Situation Assessment at Intersections
for Driver Assistance and Automated
Vehicle Control
von der Fakultät für Naturwissenschaften der Technischen Universität Chemnitz
genehmigte Dissertation zur Erlangung des akademischen Grades
doctor rerum naturalium
(Dr. rer. nat.)
vorgelegt von Thomas Streubel, M.Sc.
geboren am 6. Februar 1985 in Schlema
eingereicht am 24.11.2015
Gutachter:
Prof. Dr. Karl Heinz Hoffmann
Prof. Dr. Josef Krems
Tag der Verteidigung: 20. Januar 2016
2
Bibliographische Beschreibung
Streubel, Thomas
Situation Assessment at Intersections
for Driver Assistance and Automated Vehicle Control
Dissertation (in englischer Sprache)
Technische Universität Chemnitz, Fakultät für Naturwissenschaften, 2015
120 Seiten mit 44 Abbildungen und 63 Literaturzitaten
Referat
Die Entwicklung von Fahrerassistenz und automatisiertem Fahren ist in vollem Gange und entwickelt sich zunehmend in Richtung urbanen Verkehrsraum. Hier stellen
besonders komplexe Verkehrssituationen sowohl für den Fahrer als auch für Assistenzsysteme eine Herausforderung dar. Zur Bewältigung dieser Situationen sind
neue Systemansätze notwendig, die eine Situationsanalyse und -bewertung beinhalten. Dieser Prozess der Situationseinschätzung ist der Schlüssel zum Erkennen von
kritischen Situationen und daraus abgeleiteten Warnungs- und Eingriffsstrategien.
Diese Arbeit stellt einen Systemansatz vor, welcher den Prozess der Situationseinschätzung abbildet mit einem Fokus auf die Prädiktion der Fahrerintention. Das
Systemdesign basiert dabei auf dem Situation Awareness Model von Endsley. Der
Prädiktionsalgorithmus ist mit Hilfe von Hidden Markov Modellen umgesetzt. Zur
Bestimmung der Modellparameter wurde eine existierende Datenbasis genutzt und
zur Bestimmung von relevanten Variablen für die Prädiktion der Fahrtrichtung während der Kreuzungsannäherung analysiert. Dabei wurden Daten zur Fahrdynamik
ausgewählt anstelle von Fahrereingaben um die Prädiktion später auf externe Fahrzeuge mittels Sensorinformationen zu erweitern. Es wurden hohe Prädiktionsraten
bei zeitlichen Abständen von mehreren Sekunden bis zum Kreuzungseintritt erzielt.
Die Prädiktion wurde in das System zur Situationseinschätzung integriert. Weiterhin
beinhaltet das System eine statische Kreuzungsmodellierung. Dabei werden digitale Kartendaten genutzt um eine Repräsentation der Kreuzung und ihrer statischen
Attribute zu erzeugen und die der Kreuzungsform entsprechenden Prädiktionsmodelle auszuwählen. Das Gesamtsystem ist als Matlab Tool mit einer Schnittstelle
zum CAN Bus implementiert. Weiterhin wurde eine Fahrstudie zum natürlichen
Fahrverhalten durchgeführt um mögliche Unterschiede und Gemeinsamkeiten bei
der Annäherung an Kreuzungen in Abhängigkeit der Form und Regulierung zu
identifizieren. Hierbei wurde die Distanz zur Kreuzung und die Geschwindigkeit
bei Fahrereingaben im Bezug zur folgenden Kreuzung gemessen (Gaspedalverlassen,
Bremspedalbetätigung, Blinkeraktivierung). Die Ergebnisse der Studie wurden genutzt um die Notwendigkeit verschiedener Prädiktionsmodelle in Abhängigkeit von
Form der Kreuzung zu bestimmen. Das System läuft in Echtzeit und wurde im realen
Straßenverkehr getestet.
Schlüsselwörter: Situationsanalyse, Situationsbewertung an Kreuzungen, Kreuzungsassistenz, Fahrverhaltenanalyse, Fahrerintentionserkennung, Fahrtrichtungsprädiktion, Realfahrstudie, Verkehrskreuzungen
3
Danksagung
Ich danke Prof. Dr. Karl Heinz Hoffmann für seine Rolle als Doktorvater und als
treuer Begleiter meines wissenschaftlichen Werdegangs. Unsere konstruktiven
und offenen Diskussionen haben mir stets neue Perspektiven eröffnet und mich
motiviert eigene und andere Ergebnisse kritisch zu hinterfragen. Des Weiteren
möchte ich Prof. Dr. Josef Krems für seine Bereitschaft danken diese Arbeit
begutachtet zu haben.
Mein Dank gilt auch den Mitarbeitern der Abteilungen Advanced Technology
und Active Safety Technology der Adam Opel AG, die mich auf dem Weg zu
dieser Arbeit begleitet haben. Ich danke allen Kollegen, insbesondere Stefan
Berger, Bernd Büchs, Frank Bonarens, Gerald Schmidt, Marco Moebus, Uwe
Hahne, Christian Jerusalem und Christoph Schmidt für ihre Unterstützung und
die motivierenden Gespräche. Weiterhin gilt mein Dank meinen Doktorandenkollegen Hagen Stübing, Jonas Firl, Rami Zarife, Lena Rittger, Robert Murmann,
Falko Küster, Jens Heine, Carsten Büttner, Tobias Rückelt, Jens Ferdinand, Tirza Jung, Bernhard Wandtner und all den anderen die noch auf dem Weg sind
- ihr packt das! Die Arbeit mit euch hat mir sehr viel Spaß gemacht. In den
gemeinsamen Gesprächen erhielt ich immer wieder Impulse und konnte mich
konstruktiv Austauschen. Besonders die Bereitschaft einander zu unterstützen
u.a. bei Probandenversuchen war eine große Hilfe und nicht selbstverständlich.
Hier sei besonders Lena für die gute Zusammenarbeit und die Erkenntnisse in der
Verkehrspsychologie sowie Falko für den intensiven Austausch bei technischen
und individuellen Belangen gedankt.
Neben der gemeinsamen Arbeit sind auch einige Freundschaften entstanden, die
meine Doktorandenzeit bereichert haben und hoffentlich auch bei räumlicher
Trennung die Zukunft überdauern. Besonders unsere Doktorandenstammtische
waren kleine Highlights, die mir viel Freude und Kraft gegeben haben. Auch
den ehemaligen Studenten Dennis Abel, Igor Achieser, Hussam Al Hussein und
Sascha Gröger, deren Betreuung ich übernehmen durfte, sei herzlich für ihre
gute Arbeit gedankt.
Weiterhin möchte ich der Arbeitsgruppe am Lehrstuhl von Prof. Hoffmann danken, insbesondere Janett Prehl, Kim Schmidt und Frank Boldt für den wissenschaftlichen Austausch und die gemeinsame Lehre. Besonders die gegenseitige
Unterstützung beim Korrekturlesen von Papern auch außerhalb der üblichen Arbeitszeiten war sehr hilfreich.
Ein ganz besonderes Dankeschön geht an meine Familie und Freunde, die mich
in meiner Arbeit unterstützt haben und mich stets ermutigten meine Ziele voller
Tatendrang zu verfolgen und schließlich mit dieser Arbeit meine akademische
Ausbildung zu krönen. Hierfür danke ich besonders meinen Eltern, meiner Oma
und meinem Bruder Frank mit Familie, sowie meinem Freund Steffen besonders
für die stets unkomplizierte Unterbringung in Chemnitz, Jutta für den Zuspruch
diese Arbeit anzugehen und Sandra für den Glauben an meine Fähigkeiten.
Special thanks go to my American brother Kyle and a very special friend Emily
for the support, when I struggled with the English language.
4
Abstract
The development of driver assistance and automated vehicle control is in process and
finds its way more and more into urban traffic environments. Here, the complexity
of traffic situations is highly challenging and requires system approaches to comprehend such situations. The key element is the process of situation assessment to
identify critical situations in advance and derive adequate warning and intervention
strategies.
This thesis introduces a system approach to establish a situation assessment process
with the focus on the prediction of the driver intention. The system design is based
on the Situation Awareness model by Endsley. Further, a prediction algorithm is
created using Hidden Markov Models. To define the parameters of the models, an
existing database is used and previously analyzed to identify reasonable variables
that indicate an intended driving direction while approaching the intersection. Here,
vehicle dynamics are used instead of driver inputs to enable a further extension of the
prediction, i.e. to predict the driving intention of other vehicles detected by sensors.
High prediction rates at temporal distances of several seconds before entering the
intersection are accomplished.
The prediction is integrated in a system for situation assessment including an intersection model. A Matlab tool is created with an interface to the vehicle CAN bus
and the intersection modeling which uses digital map data to establish a representation of the intersection. To identify differences and similarities in the process of
approaching an intersection dependent on the intersection shape and regulation, a
naturalistic driving study is conducted. Here, the distance to the intersection and
velocity is observed on driver inputs related to the upcoming intersection (leaving
the gas pedal, pushing the brake, using the turn signal). The findings are used
to determine separate prediction models dependent on shape and regulation of the
upcoming intersection. The system runs in real-time and is tested in a real traffic
environment.
Keywords: situation assessment, Advanced Driver Assistance Systems for intersections, driver intention estimation, prediction of driving direction, driving behavior
analysis, naturalistic driving study, traffic intersections
5
6
Contents
List of Figures
9
Acronyms
11
1 Introduction
1.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
1.2 Outline . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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2 Fundamentals
2.1 Traffic Intersections . . . . . .
2.2 Situation Assessment . . . . . .
2.3 Prediction of Driver Intention .
2.3.1 Methods Overview . . .
2.3.2 Hidden Markov Models
2.4 Localization . . . . . . . . . . .
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3 Driving Behavior
3.1 Data Analysis . . . . . . . . . . . . .
3.1.1 Data selection and processing
3.1.2 Results . . . . . . . . . . . .
3.1.3 Conclusion . . . . . . . . . .
3.2 Naturalistic Driving Study . . . . . .
3.2.1 Background . . . . . . . . . .
3.2.2 Methods . . . . . . . . . . . .
3.2.3 Results . . . . . . . . . . . .
3.2.4 Discussion and Conclusion . .
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4 Prediction Algorithm
4.1 Framework . . .
4.2 Input data . . . .
4.3 Evaluation . . . .
4.4 Validation . . . .
4.5 Conclusion . . .
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(GUI)
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5 System Approach
5.1 Sensing . . . . . . . . . . . . .
5.2 Situation analysis . . . . . . . .
5.3 Prediction . . . . . . . . . . . .
5.3.1 Implementation . . . . .
5.3.2 Graphical User Interface
5.3.3 Testing and Outlook . .
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6 Conclusion and Outlook
107
Bibliography
109
7
8
List of Figures
1.1
1.2
1.3
World Urbanization Prospects [1] . . . . . . . . . . . . . . . . . . . .
Overview of a selection of ADAS introduced in this chapter . . . . .
Different vehicle sensors to establish a surround view [9] . . . . . . .
13
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17
2.1
2.2
2.3
Distribution of intersection angles in two analyzed areas . . . . . . .
Situation Awareness model by Endsley and system approach . . . .
Example of a intersection scenario with conflict (left) and without
conflict (right) dependent on the driving direction of the blue vehicle
Trellis structure of a HMM . . . . . . . . . . . . . . . . . . . . . . .
Schema of the Baum-Welch algorithm . . . . . . . . . . . . . . . . .
24-satellite constellation required for a full coverage [43] . . . . . . .
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46
3.23
3.24
Intersection at simTD test site in Friedberg, Germany [46] . . . . . .
Velocity for approaching the intersection on the main road . . . . . .
Velocity for approaching the intersection from the side roads . . . .
Velocity on the main road according to the direction of travel . . . .
Average acceleration for approaching the intersection on the main road
Average acceleration for approaching the intersection from the side
roads . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Frequency of turn signal usage dependent on DTI for main road sequences . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Cumulative temporal distribution of turn signal activation on main
road . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Cumulative temporal distribution of turn signal activation on side roads
Frequency of shifting dependent on DTI for side road sequences . . .
Probability density of the DTI when shifting on the main road . . .
Average yaw rate for left and right turning sequences on the main road
Test vehicle with GPS antenna on the roof . . . . . . . . . . . . . .
OSM map [17] including relevant intersections (indices and driving
direction) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
DTI on driver inputs at X-intersections according to direction . . . .
Velocity on driver inputs at X-intersections according to direction . .
DTI on driver inputs turning left at intersections of different shape .
Velocity on driver inputs turning left at intersections of different shape
DTI on driver inputs turning right at intersections of different shape
Velocity on driver inputs turning right at intersections of different shape
DTI on driver inputs going straight at intersections with different shape
Velocity on driver inputs going straight at intersections of different
shape . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
DTI on driver inputs at signposted intersections . . . . . . . . . . .
Velocity on driver inputs at signposted intersections . . . . . . . . .
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4.1
4.2
Vehicle state vector as input of the prediction framework . . . . . . .
Recognition rate varying the cluster distance method . . . . . . . . .
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2.4
2.5
2.6
3.1
3.2
3.3
3.4
3.5
3.6
3.7
3.8
3.9
3.10
3.11
3.12
3.13
3.14
3.15
3.16
3.17
3.18
3.19
3.20
3.21
3.22
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9
List of Figures
10
4.3
4.4
4.5
4.6
4.7
Recognition rate varying the number of symbols . . . . . . .
Recognition rate varying the size of the learning dataset . . .
Recognition rate varying number of hidden states . . . . . . .
Recognition rate varying number of mixtures in GMMs . . .
Validation of prediction rate varying number of hidden states
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5.1
5.2
5.3
5.4
Interface for intersection modeling (schematic)
Activity diagram of the prediction tool . . . . .
Orientation coordinate system in the vehicle . .
Prediction tool GUI . . . . . . . . . . . . . . .
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Acronyms
ADAS
ADMA
ANOVA
CAN
DTI
EU
FCA
GMM
GNSS
GPS
HMM
LDW
OSM
PRT
SA
SBSA
TTI
USB
V2I
V2V
Advanced Driver Assistance Systems
Automotive Dynamic Motion Analyzer
analysis of variance
Controller Area Network
distance to intersection
European Union
Forward Collision Alert
Gaussian Mixture Model
Global Navigation Satellite System
Global Positioning Service
Hidden Markov Model
Lane Departure Warning
Open Street Map
Perception Reaction Time
Situation Awareness
Side Blind Spot Alert
time to intersection
Universal Serial Bus
Vehicle-to-Infrastructure
Vehicle-to-Vehicle
11
12
1 Introduction
Mobility is a basic need in modern societies. In this context, road traffic plays a
major role due to individuality and flexibility. Though, the increasing number of vehicles combined with ongoing urbanization (see Figure 1.1) leads to great challenges
in traffic management and vehicle development. Many metropolitan areas worldwide
suffer from huge traffic jams in rush hours. Also, the increasing complexity of traffic
situations due to high traffic density is demanding on the drivers abilities and leads
to an increase of accidents. This is not only a threat to the safety of passengers
and other traffic participants, but also another obstruction that can result in a total
gridlock. Here, intersections are danger areas in particular because of their function
as traffic nodes.
Figure 1.1: World Urbanization Prospects [1]
The avoidance of traffic accidents is a central goal in the field of active safety in
vehicle development. Here, Advanced Driver Assistance Systems (ADAS) are developed to assist the driver in uncertain situations. While a central issue is safety,
there are also aspects of driving comfort and improving efficiency by reducing fuel
consumption and emissions. On a low level, the assistance is accomplished by presenting information to the driver to positively influence the upcoming decision. This
is mainly used for comfort features, e.g. the shift indication to improve efficiency and
induce an ecological driving behavior. Traffic Light Assist (TLA) is another example
aiming for the same goal [2]. Here, information and recommendations are provided
13
1 Introduction
to the driver for how to approach the upcoming traffic light efficiently, i.e. to either
avoid a full stop by reducing speed at an early stage (red to green phase change) or
to induce coasting when passing on green is impossible (green to red phase change).
The information is provided by the traffic light and sent to the vehicle by Vehicleto-X (V2X) communication technology. So, the driver can be informed even before
the traffic light is visible. The system also includes a safety aspect, since red light
violations were significantly reduced with the assistant system.
In more critical situations, there are systems issuing warnings in an auditive, visual
or haptic manner to draw attention to the situation and evoke an adequate reaction.
Here, existing features are Lane Departure Warning (LDW), Side Blind Spot Alert
(SBSA) and Forward Collision Alert (FCA). The first two are for lateral assistance.
While the LDW steps in when the driver is unintentionally leaving the lane, the
SBSA indicates a vehicle driving in the blind spot of the driver. The FCA is a
longitudinal feature that warns when a collision with a vehicle or other detected
object ahead is imminent.
On the highest level of assistance, ADAS intervene or take over control of the driving
task. The basic control systems are again divided into lateral and longitudinal
control and are called Lane Keep Assist (LKA) and Active Cruise Control (ACC),
respectively. Depending on the design of the system, the LKA is supporting the
driver in staying within the lane by a steering impulse to the lane center when
crossing the lane marks unintentionally (without using the turn signal). A further
advancement of this system is Lane Centering Control (LCC) where a steady control
keeps the vehicle in the center of the lane to avoid leaving the lane at all. The ACC
is an enhancement of the known Cruise Control (CC) which keeps a desired speed
determined by the driver. With the help of sensor systems in the front (usually
radar or lidar) vehicles ahead are detected and the ACC reduces the velocity to keep
a safety distance to a vehicle driving in the same lane up front.
For critical situations, there are features for brake control such as an Emergency
Brake Assist (EBA) which increases the driver’s initiated brake up to a full braking.
Autonomous Emergency Braking (AEB) systems are a further development. These
initiate a braking when obstacles ahead are detected by sensor systems and a collision
is imminent. Figure 1.2 gives an overview of the introduced systems categorized in
safety and comfort systems. The level of assistance indicates the influence of the
system between giving information up to taking control. In the near future, there
will be a combination of lateral and longitudinal control towards automated driving
applications [3] where the driver will steadily shift from having control to supervising
the control system.
14
Advanced Driver Assistance Systems (ADAS)
Safety Systems
low
Level of assistance
Lateral
Longitudinal
LDW
SBSA
LKA
LCC
FCA
(TLA)
AEB
ACC
Comfort
shift
indication
Traffic sign
recognition
CC
high
in progress
Figure 1.2: Overview of a selection of ADAS introduced in this chapter
On one side, technological enhancements stimulate the further development of new
ADAS but also governmental requirements push systems into vehicles of different
types. For example, the European Commission introduced safety standards in 2009
(Regulation No 661/2009 [4]) enacting deadlines for equipping new cars and vehicle types with certain driver assistance systems. The regulation states that Lane
Departure Warning Systems (LDWS) and Advanced Emergency Braking Systems
(AEBS) become mandatory in new trucks and buses by November 2015. Further
implementation regulations were issued for specification of the requirements of the
systems (Commission Regulation No 351/2012 [5] for LDWS and No 347/2012 [6]
for AEBS). The introduction of AEBS applies in the first instance only to utility
vehicles (admissible total weight above 8 tons) braked by compressed air. The regulation No 347/2012 further states that in the “collision warning phase [...] the
system shall provide the driver with appropriate warnings”. If there is no reaction,
it will be followed by the “emergency braking phase” in which for approaching moving objects the “subject vehicle shall not impact with the moving target” and for
stationary targets the “speed reduction of [the] subject vehicle shall be not less than
10 km/h”. This refers to approval level 1 while in the level 2 a speed reduction of at
least 20 km/h is required.
There are some critical voices requesting even higher standards that match the state
of the art especially to avoid accidents with stationary objects at all [7]. Finally, all
new types and later all new utility vehicles must be equipped with braking assistance
systems by Nov. 2016 and Nov. 2018, respectively. So far, the regulations affect
only trucks and buses, since scenarios where heavy trucks hit the tail end of a traffic
15
1 Introduction
jam or buses leave the road appear occasionally on highways and lead to terrible
accidents with high numbers of casualties.
The development and implementation of ADAS for passenger cars is especially influenced by the New Car Assessment Programs (NCAPs). These are regional institutions that perform standardized crash tests and safety assessments to provide
independent and objective safety classifications of different cars for the customer.
For this, the passive and active safety of a vehicle is evaluated according to a defined
point system (1-5 stars). On one side, the manufactures are interested in developing
their vehicles to achieve five star ratings (maximum) as an additional sales pitch.
On the other side, the customer can easily compare different vehicles in regards to
safety without a detailed look into the specifications of all systems. Recently, the
Euro NCAP agreed on a roadmap to raise the requirements for high ratings and
defined “the detailed targets and points for the years 2016 up to 2020.” [8] The focus
here is clearly on active safety and accident avoidance. Cars that have passive safety
features only will not be rated above 3 stars anymore. So, ADAS will most likely
be further implemented in new cars. Euro NCAP especially awards crash avoidance
systems but will further demand improvements in their robustness, i.e. reduction of
false positive activations.
The realization of active safety systems was enabled by the enhancements of environmental sensor systems for in-vehicle usage. New cars are equipped with many
different sensors such as infrared for short range (parking applications), radar and
lidar for mid- and long-range and cameras combined with image processing units.
The latter are preferred for lateral features especially LKA and LCC, since cameras can detect lane markings. Cameras are also utilized for pedestrian detection.
Radar and lidar sensors are mainly used to detect static or dynamic objects in the
line of sight and for the blind spot detection. For redundancy in safety features,
multiple sensors are applied and combinations of them can be used. While lidar is
less vulnerable to noise and provides better lateral resolution, the disadvantage of
high signal absorption in bad weather conditions prevails. Therefore, radar sensors
are dominating over lidar which also reduces the price due to the high number of
units produced. Additionally, vehicles can share information via Vehicle-to-Vehicle
(V2V) or Vehicle-to-Infrastructure (V2I) communication links becoming aware of
other vehicles or traffic situations out of sight. A digital map is also considered a
sensor in the broader sense providing precise information of the road infrastructure
enabling traffic sign features and lane recommendations for navigation systems. It
can also provide curvature information of the road ahead. Combined with a precise localization on lane level, it is utilized for path planning especially in inner-city
road infrastructure. All these sensors and information systems are providing great
amounts of environmental data. This raises issues like data management and software optimization. However, merging data from different sources can establish a
view of the vehicle’s surroundings (see Figure 1.3). This is called data fusion and is
16
a subject of present research. A detailed representation of the cars surrounding is
required for highly automated driving.
Figure 1.3: Different vehicle sensors to establish a surround view [9]
Technically, automated driving is operable and car manufactures are ready to introduce solutions for highway traffic since it is basically a combination of LCC and
ACC [10]. However, the regulations and laws do not yet allow autonomous vehicles
in most parts of the world. Usually, there are only special permits given for small
test fleets and scientific research. While the longitudinal control can be operated
automatically by ACC, the lateral control is still considered critical and must be
continuously controlled by the driver. Currently, LCC is only assisting the driver
while the hands have to remain on the steering wheel. Also, the driver has to be able
to override the systems at all times. So far, the ECE Regulation 79 (vehicle steering)
approves a continuous control of the steering by a system only to a maximum velocity of 12 km/h (10 km/h limit with 20% deviation) [11]. This was enacted recently
to enable automated parking assistance features (hands-free). Besides, a system was
introduced called Traffic Jam Assist (TJA) that basically controls the vehicle in stop
and go traffic up to 60 km/h. However, the system is required to be disabled when
the driver is inactive. The driver’s activity is monitored practically by sensors in the
steering system, so at least one hand has to remain on the steering wheel.
For automated control, multiple sensor systems are necessary for redundancy in
the obstacle detection and actuators for steering control. The latter is usually an
electronic steering or also called steer-by-wire system. Beside the hardware, algo-
17
1 Introduction
rithms are necessary to process the sensor data and conclude an appropriate course
of action. In this process, a situation analysis is the key aspect. Here, a human
driver is still predominant over a system due to the capability of abstraction, and
therefore, the ability to handle situations never experienced before. Another fact
in favor of a driver is the ability to learn and the expectation of behavior of other
traffic participants. For example, when a driver sees a vehicle ahead with activated
turn signal to the right, he/she is expecting the vehicle to turn and hereby reduce
the speed. Based on this expectation, the driver will most likely reduce the speed
and keep an additional safe distance. To establish system approaches with similar
functionality such as behavior prediction and situation assessment is a major focus
in research. So far, systems are able to operate under a specific range of conditions
or in predefined scenarios. This is sufficient for simple driving tasks in a controlled
environment, such as following a vehicle ahead or passing an obstacle on one-way
roads. This covers most scenarios typically found in highway traffic.
