Experimental evaluation of comfort and safety in

Experimental evaluation of comfort and safety in
light-duty vehicles
Tiago Manuel Góis Ferreira Gaspar Neves
Thesis to obtain the Master of Science Degree in
Mechanical Engineering
Supervisors:
Prof. GonçaIo Nuno Antunes GonçaIves
Prof. João ManueI Pereira Dias
Examination Committee
Chairperson: Prof. Mário ManueI GonçaIves da Costa
Supervisor: Prof. GonçaIo Nuno Antunes GonçaIves
Member of the Committee: Prof. Luı́s AIberto GonçaIves de Sousa
November 2014
Acknowledgments
First and foremost, I would like to thank my supervisors Prof. Gonçalo Nuno Antunes Gonçalves and
Prof. João Manuel Pereira Dias, for their valuable input and feedback throughout the development of
this thesis and for the availability to share their knowledge, without which a successful end would not be
possible.
A special thanks to João Freire and Nuno Duarte for their useful contribute.
To my family for their support, help and incentive during all these years and for always believing in my
capabilities. A special thanks to my father.
To the Mecânicos for their friendship and for being my comrades in this journey.
To Marta for the moral support, incentive to keep moving forward when facing hard challenges and
especially for the patience shown all these years.
ii
Resumo
O conforto e segurança representam dois dos parâmetros mais relevantes para o bem-estar dos passageiros de qualquer tipo de veı́culo, que são habitualmente avaliados de forma subjectiva em função
das condições da via ou tipo de veı́culo mas cuja forma mais objectiva será avaliar as acelerações a
que um passageiro está sujeito. Com o advento de tecnologias de baixo custo para monitorização a
bordo da dinâmica de um veı́culo é possı́vel classificar estes parâmetros sem intervenção do condutor.
Acresce ainda o elevado interesse de seguradoras em poder traçar um perfil do condutor através da
detecção de possı́veis situações de risco.
Neste trabalho, foram registados dados de aceleração, velocidade e coordenadas geográficas durante
ensaios experimentais num ambiente de condução real. Os dados de aceleração foram posteriormente
tratados e usados para a implementação de métodos já utilizados previamente por diferentes autores,
sendo usados como referência para validação dos mesmos eventos que são habitualmente percepcionados como desconfortáveis ou inseguros.
Procurou-se ainda implementar métodos alternativos para avaliação da segurança e cálculo da velocidade e distância percorrida, de modo a não só verificar a possibilidade de reduzir a dependência do
acelerómetro no caso de falha do equipamento como também se tentou inserir pequenas correcções
na informação da velocidade obtida a partir da porta OBD-II do veı́culo.
Os resultados obtidos com os métodos utilizados apresentam uma boa correlação com a percepção dos
ocupantes do veı́culo durante os ensaios, restando apenas algumas reservas quanto à classificação de
conforto e segurança de eventos induzidos por elevadas acelerações laterais.
Palavras-chave:
Aceleração; Conforto; Segurança; Dinâmica; Monitorização a bordo; Usage-
Based Insurance
iii
Abstract
Comfort and safety are two of the most relevant parameters for the well-being of the passengers of any
kind of vehicle, usually evaluated in a subjective manner as a function of road conditions or the type
of vehicle but a more objective manner is evaluating the accelerations to which a passenger is subject.
The advent of low-cost technology for on-board monitoring of vehicle dynamics makes it possible to
evaluate these parameters without any driver intervention. Added to this is the high interest of insurance
companies to be able to profile a driver through the detection of possibly risky situations.
For this work, acceleration, speed and geographic coordinate data were collected during experimental
trials in a real driving situation. The acceleration data were then processed and used for the implementation of methods previously used by different authors, with events that are usually perceived as
uncomfortable or unsafe being used as reference for validation of the methods.
Further on, alternative methods were implemented for safety evaluation and to calculate speed and
distance, in order to not only attest the possibility of reducing the dependence on the accelerometer in
case of equipment failure but also to introduce slight corrections into the speed information collected
through the vehicle’s OBD-II port.
The results obtained with the applied methods present a good correlation with the vehicle occupants’
perception throughout the trials, remaining only some reservations about the classification of comfort
and safety attributed to events where the major influence is originated by high lateral acceleration.
Keywords:
Acceleration; Comfort; Safety; Dynamics; On-board monitoring; Usage-Based Insur-
ance
iv
Contents
1 Introduction
1.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
1.2 Objectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
1.3 Structure of the Thesis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
1
1
7
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2 State of the art
11
3 Methodology
3.1 Data Collection . . . . . . . . . . . . .
3.1.1 Apparatus . . . . . . . . . . . .
3.1.2 Experimental Procedure . . . .
3.2 General Calculations . . . . . . . . . .
3.2.1 Distance Between Coordinates
3.2.2 Numerical Analysis . . . . . . .
3.2.3 Data Filtering . . . . . . . . . .
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27
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4 Dynamics
4.1 Methodology . .
4.2 Results . . . . .
4.3 Results Analysis
4.4 Conclusions . . .
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37
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5 Comfort
5.1 Methodology . .
5.2 Results . . . . .
5.3 Results Analysis
5.4 Conclusions . . .
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47
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6 Safety
6.1 Methodology . .
6.2 Results . . . . .
6.3 Results Analysis
6.4 Conclusions . . .
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63
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71
7 Conclusions and Future Work
7.1 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
7.2 Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
73
73
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References
76
Appendix
79
v
List of Tables
1.1 Summary of mentioned UBI service providers . . . . . . . . . . . . . . . . . . . . . . . . .
7
2.1 Threshold values regarding comfort conditions . . . . . . . . . . . . . . . . . . . . . . . .
20
2.2 Threshold values regarding safety conditions . . . . . . . . . . . . . . . . . . . . . . . . .
20
2.3 Abbreviated Injury Scale levels . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
21
2.4 ISO 2631-1 comfort guidelines . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
23
3.1 Differentiation and integration relations between distance, speed and acceleration . . . .
31
3.2 Qualitative comparison of the main features of Moving Average and Butterworth filters . .
34
4.1 Speeds at which the vehicles are tested to test the speedometer accuracy . . . . . . . . .
37
4.2 Resulting distances for the trip, using the three methods . . . . . . . . . . . . . . . . . . .
43
5.1 ISO 2631-1 comfort guidelines . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
47
5.2 Mean value of the RMS of each axis during the trip . . . . . . . . . . . . . . . . . . . . . .
50
5.3 Lower and upper limits from ISO 2631-1 . . . . . . . . . . . . . . . . . . . . . . . . . . . .
52
5.4 Comparison of the percentage of time spent in each comfort condition, using either the
lower or upper limits of magnitude of vibration total values . . . . . . . . . . . . . . . . . .
52
5.5 Guidelines for comfort levels on speed bumps using speed as a reference . . . . . . . . .
58
6.1 Reference safety levels . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
64
6.2 Percentage of time in each level, using the different evaluation methods . . . . . . . . . .
67
vi
List of Figures
1.1 Factors ranked as the highest priority in the new vehicle purchase decision . . . . . . . .
2
1.2 Vehicle features ranked as most important in the new vehicle purchase . . . . . . . . . . .
2
1.3 Importance attributed to certain vehicle features amongst all drivers surveyed . . . . . . .
3
1.4 Scheme of the Progressive Corporation patented system . . . . . . . . . . . . . . . . . .
1.5 Market size - share of the telematics-enabled policies in Europe and the US . . . . . . . .
5
6
2.1 Conflict graph with definition of serious conflict . . . . . . . . . . . . . . . . . . . . . . . .
14
2.2 Continuum of traffic events from undisturbed passages to fatal accidents . . . . . . . . . .
14
2.3 Scores for the three observers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
15
2.4 Effects of braking instruction and speed on the mean maximum deceleration as reached
during the control of braking . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
16
2.5 Relation between speed and crash rate on urban 60 km/h and rural 100 km/h roads. The
figure refers to a self-report study, developed by stopping drivers considered as fast or
slow according to the traffic speed distribution and asking about their crash history. . . . .
17
2.6 Relationship between average speed and crash frequency on four urban road types . . .
18
2.7 Illustration of measures of acceleration (g) and jerk (g/s) during a brake manoeuvre
. . .
19
2.8 A conceptual description of the event data recorder . . . . . . . . . . . . . . . . . . . . . .
19
2.9 Basicentric axes of the human body - seated position
. . . . . . . . . . . . . . . . . . . .
22
2.10 Health guidance caution zones . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
24
3.1 GPS receiver antenna . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
28
3.2 The inside of the OBU . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
28
3.3 OBU installed in the vehicle . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
29
3.4 Map showing the route used during the tests . . . . . . . . . . . . . . . . . . . . . . . . .
30
3.5 Comparison of the results applying the Moving Average (top) and Butterworth (bottom)
filters to the longitudinal axis data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
34
4.1 Coordinates slightly deviated from their real position . . . . . . . . . . . . . . . . . . . . .
39
4.2 Different speeds, including the integration of acceleration in a sample of five minutes . . .
40
4.3 Different total distances, including the integration of acceleration in a sample of five minutes 40
4.4 Flowchart of the dynamic speed prediction model . . . . . . . . . . . . . . . . . . . . . . .
41
4.5 Speeds comparison including the dynamic prediction . . . . . . . . . . . . . . . . . . . . .
42
4.6 Total distances comparison including the dynamic prediction . . . . . . . . . . . . . . . .
4.7 First model of the dynamic speed prediction, without any applied constraints . . . . . . .
43
44
5.1 Filtered longitudinal acceleration, with indication on hard braking events . . . . . . . . . .
48
5.2 Filtered vertical acceleration, with indication on high speed crossing of speed bumps and
crossing of a cobblestone section . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
vii
48
5.3 Longitudinal acceleration: filtered data (blue) and RMS value (black) . . . . . . . . . . . .
50
5.4 Resulting magnitude . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
51
5.5 Resulting magnitude, with the upper limit reference line for each comfort level . . . . . . .
52
5.6 RMS of the longitudinal component in the cobblestone pavement . . . . . . . . . . . . . .
53
5.7 Magnitude compared to the magnitude without including the root mean square of the
longitudinal acceleration in the cobblestone pavement . . . . . . . . . . . . . . . . . . . .
54
5.8 Speed profile vs. magnitude, while crossing the cobblestone pavement section . . . . . .
55
5.9 Speed, longitudinal acceleration and vertical acceleration variation while approaching and
crossing a speed bump at approximately 30 km/h . . . . . . . . . . . . . . . . . . . . . . .
55
5.10 Magnitude and root mean square of the longitudinal and vertical accelerations, crossing
a speed bump at approximately 30 km/h . . . . . . . . . . . . . . . . . . . . . . . . . . . .
56
5.11 Magnitude and root mean square of the vertical acceleration, crossing a speed bump at
approximately 50 km/h . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
57
5.12 Magnitude obtained while performing a hard braking event . . . . . . . . . . . . . . . . . .
58
5.13 Comparison of the root mean square of the lateral acceleration and the magnitude while
performing a left-right turn sequence . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
59
5.14 Classification in terms of safety using lateral acceleration in the left-right turn sequence .
60
6.1 Longitudinal Butterworth filtered acceleration . . . . . . . . . . . . . . . . . . . . . . . . .
65
6.2 Lateral Butterworth filtered acceleration . . . . . . . . . . . . . . . . . . . . . . . . . . . .
65
6.3 Comparison between the three different methods to obtain longitudinal acceleration, in a
set of 500 seconds . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
66
6.4 Longitudinal and lateral acceleration components with the reference lines of each safety
level . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
67
6.5 Longitudinal acceleration components obtained from the OBD and GPS with the reference
lines of each safety level . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
67
6.6 Lateral acceleration (blue) and dynamic speed prediction (black) during left-right turn sequence. The reference lines (cyan) represent the lateral acceleration safety levels. . . . .
68
6.7 Lateral acceleration (blue) and dynamic speed prediction (black) during cross of a roundabout. The reference lines (cyan) represent the lateral acceleration safety levels. . . . . .
69
6.8 Longitudinal acceleration during the braking event . . . . . . . . . . . . . . . . . . . . . .
70
6.9 Longitudinal acceleration during the braking event, obtained from the GPS coordinates . .
70
6.10 Longitudinal acceleration during the braking event, obtained from the OBD data . . . . . .
71
7.1 Coefficients of friction for different roadway surfaces, comparing dry and wet condition . .
75
viii
Nomenclature
av
Acceleration vibration total value
aw
Weighted axial root mean square acceleration
ABS
Anti-lock Braking System
AIS
Abbreviated Injury Scale
ASI
Acceleration Severity Index
EDR
Event Data Recorder
GPS
Global Positioning System
LDV
Light-Duty Vehicles
MSDV Motion Sickness Dose Value
NCAP New Car Assessment Programme
OBD-II On-board Diagnostics II
OBU
On Board Unit
OEM
Original Equipment Manufacturer
PAYD Pay As You Drive
RMS
Root Mean Square
SPS
Standard Positioning Service
TA
Time to accident
UBI
Usage-Based Insurance
UNECE United Nations Economic Comission for Europe
UTACV Urban Tracked Air Cushion Vehicle
VTV
Vibration Total Value
ix
Chapter 1
Introduction
1.1
Motivation
In today’s world, there are more than one thousand million cars in operation. Out of this amount of
cars, more than half is concentrated in the northern hemisphere, mostly in the developed countries.
Looking more closely to data available for the United States, one can see that more than 239 million
vehicles were in operation in 2010, making the country’s vehicle-to-person ratio the highest in the world,
at 1:1,3 [1]. On average a driver spent 76 minutes per day driving his private vehicle and each private
vehicle was driven slightly more than 10.000 miles per year [2]. These data clearly show how important
private vehicle transportation is nowadays in a developed country, and with it comes a concern with
the possibility of developing better products in terms of safety and comfort for drivers and passengers.
This is clearly stated in Mohr et al [3], that specifically for the premium segment, automotive original
equipment manufacturers (OEMs) could differentiate themselves with the help of design elements, new
features in infotainment and innovations directed at safety and comfort.
