Multi-Agent Situation Awareness Error Evolution
in Accident Risk Modelling
Sybert Stroeve, Henk Blom, Marco van der Park
National Aerospace Laboratory NLR, Amsterdam
5th USA/Europe ATM R&D Seminar, Budapest, 23-27 June 2003
Contents

Problem background

Situation awareness: definition & model

Situation awareness in accident risk modelling

Initial results

Conclusions
2
Problem background

Accident risk modelling for ATM
– Human operators
– Technical systems
– Procedures
– Nominal and non-nominal conditions

Situation awareness errors represent important class of hazards

Situation awareness error evolution due to multi-agent
interactions

Application example: active runway crossing operation
3
Contents

Problem background

Situation awareness: definition & model

Situation awareness in accident risk modelling

Initial results

Conclusions
4
Situation awareness in the literature

Situation awareness (Endsley, 1995):
– The perception of elements in the environment within a volume
of time and space
– The comprehension of their meaning
– The projection of their status in the near future

Errors in situation awareness at each of these levels (Endsley,
1995)
5
Situation awareness in a multi-agent environment

Situation awareness error evolution due to intra-agent interaction

Definition of agent: entity which may possess situation awareness
of the environment

An agent may be:
– Human operator
– Technical system(s)
6
Multi-agent representation of application example
7
Situation awareness vector
i
 t, j
 identity 


 state 


mode


 intent 
i
Situation awareness 
t, j at time t of agent j about agent i
8
Examples of situation awareness updating processes
Observation
state
agent 1
SA
agent 2
Communication
SA
agent 1
SA
agent 2
Reasoning
SA
agent
decision
rules
9
Integration in human cognitive
performance modelling

Contextual control mode model (Hollnagel, 1993)
– opportunistic/tactical control modes

Multiple resources model (Wickens, 1992)

Human errror (e.g., Kirwan, 1994)

Human error recovery (Amalberti, 1997)
10
Contents

Problem background

Situation awareness: definition & model

Situation awareness in accident risk modelling

Initial results

Conclusions
11
Active runway crossing operation

Main human operators:
– pilots
– runway controller

Main technical systems:
– R/T communication systems
– active stopbar
– stopbar violation alert
– runway incursion alert
12
Collision risk tree (simplified)
Total collision risk
Initial SA
difference
No
SA pilot
taxiing a/c
Cross runway
ATC alert
systems
Communication
systems
Yes
Up
Up
Proceed taxiway
Down
Down
Up
Up
Down
Up
Down
Down
Up
Down
13
Contents

Problem background

Situation awareness: definition & model

Situation awareness in accident risk modelling

Initial results

Conclusions
14
15
Initial collision risk results
Total collision risk
Initial SA
difference
No
SA pilot
taxiing a/c
ATC alert
systems
Communication
systems
Yes
Cross runway
Up
Up
8.8e-10
Proceed taxiway
Down
Down
9.8e-14
Up
Down
3.8e-14 4.3e-18
Up
Up
1.0e-8
Down
Down
1.3e-12
Up
5.3e-13
Down
6.3e-17
16
Initial collision risk results
Total collision risk
Initial SA
difference
No <<1e-8
SA pilot
taxiing a/c
ATC alert
systems
Communication
systems
1.1e-8
Yes 1.1e-8
Cross runway 8.8e-10
Up 8.8e-10
Up
8.8e-10
Down
9.8e-14
Proceed taxiway 1.0e-8
Down 3.8e-14
Up
Down
3.8e-14 4.3e-18
Up 1.0e-8
Up
1.0e-8
Down
1.3e-12
Down 5.3e-13
Up
5.3e-13
Down
6.3e-17
17
Collision avoidance percentages of human operators
Human operators
Pilots
Runway ATCo
(additional effect)
SA PF crossing a/c
Proceed
Cross
taxiway
runway
99.6%
99.997%
15%
15%
18
Contents

Problem background

Situation awareness: definition & model

Situation awareness in accident risk modelling

Initial results

Conclusions
19
Conclusions

Mathematical situation awareness model
– Represents situation awareness components of Endsley (1995)
– In addition, it accounts for error evolution in a multi-agent
environment including technical systems

Accident risk modelling
– Technical systems & procedures & human operators
– Dynamic evolution of nominal & non-nominal conditions


Results:
– Accident risk
– Safety critical conditions
– Conflict resolution probabilities of human operators
Next step is bias and uncertainty assessment
20