The variety and complexity of situations is particularly relevant in urban traffic.
Here, ADAS are just evolving and automated driving is still the object of research.
The innumerable mass of scenarios are most challenging for rule based system approaches. Still, the complex scenarios are very demanding on the cognitive abilities
of the driver leading to an increase in the error rate. This illustrates the high demand on assistance. Here, intersections are of especially high interest in the urban
environment as a basic attribute in street networks of cities.
1.1 Motivation
The development of situation assessment is necessary to enable driver assistance
especially in an urban environment. Also, it is essential for the realization of fully
automated driving which will be a major step in vehicle enhancement. The idea of
autonomous road traffic in cities is appealing for multiple reasons. Amongst others,
automated vehicles have the potential to reduce traffic accidents and accordingly
keep road fatalities to a minimum. This reduction will not only save lives, which is
definitely the major drive for ADAS development, but will also prevent injuries and
reduce accident related economic costs. Besides, the resulting traffic synchronization
will increase the traffic flow and reduce congestions. Here, intersections are particularly fragile. The overall traffic efficiency will improve, reducing the individual fuel
consumption (per vehicle) and emission accordingly.
Traffic optimization is one object of research in the field of intelligent transportation
systems. So, an intersection with automated vehicle handling was introduced in [12]
using a Cooperative Vehicle Intersection Control (CVIC) algorithm which showed
a time reduction of 33% and a reduction of fuel consumption of 44% compared
to conventional intersections. Furthermore, the vehicles had almost no stop delay
18
1.1 Motivation
(reduction of 99%). Emissions are also an important issue that need to be handled
concerning the threat of smog in downtown areas. As long as there are no emission
free modes of transportation widely spread, the emissions especially in urban areas
ought to be minimized. Autonomous vehicles could contribute to this goal.
The European Commission states similar goals in their “Roadmap to a Single European Transport Area” in 2011 [13].
“Cities suffer most from congestion, poor air quality and noise exposure.
Urban transport is responsible for about a quarter of CO2 emissions from
transport, and 69% of road accidents occur in cities.”
There is also mentioned a “60% emission reduction target” (of the emission in 2010
reached by 2050).
Besides, the high number of 25.700 road deaths within the
European Union (EU) in 2014 is to be reduced [14]. The goal is to halve the fatalities of 2010 by 2020 (see quote below). Looking further in the future, the EU is
even more ambitious. While ADAS already reduced traffic accidents considerably,
one future goal is to zero the fatalities occurring in road traffic. There are different
initiatives aiming for the so called “Vision Zero” with different approaches and it is
also on the agenda of the European Commission [13].
“By 2050, move close to zero fatalities in road transport. In line with
this goal, the EU aims at halving road casualties by 2020.”
Other goals mentioned there are the break of oil dependency and reduction of emissions.
Today, accidents occur increasingly in urban areas also due to the urbanization
previously illustrated in Figure 1.1. The German traffic accident statistic indicates
that over 74% of the recorded 2.4 million accidents happened on urban roads in
2014 [15]. This is a raise of 2.5% compared to the year before, while the overall
amount of accidents slightly decreased. Personal injuries occurred in about one of ten
accidents in urban areas (11.7%). Thereby, more than a quarter of a million people
were injured and 983 lost their life in urban traffic. The considerable increase of the
casualties of about 5% compared to the year 2013 is alarming, since it almost reaches
the level of 2011 reversing the declines of the past years. This shows certainly the
necessity for further developments in safety systems for urban traffic environments.
Table 1.1 gives an overview of the ratio of urban accidents and casualties compared
to the overall numbers for the past years.
The German traffic accident statistic also provides more detailed information categorizing the accidents (see Table 1.2) [15]. In the following, the focus will be on
accidents with personal injuries in urban traffic investigating their appearance related to intersections. The numbers presented are from the year 2014. The kind
of accident criteria describes the entire course of events and there are ten different
kinds listed in the statistics. Here, a “Collision with another vehicle which turns
19
1 Introduction
Table 1.1: Statistic of traffic accidents in Germany over the last years [15]
Traffic accidents
overall (registered by police)
on urban roads
with personal injuries
on urban roads
2011
2012
2013
2014
2,361,457
1,743,065
73.8%
306,266
210,427
68.7%
2,401,843
1,751,166
72.9%
299,637
206,696
69.0%
2,414,011
1,746,474
72.3%
291,105
199,650
68.6%
2,406,685
1,789,278
74.3%
302,435
209,618
69.3%
396,374
255,405
64.4%
4,009
1,115
27.8%
387,978
251,371
64.8%
3,600
1,062
29.5%
377,481
242,498
64.2%
3,339
977
29.3%
392,912
254,454
64.8%
3,377
983
29.1%
Casualties
overall
on urban roads
Fatalities
on urban roads
into or crosses a road” appears most likely on urban roads leading to 33% of the
accidents and to one third of the casualties. 216 people died in this kind of accident.
Another category is the type of accident which describes the conflict situation leading to an accident. In contrast to the kind of accident, the type is indicating the
initial conflict leading to the collision instead of describing the collision itself. This is
especially relevant for the accident analysis and for potential actions to resolve conflicts in hazard spots. Here, seven types of accidents are distinguished of which two
Table 1.2: Relevant categories of accidents in German accident statistics [15]
Accidents on urban roads
Kind of accident
collision with another vehicle which
turns into or crosses a road
Type of accident
accident caused by
turning off the road
accident caused by
turning into a road or by crossing it
Characterization of accident site
intersection
road branch
20
with
injuries
thereby
casualties
thereof
fatalities
209,618
254,454
983
69,124
33.0%
84,814
33.3%
216
22,0%
34,146
16.3%
55,194
26.3%
42,146
16.6%
67.333
33.3%
105
10.7%
166
16.9%
50,425
24.1%
49,434
23.6%
64,717
25.4%
59,314
23.3%
198
20.1%
185
18.8%
1.1 Motivation
are relevant for intersections. First, there is the type “Accident caused by turning
off the road” which is described in [15] as:
“The accident was caused by a conflict between a vehicle turning off and
another road user approaching from the same or opposite direction (incl.
pedestrians) at crossings, junctions and entries to premise or car parks.
Whoever follows the priority turn of a main road is not considered as
turning off.”
This type occurred in 16.3% of the accidents in urban environment with personal
injuries. While the proportion of the casualties is similar, the fatality ratio is lower
with 10.7%. This is also the case for the second type called “Accident caused by
turning into a road or by crossing it” which is defined as:
“The accident was caused by a conflict between a road user turning into a
road or crossing it and having to give way and a vehicle having the right
of way at crossings, junctions, or exits from premises and car parks.”
This type of accident has the highest appearance on urban roads with a rate of 26.3%.
As mentioned before, the fatality ratio here is significantly lower with 16.9%. So,
considering the types of accidents, 42.6% are related to turning on or off an urban
road leading to a similar ratio of casualties. The lower fatality ratio is most likely
related to the overall lower speeds in inner-city traffic but also the reduction of speed
when turning. Also, the high standards in passive safety might be a reason.
In the statistics the accident site is also characterized. Here, the two relevant sites
are intersections and road branches. The latter is related to junctions where one road
meets a continuous road and ends there. A typical example is a T- or Y-junction.
There is no indication of the traffic control for both types. As expected from the type
of accident criteria, almost half of the accidents with injuries in urban environments
happened at intersections and road branches about equally shared. Table 1.2 gives
an overview of the categorized accidents and casualties that occurred on urban roads.
The statistics also include data about misbehavior of the driver causing an accident.
Here, the “violation of right of way” is of interest. This was the cause of an accident
with injuries in 17.6% of the recorded violations. Another typical misbehavior that
occurs at intersections is “mistakes made when turning” and led in 10.1% of the
cases to an accident. Considering the numbers of casualties from Table 1.2 resulting
from these mistakes, a further development of ADAS for intersection scenarios has
the potential to reduce these comparatively high numbers of accidents and injured
persons. Here, the situation assessment plays a key role to establish these assistance
features that aim to reduce the risk of accidents.
21
1 Introduction
1.2 Outline
The following chapter “Fundamentals” includes basics about traffic intersections with
a short analysis of typical intersection types as well as the concept of situation assessment in the context of Situation Awareness (SA) by humans. Further, an overview
of prediction methods for the driver intention is given and the used method is briefly
introduced. The basic understanding of the localization with satellite systems is also
covered at the end of the chapter which is required for the determination of distances
relative to the intersection.
Chapter 3 includes an analysis of driving data at a particular intersection and a
natural driving study. The first was performed to retrieve a better understanding
of the approaching behavior at an intersection depending on the driving direction.
This led to the identification of parameters for the prediction algorithm, which is
introduced in chapter 4. The driving study was conducted to obtain more detailed
information about the preparatory behavior especially about when and where the
driver inputs apply. Also, typical intersection shapes were varied to adapt the prediction models to particular intersection types. The retrieved data was also used to
train the prediction models and test the in-vehicle application in chapter 5.
In “Prediction Algorithm” the prediction algorithm for the intended driving direction
at intersections is introduced. The prediction is evaluated with different parameter
setups and tested with the database analyzed in the first part of chapter 3. Furthermore, different designs are tested for the link between the input data and discrete
states of the prediction model.
The prediction algorithm is finally integrated in a real-time system for situation
assessment and implemented in a vehicle environment which is described in chapter 5
“System Approach”. The choice of models for the system are influenced by the results
from the natural driving study from the second part of chapter 3. The system was
also tested in a vehicle driving in a real traffic environment.
The last chapter “Conclusion and Outlook” summarizes the results and gives a brief
outlook.
22
2 Fundamentals
2.1 Traffic Intersections
Intersections are a common element of traffic infrastructure and are fundamental
components in urban environments. An intersection is defined as a structure where
multiple roads meet or cross. The intersection area is the space that is used by
vehicles approaching from different directions. Especially, the limited space used by
multiple traffic participants leads to an increase in collision risks and is therefore of
interest in the research on traffic safety in urban environments. Typical shapes are
three-way and four-way intersections. Three-way intersections occur for example,
when a secondary road merges into a main road forming a T-junction or when a
road splits in two directions forming a Y-junction. The shape does not necessarily
reflect the priority of the roads. Four-way intersections are usually X-shaped. More
complex intersections with more than four branches exist, but are not prevalent.
Also, traffic circles could be considered as a special intersection type. Further details
on the frequency of occurrence and typical intersection angles are introduced below
in this section.
Another characteristic is the traffic regulation at an intersection. Traffic lights are
commonly used in areas with high traffic volume. A traffic light changes frequently
the accessibility of the intersection area for different road users reducing or even
avoiding conflicts. However, this regulation limits the flow capacity resulting in a
typical bottleneck in urban traffic. An explicit static regulation is realizewed with
yield or stop signs and corresponding right of way signs on the main road. In Germany, these traffic signs are always combined to indicate a right of way route, which
is not necessarily straight. Vehicles coming from a side branch according to this
route will face a yield or even stop sign. Constructs like all-way stop, commonly
known in the USA, are not existing in Germany. In case of an unregulated intersection, i.e. without the presence of traffic lights or signposts, the “priority to the right”
rule applies (see § 8 “Vorfahrt” in StVO [16]). Drivers approaching the intersection
yield to vehicles coming from the next branch right of them. When the rare situation
occurs that vehicles from each direction reach the intersection simultaneously, the
drivers need to agree on who will go first.
The shape and regulation of inner-city intersections and their frequency of occurrence were subject of an analysis1 . For the development of route guidance systems,
1
performed within the framework of an internship by Igor Achieser
23
2 Fundamentals
different map databases are established with extensive road data. One of them is
Open Street Map (OSM) [17]. Here, Global Positioning Service (GPS) tracks are
provided by users (crowdsourcing) to establish an open source database of the road
infrastructure. This database was easily accessible and therefore used to retrieve
information about intersections in a certain area. A software tool was implemented
in Java to search for intersections in a predefined area selected by a bounding box.
This rectangular box was positioned to enclose most of a city area.
The shape is categorized between three-way and four-way intersections and traffic
circles. Further, the intersection angles were evaluated. For the traffic regulation,
only traffic lights are included in the data. The analysis was performed for several
urban areas in particular for Ruesselsheim and Chemnitz, both located in Germany.
Since the bounding box was rectangular, the focus was to include the urban area of
the cities. So, for Chemnitz the box included only an area of about 58 km2 around
the city center. The results for Ruesselsheim were validated with the official data
provided by the responsible department of the city administration. The results are
shown in Table 2.1.
Ruesselsheim, Germany
official statistics OSM analysis
overall intersections
Regulation
traffic lights
Shape
traffic circle
three-way intersections
four-way intersections
Chemnitz, Germany
OSM analysis
767
846
1,453
32
21
131
3
579
180
3
652
188
2
1,013
433
Table 2.1: Intersections Ruesselsheim, Germany
According to the official count, there are 767 intersections on public roads in Ruesselsheim, Germany. The OSM tool identified about 10% additional junctions. This
has multiple reasons. One is that roads with branches to parking lots or driveways
were counted if they are not tagged correctly in the database. Besides, roads with
separated lanes in each direction are treated as independent segments in the OSM
data handling. This way, a big four-way intersection appears as four intersections in
the data. Although, this was partially resolved by implementing a range detection
around each intersection and merging those that are located within close range. Further, the database is not containing information whether a road is private or public.
This is also leading to an offset displayed by the high difference in the number of
three-way intersections.
There is also an offset in the detection of traffic lights. Here, the reason is basically
the lack of information in the database. Open Street Map is based on user information and community support. Hereby, traffic lights have to be tagged manually
24
2.2 Situation Assessment
which is optional for submitting road data. However, traffic lights are rather few
compared to the overall number of intersections in an urban environment. Since
they are usually installed on intersections with high traffic volume, they only appear
on the main routes through a city road network.
Overall, the tool gives a rough overview of the typical intersection shapes. The
numbers show a ratio between three-way intersections to four-way intersections of
about 3:1 for Ruesselsheim and 2:1 for Chemnitz, respectively. Traffic circles are very
rare in both cities. Further, the intersection angles were retrieved from the database.
Hereby, the angle between each two branches of an intersection is extracted which
leads to angles between 0◦ and 180◦ . This results in three intersection angles for
three-way intersections and six for a four-way intersection. So, the latter are slightly
overrated. The distributions for both city areas are shown in Figure 2.1.
Relative frequency [%]
50
Ruesselsheim
Chemnitz
40
30
20
10
0
10
30
50
70
90
110 130
Intersection angle [◦ ]
150
170
Figure 2.1: Distribution of intersection angles in two analyzed areas
The histogram indicates that the intersection angles are similarly distributed in both
observed urban environments. Obviously, the predominant intersection angle is close
to perpendicular (around 90◦ ) with about 46%. The nearly straight angles typical
for T-junctions are also prevalent with about 30%. Conclusively, the X-intersections
and T-junctions with nearly perpendicular intersection angles seem to be the predominant intersection shapes in urban environments.
2.2 Situation Assessment
The driving task requires the perception of relevant objects in a traffic situation by
the driver. Further, the objects such as other vehicles, traffic signs etc. have to
be identified and understood in their context. Also, presumptions of the objects
are made. For example, in a situation approaching an intersection, the driver is
25
2 Fundamentals
spotting a vehicle coming from the right. Now, assuming the “priority to the right
rule” applies and the driver is aware of that, he/she will expect the other vehicle
to enter the intersection. The driver will adapt his/her actions to yield and let the
other vehicle pass the intersection first.
A widely used concept to describe this process and the state of recognizing a situation was introduced by Endsley in the field of human factors [18]. Originally, it was
applied to describe the process of perception, interpretation and prediction of situations for pilots operating airplanes. The concept is called Situation Awareness (SA)
and is defined by Endsley as “the perception of elements in the environment within a
volume of time and space, the comprehension of their meaning, and the projection of
their status in the near future.” [18] So, while SA is “a state of knowledge”, situation
assessment is understood as “the process of achieving, acquiring, or maintaining SA
(situation awareness)”. This conception relates to the cognitive process by a human
to describe the state of the environment based on the sensory perception.
The SA is structured in three hierarchical levels: perception, comprehension and
projection (see top of Figure 2.2). In the first step, the state and the attributes of
relevant objects is perceived. The next step is the processing of the information to
form an overall picture of the situation. Here, the meaning of the single elements is
retrieved and their significance. Finally, anticipations are made of future states of
objects, especially when identified as dynamic objects in the comprehension level.
Situation Awareness model by Endsley
Perception
Comprehension
Projection
Transferring to system
Sensing
Prediction
Situation analysis
Focus
Figure 2.2: Situation Awareness model by Endsley and system approach
The Situation Awareness model is complemented by theories of cognitive psychology in order to describe the human process obtaining Situation Awareness [19].
This relates especially to the creation of a representation of a situation and the
consequential choice of action. Here, situation assessment as process to establish
Situation Awareness is described as a process of understanding (Verstehensprozess).
This leads to a situation model containing all relevant elements and representing
26
2.2 Situation Assessment
the entire situation. This model is the basic for any action taken. However, these
actions change the situation which leads to an update of the situation model. This
interaction is explained in detail in [19]. The central resource for this process is the
working memory.
There is no consistent definition of the term situation in the driving context [20].
Three different terms are defined in [21] - traffic situation, driving situation and
driver situation. Here, a traffic situation is described as objective, spatial and temporal constellation of all traffic related measures of the surrounding of traffic participants. This includes basically all relevant objects even those unperceived by the
driver, for example because of line-of-sight obstructions. A driving situation is a
subset of the traffic situation which is in principle perceivable by the driver. Finally,
the driver situation is the actual assessed situation the driver is aware of. Further
definitions for situations in the driving context are assembled in [20].
In conclusion, the situation perceived by a person is always a subjective representation of the environment. It consists inherently of an interpretation of the reality
based on the sensory inputs. The human information processing filters the relevant
information and constructs a reasonable image. This established representation is
the foundation for selected actions. So, the action is based on the definition of the
situation by an individual. This interrelation between interpretation of a situation
and resulting actions is similarly described in the field of sociology as basis for the
Thomas theorem [22]:
If men define situations as real, they are real in their consequences.
This thesis focuses on a system approach for the process of situation assessment.
The transfer of the Situation Awareness model as concept for a technical system is
presented in Figure 2.2 (at the bottom). The human perception is operationalized by
the sensor system of a vehicle. Meanwhile, the sensing is highly advanced and covers
the whole vehicle surrounding (see Figure 1.3). Furthermore, this is extended by the
recently developed Vehicle-to-X (V2X) communication technology and the electronic
horizon retrieved from digital maps. So, a representation of the vehicle’s environment
is established way beyond the range and capacity of the human perception. In
contrast, a human driver is still predominant on the comprehension level due to
the abstraction abilities. Indeed, the sensors are capable of recognizing objects and
identifying other vehicles or pedestrians, but it is still a great challenge to merge
this to a holistic representation of the situation and especially derive reasonable
intervention strategies. Existing approaches for the data fusion and environmental
modeling require multiple processing units and are yet only realized for automated
driving in test vehicles for research.
A comprehensive logical system is required to handle every possible situation which
is most challenging in urban traffic environments. Human long term memory and
ability to adopt experiences to unknown situations remain unmatched. This applies
27
2 Fundamentals
also to the projection (SA level 3). The extensive range of experience and its rapid
application to any situation is unreproducible by a system yet. However, for dynamic objects a limited prediction could be achieved by extrapolation of the current
trajectory. Further approaches for a prediction especially of the driver intention is
subject of present research. So, it was a key aspect in a recent research project for
urban traffic called UR:BAN (Urban Space: User oriented assistance systems and
network management) [23]. The following section presents the state of the art in
this matter and introduces different methods for prediction driving behavior.
2.3 Prediction of Driver Intention
For situation assessment it is essential to predict the driver intention. This way,
a possible conflict can be detected in advance and warning or even intervention
strategies can take action. In the dynamic process of driving, an analysis of the
current driving situation is just insufficient in complex scenarios. So, it is called for
an estimation of the further process of a situation.
Hereby, an existing realization is for example an Emergency Brake Assist (EBA).
The sensors detect obstacles in the path. Further, the distance to the obstacle
is determined and a time to collision (TTC) is calculated under the assumption
of constant velocity. So, the path is extrapolated. Once a threshold is reached
warnings are issued or further interventions are initialized. This logical and rulebased procedure is a constructive concept for implementing features applicable to
simple and clear situations. However, this is harder to apply in complex scenarios,
e.g. in an urban traffic environment especially at intersections. Here, multiple factors
influence the situation and its progression. It is hardly feasible to consider all possible
situations that may arise and provide adequate actions to react. A decisive role for
the development of the situation is played by the driver intention. This is illustrated
in Figure 2.3. A critical situation only arises, if the driver in the blue vehicle is
intending to turn left (scenario in the left picture). In contrast, the noncritical
situation is displayed in the right picture where both vehicles cross the intersection
straightly.
So, the driver intention is crucial for the progress of a situation and its criticality.
This, in turn, determines the need of a warning or intervention. So, several research
teams work on the recognition of driver intention using different methods. This leads
to intelligent system approaches, which will increase the performance and acceptance
of assistant systems. Further, the prediction is essential for the development towards
higher automation. Vehicles that are able to take over the driving task partially or
fully must be capable to estimate the movement of other vehicles. This is especially
relevant in the transition phase where automated and manual operated vehicles will
drive in the same environment. The system requires a prediction performance similar
28
2.3 Prediction of Driver Intention
Figure 2.3: Example of a intersection scenario with conflict (left) and without conflict
(right) dependent on the driving direction of the blue vehicle
to the human skill described in the process of situation assessment in the section
before.
In the following, various methods are introduced to achieve a prediction of driving
behavior in different scenarios. The method utilized in this thesis is described in
more detail after that including the mathematical structure.
2.3.1 Methods Overview
The prediction of the driving behavior is divide into an estimation of the trajectory of
the vehicle or a maneuver based prediction according to the use case. The trajectory
prediction is highly relevant in situations when the dynamic objects such as vehicles
come close towards each other, e.g. in an intersection area or on highways passing
each other. A maneuver based prediction is aiming rather for an early estimation
of the further progress on a scenario level. This means determining if a driver is
about to perform an overtaking maneuver on a highway or where the driver intends
to go on an intersection. While the trajectory prediction is limited to a small time
horizon, the prediction of a maneuver covers a larger time span. However, the latter
comes with a high uncertainty where in the future the vehicle will be located exactly.
A trajectory prediction can be established with several methods. The straight forward approach is to simply extrapolate the present state of the vehicle based on
an underlying motion model. Here, an exact dynamic model including all physical
attributes of the motion is usually undesired and lacks real-time capability. Therefore, kinematic models are utilized of which the bicycle model is the most common
one. It approximates a vehicle as having only two tires and front wheel-drive. It is a
simple approach still taking some dynamic characteristics of a vehicle into account.
Other simple models are based on assumptions such as the Constant Turn Rate and
Velocity (CTRV) and Constant Turn Rate and Acceleration (CTRA) model which
are capable to depict a vehicle related lateral motion by considering yaw in the state
29
2 Fundamentals
vector of the vehicle. The prediction is established by extrapolation of the motion
based on the chosen model in case the present state is known. This is shown by
Ammoun et al. in [24] using a bicycle model. To model the increasing uncertainty
of the progression of the trajectory, Gaussian noise is used here. Other model based
approaches were introduced using a mathematical driver model called Intelligent
Driver Model (IDM) [25] or an elastic band approach in a potential field [26]. The
limitation of these approaches are inherent in the motion model utilized and due to
the extrapolation only reasonable within a time span of 1 s.
For a long range trajectory prediction, dynamic and environmental constraints are
taken into account by retrieving a dataset of realistic trajectories. This is accomplished by recording real driving data or simulating realistic driving data. The latter
can be realized by using Monte Carlo Simulation [27, 28]. The dataset is clustered
obtaining representative trajectories usually linked to certain maneuvers. The prediction is performed using classification algorithms, i.e. a part of the currently driven
trajectory is compared to the clustered trajectories and matched using a certain metric. So, possible criteria are the mean Euclidean distance between trajectories or a
method called the Longest Common Subsequence (LCS) and Quaternion-based Rotationally Invariant LCS (QRLCS). The last two mentioned were used in [29] to find
a set of potential future trajectories based on weights considering road constraints.