From the driver point of view, the perception of both comfort and safety is very important. In Koppel et
al [4], data from Spanish and Swedish consumers was analysed from the answers to a questionnaire.
The study found that vehicle safety is a top priority for the customers while comfort is at an intermediate
to high level of importance during the purchase process. In general, participants were more likely to
select both safety (e.g. Euro NCAP) related factors and safety related features as their highest priority in
the vehicle purchase process, thus showing that the perception of safety is a big concern for European
consumers. The results of the study can be seen in Figures 1.1 and 1.2.
1
Figure 1.1: Factors ranked as the highest priority in the new vehicle purchase decision [4]
Figure 1.2: Vehicle features ranked as most important in the new vehicle purchase [4]
Vrkljan and Anaby [5] tried to find out what are the most important vehicle features for Canadian customers, from a list that included: storage, mileage, safety, price, comfort, performance, design, and
reliability. Similarly to Koppel et al [4], safety was found to be a major concern for car buyers. The
maximum value for importance was selected by more than 50% of the customers for three of the eight
features surveyed, specifically: safety, reliability and mileage. Figure 1.3 shows the level of importance
of each of the evaluated features.
2
Figure 1.3: Importance attributed to certain vehicle features amongst all drivers surveyed [5]
The main findings of this work led to the conclusion that compared to all other features, except for
reliability, safety had the highest importance rating, with reliability being given a similar rating. Basically,
safety and reliability were found to be the most important features as opposed to the two least important,
performance and design [5].
Contrary to the results of the previous studies, a study with Australian consumers interviewed both
pre- and post-purchase [6], came to the conclusion that nor safety nor comfort are among their main
priorities. For this study a list of 20 factors was created, where four were safety related (ANCAP, USCR,
pedestrian aggressivity and vehicle aggressivity) and comfort was also included. Price, fuel efficiency
and reliability were the three factors selected more times as ”high” in priority, immediately followed by
the ANCAP rating, which was selected by 30 percent of participants. Comfort and performance ranked
lower and the other safety-related factors were ranked lowest in priority. The factors ranked in the same
order in the post-purchase survey.
While both consumers and car manufacturers are interested in comfort and safety, their interest is more
focused on the practicality of the subject, which is more useful from a marketing point of view. While for
safety the NCAP (New Car Assessment Programme) establishes the criteria for the quantification of the
conditions in case of an accident, the evaluation of comfort is highly subjective and depends on each
person’s perception.
The idea of establishing an objective evaluation for both comfort and safety has been the subject of
various researches since the 1940s [7], with relative success but with results that are very different from
each other, turning the establishment of threshold values that are in accordance with the perception
of an average person as the biggest challenge in this area. Techniques used for this have always
revolved around vehicle dynamics, in-car observations and behaviour questionnaires but none of those
represents an easily replicable method, that can be used in large numbers in normal day driving with
small financial investment. That is where third parties such as insurance companies interested in finding
an objective way to evaluate comfort and safety conditions come into play, especially trying to develop
new ways to define the premium for a customer. The traditional approach to classify risk in automobile
liability insurance is to assign an insured to a tariff class. The various tariff classes are based on many
criteria, including, for example, type of car, insured’s occupation, region (urban vs. rural), and crash
history [8]. Of all these criteria, driving history is the hardest to evaluate and to verify, due to the
possibility of unreported occurrences. The use of data logging for assessment of the different dynamic
characteristics of the vehicle can provide the data that is needed to develop a more accurate system,
3
in which the driving behaviour is analysed to determine if a certain driver is more accident prone or,
at least, takes bigger risks behind the wheel by recurrently going over the safety limits. This kind of
evaluation using telematics is already being used both in the US and in Europe, usually to develop Pay
As You Drive (PAYD) systems.
The concept of Pay As You Drive, from here on called Usage-Based Insurance (UBI), differs from the
traditional insurance because of the amount and type of information taken into account. While traditional
insurance only takes into account data like driver’s age, accident history, type of vehicle and the age of
the vehicle, the UBI model can take into account not only these criteria but also intends to reflect current
pattern behaviours of a driver, thus allowing for a dynamically adjusted insurance premium.
The emergence of low cost and widely available computers and electronic devices, allowed the development of small size equipments, usually called Event Data Recorder (EDR) or On Board Unit (OBU).
These equipments are built using basic electronic components such as accelerometers, GPRS and GPS
modules, among others and are easily installed even inside a small city car without affecting the normal use of the vehicle, making the use of dynamic quantities like acceleration and speed much easier
and more interesting for the development of new methods to assess comfort and safety conditions and
raising interest for potential investment in a new business area.
Nowadays, most of the equipment in use works through a connection to the on-board diagnostic port
(OBD-II), mandatory since 1996 in the United States and 2000 in the European Union. The most basic
systems are based on travelled distance read from the odometer or GPS, amount of time that the car
is driven, and in some cases, information about speed and the time of the day during which the car is
driven. Trying to get a deeper knowledge about the driver, it is possible to add more details that can
affect the premium, such as hard braking, rapid acceleration or hard cornering.
In the US market, one of the first companies to offer a UBI product was GMAC Insurance in 2004, available for customers subscribing to OnStar. This system connects to the OBD-II port, giving information
about the total mileage driven that latter on is used to set how much the insured can save. Previously,
Progressive Insurance had already filed for a patent registry in 1996, for a system with the capability to
monitor and record: miles driven, type of road, speed, safety equipment used, time of the day driven,
rate of acceleration, rate of braking and observation of traffic signs [9]. These features were clearly
ahead of its time, as will be seen ahead in this chapter, as only recently most of them started to be used.
Figure 1.4 shows a scheme of how the system works.
4
Figure 1.4: Scheme of the Progressive Corporation patented system [9]
In spite of all the features of this system, the Snapshot insurance program created by Progressive
Corporation only transmits information about the amount of hard brakes per 100 miles, time of day,
specifically how often the customer drives between midnight and 4 AM and miles driven and taking no
location information through the GPS signal.
In 2012, All State launched the Drivewise program that intends to reward safe driving. This system
records mileage, braking, speed and time of day when a customer is driving, by connecting to the OBDII port. Apart from allowing the customer to get a lower premium, the recorded information can be
accessed by the customer in a website, to get some feedback on his behaviour and possibly adapt the
way he drives.
In the Portuguese market, the OK GPS service is available. The service provides the possibility to
benefit of a discount in the premium as long as the insured fulfils some requisites. The requisites are
related to the data acquired by the company in partnership with the Italian company Octo Telematics
including distance driven, speed, hours of utilization and location where the car was driven. The service
also includes the possibility of sending the GPS signal to the assistance service, in case of theft if the
insured signals the situation and in case of accident if the system detects an impact with an acceleration
over 2,5g.
A look at Bruneteau et al [10] shows that more insurance providers all over the world have been looking
at this new business area, confirming that there is a tremendous potential for investment. The study
counts 130 UBI trials and launches, with about 5 million UBI policies worldwide. According to the study,
2013 was expected to be the year of maturity for the UBI technology.
5
In terms of generated value, according to a report by SAS [11] Progressive reported over $1 billion in
premium revenue for UBI policies. The same report shows a chart (Figure 1.5) from “Global Insurance
Telematics Study, 2012” by PTOLEMUS, that predicts the UBI market growth until 2020, by insurance
market share.
Figure 1.5: Market size - share of the telematics-enabled policies in Europe and the US [11]
The US market will have more than 25% of its auto insurance revenue generated via telematics, growing
to a total of $30 billion. The European market is forecast to represent more than A
C50 billion.
A Croatian company, Amodo, published an initial research outcome in 2013, showing that values of the
driving parameters related to risk of accident, decreased on average by 38% and a significant improvement of driving behaviour could be identified at 70% of the study participants [12].
As seen in Llaguno and Harbage [13], the main concern with this insurance system for the survey
participants is the possibility of privacy infringement, by having their driving locations monitored and
recorded and also with the possibility of data being shared with third parties, only surpassed by concerns
with a possible premium increase. As a possible counterbalance, as already mentioned, the insurance
companies usually offer the possibility of GPS tracking in case of theft, automatic position signalling in
case of accident and feedback on the driver behaviour.
Table 1.1 presents a summary of the previous information, listing all the mentioned services and the
technologies in use by each one of them.
6
Company
Service name
Country
Year
Features
Progressive Corporation
Snapshot
USA
1998
Records hard braking events,
time of driving and mileage; no
GPS information is taken
GMAC Insurance
OnStar
USA
2004
Counts total mileage driven
All State
Drivewise
USA
2012
Records speed, mileage, time of
driving and braking; the client
can have online access to information about his driving behaviour
Ok! teleseguros
Ok! GPS
Portugal
2012
Records speed, distance, hours
of driving and driving location;
GPS signal can be used in case
of theft or need of assistance;
accident signal if over 2,5g
Table 1.1: Summary of mentioned UBI service providers
1.2
Objectives
Taking into account all the introductory information presented in Section 1.1, it becomes clear that the
ability to objectively evaluate comfort and safety conditions is of interest for a specific industry. Accompanying that interest, technological evolution has put us closer than ever to that goal, something that is
proven by the number of players in the insurance business trying to make use of this technology.
The perspective of fast development of On Board Units and its capability to collect data lead to two
fundamental questions that the work tries to answer:
• Is it possible to evaluate comfort and safety conditions of the occupants of a light duty vehicle just
by taking into account dynamic variables, such as acceleration?
• If possible, are the methods in use by other authors valid for practical application?
The best way to answer the first question is by gathering as much information as possible concerning
methods and evaluation criteria from articles, previous experimental studies and international standards
to validate the idea of performing the mentioned evaluations.
In order to answer the second question, the use of an available On-Board Unit is required, providing the
capability to collect all the data concerning any events of interest in normal day driving. The collected
data shall then be used to implement the calculation methods needed in order to validate and compare
any results.
In order to develop the work, the use of the On Board Unit will follow the steps presented in the following
list:
• Track a vehicle in a real scenario, to collect data related to the dynamic behaviour in normal driving
conditions
• Establish criteria for the evaluation of comfort and safety conditions
7
• Develop a computational model to assess the comfort and safety conditions in each moment
The ultimate goal of this work is to develop a state machine that provides information on both safety and
comfort conditions of the vehicle occupants every second.
All these steps and the objectives of this work will be fulfilled using a short sample based on one vehicle
and one, maximum two drivers. This of course means that any of the methods that will be presented in
Chapter 2 requiring the use of very large samples of drivers are out of question for implementation. It
goes without saying that methods based on vehicle observation or evaluation from the outside are also
interesting to mention to contextualize but not to implement since that would go against the idea of using
an On-Board Unit.
When available, guidelines previously established by international standards shall be used and subject
to the same critical evaluation.
As a final consideration, the opinions of the trials’ participants shall be taken into account whenever there
are doubts about the validity of the results, in order to evaluate the accuracy of the available guidelines
compared to the perception of the vehicle occupants. Due to the small size of the sample, these opinions
were obtained during informal conversations without any kind of questionnaire being prepared for such
purpose.
1.3
Structure of the Thesis
The thesis is divided in seven chapters:
The first chapter features a short introduction, explaining the motivation for the development of this work,
followed by a list of questions that are intended to be answered, the objectives of the thesis and a short
explanation on the structure of the thesis.
In the second chapter, a summary of the current state of the art is presented, including all the information
concerning the evaluation of comfort and safety and new areas that have potential for future development
with the use of on-board monitoring techniques.
The third chapter of the work is focused on the methodology, divided in two parts: data collection and
generic calculations. In the data collection section the apparatus is presented, namely the components
and characteristics of the OBU (On-Board Unit), followed by the experimental procedure. The generic
calculations section is focused on equations that are used throughout the rest of the work. This includes
the presentation of the conversion from GPS coordinates to a distance between two points, numerical
analysis methods and the signal filtering.
The fourth chapter is dedicated to dynamics. More precisely, this chapter is focused on trying to correct
and further improve the quality of the data related to speed obtained from the vehicle, by combining it
with information from the accelerometer in order to improve the resolution.
The fifth chapter is focused on comfort, being based mostly on using the international standard ISO
2631-1, its applicability and possible problems, including specific events that are used as examples.
In the sixth chapter, the method used to evaluate safety is explained, including the choice of the reference values being used and a discussion on the effectiveness of each of the different methods used to
calculate safety conditions. Again, specific events are presented as examples.
8
The seventh and final chapter is where conclusions relative to the accuracy and reliability of the applied
methods are presented, especially concerning some specific situations, summarizing the conclusions
from the previous chapters. Future developments are also discussed, with suggestions on how to improve the three focused areas of dynamics, comfort and safety and possible new applications for the
On-Board Unit.
9
Chapter 2
State of the art
According to Faris et al [14], ride comfort is defined as how a vehicle responds to road conditions or
inputs other than its occupants. Until now, most of the testing for ride comfort has been done in a
subjective way as there is not an objective testing developed in a standardised way. Most research in
ride quality focus in one of three areas: human response to vibration, vehicle response to excitation and
ways of testing and evaluating the ride quality [14].
Temperature feeling, feeling of air speed in the cabin, the feeling of vertical acceleration, the feeling of
light type and intensity in the interior and of course ergonomics, are the main factors on which a human
being bases its opinion on comfort [15].
The work of Smith et al [16] provides good insight into what to evaluate and how to evaluate comfort conditions. The method used in this work was based on both subjective and objective measurements. For
the subjective part, ratings by the car passengers were used. For the objective part, acceleration measurements were used, including both vertical and lateral floorboard and lateral seat/passenger interface
accelerations.
The study compares passenger ratings with ISO standards, UTACV (Urban Tracked Air Cushion Vehicle)
Specification, a boundary below which the power spectral density of the ride accelerations must be at
all frequencies. For the development of weighting functions, the two previous methods and also the
Absorbed Power method of Lee and Pradko, that relates the comfort condition to the average power
absorbed by the passenger, calculated as a weighted integral of the acceleration spectral density [16],
were used.
For the UTACV the spectral density was calculated, with the result being that all the automobile rides
had spectra that exceeded the UTACV boundaries at some frequencies, indicating that the boundary
might be too conservative. For the comparison with ISO boundaries, the RMS acceleration was used.
Most of the ratings were rated as smooth.
Each of the methods selected by the authors allowed for the development of a weighting function. These
were compared with the “unweighted” RMS value of the ride acceleration (equivalent to using a function
that gives all frequencies the same weight), showing that the “unweighted” versions were not worse
predictors of ride quality than the weighted ones, contrary to what was expected.
A statistical study shows a linear correlation between the weighted indices and the mean personal
ratings. The authors then proposed two equations, one for comfort and another one for discomfort. For
11
the case of comfort, if the magnitude RMS index is used, the same linear equation can be used for both
floorboard and seat vibrations. The resulting least squares fit equation is:
R = 5, 43 − 40, 0α
(2.1)
where α is the magnitude RMS acceleration at either the floorboard or the seat and R is the ride rating.
Since the previous equation results in a measure of the ride which decreases as a function of the level
of vibrations, it makes sense to convert to the form of a discomfort equation such that
D = 5, 0 − R
(2.2)
where D is defined as the discomfort of the ride. Substituting Equation 2.1 into Equation 2.2,
D = −0, 43 + 40, 0α
(2.3)
where D varies from 0 (“the best ride you can imagine”) to 5 (“the worst ride possible”) [16].
The evaluation of the vibrations can be done using unweighted acceleration spectra for floor or seat
data in the vertical and transverse directions. A magnitude of the RMS values defined as the square
root of the sum of the square of the vertical and lateral RMS acceleration is recommended for either of
the locations. The values of these magnitude weighted RMS values will range roughly from 0 to 0,04 g
for smooth (interstate highway) rides, 0,04 to 0,06 g for medium rides, and above 0,06 for rough rides
which could be used to predict statistically general passenger rating of the ride [16].
The research done by P.S. Els [17], intended to find out which was the best objective method to evaluate
comfort in use. The comparison was done with ISO 2631, BS 6841, Average Absorbed Power and VDI
2057. The ISO 2631 standard is used mainly in Europe and the British Standard BS 6841 in the United
Kingdom. Germany and Austria use VDI 2057 while Average Absorbed Power or AAP is used by the
United States of America and by NATO in the NATO Reference Mobility Model. To perform the tests, a
military vehicle was driven over different terrains, chosen to excite significant amounts of body roll, pitch
and yaw motion, using various vehicle speeds and tyre pressures. Aside from the data monitored and
recorded during the test, all participants were asked to answer a questionnaire to evaluate the correlation
between the threshold values of each standard and the participants feeling.
As a result of the analysis, it was concluded that the vertical acceleration gives the best correlation
between subjective and objective ride comfort values. The respondents experienced pitch and roll of the
vehicle body as vertical acceleration [17].
Research by Emmanuel Leon Felipe [18] addressed the problem of highway design, more specifically
the building of horizontal curves. For a first experiment at the Pacific Traffic Education Center, a video
camera and a three-axis accelerometer were used to ride in two courses, a closed eight loop and a
figure “S” loop. Two experienced drivers were required to drive at the maximum safe speed before the
car started to skid. For all the drivers, a comfortable or an easy ride is under the region of 0,4g of lateral
acceleration. At the same institution, in 1992 the Royal Canadian Mounted Police performed a skid test,
with three different cars, obtaining a mean value of 0,84g for longitudinal acceleration. The distribution of
the load on the four cars’ tyres is different when braking than when cornering. Therefore, the skid pattern
when cornering, happens usually with a lower lateral acceleration ay than the longitudinal acceleration
12
ax needed to skid when braking hard. In this case, the value of ay can be approximated by ay = 0,9ax.
The tests at the loops were performed under two different scenarios, characterised by the speed. Scenario V1 corresponded to comfortable speed and V2 to fast speed. Each was driven by two expert
drivers and two groups of four regular drivers. The conclusions include information on the “trade-off
between speed and lateral acceleration:
• For scenario V1, people tend to drive at a speed which corresponds to a lateral acceleration of
0,35 - 0,40g in sharp curves. However, on flatter curves, the lateral acceleration seems to no not
influence the speed selection. The drivers selected their comfortable speed mostly based on the
speed itself.
• For scenario V2, the regular drivers’ speeds in sharp curves are almost the experts’ speeds. The
gap between the two set of speeds increased thereafter with the increase of radius. Regular
drivers, adjusted their maximum ’safe’ speed in flatter curves mostly based on the speed itself.” [18]
Another study [19] on comfort was done by evaluating how effective speed bumps are in inducing discomfort on passengers. For this work, the apparatus included a biaxial accelerometer (fixed on the
outside of a car door) connected to a USB data acquisition system with a 100 Hz sampling frequency.
The experimental procedure included several drives over a speed bump, at four different speed levels,
two of them lower than or equal to the local speed limit and the other two above the speed limit.
For data processing, to eliminate noise present during the data acquisition, every five consecutive data
points were averaged giving an effective measuring rate of 20 Hz. These points were then integrated
applying the root mean square method for the vertical acceleration. During the study, it was noticed
that longitudinal acceleration was present when the vehicle impacted a speed bump but in negligible
amounts and so its effect was neglected. It was concluded that while going over a speed bump at
speeds exceeding the speed limit the passengers experience uncomfortably high vertical accelerations.
Vertical acceleration increases exponentially with linear speed increase [19].
Regarding safety conditions, risky behaviour like inadequate speed or too short distances to the preceding cars show a relation with the number of conflicts and accidents in which a driver is involved [20].
The Swedish traffic conflicts technique was the result of research by Christer Hydén in 1987 [21] [22],
where the detection of safety critical events is done based on two main factors: time to accident and
conflicting speed. Time to accident is defined as, “The time that remains from one of the road users
have started an evasive action, until a collision would have occurred if the road users had continued with
unchanged speeds and directions”. Time to accident is calculated based on distance and a conflicting
speed. With these data it is possible to classify each situation according to Figure 2.1.
13
Figure 2.1: Conflict graph with definition of serious conflict (TA: Time to accident) [22]
A serious conflict is characterised by suddenness and harshness in action of at least one of the involved
and for being a situation in which drivers say they would never like to be involved [22]. The main
drawback of this method is that it is based on trained observers evaluating the seriousness of conflicts
in specific locations. On the other hand, the technique shows that conflicts and accidents belong to
the same process, just with different degrees of seriousness (most often), since the patterns are very
alike [22]. Figure 2.2 shows the scale used to classify the different level of conflicts.
Figure 2.2: Continuum of traffic events from undisturbed passages to fatal accidents, as originally developed Christer Hydén in 1987 [23]
Finally, the definition of conflict used by the author implies that two road users must interact during a
conflict.
14
Other studies based on in-car observations have been done, like the ones by Hjälmdahl and Várhelyi [24]
and Oscar Siordia [25]. In the former study [24], a car was equipped with three video cameras and data
loggers, registering the distances to both the front and the back. A laser radar was also used to measure
the distances between cars and a GPS signal was used to register information about time, positioning,
speed and speed limit, sampling at a frequency of 5 Hz. Three individuals with basic traffic engineering
knowledge were given training and then asked to analyse the risk level achieved by different drivers on
a test route. A disadvantage of this method is the influence of the environment on the observers, as for
example in a test performed during rush hour in the afternoon (test number 4), where the results were
very poor, indicating that fatigue due to a bigger amount of observation events plays an important role in
the reliability of this method, as can be seen in Figure 2.3. The results show that the method is valid but is
much dependent on the observer conditions, such as a situation in which one of the observers overslept
in the morning and then performed badly during the test or the situation mentioned before about the
effect of fatigue. In the latter [25], data was acquired in a highly realistic truck cabin simulator, with
sessions performed in four different scenarios. Data obtained included registers of the vehicle dynamics
and road characteristics and visual information obtained from two cameras, one of the driver’s top view
and the other of the simulator main view. Then, a visual analogue scale from 0 to 100 was established
from the classification of three experts and then five data mining algorithms were trained to predict the
driving risk level, based on driver, vehicle and road information, with all tests being performed in a truck
cabin simulator. In this case, the main problem found was that since the algorithms were trained using
four different environments (urban, mountain, interurban and circuit), when trying to use an algorithm to
evaluate a different environment the performance was very poor.
Figure 2.3: Scores for the three observers [24]
The research of van der Horst [23], used recordings of traffic conflict situations. The procedure consisted
in making video recordings with one or more fixed cameras on the spot and an offline analysis. By using
reference points in video stills it was possible to translate the x- and y- coordinates into the road plane.
From the analysis of consecutive video stills, it was possible to calculate speed, acceleration, time to
collision and heading angle. The work resulted in the conclusion that in normal situations, only taking
into account situations related with the “static” environment, braking goes up to -4 m/s2 (for example, at
a railway crossing).
Further analysis shows that for hard braking the limit goes from -6,5 m/s2 up to about -7,5 m/s2 and for
normal braking goes from approximately -4,0 m/s2 to -5,5 m/s2 , depending on the speed. These results
15
can be seen in Figure 2.4. This last experiment, called “The Vancouver Experiment”, was performed
with the subjects driving at three different levels of speed, wearing liquid crystal glasses that could be
controlled to suppress visual information. The drivers should brake only after being given an order to do
so, braking either normally or hard while feeling that the distance to an obstacle was still safe to avoid
hitting it. To register data, an on-board computer was used, registering speed and distance by counting
impulses in a Hall transducer mounted on the driveshaft with the status of ten binary input lines at a
sample rate of 10 Hz. One binary input line was connected with a switch on the brake pedal and another
with a pulsed beam infrared detector that fired at the moment each one of the two reflector poles were
passed. These registration enabled the measurement of distance travelled with time, longitudinal speed
with time, moment of initiating braking action and moment of passing reflector poles [23].
Figure 2.4: Effects of braking instruction and speed on the mean maximum deceleration as reached
during the control of braking [23]
It must be noted that the occlusion referred in Figure 2.4 is related to the methodology used in [23],
where the driver’s vision was affected and it is considered to be out of the scope of this work.
Research by Timo Lajunen [26], tried to find a relation of speed and acceleration as measures of the
driving style of young male drivers. For data acquisition, the subjects drove a test vehicle equipped with
two video cameras (one pointing straight ahead and another point to the right side). The pictures were
mixed into the same video screen, overlaid with VGA graphics with digital data from acceleration sensors
in the car and controls and stored on a videotape. The use of controls, speed and two accelerations
(lateral and longitudinal) were also stored on a computer file at 15 Hz.
To evaluate the behaviour at specific locations, the authors analysed the behaviour on a crest and in
both gentle and sharp curves. While evaluating site-specific measurements, it was concluded that on
a crest there was no relation of the behaviour with experience or accident involvement. As for the
curves, in the sharp curve prior accident involvement was related with the maximum acceleration used,
as drivers with more accidents tend to use higher accelerations. The same relation was not found in the
gentle curve. In the case of speed, in the gentle curve there was no relation with accident involvement
or experience, while for the sharp curve there was a clear relation between higher speeds and prior
accident involvement.
A two-way analysis of variance (accident involvement vs. driving experience) indicated that neither
prior involvement in accidents nor driving experience were related to any effects on the longitudinal
16
acceleration or deceleration. The same analysis performed for lateral acceleration, showed that the
drivers with more than one accident use higher left accelerations (in right-sided curves). In the case of
experience, there was no relation with the use of higher or lower lateral acceleration.
The two-way analysis of variance showed that there is a high relation between the use of higher speeds
during the tests and prior accident involvement while there is no relation between experience and speed.
In the case of equivalent vector acceleration (the vector sum of all the weighted acceleration components), the two-way analysis showed a relation between higher equivalent acceleration and prior accident involvement.
The authors concluded that maximum speed is a better way to gauge safe driving style than acceleration
signature or other quantities derived from it. Anyway it is considered that site-specific measures and
acceleration signature should not be completely abandoned [26].
In [27], important empirical studies on speed and crash rate were reviewed. The review included selfreport and case-control studies, with all the studies concluding that the crash rate increases with the
increasing of speed. Two of the referenced authors go even further, concluding that with increasing
speed on urban roads, the crash rate increases faster than on rural roads (Figure 2.5). In the authors
opinion, the best results are provided by the reviewed case-control methods, meaning that crash rate
increases exponentially for individual vehicles that increase their speed and increases faster, with a
particular increase in speed, on minor/urban roads than on major/rural roads. This makes clear the fact
that in roads designed for lower speeds an increase in speed causes a faster raise in crash rate than
increasing speed in roads designed for higher speeds.
Figure 2.5: Relation between speed and crash rate on urban 60 km/h and rural 100 km/h roads. The
figure refers to a self-report study, developed by stopping drivers considered as fast or slow according
to the traffic speed distribution and asking about their crash history [27]
Aarts and van Schagen [27] also reviewed the influence of speed differences at road section level, with
results showing that increasing average speed results in an increase of the crash rate for each of the
road types presented in Figure 2.6.
17
Figure 2.6: Relationship between average speed and crash frequency on four urban road types [27]
In 1987, Christer Hydén suggested that by studying fluctuations in the acceleration and deceleration
profiles it is possible to detect possible safety critical behaviour [28]. During normal day driving, a driver
needs to brake regularly in a controlled manner, in a range of situations that goes from achieving slight
speed reductions using light engine braking to very powerful braking, in order to stop the vehicle as fast
as possible.
Evidence was found that jerks or suddenness of braking can be used as a measure of safety critical
driving behaviour. The study involved over 200 passenger cars equipped with an Intelligent Speed
Adaptation (ISA) system and data loggers, which recorded driving data such as the actual speed of
the vehicles with a 5 Hz sample rate by means of a CAN (Controlled Area Network) bus. The ISA
functionality uses the speed and position of the vehicle to inform the driver about speed limits, and to
give warnings if the driver exceeds the speed limit, by comparing the driving data with the digital road
map incorporated in the ISA equipment [28]. This data was then analysed to compare the drivers’
self-reported accident involvement with the recorded jerk rate.
The authors also show some concern with problems caused by the used sample rate being too low,
stating that in future trials the sampling frequency should be at least 10 Hz, preferably 20-50 Hz in order
to decrease the risk of sampling distortion, due to noise, that could otherwise affect the data during the
necessary smoothing and filtering of the raw data [28].