It was further applied in scenarios including an interaction with a preceding vehicle
[30].
The maneuver based prediction aims to recognize a sequence of actions the driver
is about to execute. Typical scenarios are lane changes on multi-lane roads and
turns at intersections. The uncertainty of the driver’s intention calls for probabilistic
methods. Bayesian networks are utilized to infer the driver intention in lane changing
scenarios [31, 32] as well as in intersection approaches to determine the turning
intention [33]. Both scenarios are covered in an exploratory approach [34]. A generic
classification of driving situations using Bayesian networks is established in [20].
Another method are neuronal networks to model the driver behavior [35, 36]. In
[36], a multilayer perceptron is used with situation specific learning in scenarios
with traffic lights and preceding vehicles. Here, the prediction horizon is 3 s. While
Bayesian and neuronal networks are an efficient method to model uncertain systems,
the dynamic aspects of a driving situation are not inherently implemented.
This is resolved using dynamic Bayesian models and in particular Hidden Markov
Models (HMMs). These are a capable method to model dynamic stochastic processes. There have been approaches to use HMMs for driver behavior recognition
[37]. In this case, a system was introduced to predict the turning intention at a
T-shaped intersection using only the steering angle. The Hidden Markov Model was
modified to process continuous data. Also, traffic light violation behavior was estimated with this method [38]. Another approach was using Hidden Markov Models to
30
2.3 Prediction of Driver Intention
determine the driver intended direction (left or right) at an intersection [39]. Again,
the steering angle was used as single sensor input. Since the steering is the last
action of many when turning at an intersection, better results can be accomplished
involving additional vehicle information such as velocity. In [40], HMMs were used
for estimation vehicle states and maneuver prediction. In this thesis, Hidden Markov
Models are utilized in a similar way, but in addition the it is further investigated
how the performance of the prediction can be improved by a larger learning database
and what optimal model parameters are.
2.3.2 Hidden Markov Models
A Hidden Markov Model (HMM) is a stochastic model used to describe a dynamic
process. It combines two Markov Chains of which one is hidden, giving the method
its name. The hidden chain represents the state of the system and is not observable.
N is the number of possible states S = (S1 , ..., SN ) and determines the system’s degree of freedom. In each time step (discrete), a hidden state qt is adopted dependent
only on the previous hidden state qt−1 (first-order Markov Chain). This restriction
is the Markov attribute which is a simplification indicating the model character. It
is also referred to as memory free process. Various applications show that this is a
reasonable approximation in this driving context.
Mathematically, a HMM is described by the transition matrix A, the observation
matrix B and the starting distribution π. The way the system changes between
hidden states in discrete time steps t is determined by the transition matrix A:
A = {aij } ⇒ aij = P (qt+1 = Sj |qt = Si )
So, A is a N × N matrix. The initialization of the sequence is given by the probabilistic distribution of the first state πi = P (q1 = Si ). This vector is of length N
and its entries sum-up to 1. So, the hidden state sequence q = (q1 , ..., qT ) of length
T is created.
While the states are unobservable, the observation sequence O = (O1 , ..., OT ) results
by emitting a certain symbol R = (R1 , ..., RM ) each time step. Here, M is the number of discriminable symbols each connected with the states S by the probabilistic
distribution in the observation matrix B. Thus, the probability of an observation
Rj at the time t while in state Si is determined by the observation matrix B which
is of size N × M .
B = {bij } ⇒ bij = P (Ot = Rj |qt = Si )
In a HMM, the state space is always discrete, while the observation space can also be
continuous. This is established by incorporating a Gaussian Mixture Model (GMM).
For further references see the implementation in section 4.2.
31
2 Fundamentals
So, a discrete Hidden Markov Model λ(A, B, π) is defined by the above explained
parameters. Graphically, the process can be displayed in a Trellis structure as shown
in Figure 2.4.
𝐴
𝐴
qt-1
𝐵
Ot-1
𝐴
qt
𝐴
qt+1
𝐵
Ot
𝐵
Ot+1
Figure 2.4: Trellis structure of a HMM
There are three essential problems working with HMMs. The first is to evaluate how
well the model represents a certain set of data (sequence). Another one is to decode
an observation sequence to retrieve the optimal state sequences given a model. The
last is to learn the model parameters with a set of training data sequences. There
are algorithms for each of these problems. Two of them are used in this thesis
and therefore are briefly introduced in the following. Further details are given in a
tutorial by Rabiner [41]. The evaluation and the learning procedures are answering
the following questions.
Evaluation: What is the probability for an existing observation sequence O being
created by a given HMM λ? Here, the probability P (O|λ) is calculated.
Learning: What are the optimal model parameters so the probability for an
existing observation sequence O is maximized? Here, the model parameters of the
HMM λ(A, B, π) are determined so P (O|λ) is maximal.
The algorithm solving the first problem is called forward algorithm. Although, the
probability P (O|λ) can be calculated with a brute force approach, this algorithm is
much more efficient due to the inductive procedure. Initially, a forward variable is
defined as the probability of an observation sub-sequence of length t adopting the
system state Si at the end of the sub-sequence given the HMM λ.
αt (i) := P (O1 O2 . . . Ot , qt = Si |λ)
So, the initialization of the forward variables is easily retrieved from the starting
distribution π and the observation probability for O1 .
α1 (i) = P (O1 , q1 = Si |λ) = πi bi (O1 )
1≤i≤N
Though, there are N forward variables to be calculated for the initial time step. The
induction formula is given by:
32
2.3 Prediction of Driver Intention
"
αt+1 (i) =
N
X
#
αt (h)ahi bi (Ot+1 )
h=1
1≤i≤N
This algorithm is utilizing the simple fact that each state can only be reached from
one of N possible states in the time step before. So, there are only N · T forward
variables to be calculated where T is the length of the observation sequence O.
The sum over the last alphas (t = T ) is the wanted probability, since each alpha
represents the probability of the observation sequence ending in one of N possible
states.
P (O|λ) =
"N
X
#
αT (i)
i=1
1≤i≤N
This algorithm is superior over the brute force approach where the probability of all
possible state sequences is calculated. Since there are N T sequences, the calculation
time is rising exponentially the longer the observation sequence is. This is inefficient
and unfeasible even with decent computing capacity. However, the computing cost
for the forward algorithm is in the order of N 2 T .
The second problem is to retrieve optimal model parameters with a given set of data.
There is no known analytical algorithm for this problem. A common numerical solution gives the Baum-Welch algorithm, which is a type of Expectation-Maximization
(EM) algorithm (see Figure 2.5). First, initial values λ0 are determined for the HMM
parameters. This is established either by random choice or manually in case there
is a priori knowledge of the model parameters available. Given the initial parameters, the expected frequencies of occurrence of system states and the frequencies of
transitions between them are calculated taking into consideration the observation
sequence. This is called the expectation step. Further, in the maximization step
the model parameters are adapted according to the previously retrieved frequencies
using the principle of counting events. For example, in case the first state S1 was
expected to be reached 100 times and there were 20 expected transitions to the
second state S2 the new parameter in the transition matrix A for a12 is set to 0.2.
The new model parameters λ̄ are used again to calculate new frequencies of states
and translations in another iteration. Baum and Welch have proven that the probability that the given observation sequence was created by the new model is the same
or higher than with the previous parameters. Furthermore, this algorithm converges
quickly to a local optimum. However, the global optimum remains uncertain while
the parameter space is complex with multiple local optima. Therefore, it is recommended to run the algorithm several times with different starting conditions varying
the initial values of the HMM parameters. So, they should not only be randomized
but also scattered evenly through the probability space. In some applications ex-
33
2 Fundamentals
„Calculate expected state transition and observation values“
𝜆0
Expectation-Step
𝑃(𝑂|𝜆) ≥ 𝑃(𝑂|𝜆)
Maximization-Step
„Adaption of model parameters“
Figure 2.5: Schema of the Baum-Welch algorithm
pert knowledge can be applied to approximate some parameters to fit in a certain
pattern.
Besides, the learning procedure requires a certain amount of training data to retrieve
meaningful parameter values. Here, the choices of the number of hidden states
N and number of symbols M correlates with the size of the required dataset for
learning. This means more states and symbols let the model parameters grow or
more precisely the matrices A and B increase in size requiring more information
to determine the parameters in these matrices. So, the amount of learning data
needs to fit the number of parameters. Otherwise, typical problems might occur
working with learning procedures. In case of a small dataset for learning, the model
recognizes only the learned sequences but is unable to identify similar patterns.
So the intended classification of a wider group usually fails, since small differences
compared to the learning sequences lead to low probability rates. Also, the quality
of the data is critical for the performance of the model. When the learning dataset
is very homogeneous, sequences with high noise rates result in a poor recognition
rates. Conclusively, it seems reasonable to evaluate the modeling according to the
problem by varying the model parameters to find an optimal set.
2.4 Localization
For a precise localization today, a Global Navigation Satellite System (GNSS) is used.
It is applicable for navigation of airplanes, ships and ground vehicles. This section
gives a short overview based on [42]. This book is recommended for further details.
The principles of localization are followed by a calculation method to transfer GPS
coordinates into metric coordinates. This is needed for the localization of vehicles
relative to objects and within the road network. It is used in this thesis to determine
the distance to an intersection.
Today, GPS is the most commonly one in use which is administrated by the United
States Department of Defense. Since 2000 when the Selective Availability was turned
34
2.4 Localization
off, the precision raised from about 100 meters up to 3.5 meters today [43] (horizontally) for civilian applications (Standard Positioning Service). This enabled an
effective use of GPS for navigation systems especially for vehicles. Also, the low price
and development in GPS receivers spread the availability further, so most modern
smartphones are equipped with a receiving unit. Other than GPS, there are also
efforts in other Countries to establish their own GNSS, e.g. GLONASS (Russia),
BeiDou (China) and Galileo (European Union). The latter two are still under construction, i.e. more satellites will be deployed in the orbit. However, GLONASS is
fully operating and several localization units (such as smartphones) include chips capable of receiving and processing data from GLONASS. Speaking about localization
services, usually the name GPS is used as substitute for any satellite based system.
The existing GNSSs require a minimum of 24 satellites permanently online orbiting
the planet in a certain constellation (see Figure 2.6). These transmit radio signals
(GPS frequencies between 1.1 - 1.6 GHz) including their position and an exact time
stamp when the signal leaves the satellite. The localization is based on the principle
of time of flight (TOF) between the satellite and the receiver. Mathematically,
only 3 satellites are required for an accurate localization in 3-dimensional space.
However, the clock in the receiver is not synchronized with the GPS time. Since the
communication is unidirectional, this leads to a systematic error in the calculation
of the distance also called pseudo-ranging. This is overcome by considering a fourth
satellite signal to determine the time for the receiver (set of linear equations with 4
variables). Additional satellite signals can improve the localization further.
Other effects influencing the precision are signal delays mainly in the ionosphere but
also in the troposphere, shadowing (signal damping) and multipath (signal scattering
due to reflections). While for the signal delays corrections are available by sending a
second signal with a different frequency, the shadowing and multipath is tackled by a
reference signal from a ground station. These correction signals are generated on the
ground and further distributed via satellite (SBAS: Satellite Based Augmentation
System). The system for North America is called WAAS (Wide Area Augmentation
System) while in Europe the EGNOS (European Geostationary Navigation Overlay
System) is established.
To determine the coordinates of a position on earth using the positioning explained
above, a unified reference system is required (global coordinate system). The World
Geodetic System 1984 (WGS 84) is used with GPS and approximates the globe to a
rotationally symmetric ellipsoid [44]. So, the positioning retrieved from a GPS device
is in polar coordinates. The zero meridian passes Greenwich (GB) and the reference
for the latitude is the equator. For simplification, the earth could be approximated as
a sphere to calculate distances on the surface. However, the ellipsoid in the WGS84
model is defined by the semimajor axis R (equatorial radius) and the flattening f .
35
2 Fundamentals
Figure 2.6: 24-satellite constellation required for a full coverage [43]
R = 6, 378, 137.0 m
f = 1/298.257223563 (≈ 3.35%)
Using these two parameters, the ellipsoid is clearly defined. The semiminor axis r
(polar distance) results to
r = (1 − f )R = 6, 356, 752.3142 m
and the numerical eccentricity en to
r
en =
1−
r2
= 0.0818191908426
R2
For the calculation of the distance between two points on the surface, the polar
coordinates have to be transformed into a metric coordinate system on the surface
[44]. First, two radii of curvature are determined one applying to the latitude (RN )
and the other to the longitude (RE ). These radii are depending on the latitude (lat)
position on the ellipsoid.
RN (lat) =
RE (lat) =
R(1 − e2 )
3
(1 − e2n sin2 lat) 2
R
(1 −
1
e2n sin2 lat) 2
Further, this is necessary to retrieve scaling factors to convert two points given
their GPS position (lat, long) into points in metric coordinates. Hereby, one point
is a reference point (lat0 , long0 ) where the origin of the metric coordinate system
is placed. The other point is determined relatively to the reference point. Since
36
2.4 Localization
the above radii refer to the surface of the ellipsoid, there is an offset if one is on
an elevated position on earth. However, the GPS receiver provides a height with
regards to the mean sea level (msl). This is a typical reference surface of a geoid
characterized by a constant gravity. The deviation between the ellipsoid and the
mean sea level is called undulation. This error is avoided by most GPS receivers
calculating the undulation and providing the height in relation to the msl (hmsl ).
SFN = (RN (lat0 ) + hmsl ) ·
SFE = (RE (lat0 ) + hmsl ) ·
π
180
π
· cos(lat0 )
180
These scaling factors have a unit of meter per decimal degrees (m/◦ ) and are finally
used to calculate the lateral and longitudinal distance between reference point and
actual position in metric coordinates.
X = (lat − lat0 ) · SFN
Y = (long − long0 ) · SFE
Finally, the overall distance results to:
d=
p
X2 + Y 2
37
38
3 Driving Behavior
Driving behavior is a fundamental element for situation assessment. As long as the
driver is fully in charge of the vehicle, he/she plays an active role in road traffic.
Hereby, the driver’s behavior has a direct influence on the development of traffic
situations. A decisive role is the intention of the driver. As presented in section 2.2,
on maneuver level, the driver’s action is a consequence of the perception and assessment of a situation with the prior goal of following a certain route. In intersection
situations, this is reflected in choosing the optimal direction to reach the destination.
Today, navigation systems support the driver in the routing task while driving in
unknown areas. However, the execution of maneuvers on intersections is still performed by the driver. The driving behavior prior to entering the intersection leads
to the corresponding driving direction. So, the examination of the preparatory behavior at intersections is of interest to determine how this is linked to the maneuver
executed at the intersection when driving in one of multiple directions.
In a first step, driving data of intersection approaches was analyzed to identify
differences in the preparatory behavior driving in different directions. The database
contained data of only one intersection. The accordant analysis is presented in this
chapter. Further, the data was used to evaluate the prediction algorithm introduced
in chapter 4. For an extension of the analysis and a generalization, a naturalistic
driving study was conducted to examine the behavior at different intersection types,
i.e. with different regulation and shape. Still, the focus was on differences dependent
on driving direction. However, another aspect was to find similarities when passing
intersections of different shape in the same direction. This is examined to reduce
complexity in the prediction framework. In case no similarities are found, each
direction at each different intersection would require a new model. The data collected
during the study was also used for the implementation of the prediction application.
For a detailed explanation see chapter 5.
3.1 Data Analysis
This data analysis was performed in a first step to identify differences in the driving
behavior when approaching an intersection. The main results were published in [45]
and presented at the Automotive meets Electronics conference 2014. The focus was
to find variables and according time frames where differences appear dependent on
39
3 Driving Behavior
the direction of travel. Also, we wanted to establish a qualitative description of the
process of actions taken by the driver. The research questions of interest were:
How does the preparatory behavior of drivers differ between the intended direction?
At which distance (spatial and temporal) does the driving behavior change when
turning compared to going straight?
The database used here, was established within the project simTD (Safe and Intelligent Mobility Test Field Germany). This was a large-scaled field test for Vehicleto-Vehicle (V2V) communication. For the analysis, data was used from two days
of testing on the project’s test ground in Friedberg, Germany. There, a major
intersection was installed which had to be passed frequently during the test (see
Figure 3.1). The dataset consisted of driving data of approximately 30 vehicles of
different brands. The drivers had no special training. The test was not related to
this analysis and the drivers were driving without any instruction for which direction
to choose at the intersection. The existing traffic light was deactivated and the main
road was in west-east direction. Vehicles coming from south or north had to yield.
Intersection area
Figure 3.1: Intersection at simTD test site in Friedberg, Germany [46]
3.1.1 Data selection and processing
Since the test vehicles were of different brands, the data has been preprocessed
and harmonized by a defined simTD protocol. This protocol managed the recorded
signals and assigned standard identification numbers to all variables. The datasets
were stored in multiple TXT files by vehicle. Due to differences in the data metrics
and sensors between the brands, some datasets were incomplete or asynchronous and
40
3.1 Data Analysis
were discarded accordingly. Also, the frequency of recording was not homogenous
over all datasets. For the comparison of different datasets, this was overcome by
interpolation and extrapolation to retrieve all relevant data at a time interval of
100 ms. Multiple final plausibility checks excluded datasets with unrealistic values
for speed, acceleration and yaw rate.
For each vehicle the route driven was retraced using GPS data. This was used
to determine the distance to intersection (DTI) for each time step. The precision
was approximately 3 m according to the data specification of the used GPS systems.
Unfortunately, the positioning of the intersection markings was not measured. It was
impossible to be carried out later, because the simTD project was already finished
and the intersection had been removed before this analysis was conducted. Thus,
to use the intersection as reference system, its boundaries were retrieved from map
data using Google Earth. The intersection area was defined as the space which is
passed by vehicles approaching from different directions (cp. Figure 3.1). The center
of the intersection served as reference point. Accordingly, all GPS coordinates were
transformed into metric coordinates to easily retrieve the distance to intersection
(cf. section 2.4). Here, the DTI refers to the distance between the GPS data in
the database and the map-based GPS points of the intersection area. So a DTI of
zero indicates an entering into the intersection. However, the position of the GPS
antenna on the vehicles was unknown and therefore remained unconsidered.
The preprocessed database was now searched for intersection crossings. This was realized using a search algorithm finding points within the intersection area. Since the
approach of the intersection was of interest, sequences were extracted from 100 m
distance to the intersection up to the exit of the intersection area. The sequences
were labeled according to the direction of origin (north, east, south, west) and the
executed maneuver (turning left, turning right, going straight). The latter was established by tracking the coordinates where the vehicle entered and left the intersection.
Overall, 2492 sequences were extracted and used for the evaluation (see Table 3.1).
Since the direction of travel was chosen by the driver, there are high variations of
sequences of different types. For this reason, the data analysis has a descriptive
character.
Table 3.1: Number of approaches according to origin and driving direction
origin
left
east
west
north
south
6
143
18
61
driving direction
right
straight
267
171
102
401
659
482
58
124
41
3 Driving Behavior
In [47], the velocity, yaw rate and steering wheel angle have been identified as suitable
prediction variables for the complexity of intersection situations. So, the variables of
interest were chosen to be velocity, acceleration and yaw rate to describe the vehicle
dynamics and further the gear, turn signal, steering wheel angle and steering wheel
angle velocity as driver inputs. For the analysis of the gear usage, all sequences
of vehicles with automatic transmission were excluded, since the human shifting
behavior was of interest. The turn signal status was either retrieved through the
turn indicator lever position or the blinker signal (the status of the actual turn
indicator light).
3.1.2 Results
In the publication [45], the approaches from east and west are grouped to main road
sequences, which are considered to be similar independent of the origin. For the
side road (approach from north and south), this is done accordingly. However, the
geometry of the intersection is not symmetrical (cp. Figure 3.1). While the branch
to the south connects perpendicularly, the branch to the north meets at an angle of
approximately 70◦ . This is expected to influence the turning behavior on the main
road as well as the approaches from the side roads. Therefore, turning on the main
road and the side road approaches are investigated in further detail. The groups are
only clustered to main road or side road sequences when similarities appear.
For approaches on the main road, the analysis concentrates on turning maneuvers,
since going straight on main roads implies a simple driving task without significant
variable changes. Further, it is distinguished between turning left, turning right and
going straight when approaching from the side road. Here, differences dependent on
the direction of travel are not expected, since the prior concern should be to yield
at the intersection.
All sequences start at a DTI of 100 meters. Here, the dynamic variables (velocity,
acceleration, yaw rate) are examined in their progress over the entire distance while
the state variables (gear, turn signal, begin of turning) are of interest when changing
dependent on the DTI and time to intersection (TTI). The latter refers to the real
time before the vehicle entered the intersection which is retrieved from the data and
not the estimated value commonly calculated through DTI divided by the velocity.
Velocity profile
The velocity sequences are averaged as a function of DTI according to the origin
(east, west, north, south) and the direction of travel (left, right, straight). First,
the turning sequences on the main road are examined comparing the driving origin
to determine the expected influence of the geometry. For going straight on the
main road (coming from east or west) there are no differences as expected. The
comparisons of the origin for turning on the main road are displayed in Figure 3.2.
42
Average velocity [km/h]
3.1 Data Analysis
60
60
50
50
40
40
30
30
20
20
10
0
100
east left
west left
80
60
40
20
0
Distance to intersection [m]
10
0
100
(a) turning left
east right
west right
80
60
40
20
0
Distance to intersection [m]
(b) turning right
Figure 3.2: Velocity for approaching the intersection on the main road
For turning right, the averaged velocity profiles are very similar (cp.
Sub-
figure 3.2(b)). The mean velocity is close to 50 km/h (sd = 10 km/h) at a distance
of 100 meters to the intersection and decreases down to 33 km/h on average for
approaches from east and to 28 km/h for approaches from west, accordingly. In
both cases the standard deviation when entering the intersection is about 5 km/h.
The slight differences can be explained by the geometry of the intersection. The
branch to the north allows turning right while coming from the east at a higher
velocity due to the smaller angle of intersection compared to coming from the other
direction where the angle is perpendicular. However, the similarities prevail and
therefore both are combined to “turning right on the main road” to compare the
profiles according to the direction of travel.
This is not applicable for turning left on the main road. Here, the averaged sequences differ noticeably (cp. Sub-figure 3.2(a)). While approaches from the east
start at a comparatively high velocity of about 58 km/h (sd = 12 km/h) and decline
to about 27 km/h (sd = 6 km/h), vehicles coming from the west approach at an average velocity of about 46 km/h (sd = 8 km/h) and drop in speed to about 21 km/h
(sd = 9 km/h). The differences are again explainable with the geometry similar to
the right turns. This time, coming from the west and turning left requires a reduced
speed due to the greater turning angle compared to the other direction. Another
effect is that the traffic was not controlled and sequences are included where oncoming traffic interfered. With this in mind, the biggest weakness of the comparison
is the great difference in the number of available sequences (see Table 3.1). There
are only six left turning sequences coming from east calling the informative value
into question. Therefore, only the left turning sequences when approaching from the
west are used for “turning left on the main road”.
43
3 Driving Behavior
The velocity profiles of both side road branches differ significantly according to the
origin (cp. sub-figures in Figure 3.3). Especially, the first part of the sequences are
related to the preceding curves before the intersection. While the north branch has
a drawn-out curve passed with an average velocity around 30 km/h, there is a sharp
perpendicular curve before entering the intersection from the south at a distance
of about 40 m. This curve is also passed at around 30 km/h. In the close range,
the approach is similar again and the velocity is reduced down to around 10 km/h.
Here, the sequences are included where the driver had to stop, because of cross
Average velocity [km/h]
traffic. This is why the mean velocities are rather low.
60
60
50
50
40
40
30
30
20
10
0
100
north left
north right
north straight
80
60
40
20
0
Distance to intersection [m]
(a) coming from north
20
10
0
100
south left
south right
south straight
80
60
40
20
0
Distance to intersection [m]
(b) coming from south
Figure 3.3: Velocity for approaching the intersection from the side roads
Comparing the sequences within the sub-figures, it stands out that the velocity
profiles are alike independent of the direction of travel. The drop in velocity at close
range seems to be a reaction to the upcoming intersection and in this case the yield
sign. The similar progress indicates that the reaction to the yield sign is dominant
in the approach behavior independent of the intended driving direction.