Further development was achieved by Bagdadi and Várhelyi. In this work [29], the problem of false
detections of critical situations was addressed, based on the fact that safety critical situations need to
be distinguished from powerful but controlled braking. While braking, the vehicle is subject to negative
acceleration which tends to diminish as the speed is reduced. The magnitude of a jerk is greatly influenced by the rate at which the acceleration begins and how it is carried out. The more abruptly the
braking starts and how the rate decreases, the more powerful the produced jerk is. In spite of acceleration being strictly negative while braking, a jerk can have positive and negative values, independent
of the final condition of the vehicle. An example of how jerks and acceleration are measured during a
brake manoeuvre can be seen in Figure 2.7.
18
Figure 2.7: Illustration of measures of acceleration (g) and jerk (g/s) during a brake manoeuvre [29]
To perform the tests, an Event Data Recorder (EDR) was developed, capable of measuring both longitudinal and lateral variations in acceleration. Data was continuously monitored by the EDR and recorded
onto a hard drive for a predetermined time period before and after the occurrence of safety critical driver
behaviour. The main parts of the EDR were the data acquisition unit and a dual-axis accelerometer, as
seen in Figure 2.8. The sampling frequency in use was higher than the limit suggested in [28], being set
at 100 Hz. In order to calculate jerks based on measured acceleration data it is important to reduce the
noise as much as possible. Problems caused by noise were addressed by testing a few different filters,
with Savitzky-Golay being considered the one with the best performance.
Figure 2.8: A conceptual description of the event data recorder [29]
The main goal of this project was to develop a new method, named critical jerk method. During the
pilot study, a visual analysis suggested a jerk value of approximately 1,5 g/s for critical situations while
a threshold value of approximately 1,0 g/s is sufficient for detecting potentially critical events. These
values were later confirmed by performing a naturalistic driving study [29].
19
Commercially available equipments use much lower longitudinal acceleration threshold values. Two
examples are the values used by Geotab Inc. and Autel Company. The first company, a leader in
telematics usage for fleet management, in [30] presents the acceleration threshold values used. In the
case of deceleration, under default conditions, the company suggests a value of -4,76 m/s2 to define a
harsh longitudinal deceleration. The same value (absolute value) is considered for lateral acceleration.
In the case of Autel, in the Maxi Recorder User’s Manual [31], two different levels are defined, hard and
extreme braking. These values can be changed using the available software, but the default values are
-3.4 m/s2 for hard braking and -4.91 m/s2 for extreme braking.
Tables 2.1 and 2.2, summarize the threshold values obtained for both comfort and safety from various
authors.
Magnitude (m/s2 )
Craig C. Smith, 1976
Lat. Acceleration (m/s2 )
0,59
Felipe, 1996
3,92
Table 2.1: Threshold values regarding comfort conditions
Lateral
Longitudinal
Acceleration(m/s2 )
Acceleration(m/s2 )
Nygard, 1999
Jerk(m/s3 )
-9,90 to -12,60
van der Horst, 1990
-6,50
Bagdadi and Várhelyi, 2013
9,81
Felipe, 1996
7,42
-8,24
Autel Company, 2009
-4,91
Geotab Inc., 2011
4,76
-4,76
Table 2.2: Threshold values regarding safety conditions
As the tables show, especially when the concern is safety, acceleration values vary widely. More specifically, values obtained from academic research almost double the values of acceleration used by companies that provide fleet management services. Possible explanations for these variations are the different
approaches used to solve the problem, either during data collection or analysis methodology, or the goal
of each work, as all of the referenced values apart from those used by fleet management companies,
were obtained trying to analyse safety critical events. The use of equipment or vehicles based on older
technologies can possibly justify the higher thresholds obtained in the 1990s.
The values used as default by the two fleet management companies are more conservative than possible, as both let the user change the parameters and Geotab mentions a ”too forgiving” level with a
deceleration threshold of -5,64 m/s2 .
A noteworthy use of EDRs, already outside of the scope of this work, is the study of injury risk in
a collision with roadside hardware. The work developed by the authors of [32] intends to validate the
correlation between Acceleration Severity Index (ASI) and the potential for occupant risk in crash events.
"
ASI(t) =
āx
âx
2
+
20
āy
ây
2
+
āz
âz
2 # 12
(2.4)
Equation 2.4 is represents the method for the computation of ASI, where āx , āy and āz represent the
50 ms averaged component vehicle accelerations and âx , ây and âz correspond to threshold values for
each component direction.
The experiment included following vehicles with an EDR installed, leading to the collection of data from
collisions of more than one thousand cars. The data stored in a database included seat belt status for
the driver, airbag trigger time and longitudinal velocity vs. time sampled at 10 ms intervals during the
crash. Pre-crash the collected data included vehicle prior to impact, engine throttle position and brake
status for five seconds preceding the impact.
In order to narrow the crash events to be analysed, a list of criteria was established to select events
among the whole database:
• Airbag deployment (according to the authors, the velocity change threshold is approximately 5 m/s)
• Recorded EDR velocity data
• Available injury data for either the left or right front seat occupant
• Belted occupants only
• Comprised of a single impact only
• Frontal collision
• No vehicle rollover
These criteria resulted in a sample of 120 different cases. In order to validate the use of EDR data, after
calculating the ASI value with the EDR data the results were compared to six NCAP tests. Considering
the NCAP results as correct, all of the cases resulted in an error of less than 10% in the ASI value.
The final objective of the work was quantifying occupant injury. For these purpose, the authors used the
Abbreviated Injury Scale (AIS) and considered that an injury corresponding to the recommended limit
for ASI of 1,0 is equivalent to an AIS injury of level 1 or less. The Abbreviated Injury Scale is represented
in Table 2.3:
AIS value
Injury characterization
0
No injury
1
Minor
2
Moderate
3
Serious
4
Severe
5
Critical
6
Maximum/Fatal
Table 2.3: Abbreviated Injury Scale levels
The authors concluded that there is a correspondence between the recommended limit of ASI 1,0 and
the condition of ”minor/no injury” and that ASI, with respect to the currently in use thresholds, is a good
indicator of occupant injury for belted and airbag restrained occupants involved in frontal collisions [32].
21
International ISO Standard 2631-1
As mentioned before, ISO 2631-1, under the title “Mechanical vibration and shock - Evaluation of human
exposure to whole-body vibration”, establishes criteria for the selection of the method of measurement
and analysis of the vibration environment and presents an approach to the application of the results [33].
Also, with the objective of raising awareness of the complexity of human physiological/pathological response as well as behavioural response to vibration, some guidance on the effects of vibration on health,
comfort and motion sickness is given.
One must be aware that in case of extreme-magnitude single shocks, such as in case of vehicle accidents, the methods used are not applicable.
Since this work is focused on safety and comfort in a car, both driver and passengers will be in a seated
position and so, the following figure applies in terms of position and axes:
Figure 2.9: Basicentric axes of the human body - seated position [33]
The international standard states that vibration shall be measured according to a coordinate system
originating at a point from which vibration is considered to enter the human body. The location of
measurement shall be in an area of contact between the body and the vibrating surface. As seen in
Figure 2.9, for a seated person the main contact surface for measurements are the supporting seat
surface, the seat-back or the feet. Regarding the signal conditioning, the only option given by the norm
is to use a low pass filter if needed.
The primary quantity to measure comfort conditions is acceleration.
The basic way to evaluate acceleration is through the weighted root mean square method, expressed in
m/s2 , defined by Equation 2.5
"
1
aw =
T
ZT
# 21
a2w (t)dt
0
22
(2.5)
Where aw(t) is the weighted acceleration as a function of time, in m/s2 , and T is the time of the measurement, in seconds. Depending on the location of the measurement points, different weighting factors shall
be applied for the calculation of acceleration values.The frequency weighting factors are dependent on
the band of vibration frequencies in which the vibration is measured. For calculations, one shall select
the weighting factor, Wi, according to the band of frequency to which the system is subject. Also, to calculate the RMS acceleration for the different situations (health, comfort or motion sickness), a multiplying
factor is defined for each axis, taking into account the position of the subject.
In a situation where the vibration exposure consists of two or more different magnitudes and durations,
the following equation applies:
aw,e =
! 12
P 2
awi Ti
P
Ti
(2.6)
Where aw,e is the equivalent vibration magnitude (RMS acceleration in m/s2 ) and awi is the vibration
magnitude (RMS acceleration in m/s2 ) for the time of exposure Ti.
In case of a need to combine accelerations in various directions, the total value can be calculated as
follows:
1
av = (kx2 a2wx + ky2 a2wy + kz2 a2wz ) 2
(2.7)
Where ki are multiplying factors, defined in accordance with the position of the passenger and the location of the measurement equipment.
Comfort
As mentioned before, the text for ISO 2631-1 establishes guidelines for the values of the magnitude of
acceleration to evaluate the effects of vibration on comfort and perception in public transport.
Magnitude (m/s2 )
Comfort Level
≤0,315
Not uncomfortable
0,315 to 0,630
A little uncomfortable
0,500 to 1,000
Fairly uncomfortable
0,800 to 1,600
Uncomfortable
1,125 to 2,500
Very uncomfortable
≥2,000
Extremely uncomfortable
Table 2.4: ISO 2631-1 comfort guidelines
Health
Concerning the effects of vibration on health, although this is done in a very basic way due to a lack of
data to establish a relation between vibration and the effects on health, the norm still provides guidelines
for the assessment of health risk. Due to very limited experience in the application of ISO 2631 to assess
23
health in most of the body positions, the guidance provided is based almost entirely on research that
only takes into account the z-axis of a seated person.
Basic health risk can be assessed by two different equations. Both of them define when two daily
vibration exposures are equivalent:
1
1
aw1 ∗ T12 = aw2 ∗ T22
(2.8)
1
1
aw1 ∗ T14 = aw2 ∗ T24
(2.9)
These two equations are used to draw the plot in Figure 2.10. As seen in the figure, both equations
establish an upper and a lower limit of acceleration depending on the time of exposure.
Figure 2.10: Health guidance caution zones [33]
The shaded area, between 4 and 8 hours is the one for which most occupational observations exist.
This section of ISO 2631-1 is applicable to situations where the body is exposed to long-term highintensity vibration, while at work, travelling or during leisure activities. The main concerns are risks for
the lumbar spine and the nervous system, that are generally affected only after years of exposure.
Motion Sickness
Motion exposure for long periods of time may lead to symptoms of motion sickness. It is possible for a
subject to adapt in case of exposure for very long periods (e.g., a few days) and even some adaptation
can be retained for future situations.
24
The international standard presents two options for the measurement of the motion sickness dose value
(MSDV).
If possible, the MSDV shall be determined from measurements taken throughout the full period of exposure. It can be calculated by:
# 21
" ZT
M SDVz =
2
(aw (t)) dt
(2.10)
0
where aw(t) is the frequency-weighted acceleration in the z direction and T is the total period during
which motion could occur, in seconds. This calculation gives a result in m/s1,5 .
If the exposure is continuous and has an approximately constant magnitude, the MSDV can be estimated
from the frequency weighted RMS determined over a short period. For the exposure duration T0, the
MSDV can be calculated by
1
M SDVz = aw T02
In case of using Equation 2.11, the measurement period should not be less than 240 seconds.
25
(2.11)
Chapter 3
Methodology
This chapter is divided in two parts. In the first part, the data collection procedure is explained with
details on the On-Board Unit use and the selected route. The second part is focused on calculations
that are necessary for the proceeding chapters and data filtering.
3.1
Data Collection
The development of this work required the collection of data from real scenarios, using an instrumented
vehicle. In order to collect data the On-Board Unit was used, along with a laptop computer and, for the
second trial, a video camera.
For the experimental procedure a route including as many comfort and safety related events as possible
was selected in an urban setting in the Lisbon area.
3.1.1
Apparatus
The apparatus to perform the experiment consisted of a common hatchback vehicle, an On-Board Unit
for data recording and a laptop with Bluetooth communication capability.
The On-Board Unit is basically a small box, encasing a three-axis accelerometer, a Bluetooth module, a
GPRS module, a SD card holder, a GPS signal receiver and a barometric altimeter. The OBU is powered
through the OBD port connection [34]. The apparatus can be seen in Figures 3.1,3.2 and 3.3.
27
Figure 3.1: GPS receiver antenna
Figure 3.2: The inside of the OBU
28
Figure 3.3: OBU installed in the vehicle
The positioning chosen for the OBU is related with the ISO 2631-1 standard. As seen in Chapter 1, the
measurements for comfort shall be made on one of these positions: feet, seat-back or supporting seat
surface. Of these three possible locations, the decision was made to place the OBU as close as possible
to the supporting seat surface to allow for an easier setup of the equipment while avoiding discomfort for
the driver.
Only during the second trip, a video camera was added to the apparatus in order to ease further analysis.
3.1.2
Experimental Procedure
For the development of this project, it was needed to perform an experiment where all the events of
interest that can happen during a normal car travel would be included. For this, a route was selected,
comprised of relatively well-known urban and suburban roads in the Lisbon Area. As will be explained
later on, some problems arose during the first trip, creating the need to repeat the experimental procedure in a second trip. For the second trip, as a way to maintain the conditions as close as possible to
the first time to allow for the direct comparison of the data obtained, the same route was used whenever
possible.
29
Figure 3.4: Map showing the route used during the tests
The events mentioned in the previous paragraph were all, as said before, representative of common
situations that can happen to any driver during normal day travelling. The events that were included at
least once during the trips and to which a marker was attributed in the data recording, were: acceleration,
braking, right turns, left turns, roundabouts, lane changes, potholes, speed bumps and cobblestone
pavement. The markers were introduced as user inputs though a laptop computer during the trips and
represent a feature that is only available for the purpose of testing and not for use by a final consumer.