However, there are differences in the main road approaches according to the direction
of travel. The velocity sequences are grouped as mentioned above and averaged
over the DTI. So, the velocity profiles for driving on the main road are shown in
Figure 3.4. The shades indicate the standard deviation, which was left out for going
straight. These sequences show a constant velocity around 50 km/h with a standard
deviation of about 11 km/h over the entire distance. This was also the speed limit on
the test ground. The high deviation shows that there was a wide range of accepted
speeds for the drivers.
The average velocity when turning right is similar compared to going straight for
the first part of the approach. It decreases monotonically coming closer to the
intersection. Also, the standard deviation is decreasing showing that the speed range
44
3.1 Data Analysis
Average velocity [km/h]
60
50
40
30
20
main left
main right
main straight
10
0
100
80
60
40
20
Distance to intersection [m]
0
Figure 3.4: Velocity on the main road according to the direction of travel
adjusted by the drivers is smaller for a turning event. However, the velocity does
not differ distinctly until about 20 meters before entering the intersection from the
average straight profile. Here, the decline is proceeding stronger down to an average
speed of approximately 31 km/h (sd = 5 km/h) when entering the intersection. The
average velocity for turning left starts slightly lower compared to turning right. Both
graphs decrease parallel up to about 30 meters before the intersection. There, the
velocity is reduced considerably down to about 21 km/h (sd = 9 km/h) on average
when entering the intersection area. The high deviation is explained by the traffic
interference which is not excluded from the data.
Deceleration process
The deceleration process can already be estimated from the velocity profiles. Unfortunately, the brake pedal signal was either unavailable in the database or in other
cases unreliable because of many missing data. Therefore, the acceleration values
were evaluated to estimate the usage of the brake.
The average deceleration is close to zero at a DTI of 100 meters for all main road
sequences. This value is nearly constant over the entire approaching phase when
going straight and there appears to be no braking as expected. For all maneuvers,
the deceleration is shown in Figure 3.5. As mentioned above, turning left at the
main road only includes approaches from the west. The graphs are similar for the
first half of the approaching distance. There is a slight deceleration averaging up to
0.5 m/s2 for both turning maneuvers which is obtained presumably by leaving the
gas pedal and caused by the engine brake, the aerodynamic resistance and the rolling
friction. This is commonly known as coasting. An actual braking is above this value
and appears subsequently after coasting for both turning maneuvers. However, the
average gradient of the deceleration is greater for left turning sequences than for
45
3 Driving Behavior
right turning sequences. The right-turning vehicles decelerate continuously up to
1.8 m/s2 (sd = 0.9 m/s2 ) when entering the intersection. The mean acceleration for
left turns shows a minimum at a distance to intersection of about 13 meters (see
Figure 3.5). Here, the average deceleration is about 1.9 m/s2 (sd = 1.0 m/s2 ) which
is marginally more than for turning right. Since the left-turning vehicles have to
yield to the oncoming traffic, this extremum indicates that the drivers decelerate
in advance before they turn to avoid oncoming vehicles foresightedly. Also, the
Average acceleration [m/s2 ]
sequences where oncoming traffic was present were not extracted from the analysis.
1
0
−1
main left
main right
main straight
−2
−3
100
80
60
40
20
Distance to intersection [m]
0
Figure 3.5: Average acceleration for approaching the intersection on the main road
The deceleration process on both side roads (north and south) was highly influenced
by their geometry, especially through the sharp curves on both sides before the
intersection. This was already indicated by the velocity profiles. Further, there
appeared to be no difference dependent on the direction of travel at the intersection.
So, the acceleration profiles were combined for all directions (see Figure 3.6). For
the approaches from the north, the first deceleration is a reaction to the curve before
the intersection. The actual braking seems to start around a DTI of 36 m when the
acceleration drops rapidly in Sub-figure 3.6(a). The standard deviation indicated by
the dotted lines is comparatively high especially at the minimum of the acceleration
of -1.8m/s2 (sd = 0.9 m/s2 ) at a distance of about 16 meters before entering the
intersection. The acceleration profile for approaches from the south branch start out
with a deceleration of about 1.0m/s2 increasing to 1.6m/s2 over the first 20 meters
and remaining there for another 30 meters. This braking with a high variance (see
Sub-figure 3.6(b)) reflects the approach of the sharp left curve before the intersection
coming from the south branch. The curve is passed without braking indicated by
the shape of an arch in the graph. Obviously, there is even an acceleration out of the
turn in some sequences with a maximum of averaging about 0.3m/s2 at a distance
of 30 meters. Afterwards, the actual braking as a reaction on the intersection starts
46
3.1 Data Analysis
and reaches the minimum acceleration of -1.7m/s2 (sd = 0.9 m/s2 ) at a distance of
Average acceleration [m/s2 ]
approximately 10 meters to the intersection.
1
1
0
0
−1
−1
−2
−2
north all
−3
100
80
60
40
20
Distance to intersection [m]
(a) coming from north
south all
−3
0
100
80
60
40
20
0
Distance to intersection [m]
(b) coming from south
Figure 3.6: Average acceleration for approaching the intersection from the side roads
Conclusively, the intensity of the deceleration does not exceed 2.0 m/s2 on average.
However, the standard deviations are high especially for the side road approaches.
This reflects high variations in the driving behavior. Besides, the vehicles have to
yield and therefore in some sequences come to a complete stop when there was
cross traffic present. The traffic situation is included in the data and cannot be
reconstructed.
Turn indicator usage
Of the 1169 turning sequences, there were only 938 cases where turn signal information was available from the turn signal lever or one of the four blinker signals. It
is unclear, whether the missing turn signal data is caused by a missing driver input
or a failure in the data recording. However, the large gap suggests that problems
in transferring the data into the unified simTD protocol caused the missing data. In
the available data, there were 18 misuses or rather missing turn signal activations;
10 times when turning left and in 8 right turning sequences. So, the majority of test
drivers used the turn signal correctly considering the available data.
The beginning of the turn indication varied intensely and was obviously influenced by
the lane structure of the intersection. There was a left turning lane and a combined
right turning and straight lane each on the main road (cp. Figure 3.1). The bus
lane was used for turning left approaching from east. So, when turning left the
turn signal was already used to show the intention of changing into the left turning
lane. For turning right, it depends on which lane the driver is in when approaching
the intersection. So, the turn signal indicates either a lane change to the right or
47
3 Driving Behavior
the actual turning intention. This was not distinguishable, because a lane level
localization was unrealizable with the GPS system used.
indicator right
indicator left
Frequency
40
30
20
10
0
100
80
60
40
20
Distance to intersection [m]
0
Figure 3.7: Frequency of turn signal usage dependent on DTI for main road
sequences
The DTI when starting the turn signal is displayed in Figure 3.7 and shows the
spatial distribution of the activation of the turn signal on the main road. The left
turning sequences have early turn signals, which is a sign of the assumed lane change.
The mean distance when starting the turn signal is about 80 m (sd = 23 m) when
turning left. For right turns, the turn signal is spread widely over the examined
approaching distance. Here, the mean DTI is 51 m (sd = 28 m). The high standard
deviations and the distribution displayed in Figure 3.7 indicate that the usage of
the turn signal especially for right turns seems spatially randomized and there is
no pattern observable. However, when analyzing the temporal distance (time to
intersection), i.e. the actual time before entering the intersection and starting the
turn signal, there is a connection observable (see Figure 3.8). The graph shows the
cumulative relative frequency for a better comparison of the two directions because
of the high differences in the amount of data for each direction. About half of the
drivers start the indication 7.9 s before entering the intersection when turning left
and 4.1 s for right turns accordingly. The graph shows also that about 2 s before
the intersection is reached, 80% of right-turning vehicles and all of the left-turning
ones have activated the turn indicator respectively.
The shape of the graph in Figure 3.8 indicates that the activation of the turn signal
is related to the velocity rather than the distance to the intersection. This was tested
for right turns using an approximation of the distribution of the velocity when the
turn signal is activated. The results show that the velocity at turn signal activation
turning right on the main road is nearly Gaussian distributed with a mean velocity
of 45 km/h and a standard deviation of 8.7 km/h.
48
Cumulative relative frequency
3.1 Data Analysis
1
0.8
indicator left
indicator right
0.6
0.4
0.2
0
20
15
10
5
Time to intersection [s]
0
Figure 3.8: Cumulative temporal distribution of turn signal activation on main road
For the side road, there was only one lane coming from north but an extra left
turning lane coming from south. Accordingly, drivers approaching from south might
have activated the turn signal to indicate the lane change. Due to the differences in
the shape of the side roads depending on the branch, the turn signal activation is
examined separately. In contrast to main road approaches, the turn signal activation
is localized in a smaller interval for the side roads. For turning right, there are
normal distributions to approximate the distance when the turn signal is activated
while the sample size for turning left approaching on the side roads is too small. It
stands out that despite the differences in shape of the side road branches and the
previous curves before entering the intersection, the turn signal usage for right turns
is similar approaching from either side, north or south. So, the turn signal is on
average activated at a DTI of 23 m (sd = 13 m) coming from north and of 24 m
(sd = 10 m) coming from south respectively.
The temporal distance is also similar and is displayed in Figure 3.9. There were
only a few sequences turning left approaching from the north and therefore are not
comparable. All other graphs show strong similarities even between left and right
when approaching from the south. The majority of the drivers seem to activate the
turn signal at similar temporal distances. For turning right when coming from the
north, half have activated the turn signal before 4.7 s. For the approaches from the
south, most activations appear at a TTI around 3.8 s when turning left and around
4.4 s when turning right respectively. All turning right sequences combined also
showed a nearly Gaussian distributed velocity at turn signal activation with a mean
value of 25 km/h and a standard deviation of 10 km/h.
49
Cumulative relative frequency
3 Driving Behavior
1
0.8
1
indicator left
indicator right
0.8
0.6
0.6
0.4
0.4
0.2
0.2
0
20
15
10
5
Time to intersection [s]
indicator left
indicator right
0
0
20
15
10
5
Time to intersection [s]
(a) coming from north
0
(b) coming from south
Figure 3.9: Cumulative temporal distribution of turn signal activation on side roads
Gear shift behavior
For the gearshift usage, the point of reference was the last manual gear change before
entering the intersection. Vehicles with automatic transmission were ignored, since
only human behavior was relevant. Unfortunately, the majority of vehicles in the
test were automatic. To filter for manual gear changes, the presence of the signal for
the clutch pedal was sought. Still, there were missing gear signals in the remaining
data. Overall, only 271 relevant sequences were extracted for the evaluation.
Since the observed behavior was similar in the approaches on both side road
branches, the approaching sequences are grouped together for the evaluation of the
gear shift behavior. Especially, the last part of the approach where the gear shift is
expected, is dominated by the preparation to yield to the cross traffic. For the main
road sequences only the turning scenarios are analyzed, since shifting is unnecessary
when driving with nearly constant speed. In the following, the gear selection was
examined and the DTI where gear changes appeared.
For all approaches on the side road, the average DTI when shifting was 22 m (sd =
20 m). The allocation of the DTI is presented in Figure 3.10. Thereby, downshifting
is predominant as expected. There were mostly gear changes from third as well as
from second gear into first gear (each with 32%) which is the most common gear
when entering the intersection. This indicated multiple full stop scenarios on the
side road. Another downshift from third into second gear appeared in 27% of the
cases. Here, drivers probably reach the intersection with no cross traffic present and
therefore can pass without stopping.
The intersection approaches on the main road were differentiated between left and
right turns. There were only a few sequences available. Therefore, the distribution
of the DTI when shifting is presented in Figure 3.11 as a probability density func-
50
3.1 Data Analysis
20
side road all
Frequency
15
10
5
0
100
80
60
40
20
Distance to intersection [m]
0
Figure 3.10: Frequency of shifting dependent on DTI for side road sequences
tion approximated from the data. Qualitatively, the gearshift appears closer to the
intersection when turning right compared to turning left. This corresponds to the
findings in the deceleration process (cp. Figure 3.5).
3
· 10−2
Probability
main left
main right
2
1
100
80
60
40
20
Distance to intersection [m]
0
Figure 3.11: Probability density of the DTI when shifting on the main road
For both directions, the gear was most often shifted from third into second (44%
left turning, 42% right turning). When turning left on the main road, a downshift
into first gear was observed in 28% of the cases. This seems to indicate a situation
with oncoming traffic that forces the driver to stop or nearly stop before turning.
Driving in either direction, the intersection was predominantly entered in second
gear. However, the gear choice may not be solely dependent on the driver’s preference
but also on the motorization of the vehicle and the transmission ratio which differed
in this analysis due to the different cars used for the test.
51
3 Driving Behavior
Turning behavior
The analysis of the turning behavior intends to identify the starting point of a
turning maneuver. This point was determined by the yaw rate, since the steering
wheel angle was not evaluable due to missing and implausible data and the extreme
noise in the steering wheel angle velocity. The turning behavior was only examined
for approaches of the intersection on the main road. This was because the curves
on the side roads before the intersection distort the examination and also due to an
insufficient amount of data especially for left turns.
Since a vehicle is slightly yawing even when going straight, turning is not indicated
by a yaw rate other than zero. Therefore, to determine a threshold for a turning maneuver, the going straight sequences were selected (TTI ∈ [0, 10]) and the standard
deviation of the yaw rate was calculated as a function of time. The extremum of
this function was chosen to be the threshold. In this case, the amount was 0.56◦ /s.
When the yaw rate first exceeds the absolute value of this threshold while the vehicle is within a DTI of 50 meters, this is declared the starting point of the turning
maneuver. Here, turning left implies a positive yaw value, while turning right is
negative.
The mean yaw rate profiles for left turns and right turns on the main road are
presented in Figure 3.12. In the figure, the dotted lines represent the standard
deviation. The starting point of the turn in the average curve is indicated by the
vertical gray line. Obviously, the left turn starts on average at a larger distance to
the intersection compared to the right turn. Also, the deviation is greater for turning
left. This difference appears because of the larger curve radius when turning left,
which is why the yawing is starting earlier. The intersection on the test area was
relatively big with multiple lanes. So, while the driver execute a right turn driving
similar paths, the left turn can vary more between the drivers. It depends on the
individual preferences to which extend a corner cutting is accepted. This leads to a
wider range of driven trajectories indicated by the high standard deviation for left
turns.
The evaluation of the sequences individually confirmed the results from the average
profiles. Here, the DTI and TTI at the starting point of turning was determined in
each sequence according to the process mentioned above. The beginning of a left
turn is 1.8 s (sd = 0.7 s) before entering the intersection and at a DTI of about 12 m
(sd = 8 m). For right turns, the yawing is initiated latter at an average TTI of 0.7 s
(sd = 0.4 s) and an average distance of 6 m (sd = 4 m) to the intersection. The
values indicate that a turning event initialized by the steering input of the driver
and consequently the yawing of the vehicle is distinguishable shortly before entering
the intersection. The steering is the final step of action clearly displaying the turning
intention. In terms of a warning or intervention strategy in case of a collision risk,
the time before entering the intersection area seems critically low.
52
Average yaw rate [◦ /s]
3.1 Data Analysis
main left
20
20
10
10
0
0
−10
−10
−20
−20
50
40
30
20
10
0
Distance to intersection [m]
(a) turning left
50
main right
40
30
20
10
0
Distance to intersection [m]
(b) turning right
Figure 3.12: Average yaw rate for left and right turning sequences on the main road
So, the location where the turn begins is clearly defined and is dependent on the
geometry of the intersection. The velocity as well as the acceleration at this point
seem normally distributed by approximation for both directions. The average speed
is 27 ± 13 km/h for left turns and 35 ± 6 km/h for right turns accordingly. The
higher variation in the velocity when turning left is explained by the interference
of oncoming traffic included in the data. The acceleration is more alike and the
drivers seem to brake in both maneuvers when starting to turn. For left turns,
the average acceleration is −1.4 m/s2 (sd = 1.2 m/s2 ). In contrast, the average
acceleration when the right turns start is −1.6 m/s2 (sd = 0.9 m/s2 ). Again the left
turn sequences have higher variations which relates to traffic interference.
In addition, the velocity at the beginning of the turn correlates with the distance
to intersection for turning right (r = 0.60) as well as for turning left (r = 0.86)
maneuvers. This means, drivers that start steering earlier with a tendency to cut
the corner are faster than others who tend to turn later driving a sharper curve
(smaller curve radius). This is reasonable regarding the physical perspective of a
turning maneuver. The maximum speed for driving stabilized in a curve is direct
proportional to the curve radius. A fast corner cutting turn is referred to as sporty
driving style.
3.1.3 Conclusion
The approaching behavior on the main road differed considerably according to the
driving direction. All differences are shortly summarized in the following boxes.
53
3 Driving Behavior
Turning left
average
velocity
Turning right
at
intersection
average
velocity
at
intersection
22 km/h
32 km/h
extremum in deceleration
monotonous progress of deceleration
early turn signal activation (turning
temporal distance when turn signal
lane) . all before TTI of 3 s
activation app. normally distributed
gearshift further away
gearshift closer
begin of turning further out
begin of turning closer to intersection
While the velocity is reduced with a higher intensity and also to a lower amount
when turning left, the right turn sequences show rather gradually braking. However,
turning left required yielding to oncoming traffic which was not excluded from the
data since information about the surroundings was unavailable. This is also an
explanation for the minimum in the average acceleration for left turning sequences.
The findings in the gear shifting behavior correspond to the velocity and acceleration
profiles and indicate that the shifting is executed earlier when turning left compared
to turning right. Also, the yawing starts even further before entering the intersection
due to the bigger curve radius when turning left.
On the side road, the preparation to yield to possible cross traffic was the predominant goal. Differences according to the direction of travel were unobservable.
Though, the shape of the road before entering the intersection was a factor. Here, the
differences influenced the deceleration process and the velocity respectively. Coming
from north, there was a stretched curve leading to the intersection while from the
south, there was a sharp left turn followed by a straight segment merging to the
intersection. This especially influenced the velocity shortly before the intersection
and also the visibility of the upcoming intersection. Although, the drivers passed
through the intersection multiple times, eventually learning its shape and position.
However, the intensity of the deceleration was similar for approaches from both sides.
Also, the usage of the turn signal was in corresponding temporal intervals before the
intersection especially for turning right. The gear changes were combined for all
side road approaches due to a lack of data and indicated the majority of gear shifts
roughly in-between a DTI of 5 to 25 meters. Also, the first gear as the predominant
choice shows that several drivers had to yield to cross traffic and therefore come to
a complete stop. Overall, the data for the side road can be utilized to develop a
compliance behavior prediction for intersection approaches with yield signs.
54
3.2 Naturalistic Driving Study
3.2 Naturalistic Driving Study
While in the first analysis the driving behavior was examined at one particular intersection, hereupon an exploratory naturalistic driving study was conducted. The
main goal was still to find different behavior patterns dependent on the direction of
travel at an intersection. Here, the focus was on driver inputs, e.g. brake pedal usage
and as previously mentioned the activation of the turn signal. The goal was to find
differences as a basis for the prediction algorithm introduced in chapter 4. Besides,
the influence of differences in the characteristics of the intersection were of interest
to determine possible similarities. This could potentially reduce the number of necessary prediction models for different intersection types and reduce the complexity
of the approach. The test drives were performed on public roads in Ruesselsheim,
Germany, with no influence on the traffic conditions.
Parts of this study were published in [48] and presented at the ITS World Congress
2015 in Bordeaux. This publication includes only the analysis of braking behavior
and the usage of the turning indicator with a smaller data sample (16 probands).
The usage of the brake when approaching an intersection without traffic present is
an indication of a reaction to the upcoming intersection. Here, the previous action
of leaving the gas pedal was also determined to examine the approaching behavior
in more detail. This provides a closer look at the transition from leaving the gas
pedal to pushing the brake (including a possible coasting phase). Also, the study
was later extended by 15 further probands driving the same route. This was also
used to validate the first results.
Turning right at an intersection without traffic signs (priority to the right) does
not require yielding to any other approaching vehicle. Here, deceleration if at all
is only necessary for a smooth turning maneuver and depends on the geometry of
the intersection and the approaching speed. On the opposite, turning left or going
straight at the same intersection (assuming it is X-shaped) requires looking out for
other vehicles. It is assumed that this increase in complexity of the driving task
leads to initiation of a deceleration process at an earlier stage further away for the
intersection. Comparing intersection layouts, it is expected that X-intersections are
perceived as more complex than T-junctions and therefore, the onset of braking is at
a greater distance to intersection (DTI). When approaching intersections with yield
and stop signs, there are no differences expected in brake onset varying the driving
direction. However, data in these situations might show a typical stopping behavior.
This could be used to create prediction models for non-compliant behavior.
3.2.1 Background
The observation of driving behavior in naturalistic intersection situations is crucial
to develop Advanced Driver Assistance Systems (ADAS) to reduce the high numbers
55
3 Driving Behavior
of accidents occurring at urban intersections (see section 1.1). Research has shown
that driving behavior when approaching urban intersections depends inter alia on
surrounding traffic [49], driving styles and driver states [50] and initial speed [51].
The previous data analysis also indicated differences related to the direction of travel
(see section 3.1). Also, the individual driver type plays a role especially for the
operated speed [52].
An overview of the recent development in the research of driving behavior at intersections is given in [53].
In this paper, the driving behavior is grouped in
prediction-based behavior and safety-based behavior analysis. The latter concentrates on safety issues when approaching an intersection, e.g. Perception Reaction
Time (PRT), dilemma zone and gap acceptance. Hereby, the dilemma zone is a
focus of research at signalized intersections where the driver behavior that leads to
red light violations is examined. Depending on the velocity driven and the traffic
light pre-emption, there is a dilemma zone. In case of a phase change from green to
amber, this is defined as the area where the driver is neither able to safely stop before
the traffic light nor to pass the intersection before the change to red. An overview
of the research on this topic is provided by Zhang et al. [54]. The prediction-based
behavior analysis focuses on the evaluation of data from human factors and vehicle
dynamics to create systems for maneuver predictions; mainly turning at the intersection. This prediction is fundamental for the development of ADAS in an urban
environment (cp. section 2.3), e.g. for a Left Turn Assist (LTA).
3.2.2 Methods
Participants
Overall, 31 drivers (five female) between the age of 22 and 48 participated in the
study. The mean age was 29.5 years (sd = 5.5 years). The mean driving mileage of
the past year was about 17,300 km (sd = 8,600 km) with approximately 25% in an
urban environment, 22% on rural roads and 53% on highways. All drivers were in
possession of a driver’s license for several years.
Test vehicle
In the study, an Opel Zafira Tourer was used with 130 hp (96 kW) and a 6 gear
automatic transmission. The vehicle is equipped with an Automotive Dynamic Motion Analyzer (ADMA) including a GSM modem and a GPS receiver for global
positioning with differential GPS correction. Further, there is an external GPS antenna on the roof (see Figure 3.13) for improved signal reception. This system has a
localization precision between 1.8 m down to 2 cm using real time kinematic (RTK)
[44]. The latter requires a registered chip in the GSM module and a stabled mobile network connection. Additionally, there is a separately installed GPS mouse
56
3.2 Naturalistic Driving Study
as backup system. This device has an accuracy of around 3 meters (depending on
shadowing effects and multipath scattering). The entire data logging was performed
with a CAN box by Vector which was connected to the CAN bus of the vehicle.
This box was connected via USB to a laptop to retrieve the vehicle’s state (velocity,
acceleration, yaw rate) and the driver inputs such as gas pedal position, brake pedal
position, steering wheel angle and turn signal lever status. Furthermore, there were
two USB cameras installed to record the scene in front of the vehicle and the driver.
The video sequences were only used post-hoc for the identification of surrounding
traffic and the validation of dynamic driving data.
Figure 3.13: Test vehicle with GPS antenna on the roof
Route and intersections
The route was on urban roads in Ruesselsheim, Germany. Overall, the drivers passed
20 relevant intersections and two of them twice from different directions leading to
22 relevant intersection approaches. For the evaluation, 15 of them were considered and subdivided according to the present traffic regulation and the shape of
the intersection (see Figure 3.14). The others were discarded because of irrelevant
intersection types, undesired anomalies in the geometry or high traffic volume. The
latter was an undesired and uncontrollable influence especially through preceding
vehicles. The intersection approach indices and variations are shown in Table 3.2.
The speed limit was 30 km/h along the entire route. All intersections had single-lane
approach roads. The area was mostly a residential estate with moderate traffic during the time the study was conducted. Surrounding and geometry of the evaluated
intersections were similar. The intersection angles were nearly perpendicular in all
cases.