For the first trip, taking into account all the information gathered from previous works, it was decided
to setup the accelerometer sampling frequency at 100 Hz. This value was the highest found as a
suggestion in the whole bibliography review. Also, for data transmission, it was decided to use the
Bluetooth connectivity saving the data directly to the computer. During the first test, the input of the
markers revealed itself to be fallible due to a delay of three to four seconds in relation to the time of
input. After this test, some problems were found while analysing the data as a few parts of the collected
data were unusable due to incorrect registration to the text file. The probable cause for the issues
concerning the input of markers and file writing was the use of the Bluetooth connectivity while writing
to a file at the same time, requiring the use of too many resources at once. The visible effects of this
problem in the text file were lines with half the information available, lines overwritten with other lines
and most of all, the incapability of the OBU to keep the data transmission at the intended frequency.
The problems were exacerbated whenever more intense events happened during the trip, such as hard
braking or travelling through cobblestone pavement.
For the second trip, the same sampling frequency was used. The difference in the setup was the saving
of the data directly to the SD card placed inside the OBU. This proved to be a good decision, as the
resulting files were almost 100% problem free, except for a whole second that was not registered when
the car engine went off after an extreme braking situation. The only technical problem found during the
trip was that the OBU sometimes tried to connect to a server for data transmission, which was making it
impossible for the user to introduce the markers to identify the events for a few seconds. This problem
was solved remotely during a short stoppage.
30
Concerning the collected data, as already mentioned, acceleration was sampled at 100 Hz in the three
axis. Other information collected during the trips included latitude and longitude from the GPS and
speed information from the OBD port, all of them with a sampling frequency of 1 Hz. The capability for
a user to insert markers during the trip while using a laptop was also used, in order to allow an easier
analysis later on.
As mentioned in section ??, for the second trial a video camera was used with a vehicle occupant
operating it. Later on during the data analysis, the availability of the video represented a helpful addition
as any doubts on the kind of event that led to variations in the collected data were easily dissipated or
whenever the perception of the participants did not match the results.
3.2
General Calculations
During the experimental procedure data was collected from the OBU and the vehicle itself. While the
main idea of this project was at first to determine how a passenger perceives comfort and safety conditions in a light-duty vehicle using the information obtained with an accelerometer, questions were raised
concerning the possibility of some occurrence that would lead to accelerometer data unreliability, be it
a malfunction or incorrect positioning of the OBU. This created the need to look for alternative ways to
calculate approximations of the quantities of interest.
Table 3.1: Differentiation and integration relations between distance, speed and acceleration
3.2.1
Distance Between Coordinates
Two of the recorded data were latitude and longitude. While both of them by themselves are not of great
use, the combination of the two coordinates to calculate distances is quite useful.
As seen in Chapter 2, travelled distance is one of the most common criteria used by insurance companies to determine the cost of an insurance policy, which leads to the interest of including in this project a
method to calculate the travelled distance. For this purpose, Equation 3.1 can be used to calculate the
distance on a sphere, with both latitude and longitude in radians:
e = arccos[sin(Lat1 ) × sin(Lat2 ) + cos(Lat1 ) × cos(Lat2 ) × cos(Long1 − Long2 )]
(3.1)
Finally, taking into account the radius of the equator, the final equation results in:
distance = e × 6378137
31
(3.2)
where 6379137 is the radius of the equator in meters.
This method assumes a spherical shape for the Earth. It must also be noted that Equation 3.1 is only
applicable in locations in the northern hemisphere and west of Greenwich. As explained in [35], in order
to be used in the southern hemisphere or east of Greenwich, the coordinates have to be multiplied by
-1.
3.2.2
Numerical Analysis
Due to the characteristics of the problem being studied, namely the impossibility of having a function that
provides an exact solution for the sampled data, the use of numerical analysis is needed. The numerical
methods were used for both integration and differentiation and chosen according to the information
available in the literature. Also, while other parts of this project had standardized methods to perform
the calculations, for the numerical analysis there is a huge amount of options to choose from.
During the selection process, care was always taken to make sure that the selected methods require as
little computational effort as possible if these methods are to be implemented in an OBU, that has limited
computational power.
Numerical Differentiation
In order to obtain the first and second derivatives, the most widely accepted method is the Finite Difference method. For the first derivative of either position or speed, it was used a first order central difference
(Equation 3.3), which tends to provide a smaller error than progressive or regressive differences. For the
second derivative, used only to obtain the longitudinal acceleration from the known position, the second
order central difference method (Equation 3.4) was used.
f (x1 + h) − f (x1 − h)
2h
(3.3)
f (x − h) − 2f (x) + f (x + h)
h2
(3.4)
Dh f (x1 ) =
Dh2 f (x) =
where h represents the step (time interval between two values of f(x)). This value can be manipulated in
order to improve the results.
Numerical Integration
As mentioned previously, special care was taken with the computational effort of the selected methods.
Due to this, the decision was made to stick to the basic rules, namely the Midpoint Rule, the Trapezoidal
Rule and Simpson’s Rule. While the lack of an exact solution makes it impossible to calculate the
numerical error associated with any option, all these three methods have been highly tested and so it is
fairly easy to find articles or books where comparisons between the three methods are presented.
As can be seen in [36] and [37], examples with an exact solution clearly show that Simpson’s rule
always gives a result with a much smaller error than both Midpoint and Trapezoidal rules. In [37] it is
32
also explained why this much smaller numerical error is achieved with almost the same computational
effort.
The definition of Simpson’s Rule is:
Ih (f ) =
x1 − x0
x1 + x0
[f (x1 ) + 6f (
) + f (x0 )]
6
2
(3.5)
The derivatives are used in order to obtain values of speed from distance and acceleration from both
distance and speed, while the integration method is used for the inverse operations.
3.2.3
Data Filtering
The collected data, in a raw condition, presents a very noisy behaviour. Possible sources of noise are
vibrations of the plastic panel to which the OBU was attached ( 3.3), an accelerometer that does not have
the right noise specifications for the application or signal losses caused by data transmission through
cables. This leads to difficulties in analysing data, specially in the case of safety where points that are
clearly wrong can lead to wrong conclusions, as that component of this work uses data without any
intermediate calculations.
The option to low-pass filter any accelerometer data, according to ISO standard 2631-1 is left at the
user’s discretion. One of the main characteristics of a low-pass filter is that short-term fluctuations are
eliminated while the longer-term trend of the signal is kept, resulting in a smoother form of the signal.
Three different relatively simple filters were tested: a simple low-pass filter, a moving average and a lowpass Butterworth filter. In the case of the simple low-pass filter, many isolated peaks were compressed
too much, what could lead to wrong results while evaluating safety conditions, as safety risk events tend
to be short in time but with a very fast increase and decrease.
While comparing the results obtained with the moving average and Butterworth filters, it is relatively easy
to see on Figure 3.5 that the results are very similar between the two of them.
33
Figure 3.5: Comparison of the results applying the moving average and Butterworth filters to the longitudinal axis data. Raw data (blue) vs. Filtered data (red)
The final decision was in favour of using Butterworth filtering. While Butterworth filtering has the disadvantage of introducing a small phase distortion and moving average filtering is much simpler and easier
to apply, the Butterworth method allows for better control of its behaviour and more versatility for future
developments, especially if a frequency domain analysis is carried out. In [38], a summary of the main
features of the two filters is available:
Moving Average
Butterworth
Moving averages can indeed smooth noisy
Excellent passband response
signals
They cannot separate out different frequency
Arbitrarily sharp roll-off can be achieved by in-
components (cannot pick a cutoff frequency)
creasing filter order
Introduces a phase (time) distortion into
Have poor (“gradual”) roll-off characteristics
smoothed data (but this can be easily corrected)
Basically is a sophisticated weighted, recur-
Have very poor stopband attenuation
sive, moving average filter
In most cases, there will be a better option
than using a moving average
Table 3.2: Qualitative comparison of the main features of Moving Average and Butterworth filters
As mentioned in Table 3.2, the moving average does not allow to pick a cutoff frequency, which is crucial
if there is interest in a frequency domain analysis. Also, being a more sophisticated version of the
same filter and the requirement of ISO 2631-1 to use a filter with Butterworth characteristics to establish
band-frequency limits to apply frequency weightings, tips the balance in favour of the Butterworth filter.
Apart from the previously mentioned options, the Kalman filter is also a common option for accelerometer data filtering. Although being called a filter, the Kalman filter works more as a predictor, producing
34
estimates of the variables while taking into account the respective uncertainties. This filter is very appreciated not only for academic purposes [39] but also among robotics communities, especially for position
tracking or when developing methods to stabilize quadcopters during the flight. The main drawbacks
of this filter are requiring not only the availability of more data apart from the acceleration and also the
difficulty of implementing it on some hardware [40].
35
Chapter 4
Dynamics
During the development of this project, questions were raised regarding the validity of both the speed
indicated by the OBD and derived from the GPS coordinates and also the distance calculated from both.
Regarding the information obtained from the speedometer, one of the problems is related to and explained by UNECE regulations, enforced by European legislation applicable to vehicle production. While
in [41] a complete set of rules is established, taking into account all aspects related to a car speedometer, from the positioning of the equipment in the car to the marked scale, only the test conditions are of
interest for this work. Table 4.1 includes the speeds at which the tests shall be performed [41]:
Maximum design speed (Vmax) of the vehicle
Test speed (V1) (km/h)
specified by the vehicle manufacturer (km/h)
Vmax ≤45
80 % of Vmax
40 km/h and 80 % Vmax (if the resulting speed
45 <Vmax ≤100
is ≥55 km/h)
40 km/h, 80 km/h and 80 % Vmax (if the result-
100 <Vmax ≤150
ing speed is ≥100 km/h)
150 <Vmax
40 km/h, 80 km/h and 120 km/h
Table 4.1: Speeds at which the vehicles are tested to test the speedometer accuracy
Combining with the values in Table 4.1, there are two conditions directly related to the speed information
given by the speedometer [41]:
• The speed indicated shall not be less than the true speed of the vehicle.
• At the test speeds specified in Table 4.1, there shall be the following relationship (Equation 4.1)
between the speed displayed (V1) and the true speed (V2)
0 ≤ (V1 − V2 ) ≤ 0, 1 × V2 + 4km/h
(4.1)
Then, as an example, according to Equation 4.1 a vehicle at 80 km/h must have an indication no lower
than 80 km/h and no higher than 92 km/h.
The OBD speed collected for this work already includes a slight correction, considering an approximation
of the speedometer speed minus 3 to 4% of that same speed, since most manufacturers tend to include
37
a margin that is within the regulations and so, above the real speed.
Another concern with the values obtained for speed is related with tire wear. The speed shown to the
driver is based on RPMs and an average tire radius, somewhere between the radius of a new tire and
the radius of a well worn tire. Taking into account this condition together with the application of the
European legislation and the corrections made for the OBD speed, the relation between the different
speeds is probably as such:
VU sedT ire ≤ VOBD ≤ VN ewT ire ≤ Vspeedometer
(4.2)
Regarding the GPS receiver, the problems of the collected coordinates are related with possible connectivity problems caused by sources of interference. The United States government has an informative
website concerning the provision and maintenance of the system, where it is stated:
“The Global Positioning System uses radio signals in frequencies (spectrum) reserved for radio navigation services. Ensuring the continuity of the GPS service requires protection of this spectrum from
interference.
GPS interference can come from a variety of sources, including radio emissions in nearby bands, intentional or unintentional jamming, and naturally occurring space weather” [42].
The US Department of Defense also released a public document defining the levels of performance
made available to the GPS SPS users [43]. One of the chapters of the document is a list of all the
errors excluded due to being error sources not under direct control of the Space Segment or the Control
Segment. The following list specifies what errors are excluded due to effects of, being a bit more specific
than the previous quote:
• Signal distortions caused by ionospheric and/or tropospheric scintillation
• Residual receiver ionospheric and tropospheric delay compensation errors
• Receiver noise (including received signal power and interference power) and resolution
• Receiver hardware/software faults
• Multipath and receiver multipath mitigation
• User antenna effects
• Operator (user) error
The result of performance analysis also requires the horizontal accuracy to be in a range of ± 3 meters.
There are two other possible problems related with the calculation of distance using the GPS coordinates. One related with the approximation used by Equation 3.1, since this does not account for road
turns or altitude differences and the real behaviour of the car, using only straight lines to connect each
two coordinates and a different problem concerning incorrect positioning for a few seconds, resulting in
slightly incorrect results, as seen in Figure 4.1, a problem related with the accuracy mentioned previously.
38
Figure 4.1: Coordinates slightly deviated from their real position
4.1
Methodology
The first approach to calculate speed and distance, as mentioned before was quite straight forward,
applying directly the numerical methods presented on Chapter 3.2.2. Figures 4.2 and 4.3 show the
obvious unreliability concerning the usage of acceleration integration to obtain any results, even in a
smaller sample of the whole course. Acceleration was averaged in order to reduce the sample to a
frequency of 1 Hz.
39
Figure 4.2: Different speeds, including the integration of acceleration in a sample of five minutes
Figure 4.3: Different total distances, including the integration of acceleration in a sample of five minutes
The results for acceleration integration seen in Figures 4.2 and 4.3 are clearly unreliable due to drifting.
This drift can be caused by the effects of noise not being completely removed or non-removal of bias
(which was removed in this case). The integration error accumulates along time and in a double inte40
gration, the distance includes a double integration of the error, leading to the presented discrepancy. A
method considered as a better approach to a numerical integration of acceleration in robotics communities is working in the frequency domain and some research seems to backup the idea that results in the
frequency domain tend to be fairly accurate [44].
Another possibility is that the accelerometer itself leads to errors, as different accelerometers can lead
to different magnitudes of error when integrating acceleration [45].
Since the results obtained by integration of the acceleration were totally unreliable, both of the situations
were discarded.
As seen in Figure 4.2, the speed obtained from the first derivative of distance gives an acceptable result
but far from perfect. Situations like the one seen during the moments from approximately the 220th
second until the 240th , while clearly incorrect and unstable, have a mean value that is very close to the
OBD value.
To address the concerns with the real vehicle’s speed, a method was developed to apply a slight correction using the OBD speed and the 1 Hz sampled acceleration ( 4.4). The method involves in a first
moment counting only the OBD speed as an initial guess and from the next second onwards, the median
of the new OBD speed with the previous value plus the acceleration registered during the last second.
The GPS distance derivative was considered as unreliable due to the negative influence in the dynamic
speed prediction, caused by unstable signal transmission.
Figure 4.4: Flowchart of the dynamic speed prediction model
41
The idea with this process is to dilute possible errors from the speedometer information caused by the
1 Hz sampling frequency by taking into account the acceleration registered every second, in order to
produce a smoother and more reliable behaviour of speed by considering the car behaviour during
the time between two speed samples. Another improvement is the resolution of speed, since the data
obtained from the OBD is composed only of integer values with a resolution of 1 km/h, while the applied
correction allows for at least two decimal places.
To calculate the total distance, the same process as before was used, integrating the dynamic speed
with Simpson’s Rule.
4.2
Results
After applying the calculations, the results were translated into the following plots, on Figures 4.5 and 4.6.
For easeness of visualization and identification of the different curves, only a short sample of five minutes
is presented in the figures related to speed, both here and in Section 4.3.
Figure 4.5: Speeds comparison including the dynamic prediction
Checking the difference between OBD speed and dynamic speed prediction every second, for the data
presented in Figure 4.5 the maximum difference between two curves is of 3,32 km/h, while the maximum
value is 44 km/h for the OBD speed against a top dynamic speed of 44,04 km/h, which is a negligible
difference. For the complete trip, the maximum OBD speed is of 87 km/h, the dynamic speed prediction
peaks at 84,88 km/h, with a maximum difference of 8,82 km/h.
42
Figure 4.6: Total distances comparison including the dynamic prediction
Following the legend in Figure 4.6, the total distance obtained from each set of data was as follows in
Table 4.2:
Legend
Distance [m]
OBD Distance
19916,39
GPS Distance
20184,78
Dynamic Distance prediction
20490,67
Table 4.2: Resulting distances for the trip, using the three methods
The comparison of the three results was used as a way to validate the method developed in this chapter
for the dynamic prediction. The analysis of the results is presented in Section 4.3.
4.3
Results Analysis
The two main concerns with the results were the zero speed situations and the negative values of speed.
These two problems can be clearly seen in Figure 4.7, where the code was run without any constraints
applied. Both situations were solved by forcing the dynamic speed prediction to result in a zero in those
situations.
In the case of a null OBD speed it was considered that the dynamic speed should also be considered
as zero, since a speed equal to zero is only registered when the car is actually stopped. As seen in
Figure 4.7, without this constraint the speed prediction has no zeros that last for more than a second,
which would mean that the car never stops, a situation that actually happened multiple times during the
43
tests. The situation of negative results was easily solved by considering that these values should be
zeros as well.
Figure 4.7: First model of the dynamic speed prediction, without any applied constraints
Looking at Figure 4.5 it is possible to see that the correction made to the values of speed with the
acceleration creates a generally smoother curve. The situations where this is better noticed are peaks
on the OBD speed and sets of a few seconds where the speed originally collected varies, with lots of
ups and downs in a short span. These would represent situations that are abnormal since these would
represent a very fast increase and decrease of speed. This situation can probably be explained by an
OBD sampling rate of 1 Hz mixed up with the low resolution of 1 km/h.
Concerning the calculations for distance, the expected result was that by using the dynamic speed prediction and performing an integration the calculated distance would be between the distances obtained
from both the OBD speed and from the GPS coordinates. As seen in Figure 4.6, the result does not
confirm the expectation. One can see that for the first part of the trip the behaviour is close to the prediction, but somewhere between the 400th and the 500th seconds the lines of GPS distance and Dynamic
distance prediction cross with each other.
A possible explanation for this result is that the coordinates collected with the GPS do not take into account the travelled route between two points. This would mean that, theoretically, if two GPS coordinates
were separated by a section with an elevation in the route between them, when applying Equation 3.2
the result would only be the shortest straight line between the two points and not the real distance
between them.
44
Another possible explanation can be that the results of both the OBD and the dynamic prediction (since
it is based on the OBD data) are overestimated. This can be caused by the fact that even with the
mentioned correction applied to the speedometer speed when obtaining the OBD speed, this does not
mean that the resulting speed is the real one. In that case the speedometer still has influence over the
final results and even if all the corrections applied during the processing of data were very precise, there
would still be no guarantees that the tire radius was in accordance with the average tire wear considered
for the speed indicated in the speedometer.
4.4
Conclusions
In this chapter the idea of testing a method that improves the results obtained directly from the information collected during the trips reveals itself as a viable option. Although the results do not agree entirely
with the initial predictions, the results are considered plausible and not at all unexplainable.
In the case of speed, the dynamic prediction works well and provides good results, leaving only some
questions regarding points with a dynamic prediction much lower than the OBD speed. The primary goal
of improving the smoothness and resolution of speed was achieved but improving the dynamic speed
prediction using information about the road slope is probably a good hypothesis to test in the future. Its
implementation was put aside for this work because the sampling rate of altitude was not constant and
in some cases the very fast detected variation of altitude led to very pronounced slopes.
It is also possible to see that the GPS equipment that is being used should be used to calculate speed
only as a last resort option. Looking at the red line in Figure 4.7 areas with unreliable results are visible.
Possible causes for this are situations of multiple changes in speed and direction, crossing areas with
poor GPS signal and vibration affecting the GPS antenna, for example when riding on cobblestone
pavement (figure available in the Appendix).
In the case of distance, the results are considered acceptable since all the methods achieve similar
results and there is no exact solution to establish a better comparison. However, potential technical
issues mentioned in Section 4.2 shall not be forgotten.
Further improvements would require a lot more testing and probably specific information on the vehicle
where the OBU is installed should be available, for example, the speedometer corrections being applied
by the manufacturer.
45
Chapter 5
Comfort
In this chapter the evaluation of comfort is presented. The first part concerns the methodology and all
the calculations related to comfort, following the rules established by the ISO 2631-1 standard, including
the criteria to classify the different comfort levels.
The second section of the chapter is where the results are presented. This includes an overview of the
data collected during a whole trip, followed by the results of the calculation methods applied to each axis
and combined into magnitude.
The third section includes the analysis of the results, focusing on the values presented in Table 5.1 [33].
Magnitude (m/s2 )
Comfort Level
≤0,315
Not uncomfortable
0,315 to 0,630
A little uncomfortable
0,500 to 1,000
Fairly uncomfortable
0,800 to 1,600
Uncomfortable
1,125 to 2,500
Very uncomfortable
≥2,000
Extremely uncomfortable
Table 5.1: ISO 2631-1 comfort guidelines
An important goal of this chapter is verifying the possibility of identifying a certain event just by looking
at the magnitude or if the acceleration profile is needed. As explained below, most cases require the
use of the acceleration profile unless information about the location or input by the user is available.
To identify the events that will be closely looked at, the best way is certainly looking at either one of the
three axis filtered data, since the magnitude values combine all the axis and make it difficult to identify
any situation, except in very specific situations. For example, a hard braking event is predictably easily
noticeable in the magnitude profile, as it generates a value way above the others. Of course, looking at
the filtered signal only it is easier due to the use of positive and negative values, as in Figure 5.1.
47
Figure 5.1: Filtered longitudinal acceleration, with indication on hard braking events
For vertical events of high intensity, such as crossing a speed bump at high speed or a long stretch of
cobblestones it is also easy to see the events in the vertical acceleration graph.
Figure 5.2: Filtered vertical acceleration, with indication on high speed crossing of speed bumps and
crossing of a cobblestone section
The speed bumps when crossed at high speed induce a high vertical acceleration, while the cobblestone
pavement generates a behaviour very similar to a normal tarmac road in terms of frequency but with
much higher values of acceleration.
48
5.1
Methodology
As already mentioned in Chapter 1, the international standard ISO 2631-1 establishes reference values
of acceleration to classify comfort in different levels.
The method used in this work is called the “basic evaluation method”, which requires the use of the
weighted root mean square acceleration, as in Equation 5.1:
"
1
aw =
T
ZT
# 21
a2w (t)dt
(5.1)
0
Where T represents the time period between every two measured points which in this work is one
second. As the goal was to classify the driving condition every second, to achieve it Equation 5.1 was
applied to a set of every one hundred points, resulting in a single acceleration value for that data set and
effectively reducing the sampling rate to 1 Hz.
The second step, that allows for the effective use of the values of acceleration to evaluate comfort was
the calculation of the magnitude, using Equation 5.2:
1
av = (a2x + a2y + a2z ) 2
(5.2)
Equation 5.2 effectively combines the three considered axis into one value, enabling the evaluation of
situations where it is unclear if the acceleration in one direction is predominant over the others.
5.2
Results
Using the filtered axial accelerations obtained in Section 3.2.3, the root mean square values for each
axis were calculated, using Equation 5.1. Figure 5.3 is an example of the comparison between the
filtered and RMS longitudinal accelerations. The figures for the vertical and lateral axes are available in
the Appendix.
49
Figure 5.3: Longitudinal acceleration: filtered data (blue) and RMS value (black)
Analysing the three different RMS axial accelerations by computing the mean RMS acceleration, the
longitudinal axis presents the higher values while the vertical axis has the lowest mean value. This is
probably due to the fact that most of the driver inputs affect either the longitudinal or lateral behaviour
of the car, while the vertical inputs are caused by outside interference and less common in an urban
environment. Table 5.2 shows the mean value for each axis during the itinerary:
Axis
Mean acceleration [m/s2 ]
Longitudinal
0,6246
Lateral
0,5754
Vertical
0,3578
Table 5.2: Mean value of the RMS of each axis during the trip
The next and final step, is to apply Equation 5.2 to obtain the magnitude for every second. This is the
measure that allows to classify the different comfort levels according to the international standard.
50
Figure 5.4: Resulting magnitude
Looking at Figure 5.4, it is possible to notice some events that generate higher acceleration values but
it is impossible to tell them apart in general. This leads to the conclusion that magnitude is a simple
and fast method to evaluate comfort but further analysis of each axis root mean square individually is
required in order to narrow the list of probable causes for magnitude variations.
5.3
Results Analysis
The first goal of this chapter is to classify the comfort condition in every second of a trip, following the
guidelines of ISO 2631-1. Figure 5.5 represents the magnitude for each second of the trial including the
threshold values used for comfort throughout this work.
51
Figure 5.5: Resulting magnitude, with the upper limit reference line for each comfort level
Comfort condition
Lower limit (m/s2 )
Upper limit (m/s2 )
Not uncomfortable
0 ≤ av < 0,315
0 ≤ av < 0,315
A little uncomfortable
0,315 ≤ av < 0,500
0,315 ≤ av < 0,630
Fairly uncomfortable
0,500 ≤ av < 0,800
0,630 ≤ av < 1,000
Uncomfortable
0,800 ≤ av < 1,125
1,000 ≤ av < 1,600
Very uncomfortable
1,125 ≤ av < 2,000
1,600 ≤ av < 2,500
Extremely uncomfortable
av ≥ 2,000
av ≥ 2,500
Table 5.3: Lower and upper limits from ISO 2631-1
The values used as reference were the upper limits of the international standard. This decision was
made after a first more conservative approach, performing the evaluation with the lower limits, which
resulted in a trip that was much more uncomfortable in theory than in practice, an opinion confirmed by
the trials participants.
Comfort condition
Time using lower limits [%]
Time using upper limits [%]
Not uncomfortable
4,08
4,08
A little uncomfortable
16,08
25,45
Fairly uncomfortable
20,66
25,49
Uncomfortable
22,07
28,19
Very uncomfortable
28,57
13,83
Extremely uncomfortable
8,54
2,96
Table 5.4: Comparison of the percentage of time spent in each comfort condition, using either the lower
or upper limits of magnitude of vibration total values
52
As the results of Table 5.4 show, there is a big difference between the two conditions. While using lower
limits the total amount of time in at least an uncomfortable condition is almost 60%, using higher limits
puts that percentage at only 45%, which seems to be more realistic, but still quite high for a normal ride.
Even though the upper limits provide results more in accordance with the drivers and vehicle passengers’
perception (information obtained during informal talks with the trial participants), there is still a feeling
that the values used in this guideline are too low. This may be caused by the fact that the guideline was
created having in mind public transports, which obviously require tighter regulations.
Regarding specific situations that are considered as uncomfortable, those considered as more obvious
are events with vertical acceleration inducers, such as cobblestones pavement, speed bumps or potholes. Speed bumps are widely known for being used to cause discomfort if crossed at high speeds.
Other uncomfortable situations, related to longitudinal and lateral accelerations are more related with
driver behaviour or road conditions and usually are situations where comfort and safety evaluations will
overlap each other, e.g. hard braking.
The first situation presented is a stretch of cobblestone pavement with some very small and almost
unnoticeable speed bumps, crossed at low speed, most of the time in the range of 20 to 30 km/h. During
the presented trial, this event happened during seconds 244 to 488.
Two assumptions are made when looking at a cobblestone section. The first is that the vertical acceleration has a clear predominance over the longitudinal and lateral components on influencing the resulting
magnitude. The second assumption that seems reasonable is considering that the longitudinal component represents the smaller part of the final result of magnitude. With these two assumptions in mind,
the first analysis is done by comparing the total magnitude against the RMS of the vertical acceleration.
Figure 5.6: Magnitude and root mean square of the vertical acceleration in the cobblestone pavement
As predicted for this kind of pavement, during the analysis performed for this work the vertical acceler-
53
ation has consistently reached higher values than the other axial components. The type of pavement,
which is highly irregular, translates into an irregular acceleration behaviour. But contrary to expectations, while the RMS of the vertical acceleration is the axial component with the higher value during
most of time while crossing the cobblestone pavement section, the result is clearly far from providing a
full explanation for the values of magnitude obtained. Given the circumstances, the RMS values of the
longitudinal and lateral accelerations are relatively stable throughout the crossing of the cobblestone but
with the lateral component being almost constantly higher.
Considering all the information available, it is considered reasonable to assume that a combination of the
RMS of vertical and lateral accelerations will then provide an approximation very close to the vibration
total value (VTV).