57
3 Driving Behavior
21
22
20
4
6
15
16
14
13
17
18
8
9
10
12
Figure 3.14: OSM map [17] including relevant intersections (indices and driving
direction)
Design
The study was conducted in a partial within design with three factors: intersection
shape, regulation and driving direction. The intersection shape is varied between
T-junction and X-intersection, while the traffic regulation differs in priority to the
right, yield and stop. Each type of intersection was passed in all possible directions
(going straight, turning left or right) except for the one with a stop sign, which
was passed going straight. At a stop sign differences in the approaching behavior
dependent on the driving direction are not expected due to the prior task of stopping
at the intersection. Also going straight at a T-junction with a left branch was not
investigated since it is considered to be similar to driving down a normal road.
58
3.2 Naturalistic Driving Study
Table 3.2: Overview of analyzed intersections according to type
Intersection type
regulation
priority right
priority right
priority right
priority right
yield
stop
shape
T-junction (branch right) |–
T-junction (branch left) –|
T-junction
X-shape (crossroad)
X-shape (crossroad)
X-shape (crossroad)
Direction of travel
left
6
20
21
18
-
right
15
10 12
9
22
-
straight
13 16 17
14
4
8
All drivers included in a comparison analysis have passed the same intersections.
Unfortunately, there was a construction site on the route during one day of testing
which led to missing data at several intersections for 2 probands. Furthermore, the
route was driven only once in the first execution of the study. So, in case of obvious
traffic interference, e.g. a preceding vehicle, the intersection approach sequences was
discarded, since surrounding traffic is influencing the approaching behavior [49].
This was necessary since the traffic conditions were uncontrolled due to driving on
public roads. In case of missing information for driver inputs of one proband at just
one intersection approach, the mean value of all other drivers was inserted. In the
extension of the study, this was avoided by repeating the approach of the intersection
afterwards if necessary and driving the entire route twice.
Leaving the accelerator pedal (AccPdlLeave) and pushing the brake pedal (BrPdlPush) are indications of a reaction to the upcoming intersection. Therefore, the DTI
and velocity are monitored at the point when the accelerator pedal is released and
the point when the brake pedal is pushed. Also, the turn signal usage was evaluated
by tracking the turn switch activation (TrnSwAct). Here, again DTI and velocity
were of interest when the turn signal is activated. These are the dependent variables
of the analysis. While the DTI provides a spatial location of the reaction related
to the intersection, the combination of DTI and velocity also reveals a temporal
component by calculating the TTI. An interesting question is whether the drivers
show the same reaction at an intersection rather spatially or temporally correlated.
The DTI was not measured directly but determined through the GPS points retrieved from the measurements according to the procedure described in section 2.4.
Other than in the previous data analysis, here the intersection points were obtained
previously by a test drive with the ADMA system.
Procedure
The route was set with the navigation app Osmand which was installed on a
smartphone. This was positioned at the windshield. The app uses Open Street
Map (OSM) data [17] and was necessary to guide the driver along the predefined
59
3 Driving Behavior
route in a standardized setup. The instruction was to drive in a normal manner
without endangering anybody and to follow the route indicated by the navigation
app, which also had a voice output for the turning events. The study was conducted
under real traffic conditions. One lap took about 20 minutes. The instructor was the
front passenger and supervised the procedure by recording traffic events (e.g. crossing pedestrians). In the back seat, there was a technical supervisor who verified the
recording procedure on the laptop.
In a short questionnaire directly after the ride, the participants weighted in a five
point Likert scale their driving effort, driving performance, driving efficiency and
how safe and how natural they were driving. There was also a place for comments
to stressful and critical situations. This was conducted to determine a possible
influence of the chosen route of the behavior.
3.2.3 Results
As mentioned before, the analysis was conducted according to a repeated measures
design. Hence, the only sequences included were those in which a driver experienced
all factor combinations relevant to the respective analysis. Because of missing data
(reason explained in “Design”), the set of considered test drivers differs slightly between each analysis but is consistent within. The exact number of test drives is
reported accordingly. This was performed in a few exceptional cases to avoid an
overall data reduction.
The driving data was converted from CANape into Matlab. The detection of the
driver inputs was realized with Matlab scripts and in case of discrepancy checked
manually. This data was transferred to Excel. The statistical analysis was performed
using the Real Statistics add-in [55]. The following basics about statistical analysis
were retrieved from the Real Statistics website and the reference book by Bortz [56].
All datasets were tested for normal distribution with the Shapiro-Wilk test (α =
0.01). Another pre-condition required for a repeated measures analysis of variance
(ANOVA) is sphericity of the dataset. This means the correlations between variances
of different samples are homogeneous. If this requirement is not met, a correction
factor is used to reduce the degrees of freedom (df) for the test. Commonly the
correction factors by Greenhouse-Geisser GG which tend to be to low (conservative)
or by Huyhn-Feldt HF (slightly to high) are used to adjust the df values. In the
results, the declaration of the correction and the df adjustments is left out for the
benefit of comparability and confirmability (integer df) of the results. However, the
correction was calculated and it was checked whether significances still occurred.
If this was not the case, it is explicitly mentioned. As post-hoc tests, t-tests for
pairwise samples were used at a significance level of 5% and with Bonferroni alpha
correction. The effect size is specified by Cohen’s d. Small effects are around 0.2
while large effects are around 0.8. The graphs follow loosely a color coding for
60
3.2 Naturalistic Driving Study
the driving directions - turning left (red), turning right (blue) and going straight
(green). The error bars of the displayed values represent the confidence interval on
a confidence level of 95%.
From the questionnaire emerged that the drivers rated their driving behavior in the
test drive very natural with a mean of 4.2 (sd = 0.7) on the five point Likert scale.
Also, the driving performance and safety was assessed prevalently high with average
values of 3.8 and 3.9 (both sd = 0.8) respectively. The driving effort was moderate
in the middle of the scale with 2.6 (sd = 1.0). Most comments referred to parked
cars in combination with the narrow roads, which was a problem for the evaluation
in some places and a reason for the elimination of some intersections. Interestingly,
the subjective evaluation of the safety of the ride correlated with the average mileage
of the past year (r = 0.57). Thus, the drivers with more driving practice felt safer
on the ride.
Priority to the right intersections
For intersections without traffic signs, the priority to the right rule applies on German roads (cp. section 2.1). For this type, the study distinguished between two
shapes (T-junction vs. X-intersection) and all possible driving directions (left vs.
right vs. straight). There are three possible forms of T-junctions depending on the
direction from which the intersection is approached. Accordingly, there are only
two driving directions possible. Thereby, going straight at a T-junction with a left
branch was ignored, since it bears comparison with driving on a straight road if there
is no traffic present. Due to the missing third driving direction for T-junctions, a
combined evaluation in terms of a two-way analysis of variance (ANOVA) (factor
1 shape, factor 2 driving direction) is not applicable. In [48], this was evaded by
grouping the three forms of a T-junction and choosing representatives for each direction. Here, there are more T-junctions included in the analysis to find differences
and similarities between different orientations and to compare all of them with the
X-shape.
So, the X-intersections are examined in detail according to the driving direction
first. Here, differences in the approaching phase are of interest to infer the intended
driving direction from different behavior. Further, the results are compared with
the according variations of the T-shape where the same driving direction is possible.
Thus, the influence of the different shapes is evaluated. In this part, there were
28 complete datasets for the evaluation. Unfortunately, sequences of three drivers
were discarded due to the construction site. All others passed all intersections with
priority to the right regulation without interference.
X-intersection
The approaching behavior was compared between the three X-
shaped intersections (see Table 3.2) according to the driving direction (left, right,
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3 Driving Behavior
straight) which was the independent factor. A one-way repeated measures ANOVA
was performed for the DTI and velocity each for the events of leaving the gas pedal
(AccPdlLeave) and pushing the brake pedal (BrPdlPush). For the turn switch activation (TrnSwAct), two paired two-sample t-test were sufficient (each for DTI and
Distance to intersection [m]
velocity), since the turn signal is only activated when turning left or right.
70
60
50
left
right
straight
40
30
20
AccPdlLeave
TrnSwAct
BrPdlPush
Figure 3.15: DTI on driver inputs at X-intersections according to direction
There are significant effects for the distance to intersection (DTI) when leaving
the gas pedal (F (2, 54) = 17.48, p < .001) as well as for the DTI at the onset of
braking (F (2, 54) = 31.23, p < .001). Also, the turn signal activation is significantly
distinguishable between left and right turns (t(27) = 4.18, p < .001, d = 0.79). This
effect is considerably strong which is indicated by the high Cohen d value. It was
not detected in [48] with a smaller sample size. The results are shown in Figure 3.15
with all mean values and the confidence intervals.
It can be seen clearly that when turning right leaving the gas pedal as well as pushing
the brake pedal is executed closer to the intersection in comparison to turning left
or going straight. This was also confirmed by post-hoc t-tests with a Bonferroni
alpha correction (α = 0.0167). Turning right was significantly different from both
other driving directions (all p < .001). The turn signal is also activated closer to
the intersection when turning right compared to turning left. The turn signal was
forgotten in two approaches for turning left and three times for right turns. Data
was still available since these probands repeated the approaches using the turn signal
in one of two runs.
The alignment of the driver inputs (x-axis) in Figure 3.15 was chosen to display the
chronology of events. It shows that on average the turn signal is activated during
the coasting phase between leaving the gas pedal and pushing the brake. However,
a detailed look into the sequences for each proband individually creates a different
picture. Here, only 11 probands (38.3%) activated the turn signal while coasting
when turning left and 8 (28.6%) when turning right respectively. The same number
of drivers turning left (11/38.3%) and even half of them turning right (14/50%) are
62
3.2 Naturalistic Driving Study
starting the turn signal within 2 seconds before leaving the gas pedal. The rest
(6/21.4% each) activated the turn signal after braking. This indicates that the turn
signal by itself is not an adequate parameter for an early prediction of a turning
event.
Velocity [km/h]
40
35
left
right
straight
30
25
20
AccPdlLeave
TrnSwAct
BrPdlPush
Figure 3.16: Velocity on driver inputs at X-intersections according to direction
In addition to the DTI, the velocity was examined at the aforementioned driver
inputs and also compared according to the driving direction at the different Xintersection approaches. There is also a significant effect for all three events - leaving
the gas pedal (F (2, 54) = 23.83, p < .001), pushing the brake pedal (F (2, 54) =
18.39, p < .001) and activating the turn switch (t(27) = 7.2, p < .001, d = 1.36).
The results are shown in Figure 3.16. Here, turning left differs significantly from
both other driving directions (all p < .001). The velocity is considerably high in all
cases and even higher than the speed limit on the route. This could relate to the
characteristics of the intersection (No. 21) and the road before which was slightly
wider and the intersection was well visible.
T-intersections For the comparison of intersections of different shapes, the approaches at T-junctions and X-intersections are compared when driving in the same
direction. This means intersections where the same driving direction is possible are
compared with each other (column-wise comparison according to Table 3.2). Note
that there are multiple intersections of the same shape for T-junctions (cp. Table 3.2). Again, the DTI and velocity for leaving the gas pedal, pushing the brake
and activating the turn signal are examined. In the following, to distinguish between the three different orientations of T-junctions, they are labeled T-oriented
(T), T-junction with left branch (-|) and T-junction with right branch (|-).
Turning left is compared between the X-intersection and two T-junctions of
different shape, one T-oriented (No. 20) and another with a left branch (No. 6).
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3 Driving Behavior
A repeated measures ANOVA was performed for both distance to intersection and
velocity at driver inputs to examine differences in the approaching behavior.
The distance to intersection (DTI) shows significant effects for all three driver
inputs. The mean values with confidence intervals are shown in Figure 3.17. The
DTI when leaving the gas pedal is significantly different (F (2, 54) = 9.91, p < .01)
as well as when pushing the brake (F (2, 54) = 12.42, p < .001). While there are no
significant differences between the X-intersection and the T-oriented junction, only
the behavior at the T-junction with left branch differs significantly from the other
two shapes in leaving the gas pedal (both p < .01) as well as in pushing the brake
(-| vs. T p < .001 and -| vs. X p < .01). Here, the drivers leave the gas pedal and
Distance to intersection [m]
also brake closer to the intersection.
70
60
50
X-shape
T-oriented
branch left
40
30
20
AccPdlLeave
TrnSwAct
BrPdlPush
Figure 3.17: DTI on driver inputs turning left at intersections of different shape
The turn switch activation also differs significantly (F (2, 54) = 11.81, p < .001).
Here, the turn signal is activated closer to the intersection when turning left at the Tjunction with left branch compared to the T-oriented intersection (p < .01, d = 0.60)
and also compared to the X-intersection (p < .001). In addition to the already
reported missing turn signals for X-intersections, one driver forgot to indicate the
turn at intersection No. 6 (branch left) and two at intersection No. 20 (T-oriented).
The results for the velocity are shown in Figure 3.18. There are significant differences
in the velocity at all driver inputs. The gas pedal was released at different velocities
(F (2, 54) = 71.68, p < .001) turning left. At the X-intersection the velocity was
higher compared to the T-oriented intersection (p < .0167, d = 0.5) as well as
compared to the T-junction with left branch (p < .001, d = 0.92). There is also a
clear difference between both T-junctions (p < .001, d = 0.77). The velocity when
activating the turn signal also shows a significant effect (F (2, 54) = 7.14, p < .01).
Here, the drivers were driving slower when using the turn switch to turn left at the Tjunction with left branch as compared to both other intersections (both p < .001). It
stands out that there is a high variance in the velocity at the T-oriented intersection.
64
3.2 Naturalistic Driving Study
The usage of the brake pedal shows only differences between the X-intersection and
T-junction with left branch (p < .001, d = 0.92).
Velocity [km/h]
40
35
X-shape
T-oriented
branch left
30
25
20
AccPdlLeave
TrnSwAct
BrPdlPush
Figure 3.18: Velocity on driver inputs turning left at intersections of different shape
The steps in the velocities between the different intersections at leaving the gas
pedal and switching the turn lever are also visible in the DTI shown above (cp.
Figure 3.17). This corresponds to the closer the activation the lower the velocity. It
could be assumed that the temporal distance to the intersection is still similar in all
cases even though there are differences in the spatial distance indicated by the DTI.
A short look into the time to intersection (TTI) values calculated though dividing
the distance to intersection by the velocity confirm the assumption for leaving the
gas pedal. Here, at all intersections the average TTI is between 5.3 s (branch left)
and 5.8 s (X-shape). However, the turn signal activation and also the brake are both
still activated significantly closer to the intersection temporally.
Since the average distance between gas pedal release and brake pedal push is comparable for all three intersection shapes, the coasting phase seems similar and only
slightly shifted towards the intersection for the T-junction with left branch. The
coasting phase is spatially averaging around 15 meters which corresponds to a time
of about 1.6 s considering the driven speed.
Turning right
is compared between the X-intersection and three T-junctions
of which two are T-oriented (No. 10 and No. 12) and one with a right branch.
The reason for choosing two intersections of the same shape is to determine if the
differences found are explained exclusively by the differences in shape or if there are
other factors which were not considered but did influence the approaching behavior.
There are significant differences in the DTI when leaving the gas pedal (F (3, 81) =
10.74, p < .001) and for pushing the brake pedal (F (3, 81) = 27.61, p < .001). The
distance at turn signal activation indicates no significant effect. Figure 3.19 shows
the mean values and confidence intervals. It stands out that at intersection No. 10 the
drivers left the gas pedal and also braked further away from the intersection entrance
65
3 Driving Behavior
point. All results from the comparative t-tests (Bonferroni adjusted α = 0.0083) are
shown in Table 3.3. Here, the values above the main diagonal belong to leaving the
gas pedal, while the values below are the comparison between the intersections for
using the brake pedal.
Table 3.3: Results of post-hoc tests for turning right at intersections of different
shape
X-shape
vs.
X-shape
T-oriented (10)
T-oriented (12)
branch right
p < .001
p < .001
T-oriented
(10)
p < .001
T-oriented
(12)
branch right
p < .001
p < .001
p < .001
p < .0083
BrPdlPush
p < .001
AccPdlLeave
The results indicate differences for one of the T-oriented intersections (No. 10) even
compared to the intersection of the same shape (No. 12). A further in-depth analysis
of the video material provided an indication for this conspicuous finding. The drivers
left the gas pedal and pushed the brake prematurely at intersection No. 10 when there
were parked cars at the side close to the intersection entrance point. The parked cars
narrowed the road down leaving only space for one vehicle to pass. Consequently,
the driver inputs in these sequences could be a reaction to the parked cars rather
than the upcoming intersection distorting the actual results. This influence on the
driving behavior was not considered in the execution of the study and was not a
Distance to intersection [m]
factor. Also, it was not influenceable when driving in public traffic.
70
60
X-shape
T-oriented (10)
T-oriented (12)
branch right
50
40
30
20
AccPdlLeave
TrnSwAct
BrPdlPush
Figure 3.19: DTI on driver inputs turning right at intersections of different shape
However, while the T-junction with right branch showed no significant differences
to the X-intersection, both T-oriented intersections differ significantly in the DTI
when braking. In summary, the T-oriented intersection No. 10 stands out, because
of interference through parked cars and the different shape which led to differences
66
3.2 Naturalistic Driving Study
in the braking behavior, while the turn signal was activated in the same spatial
distance to the intersection at all four intersections. Although, a high number of
drivers missed using the turn signal especially at the T-junction with right branch.
Overall, there were 9 turn signal violations at T-junctions in addition to the 3 missed
signals turning right at the X-intersection. Two probands even forgot to activate the
turn signal multiple times at different T-junctions. Three drivers forgot it twice at
the same intersection leading to missing data (reduction of samples for TrnSwAct).
The examination of the velocity for turning right showed significant differences for
all three driver inputs, leaving the gas pedal (F (3, 81) = 5.05, p < .01), pushing
the brake pedal (F (3, 81) = 8.86, p < .001) as well as activating the turn signal
(F (3, 69) = 6.95, p < .001)1 . For leaving the gas pedal and using the brake the
velocity driven was only significantly higher for intersection No. 12 (all p < .0083).
The velocity when using the turn signal differs between intersection No. 10 and the
other T-oriented intersection No. 12 (p < .001). Another significant effect is between
the T-oriented intersection No. 12 and the X-intersection (p < .001). While at one
T-oriented intersection (No. 10) the velocity is rather lower, the drivers were going
faster at the other T-oriented intersection (No. 12) on average. The results are
displayed in Figure 3.20.
Velocity [km/h]
40
35
X-shape
T-oriented (10)
T-oriented (12)
branch right
30
25
20
AccPdlLeave
TrnSwAct
BrPdlPush
Figure 3.20: Velocity on driver inputs turning right at intersections of different shape
A look in the time to intersection (TTI) shows again, that there is an interrelation between the DTI and the corresponding velocity at the point of driver inputs. Ignoring intersection No. 10 which is already identified as an exception, the
temporal distance for leaving the gas pedal is between 4.7 s (T-oriented No. 12)
and 5.3 s (T-junction branch right). In between, there is the X-intersection with
4.7 s.
A corresponding test shows no significant differences for the three in-
tersections (F (2, 54) = 2.60, p = 0.08).
For the turn signal activation, there
is also no significant difference in the temporal distance for all four approaches
(F (3, 69) = 0.90, p = 0.44). This is simultaneously to the spatial distance where
1
smaller p with df adjustment GG = 0.55, F (1.7, 38.0) = 6.95, p < .05
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3 Driving Behavior
there was also no difference (see Figure 3.19). Though, the differences found in the
velocity when starting the turn signal are having no effect on the temporal distance.
For braking, besides the high TTI of 5 s at intersection No. 10, there is only one
significant difference between T-oriented intersection No. 12 and the X-intersection
(t(27) = 3.61, p < .0083, d = 0.68). Consequently, the braking behavior when turning right seems to be different at T-oriented intersections which is already indicated
by the comparisons of the DTI and the velocity.
Going straight at an unregulated intersection (priority to the right rule) requires
a deceleration to yield to potentially approaching vehicles from the right. Hence,
there are only two different shapes compared here, the X-intersection and three Tjunctions with right branch. Again, intersections of the same shape are included in
the comparison to identify other possible factors influencing the driving behavior.
Here, only leaving the gas pedal and pushing the brake is examined, since the turn
signal remains untouched when passing the intersection going straight. As before, the
DTI and velocity are compared at both driver inputs conducting multiple ANOVAs.
The DTI shows significant differences when leaving the gas pedal (F (3, 81) =
54.76, p < .001) as well as when initializing the brake (F (3, 81) = 62.69, p < .001).
Figure 3.21 shows clearly, that both driver inputs are executed further away when
approaching the X-intersection. This is also confirmed by corresponding t-tests. Additionally, one of the T-junctions with right branch (No. 17) also stands out when
leaving the gas pedal (all p < .001) and when using the brake (all p < .001). The
other two T-junctions with right branch (No. 13 and No. 16) show no significant
Distance to intersection [m]
differences in the DTI.
70
60
X-shape
branch right (13)
branch right (16)
branch right (17)
50
40
30
20
AccPdlLeave
BrPdlPush
Figure 3.21: DTI on driver inputs going straight at intersections with different shape
A detailed analysis of the sequences showed that the premature driver inputs at
intersection No. 17 appear consistently and are not linked to any eye-catching events
or objects traceable in the present video material. An additional inspection of the
route revealed differences in the surrounding of the intersections. On the right
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3.2 Naturalistic Driving Study
parcel before intersection No. 17, there was a high hedge which appeared to be
an obstruction of sight. Comparing the surroundings with the other T-junctions
showed that the visibility constraint present at intersection No. 17 is considerably
more severe. Consequently, the drivers can only see oncoming traffic from the right
branch when closer to the intersection. This requires a lower speed to maintain the
ability to yield. The data indicates that this it is realized by the preceding release
of the gas pedal and premature braking.
For the velocity, there are significant differences neither when leaving the gas pedal
nor when pushing the brake (cp. Figure 3.22). At the former, the drivers are
keeping a speed slightly above 30 km/h. Since the coasting phase is rather short, the
velocity declines marginally before the braking starts. With an average 15 meters,
coasting is the longest at X-intersections. The temporal distance for the driver inputs
correspond to the spatial distance. The approaching behavior shows significant
differences between the two compared shapes, X-intersection and T-junction with
right branch.
Velocity [km/h]
40
35
X-shape
branch right (13)
branch right (16)
branch right (17)
30
25
20
AccPdlLeave
BrPdlPush
Figure 3.22: Velocity on driver inputs going straight at intersections of different
shape
Signposted intersections
In the study, the drivers passed three intersections with a traffic sign and one of
those was passed twice from different directions (see yield intersection in the center
of Figure 3.14). Each direction was examined for intersections with a yield sign,
while there was only one intersection with stop sign which was passed going straight.
The approaches of the yield intersections were compared according to the driving
direction (left, right, straight) representing the independent variable. The dependent
variables again were the distance to intersection (DTI) and velocity when leaving
the gas pedal (AccPdlLeave), when pushing the brake pedal (BrPdlPush) and when
activating the turn switch (TrnSwAct). Due to a construction site on one day of
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3 Driving Behavior
testing, there was missing data for three test drivers. Though, overall there were 28
samples for the comparison of the approaches on yield intersections. Due to high
traffic interference in the approaches to the stop intersection, multiple sequences had
to be discarded. Therefore, the stop intersection is only qualitatively compared to
the yield intersection approaches. The mean values are included in the graphs of the
yield intersection.
The distance to intersection (DTI) when leaving the gas pedal at yield intersections
shows significant effects (F (2, 54) = 16.91, p < .001) as well as when using the brake
(F (2, 54) = 25.06, p < .001). In both cases, the driver inputs occur closer to the
intersection when turning left compared to turning right and going straight (all
p < .001). However, there is no effect between turning right and going straight. The
paired-sample t-test comparing the DTI at turn signal activation shows a significant
difference (t(25) = 4.40, p < .001, d = 0.88). Here, three sequences were disregarded
due to missing turn signals. The turn signal is also initiated closer to the intersection
when turning left. These effects remained undiscovered in the previous analysis [48]
with smaller sample size, although the tendency began to show there. The mean
distances with confidence intervals are displayed in Figure 3.23. Qualitatively, the
DTI at driver inputs approaching the stop intersection is similar to turning right
Distance to intersection [m]
and going straight at yield intersections.