Figure 5.7: Magnitude compared to the magnitude without including the root mean square of the longitudinal acceleration in the cobblestone pavement
The analysis of Figure 5.7 shows that most of the time the combination of the lateral and vertical acceleration represent an almost perfect approximation to the magnitude values and consequently, are
the two main contributors for the comfort classification while in a cobblestone pavement. The only moment where there is a clear deviation is for about ten seconds, right before the mark of fifty seconds.
This was caused for a moment of deceleration, caused almost certainly by braking when approaching a
pedestrian crossing.
Another important matter in this pavement seems to be the fact that the moment when a lower magnitude
happens corresponds to moments when the vehicle is moving slower, as seen comparing Figure 5.8.
This relation seems to go in the same direction as the relation seen in speed bump events [19], hinting
at the possibility of a relation between speed and comfort.
54
Figure 5.8: Speed profile vs. magnitude, while crossing the cobblestone pavement section
The next event is the crossing of speed bumps. These are quite common features in urban roads around
the world, used in order to try that drivers refrain themselves from speeding by causing discomfort if a
high speed is used. In order to evaluate the speed bumps effectiveness, the results include a speed
bump crossed at almost 50 km/h and another one crossed at approximately 30 km/h, which was the
recommended speed in the area.
Figure 5.9: Speed, longitudinal acceleration and vertical acceleration variation while approaching and
crossing a speed bump at approximately 30 km/h
55
Figure 5.10: Magnitude and root mean square of the longitudinal and vertical accelerations, crossing a
speed bump at approximately 30 km/h
Looking at Figure 5.10, it is noticeable that the magnitude shows two different phases where higher
values are reached. These two peaks are easily justified by the two individual components shown in the
same figure. When crossing the speed bump, obviously represented by the second peak, the vertical
component of acceleration represents the higher influence in the magnitude value while the first peak is
caused by longitudinal acceleration. By using magnitude solely it is not possible to understand what kind
of event caused the raise of the longitudinal acceleration, which leads to Figure 5.9. This figure shows
that the approach to the speed bump was done at a high speed and therefore the driver had to brake in
order to reduce to the desired speed of 30 km/h.
From the figure it is possible to see that while the vertical component is the main influence in the final
result, the longitudinal component makes a difference, leading to a classification of ”uncomfortable” by
a small margin.
56
Figure 5.11: Magnitude and root mean square of the vertical acceleration, crossing a speed bump at
approximately 50 km/h
The crossing of a speed bump with the same characteristics but at a higher speed, close to 50 km/h, led
to a classification of ”very uncomfortable” by a significant margin. The speed of crossing represents an
increment of 66% compared to the recommended speed in the area.
Comparison between Figures 5.10 and 5.11, shows that there is a clear influence of the speed at which
the vehicle is travelling when crossing the same kind of speed bump. It is clearly noticeable that crossing
the same type of speed bump at a speed about two thirds higher leads to a peak of vertical acceleration
(2,212 m/s2 ) that is slightly more than double the vertical acceleration achieved at a lower speed (1,090
m/s2 ), that complies with the legal speed limit of the location.
Comparing the results with the relation established by Jiang et al [19], presented in Table 5.5, where
the comfort levels were also classified according to speed, the results fail to confirm the suggested
guidelines when considering situations where a speed bump is crossed at a higher speed.
If Table 5.5 was used as the sole reference, the speed bump from Figure 5.10 would be considered
as “fairly uncomfortable”, which is in accordance with the figure if only the vertical RMS acceleration
is used but is a level short if the magnitude is considered. The same comparison for the speed bump
from Figure 5.11 would lead to a classification of “extremely uncomfortable”, but both magnitude and
the vertical RMS acceleration evaluate the speed bump only as “very uncomfortable”. So, establishing
a comparison using the same criteria as Jiang et al [19] leads to the conclusion that the suggested
guidelines only achieve results in accordance with ISO 2631-1 when applied to low speed crossing of
speed bumps.
57
Speed range (km/h)
Comfort Level
≤24
Not uncomfortable
24 to 29
A little uncomfortable
27 to 32
30 to 38
Fairly uncomfortable
Uncomfortable
35 to 43
Very uncomfortable
≥40
Extremely uncomfortable
Table 5.5: Guidelines for comfort levels on speed bumps using speed as a reference
Out of the events that mainly generate acceleration in the longitudinal or lateral directions, hard braking
is the one that creates an overlap of comfort and safety. During the trial, under controlled conditions,
a hard braking situation was performed while riding in a straight line, by braking until the ABS was
activated. As this event was intended to be used to evaluate safety conditions, it is more focused in
Chapter 6, while here only the comfort conditions are shown.
Figure 5.12: Magnitude obtained while performing a hard braking event
Looking at Figure 5.4, the event presented in Figure 5.12 corresponds to the highest peak in the whole
trip, confirming the most logical prediction that an accident or pre-accident situation would be not only a
high safety risk but a highly uncomfortable event.
While the three already mentioned situations were considered as uncomfortable by the vehicle passengers, other situations that qualify as ”extremely uncomfortable” according to the international standard
were not considered as such by the same passengers. A clear example of this is the sequence of a
left-right turn, in Figure 5.13.
58
Figure 5.13: Comparison of the root mean square of the lateral acceleration and the magnitude while
performing a left-right turn sequence
While the comfort classification could hypothetically be explained by the influence of longitudinal or
vertical accelerations, this is not the case since the profiles in Figure 5.13 are closely related, with a
peak value of 3,28 m/s2 for the lateral root mean square and 3,36m/s2 for the magnitude. A result like
this can have two possible explanations. One possibility is that the result backs up the idea that the
scale is possibly flawed due to its use of a very tight scale, probably influenced by the need to adapt
the scale to public transports. Another possibility is the road design being unable to guarantee that a
vehicle going under the legal speed limits can stay inside the comfort or even the safety limits used as
reference in this work, as seen in Figure 5.14. The event ranks as harsh, in terms of safety, as can be
seen in Figure 5.14. This event is focused also in Chapter 6.
59
Figure 5.14: Classification in terms of safety using lateral acceleration in the left-right turn sequence
5.4
Conclusions
In this chapter all the work developed regarding comfort was presented. The first part was more focused in the methodology while the second and third parts included the obtained results and respective
analysis.
The method used for the evaluation of comfort was based on the ISO 2631-1 standard and the available
references. The process to characterise comfort can be summarized as:
• Filter data with a Butterworth filter
• Calculate the RMS of each axis, Equation 5.1
• Determine the magnitude or vibration total value (VTV), Equation 5.2
• Compare the magnitude values against the guidelines from Table 5.1 or other known applicable
guidelines
Using the above list it is possible to obtain an evaluation of comfort conditions in a fast and reliable way.
Concerning the method used, the acceleration values were used by performing a single signal filtering
in order to reduce noise and leaving out the use of frequency weightings. Although the influence of the
frequency weighting factors is probably not that strong in the final result and a similar method was used
by Jiang et al [19], it should be noted that slightly different results are possible.
A question arising from the evaluation of comfort is related to how restrictive the levels are. The goal
of ISO 2631-1 in terms of comfort was to establish guidelines for public transports, that ended up being
considered by other authors for LDVs and other types of vehicles and with acceptable results.
60
Considering situations where the magnitude levels are low, the evaluations are considered as acceptable. On the other hand, in some situations the reference values seem to be very low and are not in
accordance with the feelings of the few participants in the trials. This is noticeable by the 45% of time
considered as uncomfortable or worse and in lateral events classified as ”extremely uncomfortable”, both
situations where the vehicle occupants did not feel as such.
As mentioned in the introduction to this chapter, one of the goals was understanding if just by looking
at the magnitude profile it is possible to identify a certain event. Looking at any of the events used as
a case study, it becomes clear that none of the events’ causes are identifiable just by looking at the
magnitude profile. Cases like the one presented in Figure 5.10, clearly show that the option of looking
only at the magnitude profile is not a good method.
The option of looking solely at the axial RMS is also unreliable. For example, events with a high vertical
acceleration can be anything among potholes, speed bumps or cobblestones. Very high longitudinal
RMS is mostly associated with hard braking events but for most events it is impossible to establish a
driver profile just by looking at it.
The only possible conclusion is that only the use of the raw or filtered acceleration data provide all the
information needed to identify a certain event.
An interesting possibility in this chapter was to see how the relation established for comfort and speed
by Jiang et al [19], using only the vertical acceleration instead of magnitude, fared when applied to
speed bump events data collected for this work. The results are not in complete accordance with the
suggested guidelines for comfort on speed bumps when using speed and vertical RMS acceleration as
criteria, as the results fail when evaluating events at higher speed. Since the suggested guidelines were
established by performing experiments in different types of speed bumps and possibly none of those was
of the same type as the ones used in this work, this probably explains the differences in the resulting
classifications, with the differences being noticed only in situations where higher speeds are used.
61
Chapter 6
Safety
This chapter is focused more on classifying the safety conditions for the occupants and not so much in
the perception of those. This is easily explainable by the fact that not only the objective was never to put
the participants at risk and also by the fact that the experiments were performed on public roads and so,
any activity that could pose any safety risk for the participants could possibly put the occupants of other
vehicles at risk too.
Since for the evaluation of safety conditions there are no international standards or methods that are
accepted as being the best one, the analysis performed in this chapter is based only on values that
were subject to a simple filtering and then compared to threshold values selected among those already
referenced in Chapter 1. Below is presented a summary of the steps if the accelerometer is working
properly:
• Filter data with a Butterworth filter
• Compare the longitudinal and lateral acceleration values against available guidelines
The main difference between safety and comfort, is that in the case of comfort conditions, while the three
axis are considered, the vertical acceleration has the biggest influence in the result of situations that are
comfortable or uncomfortable, in the case of safety conditions the focus is only on the longitudinal and
lateral accelerations. It must be noted that most situations that can be considered as inducing high levels
of discomfort due to longitudinal or lateral accelerations are usually also in a relatively low level of safety.
For the evaluation of safety, the derivatives of both GPS data and OBD data can be used, following the
relations established in Figure ??. The reasons for the use of the derivatives is in the following list:
• For the evaluation of safety conditions the longitudinal and lateral accelerations are predominant
• There is a high probability of an overlap between unsafe and uncomfortable situations
• The possibility of the accelerometer being defective or the equipment falling off from its place and
staying in an incorrect position
The last item of course leads to unreliable data being collected and so the derivatives of distance and
speed are possible alternatives to minimize the problems created by that situation, since those are not
dependent on the positioning of the OBU. The main drawback of using the data of distance and speed
is that both of them are only representative of the longitudinal component of the vehicle movement and
so the lateral acceleration is impossible to evaluate.
63
Like what was done in Chapter 5, in this chapter specific events are used as examples with the results
of using the different methods to obtain the longitudinal accelerations being compared to each other.
6.1
Methodology
The methodology for this section was much simpler than the one used in other chapters. The main
concern to correctly evaluate the values of acceleration in this case was choosing an appropriate filter.
Since for safety there is not a widely accepted filter as being the best, contrary to what happens in the
case of comfort, and in order to maintain the use of the OBU resources as low as possible, the same
filter that was used for comfort was used for safety. Of course, as mentioned in Section 3.2.3, the fact
that the Butterworth filter did not eliminate the peaks allows for its use in this situation.
In terms of calculations, the main concern for this part was the already mentioned need to take into
account the need to keep tracking safety conditions if any problems related to the accelerometer arise.
For this reason, the numerical differentiation methods from Section 3.2.2 were applied to both the OBD
speed and to the distance obtained from the GPS data.
f (x1 + h) − f (x1 − h)
2h
(6.1)
f (x − h) − 2f (x) + f (x + h)
]
h2
(6.2)
Dh f (x1 ) =
Dh2 f (x) =
To establish the safety reference levels the option was to use the default values for longitudinal hard and
extreme braking situations from Maxi Recorder User’s Manual [31]. The option for these values was
based on the fact that without any international standard being enforced, then the best option would be
to choose values being used by operating companies. Autel Company also establishes two different
levels which is regarded as an important measure to differentiate, at 0,5g and 0,35g.
To define the lateral threshold values, for extreme lateral acceleration the relation referenced by Felipe [18] for skidding was used, considering that an extreme lateral acceleration would be about 90% of
an extreme longitudinal acceleration and the value obtained this corresponds to approximately 0,45g.
Applying the same difference of 0,15g, a value of 0,3g is selected to be used as reference for a not so
safe approach to a curve. The values being used are summarized in Table 6.1.
Safety Level
Longitudinal deceleration (m/s2 )
Lateral acceleration (m/s2 )
Hard
≤ -3,43
≤ |2,94|
Extreme
≤ -4,91
≤ |4,42|
Table 6.1: Reference safety levels
6.2
Results
The results for this chapter are focused on the validation of the different methods used to evaluate the
acceleration values, when applied using the chosen reference values. The first goal is to perform this
evaluation using the accelerometer data, that allows the testing of both longitudinal and lateral safety
64
conditions. The second goal is to understand how accurate the alternative methods are when compared
to the previous evaluation, even with the associated limitations such as the lower sampling frequency or
being limited to evaluate the longitudinal component.
Using the data available directly from the accelerometer, the resulting profiles for longitudinal and lateral
were obtained and can be seen in Figures 6.1 and 6.2. These represent the core of the safety evaluation
process.
Figure 6.1: Longitudinal Butterworth filtered acceleration
Figure 6.2: Lateral Butterworth filtered acceleration
65
Since the longitudinal acceleration is also looked upon for safety evaluation, the comparison between
the three different accelerations is of interest. The Butterworth filtered acceleration was reduced to a 1
Hz sample in Figure in order to ease the comparison between the three images.
Figure 6.3: Comparison between the three different methods to obtain longitudinal acceleration, in a set
of 500 seconds
The OBD speed derivative and collected acceleration profiles seem to have a very similar behaviour. On
the other hand, the values of acceleration obtained from the GPS clearly show a very different behaviour,
that seems to have generally much lower values compared to the other two methods for events in the
same moment.
6.3
Results Analysis
The first goal of the chapter is to obtain the amount of time spent under each safety condition during the
trip, in order to see how each method fares against the others.
66
(a) Longitudinal acceleration
(b) Lateral acceleration
Figure 6.4: Longitudinal and lateral acceleration components with the reference lines of each safety
level
(a) OBD acceleration
(b) GPS acceleration
Figure 6.5: Longitudinal acceleration components obtained from the OBD and GPS with the reference
lines of each safety level
The reference values used are the ones presented in Table 6.1. Under those conditions, the percentage
of time under each of them was determined.
Safety Level
Longitudinal [%]
Lateral [%]
OBD [%]
GPS [%]
Normal
99,80
99,20
99,90
99,71
Hard
0,10
0,77
0,10
0,25
Extreme
0,10
0,03
0,00
0,04
Table 6.2: Percentage of time in each level, using the different evaluation methods
Looking at the figures previously presented, something that is immediately noticeable is that the values obtained from the GPS are not in line with the other longitudinal component values. Figures 6.4a
67
and 6.5a clearly show that there was an event that led to a high deceleration around the 2300 seconds of
the trip while the values obtained with the derivative of the GPS data have a peak of deceleration much
earlier, after approximately 2050 seconds, when no events were noticed. Even worse is the fact that
the GPS shows a high acceleration with a peak of 10,07 m/s2 immediately before the high deceleration
and looking at the graphic in Figure 6.5b it is possible to see that the real hard braking event performed
during the trip went unnoticed.
Comparing only the Figures 6.4a and 6.5a, the behaviour of both the profiles seems to be very similar,
with the main difference being the apparent shrinkage suffered by the profile obtained from the OBD
derivative, that leads to the braking event performed during the test not being considered as extremely
unsafe but only as a hard braking situation.
Concerning specific events, the two most interesting situations are turns with high values of lateral
acceleration and braking events with highly negative deceleration.
The first situation being looked at is the sequence of turns already mentioned in Chapter 5.
Figure 6.6: Lateral acceleration (blue) and dynamic speed prediction (black) during left-right turn sequence. The reference lines (cyan) represent the lateral acceleration safety levels.
This event was considered as extremely uncomfortable in Chapter 5 even if the occupants did not feel it
as such. Since that was the case, it is possible to assume that the vehicle occupants never felt at risk
while performing any of the two turns of this sequence and seems to be in accordance with Figure 6.6
as it qualifies only as a hard turn but not extreme.
The sequence yields a maximum of 3,81 m/s2 when turning right and a maximum of 3,86 m/s2 when
68
turning left. Combined with the information of speed this leads to the conclusion that probably the speed
at which the turn is crossed is not the main cause of the induced lateral acceleration, at least while
performing these large radius turns at relatively low speeds. There is a difference of about 6-7 km/h
while performing the two curves, with the right turn being the one performed at a higher speed around
42 km/h.
The next situation of interest is the passage through roundabout and the exit of it. This event is the one
with the highest lateral acceleration registered.
Figure 6.7: Lateral acceleration (blue) and dynamic speed prediction (black) during cross of a roundabout. The reference lines (cyan) represent the lateral acceleration safety levels.
Figure 6.7 shows the whole process, including the moment of stoppage before entering the roundabout.
While the turn performed to exit the roundabout is not a hard turn with a small radius, it has a slight slope
downwards and is slightly banked to the outside, inclining the vehicle towards its left. The main reason
for the high acceleration value is probably due to the presence of a banked turn that in this specific
situation tends to send the vehicle towards the lower part of the road.
The speed at which this roundabout was crossed was always under the legal speed limit of 50 km/h,
never even reaching 35 km/h, which clearly leaves open the possibility of the safety conditions being
jeopardized by poor road design.
The last situation being focused and relevant as a safety related event is the braking trying to simulate
an accident or near accident situation. In this case, the longitudinal component of speed is the part of
interest.
69
Figure 6.8: Longitudinal acceleration during the braking event
As can be seen in Figure 6.8, the event is clearly considered as an extreme braking event. The maximum
deceleration is of -6,827 m/s2 , a lot more than the reference limit of -4,91 m/s2 .
Figure 6.9: Longitudinal acceleration during the braking event, obtained from the GPS coordinates
70
Figure 6.10: Longitudinal acceleration during the braking event, obtained from the OBD data
The validity of both of the approximations seems to be dubious. In the case of acceleration obtained
from the GPS (Figure 6.9), the results are clearly unreliable as the acceleration profile barely crosses
the reference line for a hard braking event, with a minimum of -3,499 m/s2 . The acceleration obtained
from the OBD (Figure 6.10) results in a condition of hard braking event, with a peak of deceleration of
-4,444m/s2 . While this result is far from the values detected by the accelerometer, it still allows for a
better notion of the driver behaviour.
6.4
Conclusions
This chapter includes all the work developed concerning safety. The objectives included evaluating how
trustful the reference values used are and see how each of the methods presented in the methodology
fare against each other, when applicable.
The first and biggest problem in this area is that there are no international standards defining what is
and what is not acceptable in terms of acceleration values to characterize an event as safe or unsafe,
hence why the decision was made to use values in use in the industry. While those values seem to
be reasonably in accordance with what the vehicle occupants felt during the trials, they still lack an
explanation of how and why they were obtained. For example, the values used by Geotab are the same
for longitudinal and lateral accelerations, which does not seem correct while Autel is only concerned
with the longitudinal acceleration, with slightly higher values compared to Geotab. The main reason for
choosing the values used by Autel were due to the use of three different safety risk levels instead of
only two. Starting from there, to obtain the reference values for the lateral acceleration the equation
referenced by Felipe [18] for the risk of skidding was used, considering a limit of 90% of the limit for
longitudinal acceleration.
71
Regarding events where the lateral acceleration is dominant, it must be pointed out that research is
scarce. Nevertheless, the values used give a very high percentage of time during the trip where the
lateral conditions were considered as being perfectly safe, with the 0,03% of time considered as unsafe
not being in accordance with the participants’ feeling during the trials. While contradicting the perception
of the occupants, it is acceptable for lateral safety conditions to be stricter, as it can lead to losing control
of the vehicle due to the nature of the event.
Other source of concern, even if the vehicle occupants perception is not taken into account, is the fact
that as seen in Section 6.3, some areas generate a situation considered as extremely unsafe even when
the vehicle is travelling well below the legal speed limit, leading to two possible conclusions: either the
values being used are indeed too strict or there are effects created by a possibly deficient road design
that leads to unsafe situations.
Concerning the different evaluation methods, for events related to longitudinal acceleration, namely
braking, the results quality is varied. The collected acceleration, after filtering, seems to give results in
concordance with the feeling of the vehicle occupants. Acceleration obtained by differentiation of the
OBD data is a passable alternative if the concern is only in trying to identify a qualitative pattern in the
driver behaviour, due to the failure in correctly quantifying high intensity events. The results obtained
by differentiation of the distance calculated using the GPS coordinates are completely unreliable, barely
considering an extreme braking event as a hard event. Difficulties when using the GPS data were
also noticed when trying to find the vehicle speed which could help predicting possible difficulties when
finding acceleration.
72
Chapter 7
Conclusions and Future Work
7.1
Conclusions
The first objective of this work was to ascertain the possibility of evaluating comfort and safety conditions
in a vehicle based on a dynamic characterisation using acceleration data. The review of previous works
shows that clearly this is not a new idea, with research on this area going on since the 1940s and with
a clear evolution in the available equipment, that started as big installations prepared specifically for
this kind of work, moving to on-board systems that occupied almost the same room as a human and
vehicle observations from the outside and finally in the 1990s the advent of small On-Board Units. All
of these different approaches showed that the idea of evaluating the driving profile based on dynamic
characteristics is not out of sense.
The second objective was to define the metrics and the methods to perform the characterisation of
driver behaviour using only dynamic variables. This is based on the availability of modern low-cost and
technologically developed On-Board Units combined with the admitted interest of insurance companies
in developing a new business area by providing a new service that can lead to lower spending with small
investment.
The work was then divided in two parts: evaluation of comfort and evaluation of safety. Each part was
treated differently due to the specificities of each part and the need to use completely different methods,
as explained in Chapters 5 and 6.
Concerning comfort, it was found that an international standard is in force and so its use was considered
as mandatory in terms of calculations and the suggested reference values for magnitude were used. The
main conclusion is that, in general, the classification attributed by the norm is trustful, especially when
talking about low values of magnitude. The main concern is with events considered as uncomfortable of
worse, as the difference of acceleration limits between different levels seems to be based on a very tight
scale, even when using the more relaxed approach. The results obtained consider the trials as being
uncomfortable or worse in almost 45% of the time, not in accordance with the feeling of the participants.
Especially when considering events that lead to higher lateral accelerations, it seems that the results
are easily put in extreme levels. These very strict levels are certainly influenced by the development of
ISO 2631-1 for public transportation, where high accelerations in any direction have much more effect
than on a car. Not only passengers can travel still standing, for example, but even if seated usually the
seats are not as comfortable as what is found in a light-duty vehicle (LDV) nor the same instruments are
73
available to keep the person in place, such as seat belts.
Concerning the directions of acceleration that influence the most the comfort condition, the vertical and
lateral are the most relevant. The longitudinal component tends to represent an uncomfortable condition
under hard braking or worse, when the safety conditions are already considered risky as well.
Regarding safety, there is no specified method defined by an international standard and the available
research provides very different methods and results from one author to another. For the safety evaluation the reference values used were then based on data already in use in the fleet management industry
for longitudinal values together with information referenced by [18] to define the lateral reference values.
Three different methods were used to evaluate the longitudinal acceleration (accelerometer and differentiation of both GPS distance and OBD speed) with all of them resulting in a normal safety condition on
more than 99% of the time. The most straight forward method, using accelerometer data, was the only
one that led to a significant percentage of time under extreme safety conditions while providing reliable
results. In the case of OBD data, the results are passable as the inability of the method to detect higher
acceleration values makes it only viable as a last resort. In the case of the GPS data, the results are
completely wrong when looking for extreme events and if the tested numerical methods are to be used,
this option should not be considered unless the hardware quality improves in the future. Concerning
lateral safety, as in comfort related situations, the participants did not feel unsafe while performing any
turn, contrary to the 0,03% of time indicated by the results. While contradicting the participants’ perception, a more conservative approach to lateral safety conditions is acceptable, as events that induce
higher lateral accelerations can easily result in losing control of the vehicle.
The dynamics evaluation can be considered as an additional component of the work, intending to obtain
a more correct speed than the one obtained through the OBD and improve the resolution. The application of restrictions on the resulting dynamic speed prediction was needed to improve the results and the
method lacks the inclusion of slope effect in the results. In the case of distance, even though the results
are not in accordance with the initial expectations, the results’ proximity is enough to consider that the
developed model is valid for application.
7.2
Future Work
Suggestions for further development of the work developed for this thesis are vast. Those could be:
• On comfort:
– Perform an analysis in the frequency domain;
– Develop acceleration threshold values specifically for LDVs;
• On safety:
– Further research to improve the data regarding safety reference values;
– Perform research concerning wet road conditions. As a first approximation, data from Figure 7.1 can possibly be used;
• On dynamics:
– Include the effect of road slope, either by collecting data of the road slope itself or using
altitude values to approximate the slope value. The methodology developed by Gao [39]
74
should also be considered;
• Look for better correlation between safety, comfort and the vehicle occupants’ perception;
• Use a scale to quantify the injury level in case of accident or near-accident;
Further analysis will require the use of large samples of individuals to ensure statistical validity for the
results.
Figure 7.1: Coefficients of friction for different roadway surfaces, comparing dry and wet condition [46]
75
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78
Appendix
Additional Figures
GPS Speed Problems
As mentioned in Chapter 4.4, when travelling over a cobblestone road stretch the resulting GPS speed
is highly unstable, possibly caused by vibration affecting the GPS antenna behaviour.
Figure 2: Speeds comparison during the cobblestone pavement crossing. Problems when using the
GPS data are clear
79
Filtered and RMS Accelerations Comparison
Figure 3: Lateral acceleration: filtered data (blue) and RMS value (black)
Figure 4: Vertical acceleration: filtered data (blue) and RMS value (black)
80
Butterworth Filter
In order to filter the accelerometer data a Butterworth filter was chosen. This part of the Appendix
presents a short explanation on the theory behind the use of this filter and how it is implemented in
Matlab.
The Butterworth filter, also called Maximally Flat Response filter, is an Infinite Impulse Response filter
that has the flattest possible frequency response in the passband and the magnitude square for order n
is given by:
|H(jω)|2 =
1
jω 2n
)
1 + ( jω
c
(1)
Where n is the order of the filter and ω c is the cutoff frequency.
Figure 5: Butterworth filter with different n orders compared to the ideal response filter
The filter transfer function is obtained from:
H(s)H(−s) =
1
1 + ( jωs c )2n
(2)
And poles of H(s)H(−s) are at:
1
s = (−1) 2n jωc
sk = (e)
j(2k+n−1)π
2n
81
(3)
(4)
The poles are then represented in the unity circle. To establish a stable transfer function, the poles of
H(s) in the left-hand plan must be selected, resulting in:
1
k=1 (s − sk )
H(s) = Qn
(5)
For the code development in Matlab, the Butterworth filter was designed using the “butter” function,
which uses an analog prototyping method. selecting the order, normalized cutoff frequency between 0
and 1 (Wn in Matlab environment) and bandpass type of filter in order to define the transfer function
coefficients, b and a.
The coefficients are applied in the following transfer function:
H(s) =
B(s)
b(1)sn + b(2)sn−1 + ... + b(n + 1)
=
A(s)
a(1)sn + a(2)sn−1 + ... + a(n + 1)
(6)
The normalized cutoff frequency is calculated as:
Wn =
fc × 2
fs
(7)
where fc is the desired cutoff frequency that defines the bandpass limit and fs is the sampling frequency.
This section was based on:
“Chapter 7. FIR and IIR Filters”, Notes on Discrete Signals and Systems, 2004, Sabanci University,
Istanbul, Turkey;
“Butterworth Filters”, Lecture Notes for Signals and Systems, 2011, Massachusetts Institute of Technology, MA, USA through MIT OpenCourseWare.
”Butterworth filter design, butter function”, Mathworks Matlab 2014b documentation
82

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