60
50
yield left
yield right
yield straight
stop straight
40
30
20
10
AccPdlLeave
TrnSwAct
BrPdlPush
Figure 3.23: DTI on driver inputs at signposted intersections
The comparison of the velocity results in significant effects in all examined driver
inputs at yield intersection approaches similar to the DTI. Here, the velocity when
leaving the gas pedal (F (2, 54) = 17.73, p < .001) as well as when starting to brake
(F (2, 54) = 15.04, p < .001) is significantly lower when turning left (all p < .001).
There appears to be no difference in the velocity between turning right and going
straight at yield intersection. Although, the variance in the sequences going straight
is obviously lower. These results correspond to the DTI at driver inputs. However,
the examination of the temporal distance (calculated TTI) also indicate a difference
for the left turn sequences which is about 1 s below the TTI of the other two direc-
70
3.2 Naturalistic Driving Study
tions. While the coasting phase appears to be of similar spatial (app. 10 m) and
temporal (app. 1.2 s) length on average, the initiation of the deceleration process
just starts with an offset when at the yield intersection turning left.
Since the turn signal activation occurs considerably closer to the intersection when
turning left, the velocity here is also significantly lower than when turning right
(t(25) = 2.84, p < .01, d = 0.57). The means are displayed in Figure 3.24 including
the velocities for AccPdlLeave and BrPdlPush at the stop intersection which is
slightly above the velocities for going straight at the yield intersection. Interestingly,
the velocity when leaving the gas pedal in the turning left sequences is below 30 km/h
in average.
Velocity [km/h]
40
35
yield left
yield right
yield straight
stop straight
30
25
20
15
AccPdlLeave
TrnSwAct
BrPdlPush
Figure 3.24: Velocity on driver inputs at signposted intersections
During the execution of the study, several parked cars along the route were observable. Unfortunately, due to the residential area, parked cars at the side of the road
were unavoidable. In the approach of intersection No. 18 (yield left), the concentration of parked cars was especially high and were present for all test drives. This
was reaffirmed in an additional examination of the video material. The obstruction
narrowed the road down to a width only passable by one vehicle along most of the
latter part of the approach.
3.2.4 Discussion and Conclusion
The results indicate that there are differences in the preparatory behavior in dependency of the direction of travel as well as the shape of the intersection. The
former is shown in the analysis of the X-intersection approaches. There, the deceleration process in form of leaving the gas pedal followed by pushing the brake
was initiated spatial and temporal closer when turning right. In the comparison of
different shapes (X-intersection vs. T-junction), differences as well as similarities
occurred. So, the behavior was alike when turning left at the X-intersection compared to the T-oriented intersection. This arose from the similarities in the DTI
71
3 Driving Behavior
and in the velocity when leaving the gas pedal and even clearer when using the
brake. Similar commonalities exist between the X-intersection and the T-junction
with branch right when turning right. Here, the similar behavior is also indicated
by the spatial adjacency and related velocity at the point of braking.
Contrariwise, there are noticeable differences in the comparisons of the shape of unregulated intersections (priority to the right). In the following cases these differences
are assumed to trace back to the variation of intersection shapes. So, the deceleration process turning left at the T-junction with branch left is executed considerably
closer to the intersection compared to at the X-intersection or T-oriented intersection. This can be explained through the missing factor of possible traffic from the
right at this shape of intersection. The driver only has to yield to oncoming vehicles that are early visible and where excluded in the study by discarding sequences
with traffic present. This way the left turning maneuver is executed without interference, in contrast to turning left at a X-intersection or a T-oriented intersection
where the driver has to prepare to yield to a potentially approaching vehicle from
the right. When turning right, differences appeared at onset of braking between the
T-oriented intersection and both other shapes in the comparison (X-intersection and
T-junction with branch right). Here, the brake was pushed earlier at the T-oriented
intersection (No. 12). This could be related to the fact that when approaching a
T-oriented intersection visually the road ends looking ahead, which brings a certain
lack of clarity about the shape and geometry of the upcoming intersection. The
comparison of driving straight at the X-intersection compared to the T-junction
with branch left showed also differences in the behavior. It is assumed that the
later brake response in the T-junction layout might be related to the higher complexity of the X-intersection. Additionally, the analyzed X-intersection had a slight
misalignment, which might have additionally increased complexity in comparison
to the T-junction. All results related to differences in the shape are displayed in
Table 3.4. The same color refers to similarities while “D” stands for “different from
driving the same direction at intersections of other shape.”
Table 3.4: Overview of results from comparison according to shape
Intersection shape
X-intersection (crossroad)
T-junction (branch right) |–
T-junction (branch left) –|
T-oriented junction
left
D
Direction of travel
right
straight
D
D
D
Some differences in the approaching behavior are connected to previously unconsidered influencing factors such as parked cars alongside the road. Parked cars are
perceived as relevant objects in traffic scenes and can be potential hazards [57], e.g.
a person within the parked car opens the door or the driver pulls out of the park-
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3.2 Naturalistic Driving Study
ing lot. Thus, parked cars influence the driving behavior as previously examined in
the results of turning right at the T-oriented junction No. 10 with priority to the
right regulation. In the yield intersection approaches, the unexpected differences for
turning left and the suspicious low velocity when leaving the gas pedal are also indications for the influence of the parked cars present there. However, the reactions
seem contrary, since at the yield intersection turning left the drivers decelerated
later but were already driving slower, while at the T-oriented junction the deceleration starts earlier and there were no differences in the velocity compared to the
other approaches. It is expected that the length of the narrow zone emerging from
the obstruction (parked cars) was a factor here. In the approach towards the yield
intersection turning left, there were multiple parked cars present alongside, while
at intersection No. 10 (T-oriented turning right) only single parked cars led to an
obstruction.
In general, it stands out that the intervals (error bars) for the spatial distance when
leaving the gas pedal are obviously wider than for activating the brake. A further
look into the data shows the reason. There is a more individual preference leaving the
gas pedal depending on personal manner of driving related to coasting. Otherwise,
the onset of braking is located in a more fixed spatial frame for all drivers. So, using
the engine brake and aerodynamic resistance in the coasting phase for reducing
the velocity when approaching an intersection is believed to be an individual driving
characteristic. However, the spatial and even the temporal distance where the drivers
start to brake is of high conformity: there is a relatively small distance interval were
all drivers feel the necessity to decelerate as preparation to pass the intersection.
In contrast, the turn signal usage showed high variations in the DTI and velocity
indicated by the error bars in the graphs. This reveals an individual preference
of operating the turn switch. There are sequences in the analysis where the turn
signal was activated before leaving the gas pedal as well as such later before pushing
the brake pedal and also after the brake. At X-intersections, the turn signal is
activated spatially in average between leaving the gas pedal and pushing the brake.
The operation of the turn switch at T-junctions is equally distributed to the three
zones previously described. The only exception is turning right at T-junction No. 10
(T-oriented) where the turn signal occurs after the brake. Here, the influence of
the parked cars close to the intersection plays a superior role. Apart from that, a
late activation of the turn signal was also observed at yield intersections even to a
point directly before entering the intersection. Also, the high number of missing turn
signals was eye-catching. Overall, the turn switch remained untouched in 21 turning
sequences. The data show that one proband stands out as exceedingly oblivious
missing to indicate eight turning maneuvers. This is extraordinary in consideration
of the partial test environment created by such a study. There was no obvious reason
to explain this incident. It is expected that the average absence of turn signals is
even higher in an unobserved driving surrounding. This uncertainty and the high
73
3 Driving Behavior
variance in the location of the turn signal illustrates, that it is not an overall reliable
parameter to indicate a turn maneuver.
Another anomaly in the approaching behavior was examined at intersection No. 17
(T-junction with branch right). Here, a visual constraint was present in form of a
high hedge that reduced the visibility of possible approaching vehicles from the right
branch for the driver. When choosing a route for a study concerning intersection
crossings, a detailed characterization of the line of sights is an important requirement
since small differences can influence the driving behavior. This is a general challenge
of naturalistic driving studies conducted in a real traffic surrounding beside the
uncontrolled traffic that might interfere with the proband. The major advantage
why natural driving studies are of interest is the observation and examination of
driving behavior in a realistic driving environment. Therefore, the results are of
high external validity, since the data was retrieved in a natural driving environment.
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4 Prediction Algorithm
The prediction of the driver intention is of high relevance for the development of
driver assistance as well as automated driving. Though, critical situations may arise
dependent on the maneuver the driver intends to execute. In intersection scenarios,
the driving direction is such a crucial factor (see Figure 2.3 in section 2.3). Here, the
system approach concentrates on the prediction of the ego driver intention. However,
the algorithm is designed with the thought of adapting the system to predict the
driver intention of other vehicles using sensor data.
Since the driver is in full charge of the vehicle control at present, only the turn signal
is an indication of a turning scenario. For the prediction of the ego vehicle, it is easily
retrievable but not always reliable (cf. subsection 3.2.4). It is also used for indication
of lane changing or passing maneuvers that may occur before the intersection (cf.
section 3.1.2). Also, a misuse is possible or rather a missing deactivation after the
last utilization. However, a solid prediction should be established by taking into
account other operational input of the driver. The analysis in section 3.1 concludes
that the vehicle dynamics as a result of the driver’s operation are indications for his
or her intended direction of travel. However, the prediction algorithm introduced
here is based on probabilistic methods. In general, these methods inherently hold a
trade-off between the temporal horizon of the prediction and its validity.
For establishing a solid prediction, a framework is created and evaluated. Several
parameters and the dataset for learning is varied to find an optimal and robust design
and gain experience about the performance level of the approach. The evaluation is
conducted offline using the database introduced in section 3.1. The overall concept of
the framework and some test results were published in [58]. Further, the framework
is adapted for a real-time application. This is implemented to run in a test vehicle.
Though, the application is predicting the driving direction live in a real driving
environment. The realization in a test vehicle was published in [59] and is introduced
in the next chapter.
4.1 Framework
First of all, a prediction framework is established to determine the driving direction
when approaching an intersection. The prediction is methodologically based on
Hidden Markov Models (HMMs) which are introduced in subsection 2.3.2. Further,
a four-way intersection is considered as standard intersection. Thus, there are three
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4 Prediction Algorithm
possible driving directions. The driver can turn left, turn right or go straight. The
possibility of a U-turn remains unconsidered.
The framework consists of three different HMMs each for one possible driving direction (λleft , λright , λstraight ). This is similar to other approaches (cp. [40]). Within the
models, the observable symbols are the measurable driving data while the hidden
states are an abstract construct. They could be imagined as indefinite states in the
decision process of the driver which are not observable. However, a detailed definition or classification of the hidden states is unnecessary for the implementation,
since the prediction results from the assignment of a sequence to a model representing a driving direction. Therefore, the hidden states are not contemplated in detail
and besides are different for each model.
The transition matrix A is chosen to be a dense matrix, i.e. all hidden states are
directly connected. In this case, more parameters need to be estimated (cp. subsection 2.3.2). Although, one could argue that the decision process briefly described
by the hidden states is temporal linear inducing a left-right-model as in [39], which
would lead to a sparse matrix. However, to allow the decision to change during
the intersection approach, a dense matrix is chosen. This also increases the robustness with respect to noisy driving data. This means in detail, if there were zeros
in the transition matrix, errors in the driving data would result in implausible hidden state sequences. The consequences are errors in the forward algorithm routine,
i.e. a sequence appears unrecognized by one or more models. So, the framework
could exclude one or more driving directions during the approaching process without revision. In the worst case scenario, all models could crash leaving no result to
report. This would be an undesirable progress in the prediction, since one scenario
will definitively occur. A stop before the intersection is unconsidered.
The size of the observation matrix B is depending on the number of hidden states N
and the number of observable symbols M . The connection of the driving data and
the set of symbols in the models is explained in detail in the following subsection
“Input data”. The starting distribution π is of less importance in this application,
since the progress is more of interest than the starting state. Also, in an ergodic
model where each state is reached from any other with a certain probability, the
starting state loses relevance the longer the sequence evolves.
All parameters are determined by a learning process using the Baum-Welch algorithm (see Figure 2.5). This requires a dataset of driving sequences. Logically,
each model learns with the according sequences related to the driving direction.
The learning algorithm is run several times with different starting conditions due
to its characteristic of leading to a local optimum. Here, ten times was chosen as
a compromise between run-time and performance. The parameter set best describing the learning dataset is selected. The required driving data contains intersection
crossings including the information of the actual driving direction. The database
76
4.1 Framework
retrieved from the field test and evaluated in section 3.1 will be utilized. As mentioned in the introduction of the method, the quality and size of the dataset for
learning the parameters is decisive for the performance of the prediction. Especially,
the proportion between degrees of freedom of the model and amount of data for
learning is relevant for the generalization of the system. This refers to a memory
effect which means a stochastic model is only capable to recognize the dataset utilized for learning without the ability to find corresponding patterns in other data.
Therefore, several variations are tested in the evaluation to find a balance between
number of parameters and size of dataset. The remaining data is used for estimating
the performance of the established prediction framework.
This structure has several advantages compared to one HMM with the driving direction as hidden states. First, the parameters of the separate models can be retrieved
by the Baum-Welch algorithm. Also, the probabilities for a sequence belonging to
one of the models can be compared to determine the validity of the prediction. Besides, an advantage of the overall framework is the potential to retrieve a prediction
outcome at any time during the approaching of the intersection. Since the models
inherently contain patterns of the approaching progress retrieved from the learning
dataset, even a small sub-sequence can be evaluated and recognized. So, the point
of the prediction is not fixed spatially or temporally as it is in other approaches
with different methods (cf. subsection 2.3.1). This enables a driving prediction even
in an early state, way before entering the intersection, which is highly desirable in
terms of planning a warning and intervention strategy. However, the validity of
the prediction is questionable at high distances but increases the further the vehicle
closes in on the intersection. This corresponds to the behavior analysis, where differences observable in the driving dynamics become distinctive in close range of the
intersection.
Using the framework for prediction, a sequence of driving data serves as input.
The data consists of a set of predefined dynamics structured in an overall discrete
time interval. This means, there are values for each attribute at all contained time
steps. This sequence is evaluated by each HMM using the forward algorithm. The
resulting probabilities (P (O|λleft ), P (O|λright ), P (O|λstraight )) are compared, and the
sequence is assigned to the direction according to the model that it best relates to
(highest probability). Since the value of the probability (P (O|λ)) decreases with
increasing length of the sequence, the forward algorithm runs with the logarithm
of the probability. This is also called the log-likelihood. Also, working with the
logarithm of the probability prevents problems with the precision of the floatingpoint numbers in a computer system. So, even small probabilities are representable
without the risk of truncations to zero. Conclusively, the framework is designed to
run dynamically multiple times during the intersection approach.
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4 Prediction Algorithm
The code is implemented in Matlab. For working with HMMs, an existing toolbox
was used [60]. Here, the forward algorithm as well as the Baum-Welch algorithm
are included. The simTD data introduced and analyzed in section 3.1 serves as
database. The dynamic driving data available is chosen as input in particular the
velocity, acceleration and yaw rate. Here, the main road sequences are included
only. On the one hand, this is necessary because of the traffic interference included
in the data. Also, coming from the side branches (north, south) the approaching
behavior was dominated by the intention to yield and therefore to slow down or stop
the vehicle in case of cross traffic. So, there were no significant differences in the
approaching behavior according to the driving direction (see Figure 3.3). On the
other hand, the prediction is part of the situation assessment to identify potential
conflicts in the intersection area. This risk of a conflict declines, in case the driver
complies with the formality to yield. Also, the lack of dynamic information of a
stopping vehicle reduces the ability to predict the driver intention. Here, the turn
signal might be the best indication.
The advantage of using the vehicle dynamics rather than driver inputs is the adaptability of the framework to predict the driving direction of other vehicles as well.
This could be accomplished by sensor information. So, while from the perspective of
an ego-vehicle the driver intention of others remains unknown, the framework could
estimate the behavior based on sensor information. So, velocity, acceleration and
yaw rate were selected in main road sequences since they showed a distinguishable
progression in the approaching process. In addition to the driving data, the distance to intersection (DTI) is included as observation in the HMMs. The distance
is determined by utilizing the recorded GPS data of the vehicles and the retrieved
GPS position of the intersection from map data. For the calculation, the method
described in section 2.4 was used. While the sequence of the vehicle dynamics describes the process, including the DTI inherently encodes a spatial dependency to
observe a certain state in the vehicle dynamics. This increases the robustness of the
prediction in case of interference, e.g. by a preceding vehicle, since this changes the
process but not necessarily the outcome. For example, when a slow preceding vehicle
forces the driver to brake way before the intersection, although the intention is to
go straight, the process seems to indicate a turning intention while in combination
with the distance this assumption is weakened.
As explained in subsection 3.1.1, the driving data is preprocessed resulting in intersection approach sequences over a distance of 100 meters before entering the
intersection. The analysis showed that there are hardly differences in the driving
behavior or rather vehicle dynamics at this distance. So, a prediction before that
point seems unreasonable. Also, the dynamic driving data was interpolated to a
sampling rate of 100 ms. All sequences are label according to the the direction of
travel (left, right, straight). Unfortunately, the number of sequences for the three
maneuvers is highly diverging causing some difficulties in the testing of the frame-
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4.2 Input data
work. Overall, the utilized database consists of 146 turning left, 411 turning right
and over 1000 going straight sequences.
In the following, the integration of the recorded data as input in the HMMs is
described. Further, various parameter constellations are evaluated by determining
the recognition rate for the learning dataset. Afterwards, the models were tested
with the remaining dataset.
4.2 Input data
The four variables distance to intersection (DTI), velocity v, acceleration a and yaw
rate ψ are merged in a state vector of the vehicle (~s). This state is retrieved at
discrete time steps t and updated frequently serving as input in the framework (see
Figure 4.1). In the data the sampling rate is 10 Hz. The sequence of the state vector
represents the observation in the HMMs. Figure 4.1 illustrates the input of vehicle
data into the framework.
𝑠ego 𝑡 = DTI, 𝑣, 𝑎, 𝜓
𝜆left
𝜆right
𝜆straight
Figure 4.1: Vehicle state vector as input of the prediction framework
Corresponding to the structure of a discrete HMM as introduced in subsection 2.3.2,
the observation in each time step is chosen from a set of countable symbols. The
driving data retrieved from the Controller Area Network (CAN) bus is within a discrete interval with a certain resolution. This depends on the number of bits reserved
for the information defined in the data length code (DLC) of a CAN message. Such
a message contains signals with the information encoded. For the vehicle dynamics
the resolution is fine using a DLC up to 8 bytes. This is implemented to accomplish
high precision in the measured data. However, it also leads to 264 distinguishable
values for just one variable. This illustrates that it is inapplicable to assign a symbol to every combination of measured value. On the one hand, the B matrix would
have an extreme size requiring an enormous amount of data for the learning of the
parameters. For example, if all four variables have a detail level of 264 , the number
of symbols would be 2256 . On the other hand, in the learning data each of the symbols would need to appear at least once which is unrealistic. So, B would become a
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4 Prediction Algorithm
sparse matrix leading to errors in case a sequence for testing shows symbols missing
in the learning dataset.
One way to overcome this discrepancy is to reduce the parameter space by clustering
the learning dataset. The resulting cluster centroids represent the symbols in the
HMM. Here, the entire dataset chosen for learning consists of multiple vehicle state
vectors spread out in four dimensions, one for each considered variable (DT I, v, a, ψ).
These points are grouped using k-means clustering [61]. This algorithm is comparable to the Expectation-Maximization (EM) algorithm which is similar to the learning
process of the HMM described in subsection 2.3.2. It is available as Matlab function.
The number of cluster centers are defined in advance which fits the requirement of
the problem addressed here, since the number of symbols is a varied parameter. The
algorithm starts by randomly setting up centroids. Afterwards, the distance between
each point and all cluster centers is calculated. The points are assigned to the centroids closest to them. This is depending on the distance measure which by default
is the Euclidean distance. In the evaluation another distance measure is introduced
and tested. The assigned points are used to determine new cluster centroids. Again,
all distances are calculated and the points are assigned to the nearest centroid. This
algorithm is iterative and converts to a local optimum. Therefore, several runs with
different randomized starting values are recommended. Here, the number of runs
was set to 10. The procedure is based on the least squares method minimizing
the variances between points and their assigned centroid. Mathematically, this is
expressed by minimizing the residual sum of squares (RSS) [62].
RSS =
K X
X
k=1 ~s∈ck
(~s − µ
~ (ck ))2
Here, K is the number of cluster centroids and ck is the k-th cluster. The algorithm
is terminated either when no more changes in the centroids position appear between
iterations or after a predefined number of iterations. The assigning step in the
algorithm is used later in the evaluation to determine which symbol the vehicle
state vector (~s) belongs to. This method provides fast results to retrieve symbols
in the HMM. However, it is susceptible to outliers in the data. Since all points
are included, outliers displace the centroids. This effect is reduced the more cluster
centroids are used.
Also, using an Euclidean distance is problematic because of the different measures
and span in the measured variables. This refers to the different intervals of the
variables. While the DTI with the widest span is between 0 and 100, the acceleration
values are within a smaller interval of about [-9, 6]. So, differences in the DTI
outweigh differences in the acceleration just because of the intervals. Therefore, the
data is scaled. Since the DTI is representing the widest interval, the dynamic vehicle
80
4.2 Input data
data is expanded to fit in intervals 100 units wide. An alternative would be a scale
invariant distance measure.
Another approach to connect the hidden states with the observed measurements is
similar to clustering. Instead of fixed centroids, the mapping can be established using
multivariate Gaussian distributions. So, a hidden state is not emitting a discrete
symbol (represented by the cluster centroid) based on a discrete distribution than
rather a continuous vector (vehicle state vector) determined by a probability density
function. This function is estimated with a mixture of Gaussian distributions. This
link is established by integrating a Gaussian Mixture Model (GMM) for each hidden
state into the established model. So, it represents the distribution of the measured
data when the system is in one of the N hidden states. The observation matrix B
bi (i = 1, ..., N ).
from the discrete HMM is replaced by N GMMs λ
A Gaussian Mixture Model is an approximation of an unknown distributed set of dim
random variables using the weighted sum of M mixtures of Gaussian distributions.
The following brief introduction is based on [63]. The model is characterized by
the vectors µ
~ of size dim containing the means for each mixture, the covariance
matrices C of size dim × dim and the mixture weights ωj (j = 1, ..., M ). Here,
the set of random variables corresponds to the 4-dimensional vehicle state vector ~s
(dim = 4). Accordingly, the density of the mixed distribution for one hidden state
is given by:
b =
p(~s |λ)
M
X
j=1
ωj · g(~s |~
µj , Cj )
The mixture weights determine the influence of each component Gaussian density
g(~s|~
µj , Cj ) which is defined as:
1
1
0 −1
g(~s |~
µj , Cj ) =
exp − (~s − µ
~ j ) Cj (~s − µ
~j)
2
(2π)d/2 |Cj |1/2
The sum of the weights satisfies the constraint
PM
j=1 ωj
= 1. So, the result is an
actual probability density. Depending on the number of mixtures for the modeling,
the number of mean vectors and covariance matrices and accordingly the number
of parameters increases. These are determined similarly to the learning process of a
HMM by the Expectation-Maximization (EM) algorithm.
For the implementation in the HMM, the GMM parameters are stored in single
variables. So, the vector µ becomes a 3-dimensional array of size d × M × N , the
covariance matrices C becomes a 4-dimensional array of size dim × dim × M × N
and the weights ω are stored in a M × N matrix. This indicates that the number
of parameters that are necessary just for the matching of the hidden states to the
vehicle state vector including the four measurements (dim = 4) is 21 × N × M .
Focusing on the necessary parameters, this corresponds to a k-means clustering
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4 Prediction Algorithm
with 21 × M centroids. This should be considered when comparing both methods
in the evaluation.
4.3 Evaluation
In the evaluation phase, various parameter setups are implemented and the according
HMMs learn with a specified dataset. For a start, the setups are evaluated by
the recognition of the sequences used for learning. Hence, the performance can be
estimated and assumptions are made, that are verified in the following validation
phase. There, an offline prediction is simulated by evaluating the sequences drawn
from the rest of the database that was excluded from the learning dataset. The
learning dataset was chosen randomly but kept consistent over all tests except the
one varying the size of the dataset. So, the comparability of different variations
is independent of the dataset for parameter learning which is known to have great
influence on the results. This influence is obvious when comparing the results here
with the results in [58] where a different random learning dataset was used.
The following parameters are varied in the evaluation with discrete Hidden Markov
Models: the distance measure for clustering, the number of symbols (M ) which is
equivalent to the number of cluster centroids, the size of the dataset for learning
(LS) and the number of hidden states (N ). All these variations have a noticeable
effect on the recognition of the learned sequences. The size of the dataset for learning
is consistent between the HMMs of a setup. So, all three prediction models receive
the same number of sequences for the learning process. The number of symbols
has an effect on the size of the observation matrix and inherently on the amount of
information extracted from the measured data. The number of hidden states is also
influencing the size of the observation matrix B and also the transition matrix A.
Since it represents an abstract inherent state of the process, the variation is necessary
to find a suitable number best describing this process. In a similar implementation,
the choice of this parameter seems to be based on experiences with the method
and is set to 5 [40]. However, not all combinations of variations are considered to
reduce the complexity of the evaluation. Instead, a standard setup is introduced
with 5 hidden states, 16 symbols and 146 sequences for learning and the evaluation
is executed subsequently.
The distance measure for clustering is varied between Euclidean and Cityblock.
The latter refers to the sum over the absolute distance along each dimension. It
reduces the computing time but is also susceptible to the scale effect mentioned
previously. The standard value for the number of symbols was chosen to have two
occurrences for each dimension, i.e. the combination of two different characteristics
in the measured variable. For example, two characteristics of the velocity could be
labeled slow and fast. All four variables are combined in the vehicle state vector
82
4.3 Evaluation
which has 4 dimensions accordingly. The cluster centroids represent a combination
of these characteristics in the 4-dimensional variable space. Thus, there are 24 (16)
combinations of two characteristics in four variables. For the variation, the number
of symbols is increased to have 3 and 4 characteristics for each measured variable
leading to 34 (81) and 44 (256) symbols respectively. The entire learning dataset
is clustered together. So, the symbols represent the same area in the vehicle state
space for all three HMMs.
The number of available sequences were unbalanced for the different driving directions. There were only 146 left turning scenarios compared to 411 sequences with
right turns. Most of the drivers went straight (over 1000 sequences). A smaller
dataset for learning lacks the diversity necessary for generalization, i.e. the model
adapts to the learning dataset explicitly. This should lead to a memory effect with
high recognition rate on one side but poor performance in the prediction of unknown
sequences. So, the maximum size for the dataset is 146 sequences since there are no
more left turns and the size shall be consistent between all three models. For the
variation, this size is reduced to half (73 sequences) and quarter (36 sequences) of
the standard value to observe this effect. Finally, the number of hidden states was
varied between 2 and 10.
As described in subsection 2.3.2, learning the parameters is an iterative procedure
defined by the Baum-Welch algorithm. It determines the model parameters and converges towards a local optimum. Therefore, the learning is performed several times
with randomized starting values. This makes the comparison of different parameter sets complicated, since the results may relate to a better optimum found in the
learning process rather than the differences in the parameters. So, the log-likelihood
of the learning process is stated for the established models. This represents the probability that the complete learning dataset (LS) used for a HMM is associated with
this model (P (LS|λ)). This gives an estimation of the performance with the particular parameter set. However, the probability changes for the variation of hidden
states, number of sequences in the dataset for learning and number of symbols.
After the learning, the evaluation is performed by sampling a sequence time step
by time step. This means parts of the sequence is presented to all three HMMs
and in each time step a estimated driving direction is retrieved by comparing the
log-likelihood of each HMM. This process is running until the end of the sequence is
reached. The last prediction result is compared with the actual direction driven in
the sequence. In case of a match, the sequence counts as correctly predicted. Then,
the actual time before the vehicle entered the intersection is determined where the
correct result first appeared without any changes in between using backtracking.
This value is called prediction time to intersection (TTI).
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4 Prediction Algorithm
Distance measure for clustering (Cityblock vs. Euclidean) The quality of the
clustering is influencing the information transferred into the model. For k-means
clustering, a distance measure determined the allocation of the points to a centroid.
Since this allocation is necessary for the prediction also (to determine which symbol
to assign to the measured vehicle state vector), a second distance measure is evaluated besides the Euclidean distance. For the Cityblock distance measure, the sum
of the absolute values of the entries in the distance vector between two points is
calculated instead of the magnitude of the vector itself. So, the calculation requires
slightly less computing time. Also, the centroid is the component-wise median of the
cluster points when using the Cityblock measure instead of the mean of the points
when using the Euclidean distance.
The same learning dataset was used and the HMMs were parameterized with
the standard parameters.
(LLleft =
−1.77 · 104 , LLright
The learning results using the Euclidean distance
= −1.38 · 104 , LLstraight = −1.26 · 104 ) were slightly
better than using the Cityblock distance measure (LLleft = −1.83 · 104 , LLright =
−1.44 · 104 , LLstraight = −1.30 · 104 ). Here, the log-likelihood (LL) of each model
expresses the probability of the match between the learning dataset overall and the
created model.
The evaluation results are illustrated in Figure 4.2. The bars represent the number
of correctly recognized sequences from the learning dataset. The different color
shades indicate increments in the prediction TTI. These are from dark to light:
>3 s, >2 s, >1 s and <1 s. The turning left sequences are recognized all but one for
both variants. Although, the prediction TTI is better using the Cityblock option.
Also, there are certainly more sequences for right turns recognized in the Cityblock
section. However, the high bar in dark magenta indicates that most of the rights
turns were recognized early using the Euclidean option. The sequences for going
straight have a low recognition rate for both which is surprising. The analysis of the
data in section 3.1 suggests that going straight is a simple progress. A closer look
into some sequences shows that the high deviations in the velocity and acceleration
are a result of speed reductions through coasting. Obviously, the other two models
for turning events pick up on this process leading to false predictions. The setup
with Euclidean distance recognizes slightly more sequences going straight.
Overall, the variation of the distance measure for clustering showed balanced differences in the performance. A high prediction rate for each direction is mostly desired.
The Euclidean distance shows slightly better results for the going straight event and
also indicates a higher prediction TTI for turning right sequences. Therefore, it
is chosen the preferred distance measure in the k-means clustering for the further
evaluation. The chosen measures are too similar to find significant differences. Consequently, there is no test performed on the rest of the data.
84
4.3 Evaluation
Recognized sequences
146
120
90
Cityblock
60
Euclidean
30
left
right
straight
Figure 4.2: Recognition rate varying the cluster distance method
Variation of cluster centroids The centroids in the clustering are corresponding to the symbols in the HMM. Therefore, a higher number potentially leads to
more transitions in the hidden states. Also, more information is retrieved from the
dataset. However, the increase in parameters requires more learning data. The
number of centroids is increased in two steps from the standard value 16 up to 81
(LLleft = −3.91 · 104 , LLright = −3.17 · 104 , LLstraight = −2.91 · 104 ) and further to
256 (LLleft = −5.60 · 104 , LLright = −4.56 · 104 , LLstraight = −4.11 · 104 ). The dis-
tance measure in the k-means clustering was chosen to be Euclidean. The framework
for 16 centroids is adopted from the comparison above. Thus, the evaluation results
are identical with the Euclidean setup above. It is only included for comparison reasons. The log-likelihood is decreasing due to smaller probabilities in the B matrix
when the number of symbols rises. Comparing the models within a setup, it seems
the going straight models (λstraight ) are better adapted to the learning dataset than
the other two. However, the reason for the higher LLstraight in all setups is that the
straight sequences are shorter, i.e. less time steps because of the higher velocity and
missing deceleration. Since the log-likelihood is the multiplication of probabilities
along the sequences, the less time steps there are the larger is the probability.
The results are displayed in Figure 4.3. The color coding follows the same scheme
as in the previous comparison (from dark to light: >3 s, >2 s, >1 s and <1 s). The
higher number of centroids allows a more accurate partitioning of the observation
space containing the vehicle state vectors. This leads to the improvements that are
clearly visible. The left turns are again highly recognized. Obviously, the sequences
contained in this dataset are homogenous and considerably distinguishable from the
other two directions. However, with 81 and 256 symbols, the number of sequences
with high prediction TTI increase further for left turning sequences. For the right
turns, the model with 81 symbols recognizes two sequences more than the model
with 256 symbols. A further increase in symbols will probably not increase the
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4 Prediction Algorithm
recognition rate any further. The recognition rate for straight sequences increases
for each step up to 86% with 256 symbols.
Recognized sequences
146
120
16 symbols
90
81 symbols
60
256 symbols
30
left
right
straight
Figure 4.3: Recognition rate varying the number of symbols
Conclusively, the increase in cluster centroids and HMM symbols accordingly improved the recognition rate and also the average prediction TTI. However, the testing
below will show if the size of the learning dataset is still sufficient or if the improvement only applies to the recognition of the learned data instead of the prediction of
unknown sequences. Also, the required parameters increase by a factor of about 3
using 256 symbols instead of 81. This should be considered in the following variation,
especially since the recognition improvements are relatively small.
Variation of learning dataset size The size of the dataset used for learning is
influencing the performance of the prediction. It is expected that a reduction of
sequences for learning will improve the recognition on the cost of the prediction of
unknown sequences. Also, the number of symbols was set to 256 increasing the size of
the observation matrix B to 5 × 256. The already evaluated setup with 146 learning
sequences (LS146 ) is included for comparison reasons. Besides, two variations are
evaluated with half of the learning dataset (LS73 ) and a quarter of the standard
learning dataset (LS36 ). The learning procedure returned the the models for the 50%
dataset (LLleft = −2.63 · 104 , LLright = −2.31 · 104 , LLstraight = −1.90 · 104 ) and for
the 25% dataset (LLleft = −1.16 · 104 , LLright = −1.04 · 104 , LLstraight = −1.00 · 104 ).
The probabilities differ for each setup because the length of all learning sequences
is of course different varying the dataset. However, the size of the dataset is kept
consistent over all three scenarios because of the clustering which is performed with
the entire set. An unbalanced number of sequences would influence the cluster
centroids. This would lead to an over representation of the direction with more
sequences for learning.
The results of the comparison are displayed in Figure 4.4. Also, the y-axis represents
a relative recognition rate for a better comparability. In all three setups, left turns
86
4.3 Evaluation
were recognized nearly perfectly. This again indicates a high homogeneity in the left
sequences and patterns that are clearly distinguishable. Apparently, there are no
anomalies in the data for left turns which is remarkable since the traffic interference
is included in the data.
Recognition rate [%]
100
80
60
LS36
LS73
LS146
40
20
left
right
straight
Figure 4.4: Recognition rate varying the size of the learning dataset
The best recognition results were achieved with the smallest learning dataset as
expected. The further test with unknown sequences will prove if this high recognition
will also result in high prediction rates. While for left turns the recognition remains
stabilized, the rates reduce the larger the size of the dataset. This effect is stronger
for the straight sequences than for the right turning ones. Again, this indicates that
there are outliers in the going straight dataset.
Variation of the number of hidden states The hidden states represent abstract
states in the decision making process which are not defined in detail. Their number
influences the size of the transition matrix A as well as the size of the observation
matrix B. So, more hidden states require a larger number of parameters to be
learned. Since the time development of the hidden states sequence is the key functionality to find the patterns in the intersection approaching process, their number
is relevant for the performance. The standard value of 5 was chosen based on an
existing approach [40]. Thus, the number of states is varied widely between 2 and 10
to examine their influence in detail. Since the setup with 81 symbols showed decent
results, it is chosen for this variation, especially to keep the number of required parameters manageable in perspective of the further growth with more hidden states.
Also, the large dataset for learning is used (LS146 ). Accordingly, the setup with 5
hidden states was previously generated and is included for a better comparison.
The results of the learning process are displayed in form of the log-likelihood of the
according learning dataset in Table 4.1. From left to right the probabilities increase,
which still indicates that the lengths of the sequences are shorter from left to right
in the table. The increase in the log-likelihood with a higher number of hidden
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4 Prediction Algorithm
states N implies a better representation of the learning dataset with more hidden
states. However, this refers to the overall dataset rather than each single sequence.
It appears since more hidden states allow a stronger preference in the allocation
to the symbols. This means the probabilities in the B matrix increases for some
symbols that are frequently reached, which leads to a higher overall probability for
the dataset. The evaluation will show if the recognition rate is also rising with
increase in the number of hidden states.
Table 4.1: Log-likelihood of learning dataset with different number of hidden states
N
LLleft
LLright
LLstraight
2
3
4
5
6
7
8
9
10
−4.91 · 104
−4.50 · 104
−4.21 · 104
−3.91 · 104
−3.70 · 104
−3.61 · 104
−3.43 · 104
−3.28 · 104
−3.10 · 104
−4.01 · 104
−3.69 · 104
−3.42 · 104
−3.17 · 104
−3.03 · 104
−2.93 · 104
−2.88 · 104
−2.71 · 104
−2.63 · 104
−3.69 · 104
−3.33 · 104
−3.22 · 104
−2.91 · 104
−2.79 · 104
−2.62 · 104
−2.62 · 104
−2.43 · 104
−2.34 · 104
The results of the evaluation are presented in Figure 4.5. The recognition rates of
left turning sequences are still high even with fewer hidden states. Also, the mean
prediction TTI is above 6 s for all cases. Rights turns are also recognized early (mean
prediction TTI above 4 s) and at high rates greater than 97%. So, between one and
four out of the 146 sequences were identified mistakenly in the different variations
of the number of hidden states. The main influence of the variation appears in the
straight sequences. Here, the recognition rate fluctuates below 80% for the first
variations between 2 and 5 hidden states. With a further increase the recognition
rate rises to 88% with a decent mean prediction TTI of 5.7 s. This is the best
performance in the variation, since the recognition rate drops again with 9 and 10
hidden states respectively.
The validation of the results will show, if the variances especially in the straight
sequences is reduced to the variation of hidden states. It could also be related to
variances in the quality of the learning performance, i.e. in some cases a better local
optimum was found than in others.
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4.3 Evaluation
Recognition rate [%]
100
N =2
N =3
N =4
N =5
N =6
N =7
N =8
N =9
N = 10
80
60
40
20
left
right
straight
Figure 4.5: Recognition rate varying number of hidden states
Gaussian Mixture Model Using GMMs as link between the measured data and
the HMM was explained in detail in section 4.2. For each hidden state, a GMM
is established with M mixtures of Gaussian distributions. The number of mixtures
determines the shape of the distribution function representing the probability of
a vehicle state vector while in a certain hidden state of the HMM. This variable
is varied between 2 and 4. The more mixtures are used the better representation
but this requires also a greater amount of parameters to be learned. The other
parameters are set to 5 hidden states (standard parameter) and half of the learning
dataset (LS73 ). The latter was chosen to reserve sequences for the validation of the
left turning model. The advantage of using GMMs is that previous clustering and
scaling is unnecessary. It is expected that the information transfer into the model is
more efficient this way.
The results of the evaluation are shown in Figure 4.6. The recognition rates are
very high for all three driving directions. The color shades refer to the prediction
TTI in the same way as before (from dark to light: >3 s, >2 s, >1 s and <1 s). All
turning left sequences were correctly recognized by all three variations. However, the
model with 2 Gaussian mixtures performed slightly better identifying some sequences
earlier than in the other models. The recognition of right turn sequences is similarly
high. Only one sequences was missed by the model with 2 mixtures. The straight
sequences have also a high recognition rate. However, the light shades indicate that
a high amount of sequences is correctly identified in the last second before entering
the intersection.
The high recognition rates are promising. The validation will show if this also applies
to high prediction rates in the unused dataset. Also, it stands out that the prediction
TTI is lower in average.
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4 Prediction Algorithm
Recognized sequences
73
60
2 mixtures
40
3 mixtures
4 mixtures
20
left
right
straight
Figure 4.6: Recognition rate varying number of mixtures in GMMs
4.4 Validation
In the validation of the framework, the different setups evaluated previously are
tested with the dataset which remained unused for the learning process. The evaluation led to the following assumptions. A higher number of centroids results in a
more accurate sampling of the data which improves the prediction. However, this
requires more model parameters and increases the computing time. A decrease in
the size of the learning dataset resulted in high recognition rates. However, a smaller
dataset is corresponding to less information input into the model. The prediction
of these models is expected to decrease in performance due to a memorizing effect.
Also, the optimum number of hidden states for the HMMs with 256 symbols and
the given modeling approach seems to be 8.
The variation of the distance measure for the clustering was not validated since the
two variations differed hardly and both require scaling. For all further models, the
Euclidean distance was used as standard measure for the distance between points in
the vehicle state space. Also, the clustering required a consistent learning dataset
for all three HMMs. Otherwise, the cluster centroids would have been shifted to the
scenario with the largest dataset for learning. This restriction and the unbalanced
number of available sequences impeded the validation of the turning left scenario
since only 146 sequences were available here. The problem of consistent size of the
learning dataset for each model applies not necessarily to the approach using GMMs.
This is addressed further below.
The remaining dataset which was excluded in the learning process is tested with
the established models. Unfortunately, the sequences for left turns was completely
used for the learning in most variations except the comparing of different sizes of
the learning dataset. So, there were no sequences left for the validation. However,
the evaluation showed that the left turns were mainly recognized by all different
variations. This indicates that in this dataset the sequences are homogenous and
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4.4 Validation
clearly distinguishable compared to sequences of the other two directions. The validation was performed analogically to the recognition, i.e. the sequences are partially
presented to each model and the highest log-likelihood determines the prediction
outcome. The prediction TTI represents the time were the correct direction first
appeared without further changes.
The results of the variations of number of symbols and size of learning dataset
are displayed in Table 4.2. It starts with the standard parameters and shows all
evaluated parameter setups. The varied parameter is emphasized with bold letters.
The mean prediction TTI is averaged over all correct identified sequences. Further,
only the early and late prediction TTI slots are stated in relation to the correct
predicted sequence.
Table 4.2: Prediction results of different variations
left
Varying number of symbols
16 symbols / 5 hidden states / LS146
rel. correct prediction
mean prediction TTI
prediction TTI >3s
prediction TTI <1s
81 symbols / 5 hidden states / LS146
rel. correct prediction
mean prediction TTI
prediction TTI >3s
prediction TTI <1s
256 symbols / 5 hidden states / LS146
rel. correct prediction
mean prediction TTI
prediction TTI >3s
prediction TTI <1s
Varying size of learning dataset
256 symbols / 5 hidden states / LS73
rel. correct prediction
100%
mean prediction TTI
8.3s
prediction TTI >3s
90%
prediction TTI <1s
0%
256 symbols / 5 hidden states / LS36
rel. correct prediction
100%
mean prediction TTI
6.9s
prediction TTI >3s
89%
prediction TTI <1s
0.9%
right
straight
79%
7.5s
94%
2.3%
75%
5.5s
80%
2.2%
95%
6.1s
80%
3.1%
85%
4.3s
68%
26%
82%
7.8s
94%
0%
70%
3.9s
57%
20%
69%
4.3s
56%
18%
66%
6.9s
98%
0.6%
38%
7.9s
100%
0%
59%
4.7s
65%
3.4%
The increase in the number of symbols did not necessarily lead to a better performance. However, the results with 16 symbols show that there is a minimum not
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4 Prediction Algorithm
to be fallen below to ensure the distinguishability between the different intersection
approaching phases. More symbols improve the information transfer from the data
into the model, but require more data for a solid estimation of the model parameters.
The highest prediction rates are already established using 81 symbols. Here, 95% of
the right turning sequences and 85% of the straight sequences are correctly identified. Obviously, the size of the learning dataset is too small for 256 symbols, since
the prediction rate drops for both right turns and straight sequences. Interestingly,
the majority of the right turns (94%) is correctly predicted with both setups using
16 symbols and 256 symbols. This indicates that a high number of right turning sequences is similar and distinguishable in an early stage of the intersection approach.
The dataset with straight sequences seems to be more diverse leading to errors in
the prediction.
As expected, the prediction rate drops harshly when reducing the number of learning
sequences for the right turning and going straight scenario. However, left sequences
are estimated extremely accurate with 100% prediction rate with both half and quarter learning sequences. This leads to the conclusion that the turning left sequences
are overall very similar, i.e. the entire 146 sequences. This leads to a highly adapted
left turning model which is extremely specialized. At this point, this adaptability
is especially impressive considering that the sequences included traffic interference
which is influencing left turns in particular. Contrariwise, the straight sequences
seem very diverse and are numerously included in the dataset. So, the according
HMM has difficulties to adapt to all straight sequences equally well. Also, there
were anomalies in some sequences that are comparable to turning behavior, such
as reducing speed during the intersection approach. Therefore, certain not typical straight sequences are identified mistakenly as turning sequences. However, the
variation of the size of the learning dataset shows clearly the previously introduced
memory effect at least in the right turning and straight scenario. While the recognition rate rose in the evaluation with smaller dataset, the prediction rate drops below
70% with the half of the usual learning dataset (LS73 ) and even further below 40%
for right turns and 60% for going straight with the smallest learning dataset tested
(LS36 ). So, the models were adapted to the dataset for learning particularly which
resulted in a decrease of detection of similar sequences. This is illustrated by the
poor performance of the turning right model and especially in the prediction TTI.
All sequences that were correctly predicted, were identified at an early stage and
therefore must be similar to the learning sequences.
In the variation of hidden states, the evaluation showed best results using 8 hidden
states with 81 symbols and the largest learning dataset (LS143 ). The results of the
validation confirm this as an optimal value in this setup (see Figure 4.7). There is
a clear maximum in the performance, since the prediction rate drops again using
more hidden states. Still, it has to be considered that for each variation, the learning
process was performed leading to local optima of parameters. Also, the performance
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4.4 Validation
is dependent on the learning dataset and different setups might improve with other
data in the learning process.
Prediction rate [%]
100
N =2
N =3
N =4
N =5
N =6
N =7
N =8
N =9
N = 10
80
60
40
20
left
right
straight
Figure 4.7: Validation of prediction rate varying number of hidden states
Using 8 hidden states, 93% of the right turns and 94% of the straight sequences are
identified correctly. In this case, the mean prediction TTI is 6.9 s and 5.5 s for left
turning and straight scenarios respectively. Comparing all setups, the prediction TTI
varies intensely in the variation of hidden states. However, better performance in
the correct prediction is not necessarily leading to a decrease of the mean prediction
time as it could be assumed. The best setup for turning left is 5 hidden states (95%).
While this framework detects 5 more left turning sequences, 91 straight sequences
are missed compared to the framework with 8 hidden states.
The variation of the mixtures of Gaussian distributions utilizing GMMs shows similar
results for all three variants (see Table 4.3). Here, 5 hidden states and half the
learning dataset were used. Compared to the clustering with half the learning data,
the prediction rate is solid above 80% even for right turning and going straight.
However, the mean prediction TTI is rather low and a high number of sequences is
correctly identified less than a second before entering the intersection.
The left turns are detected almost completely and at an early stage of the approaching stage. The prediction rate of the right turns is slightly below the rate of the
straight sequences. In both cases, about half of the correct predicted sequences
is identified more than 3 s before reaching the intersection. Furthermore, in more
than 10% of the turning right and going straight sequences late prediction appears.
However, the required parameters with the three setups compares to clustering with
42 to 84 centroids. Taking this into consideration the results are better than using
clustering.
Also, the GMM approach requires no scaling and since the clustering is obsolete, the
consistency of the learning dataset for each model is unnecessary. This reduces the
computing time and eliminates the information loss due to the sharp discretization.
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4 Prediction Algorithm
Table 4.3: Prediction results for variation of mixtures in GMM
left
Varying number of mixtures
2 mixtures / 5 hidden states / LS73
rel. correct prediction
99%
mean prediction TTI
8.6s
prediction TTI >3s
90%
prediction TTI <1s
1.4%
3 mixtures / 5 hidden states / LS73
rel. correct prediction
100%
mean prediction TTI
8.8s
prediction TTI >3s
92%
prediction TTI <1s
2.7%
4 mixtures / 5 hidden states / LS73
rel. correct prediction
100%
mean prediction TTI
8.8s
prediction TTI >3s
92%
prediction TTI <1s
2.7%
right
straight
86%
3.6s
48%
24%
89%
3.6s
50%
11%
83%
3.4s
46%
24%
86%
3.6s
50%
16%
83%
3.9s
49%
17%
86%
3.3s
45%
16%
In Table 4.4 a confusion matrix is displayed showing the results with 3 mixtures, 5
hidden states and a randomized 20% learning dataset. This means one-fifth of the
data available for each driving direction is used as learning data. The remaining
80% are tested and the prediction performances illustrated in the confusion matrix.
Table 4.4: Confusion matrix using Gaussian mixtures (3 mixtures, 5 hidden states)
Predicted direction
Direction driven
left
right
straight
left
116
1
0
right
2
329
6
straight
2
72
806
mean prediction time
4.9s
4.2s
4.8s
The prediction rates are high for all three scenarios. Only one left turn is mistakenly estimated as a right turn. This is impressive considering the small amount of
sequences for learning the left turn model (26 sequences). Right turns were correctly predicted in 97% of the sequences. The few errors identified right turns as left
turns in 2 sequences and as going straight in 6 sequences respectively. Also, 92%
of the straight sequences are predicted as such. Here, a high number of 72 straight
sequences is identified as right turns by mistake. This confirms what was already
assumed before that there are several straight sequences with reduction of speed in
the approach which are therefore expected to be right turns. However, the prediction TTI is between 4 s and 5 s in average. Comparing with the clustering method,
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4.5 Conclusion
these are rather low. The validation of the variation of mixtures already showed
that there are multiple sequences detected shortly before entering the intersection.
4.5 Conclusion
A prediction framework was established to predict the driving direction at a four-way
intersection using vehicle dynamics and the distance to intersection as input data.
The framework was tested with different variations of parameter sets to examine
the influence of each parameter. To use the measured vehicle data as input to the
framework two different approaches were introduced. On the one hand, the vehicle
state vectors are clustered to find representatives that serve as symbols in the Hidden
Markov Models (HMMs). On the other hand, an alternative is to use Gaussian
Mixture Models (GMMs) to estimate the distribution of vehicle state vectors emitted
by a hidden state.
Using the clustering method, the best results were established with 8 hidden states,
81 cluster centroids and a learning dataset of 143 sequences (LS143 ). The variation
of the cluster centroids illustrated the trade-off between available learning data and
required parameters. Further, a memory effect was shown in the variation of the size
of the learning dataset for right turns and straight scenarios. Fewer learning data
was better recognized by the model but showed poor results identifying unknown
sequences.
The approach with GMM showed solid prediction rates but lower prediction TTI
values. However, its advantage is the variation of the learning dataset for each HMM.
This also leads to the capability of an online learning approach, i.e. new sequences
that are estimated can be used for learning the according model after the driving
direction is determined.
95
96
5 System Approach
The prediction algorithm is embedded in a system designed for situation assessment
based on the approach illustrated in Figure 2.2. Here, the focus is on situation
analysis and prediction. Still, the sensing requirement and current innovations are
shortly introduced. The main part is the implementation of a real-time application
running the prediction algorithm in a test vehicle.
5.1 Sensing
For the detection of objects, sensors are necessary corresponding to the human
senses. In an urban environment, there are considerably more traffic participants
present that might become relevant for the situation assessment. In the process,
line-of-sight obstructions impede the perception additionally. Here, common sensor
systems such as lidar, radar and cameras are also affected due to their dependency
on a line of sight to an object. Recently developed communication technologies
overcome this constraint, providing information about the static and dynamic surrounding beyond the range of the human perception. This advantage is used on one
side to provide this information to the driver and on the other side to enable new
features to assist the driver in an urban environment. This environmental modeling
is the basis for those applications.
Comprehensive databases are available with road data in form of a digital map. So
far, map data has already been used for navigation applications. Further development includes a more precise representation of the traffic network on at least a lane
level. This provides detailed information about the accessible space for the vehicle.
In this connection, back end solutions are developed to store the static environmental
data (road infrastructure) as well as changeable infrastructural information (traffic
signs, construction sites). The back end is kept up-to-date by a communication
link to vehicles, i.e. sensor information from vehicles is retrieved and processed into
the digital map database (personal information, IAA, Frankfurt, 2015). This advancement is capable to provide road information in a more dynamic way including
temporary constraints and traffic signs. However, a constant communication link
between vehicle and back end is required. An essential challenge is the localization
of the ego vehicle within the digital map. The quality of the position information
provided by GNSS is decreasing in urban environments due to multipath scattering
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5 System Approach
and shadowing in urban canyons. So far, a lane level localization is not realized with
common GPS receiver units.
In addition to the communication with service providers, the Vehicle-to-Vehicle
(V2V) and Vehicle-to-Infrastructure (V2I) communication technology will be introduced in new vehicles shortly (personal information, ITS World Congress, Bordeaux,
2015). This offers new approaches especially for urban applications. Through communication between vehicles, information is retrieved of other vehicles without the
constraint of line of sight. After the introduction, the benefit will rise with a increase
in market penetration. Traffic light assistance was realized in a development stage
using V2I communication. The combined information of dynamic objects and the
static traffic environment merges into a environmental representation of a part of
the traffic situation including considerably more information than contained in the
driver situation perceived by a human driver.
5.2 Situation analysis
The situation analysis corresponds to the comprehension level in the Situation
Awareness model (see section 2.2). So, it contains the classification of all relevant
objects within a certain range of the ego vehicle. The minimum requirement for a
system is to include all objects of the driving situation. This is the representation a
driver could possibly establish with the human senses. However, as in the previous
section explained, there is information available about the environment as well as
about objects that are located at further distance. This foresight is called electronichorizon and significantly improves the situation analysis. Here, the focus is on the
static traffic environment especially road attributes.
Details about the characteristics of the intersection, such as shape and regulation,
are necessary to establish an intersection model which can be visualized according
to Figure 2.3. The static information about the road network and intersections in
particular are provided by digital maps. The company HERE (former NAVTEQ) is
a map provider with an extensive database on road information. They also offer a
framework for map-based driver assistance applications called ADASRP (Advanced
Driver Assistance Systems Research Platform). The data includes a variety of road
attributes, but also speed limits and traffic signs. The structure of the data is specified in the ADASIS (Advanced Driver Assistance Systems Interface Specification)
protocol. Therein, the road network is organized in knots called stubs connected
by so called links. The combination of several links is called path. The distance
to traffic signs or stubs are indicated by an offset starting at the beginning of a
path. Also, the vehicle position along the path is stated in this way. The positioning is established correspondingly to navigation systems. A GPS receiver is used
to locate the position of the vehicle in the map. Further, an electronic horizon is
98
5.3 Prediction
OpenDrive
database
GPS receiver
Interface
Intersection
model
ADASRP
Figure 5.1: Interface for intersection modeling (schematic)
established that contains all the data including the road attributes and geometry of
the upcoming road segments to a certain predefined distance of path.
The intersection modeling is designed in a modular setup to remain independent of
the data source. Therefore, an interface is created that can process ADASIS data as
well as data in OpenDrive format (see Figure 5.1). The latter is an open data format
where the road network data is stored in a structural pattern similar to the XML
(eXtensible Markup Language) format. It is especially used in simulation applications. However, there was no data available for the region. So, some intersections
were measured and manually encoded in XML files for testing the system. The
advantage of the interface is, when using other road data sources the interface is
reworked instead of the entire application.
The data source for modeling is selectable within the interface. The ADASIS data is
provided either by running ADASRP on the same platform or by receiving the data
via CAN bus from a source within the test bed. In both cases, the actual GPS location is required for map matching. In the manually established OpenDrive database,
the position of the intersection is listed. The interface delivers the actual position
to compare both coordinates and preselects the closest intersection. Whether the
vehicle is approaching this intersection directly is determined by the heading also
provided by the GPS unit. When approaching the intersection and closing in under a DTI of about 110 m the modeling starts and a simplified graphical output is
generated in the application (see Figure 5.4).
5.3 Prediction
The prediction algorithm introduced in chapter 4 is adapted for a real time application of the prediction of the driving direction. The test field is identical with the
area of the study in section 3.2. The data collected in the study included the vehicle
dynamics next to the evaluated driver inputs. Thus, the intersection approaches are
99
5 System Approach
used to learn the Hidden Markov Models in the prediction framework. The study
included intersections of different shape and regulation. This system concentrates
on unregulated intersections, i.e. intersection with priority to the right rule. At yield
and stop intersections, the behavior is dominated by the concern to give right of way
which leads to a reduction of speed independent of the intended driving direction.
However, the different shapes (X-intersection and T-junction) are considered in the
system. Also, the conclusions of the behavior analysis are applied by merging shapes
where similar driving behavior was examined.
According to Table 3.4, two combined prediction models are created. For left turns,
one HMM for X-intersections and T-oriented junctions (λXT
left ) is created and another
l ). Another combined model is established
one for T-junctions with left branch (λTleft
r
for right turns at X-intersections and T-junctions with right branch (λXT
right ). The
other HMM for turning right refers to T-oriented junctions (λTright ). Going straight
differed significantly between both examined intersections leading to separate HMMs
Tr
(λX
straight , λstraight ). All six HMMs are learned with the according sequences provided
by the data collection in the study.
The Gaussian mixture approach with 3 mixtures and 5 hidden states is used as
prediction framework in the application. On the one side, this gives the opportunity
to implement an online learning feature and makes previous clustering and scaling
of data obsolete. On the other side, this setup showed reasonable results as shown
in Table 4.4.
5.3.1 Implementation
The intersection type is encoded in the generated XML files for the intersections
to be tested. This information is provided by the intersection model through the
interface. The application runs on a laptop connected to the vehicle CAN bus via
a Vector CANbox. So, the relevant vehicle dynamics (velocity, acceleration, yaw
rate) are retrieved over this interface using the Vector tool CANape. The prediction
algorithm is embedded in a Matlab/Simulink tool with a graphical user interface
(GUI). The tool is connected to the CANape software through a Matlab specific
interface. In case of using OpenDrive, the distance to intersection is calculated as
explained in section 2.4 using the actual GPS coordinates from the CAN bus and
the GPS coordinates of the intersection provided by the interface to the intersection modeling. This is obsolete when ADASIS data is available, since the distance
is already included there. All data is merged to a time-discrete array (sequence)
with a resolution of 100 ms using interpolation. The activity diagram in Figure 5.2
illustrates the progression in the application.
After initialization, the entire system runs permanently in a loop until it is canceled.
Here, the measured OpenDrive data was used as source for the intersection modeling. The interface to the intersection model provides the information about the
100
5.3 Prediction
start
calculate distance
to intersection d
clear buffer
calculate angle
to intersection φ
training
optional
false
d < 110 ∧
φ∼
= heading
determine actual
direction driven
true
intersection modeling
false
true
φ∼
= heading
retrieve CAN bus
data from buffer
calculate distance
to intersection
prediction
of direction
Figure 5.2: Activity diagram of the prediction tool
closest intersection by comparing the actual GPS data of the vehicle with the GPS
coordinates of the intersections in the database. This distance d to the nearest intersection is tracked once the application is initialized. Further, the direction to the
intersection from the position of the vehicle is determined by calculating the heading
angle towards the intersection φ. This angle is determined in the same orientation
system as the actual heading provided by the GPS unit (north 0◦ counting clock-
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5 System Approach
wise). The calculation is performed using the scalar product between a unit vector
pointing north and the vector ~r from the vehicle to the intersection (see Figure 5.3).
φ = arccos
~r · ~ey
|~r|
In case the vector towards the intersection has a negative value in x-direction, the
resulting angle is increased by 180◦ to follow the orientation regulation. This angle
is compared to the actual heading of the vehicle in the orientation coordinate system
(see Figure 5.3). This is performed to determine if the vehicle is approaching the
intersection rather than driving on a parallel road. In case the angles match nearly
and the distance d drops below 110 m, the intersection modeling is initialized. The
prediction is supposed to start at a DTI of 100 m, since the sequences for learning
were limited to this distance. Since the modeling consumes time and the vehicle is
in motion, a small offset of 10 m is added.
Intersection
0°
X
φ
90°
270°
vehicle heading
180°
Figure 5.3: Orientation coordinate system in the vehicle
The intersection modeling is followed by the actual prediction. Right after the
initialization of the application, CANape was started in the background to collect
constantly the relevant vehicle dynamic data from the CAN bus and store it in a
buffer. Now, this buffer with CAN data is readout and merged to a sequence combined with already retrieved data of this intersection approach. Further, the DTI
is updated using the newest position available. Finally, the prediction is executed
in the same way as in the prediction framework (cf. section 4.3). The sequence is
presented to the according models for each possible direction (three or two options
depending of intersection type). Then, the log-likelihoods are calculated using the
forward algorithm. The direction with the highest log-likelihood is the prediction
result. Further, a validity is determined based on the differences between the two
102
5.3 Prediction
highest log-likelihood outcomes. This is realized through the ratio between the difference and the overall log-likelihood of the best result. Under the assumption, that
loglik is the sorted vector (descending) with the log-likelihood values, the validity is
determined by:
loglik(2) − loglik(1)
val = min 1;
loglik(1)
The validity is small if the distance between the two best results is tight and it is
ceiled at 1 for differences between the two log-likelihoods greater than the absolute
maximum log-likelihood. So, the prediction outcome is estimated by the relation
between the single results, since the absolute value is depending on the length of the
sequence presented. The prediction result as well as the validity is presented in the
GUI of the tool (see Figure 5.4).
One prediction cycle runs approximately 1 s. However, a code optimization is expected to improve this run-time. The cycle rewinds as long as the vehicle approaches
the intersection. This is tracked by the comparison of the heading with the orientation towards the intersection. Tracking the changes in the heading and comparing
the measured values before and after passing the intersection, enables the determination of the actual driving direction. As soon as the vehicle passes the intersection,
the prediction is terminated. The GUI indicates if the final prediction was correct
by coloring the arrow displayed in Figure 5.4 green.
Before tracing the next intersection approach, there is an optional training process
implemented. Since the driving direction was determined, the entire sequences can
be used to train the according model and further improve the model adaption. This
is applicable even though the prediction failed to estimate the correct direction.
The sequence is passed to the model which matches the actual direction driven
independent of the prediction. This also enables personalization in the models in
case a driver detection is realized. At last, the buffers are cleared and used variables
are deleted before the tool resets to the idle mode, in which the next intersection is
determined and the process starts again.
5.3.2 Graphical User Interface (GUI)
The tool is simple to use with a customized Matlab GUI (see Figure 5.4). The
application is started by pressing the “Start Prediction” button. In the initialization,
the user is requested to choose a data source for the road information. The options
are ADASIS, OpenDrive or Offline. The latter allows testing of the tool with already
stored sequences. The tool runs according to the activity diagram in Figure 5.2 and
can be interrupted at any stage with the “Stop” button. From there, the tool can
be restarted or closed with “Exit”.
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5 System Approach
Figure 5.4: Prediction tool GUI
After initializing the tool, the closest intersection is displayed in written form. This
information is included only in the XML files using OpenDrive data. Further, the
DTI is shown. In a test drive, the previously mentioned conditions are checked
after the data source is determined. When the vehicle closes in on an intersection,
the modeling is executed and visualized schematically in the GUI. The prediction
outcome is indicated by an arrow as well as in written form. The validity is indicated
in percent.
The field “number of runs” shows how many prediction cycles were performed. There
are also some status notification for debugging. Errors are displayed in the “Run”
area while the “State” indicates the mode in which the tool is currently in. When
the intersection is passed, the tool freezes shortly to indicate if the prediction was
correct (arrow turns green) and to display the actual driving direction. Afterwards,
the user is asked to decide if the sequence shall be used for training the according
model or if the data is discarded.
5.3.3 Testing and Outlook
The tool showed a stable performance and was tested at several intersections in the
test area. The prediction results were comparable with the outcome of the evaluation of the prediction framework in section 4.1. Further, the integrated training
feature was tested and is assumed to lead to further improvements. However, the
computation time is in some cases interfering with the next intersection approach,
which appears especially in urban areas where intersections are close together. An
104
5.3 Prediction
alternative could be to store the sequences and perform the training afterwards or
implement a parallel process for the training.
The prediction tool is restricted to certain intersections when using OpenDrive
data, since there was no database available. This can be overcome by running
the ADASRP on the laptop retrieving ADASIS data. Also, further training data is
expected to improve the prediction performance especially in the early stage of the
intersection approach. So far, the turn signal remains unconsidered in the prediction
even though it is the only externally perceivable information of a possible turning
intention. However, as the naturalistic driving study revealed, the turn signal was
not activated several times. Also, in the evaluation of the prediction in chapter 4,
the correct driving direction was estimated before the activation of the turn signal.
Still, it seems reasonable to include the turn signal to increase the validity of the
prediction.
So far, the influence of other traffic participants is unconsidered. In the tested traffic
environment, the visibility constraints through line-of-sight obstructions impeded
the perception of other vehicles approaching from other direction to a point shortly
before entering the intersection. Still, preceding vehicles influence the approaching
behavior. This could be considered through an additional parameter in the HMMs
or through extending the prediction framework by another model (λfollowing ).
105
106
6 Conclusion and Outlook
In this thesis, a system is presented which allows an assessment of intersection scenarios. The focus was on a situation analysis including a representation of the static
environment at the intersection and the prediction of the driving direction while
approaching it. The latter was realized with a prediction framework using Hidden
Markov Models (HMMs). To determine suitable input variables for the prediction,
a thorough analysis of driving behavior at intersections was performed. For this
purpose, an existing database was used. This database was adapted and utilized for
the determination of the model parameters in the prediction framework.
The analysis of the vehicle dynamics indicated obvious differences in the intersection approach according to the driving direction. Also, the conducted naturalistic
driving study revealed differences and also similarities in the occurrence of driver
behavior at intersections of different shape. Here, the most common shapes which
are T-junctions and X-intersections with nearly perpendicular crossing angles were
examined. A self-provided analysis tool showed that this are the predominant intersection shapes in urban road networks.
In the study, the influence of preceding vehicles was eliminated by discarding corresponding data. Thus, preceding vehicles are unconsidered in the prediction framework. Also, other factors like line of sight obstructions and parked vehicles showed
influence on the driving behavior which is excluded in the prediction.
In this system approach the turn signal remained unconsidered. This was decided
because of the high variations in the turn signal activation as well as due to multiple
misses which were observed in the driving study. Besides, the framework is designed
to be applicable to predict the driving direction also of other vehicles using sensor
systems. Since a camera based detection of turn signals is complicated at intersections, the prediction is established independent of this information. However, for
the ego vehicle the turn signal could serve as additional source for the prediction.
An in-depth evaluation of the prediction framework identified optimal parameters
and showed results with high prediction rates at several seconds before reaching the
intersection. A setup was created using Gaussian Mixture Models as link between
input variables and the hidden states in the HMMs. This allowed the use of variable
sizes of datasets for learning and therefore enables an online learning approach.
Finally, a real-time assessment system was implemented and integrated in a vehicle
environment. This system provides a situation assessment including an intersection
model and the prediction for the ego vehicle. It runs stable and was tested in real
107
6 Conclusion and Outlook
traffic scenarios. The prediction results worked surprisingly well and were robust
even in cases with traffic interference. The system displays continuously an estimate
of the driving direction and a corresponding validity while approaching an intersection. It is also capable of using the recorded driving data as training input to further
optimize the inherent prediction models during the test drive or afterwards. This
increases the performance and can alternatively be used to personalize the prediction
for different drivers.
The system introduced here can be used as basic platform for the development
of Advanced Driver Assistance Systems (ADAS) for intersection scenarios. The
inherent situation assessment is also relevant for the realization of automated vehicle
control at intersections. Since the prediction is based on a stochastic model, the
validation of such a system will require new approaches. Especially the implemented
online learning process creates variations in the system outputs. These variations
have the potential to predict situations correctly which were previously not identified,
which increases the prediction performance. However, it cannot be excluded that
this overall increase of performance comes with false predictions of situations that
were previously identified correctly. This variation is relevant for the design of the
intervention strategy. While false positive activations in a warning feature are only
annoying, they might impair the situation in a control system. Conclusively, the
early prediction established here can be used in a foresightful manner to reduce the
risk of critical situations before they occur.
108
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114
Selbstständigkeitserklärung
Ich erkläre, dass ich die vorliegende Arbeit selbstständig und nur unter der
Verwendung der angegebenen Literatur und Hilfsmittel angefertigt habe.
Ich versichere, nicht bereits früher oder gleichzeitig bei anderen Hochschulen oder an
dieser Universität ein Promotionsverfahren beantragt zu haben. Diese Dissertation
ist als solche noch nicht veröffentlicht.
Ich erkenne die Promotionsordnung der Fakultät für Naturwissenschaften der
Technischen Universität Chemnitz vom 31. Januar 2011 an.
Chemnitz, 21. Januar 2016
Thomas Streubel
115
116
Curriculum Vitae
Name
Geburtsdatum
Geburtsort
Kontakt
Thomas Streubel
6. Februar 1985
Schlema
[email protected]
10/2005 - 09/2009
Technische Universität Chemnitz
Bachelorstudium Computational Science
10/2006 07/2007
Fakultät für Naturwissenschaften
10/2008 06/2009
Volkswagen AG, Wolfsburg
Tutorentätigkeit
Konzernforschung - Abt. Integrierte Sicherheit und Licht
Praktikum: Entwicklung von Software für Lichtapplikationen
Bachelorarbeit: “Realisierung einer aktiven, präventiven Bremsleuchte
für Personenkraftwagen”
10/2009 - 11/2011
Technische Universität Chemnitz
Masterstudium Computational Science
11/2010 03/2011
04/2011 09/2011
Fakultät für Maschinenbau
Hilfswissenschaftliche Mitarbeit
Implementierung nutzerspezifischer Bedienelemente in LabVIEW
Adam Opel AG, Rüsselsheim
Vorausentwicklung - Abt. Advanced Active Safety
Masterarbeit: “Artificial Potential Fields as a Concept of Environment
Modeling for Forward Directed Driver Assistance Systems”
02/2012 - 10/2015
Adam Opel AG, Rüsselsheim
Industriepromotion
SS13 &
SS14 &
SS15
Technische Universität Chemnitz
Fakultät für Naturwissenschaften
Unterstützung Lehre in Simulation Naturwissenschaftlicher Prozesse
11/2015 - 01/2016
Technische Universität Chemnitz
Fakultät für Naturwissenschaften
Wissenschaftlicher Mitarbeiter
117
118
Publications
T. Streubel and R. Zarife, Verfahren für einen adaptiven Rechtsabbiegeassistenten,
Gebrauchsmuster, Jan. 2013.
T. Streubel, “Fahrassistenzsystem, Fahrzeug mit einem Fahrassistenzsystem und
Verfahren zum Betrieb eines Fahrerassistenzsystems.” Patent DE102013013747 A1,
Aug. 2013.
T. Streubel, M. Moebus and K. H. Hoffmann, “Generische Umfeldmodellierung Autonome Fahrzeugsteuerung durch eine Risikokarte,” in Proc. of VDI - Elektronik
im Fahrzeug, Baden-Baden, Oct. 2013. (Auto-Electronic Excellence Award)
T. Streubel and K. H. Hoffmann, “Fahrverhaltenanalyse an Kreuzungen auf Basis
von Fahrzeugdaten,” in Proc. of Automotive meets Electronics 2014, Dortmund,
Feb. 2014. (Best Paper Award)
T. Streubel and K. H. Hoffmann, “Autonomous Vehicle Control through Dynamic
Traffic Scenarios Based on Artificial Potential Fields,” presentation at DPG Spring
Meeting, Dresden, Apr. 2014.
T. Streubel and K. H. Hoffmann, “Prediction of Driver Intended Path at
Intersections,” in Proc. of IEEE Intelligent Vehicles Symposium, Dearborn, USA,
Jun. 2014.
T. Streubel, “Fahrverhaltensanalyse zur besseren Fahrerassistenz,” Fachmagazin
Mechatronik, I.G.T. Verlag, München, Jun. 2014.
T. Streubel, “Driver assistance system, motor vehicle having a driver assistance
system, and a method for operating a driver assistance system.” U.S. Patent
US20150057835 A1, Aug. 2014.
T. Streubel and K. H. Hoffmann, “Realisierung eines Fahrtrichtungsprädiktors für
Kreuzungen,” in Proc. of Automotive meets Electronics 2015, Dortmund, Feb.
2015.
T. Streubel, L. Rittger, K. H. Hoffmann and J. F. Krems, “Naturalistic driving
behavior at inner-city intersections,” in Proc. of ITS World Congress, Bordeaux,
France, Oct. 2015.
119
120