Cacciabue Modelling Driver Behaviour in Automotive Environments

Critical Issues in Driver Interactions with Intelligent Transport Systems
1. Auflage 2010
ISBN: 978-1-84628-618-6
Verlag: Springer
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, 441 Seiten

This book presents a general overview of the various factors that contribute to modelling human behaviour in automotive environments. This long-awaited volume, written by world experts in the field, presents state-of-the-art research and case studies. It will be invaluable reading for professional practitioners graduate students, researchers and alike.

Carlo Cacciabue is an internationally-renowned expert on safety-critical systems and accident investigation, and an experienced consultant. He is a senior scientist in the Institute for the Protection and Security of the Citizen, at the Joint Research Centre of the European Commission, Ispra, Italy. He holds a PhD in Nuclear Engineering from the Politecnico di Milano (Italy) and retains a number of temporary academic positions in Italian and European universities. He is the author of two monographs and of several publications in journals and books relating to safety engineering and human-machine interaction in the domains of energy production and transportation. He is the principal Editor, together with E. Hollnagel, of Springer's International Journal of Cognition, Technology & Work.

Autoren/Hrsg.

Cacciabue, Carlo

Inhaltsverzeichnis

1;Title Page;3
2;Copyright Page;4
3;Table of Contents;5
4;Editorial;8
5;List of Contributors;12
6;I International Projects and Actions on Driver Modelling;15
6.1;1 Modelling Driver Behaviour in European Union and International Projects;16
6.1.1;1.1 Introduction;16
6.1.2;1.2 Evaluation of Driver Behaviour Models;17
6.1.2.1;1.2.1 Michon's Hierarchical Control Model;17
6.1.2.2;1.2.2 The GADGET-Matrix: Integrating Hierarchical Control Models and Motivational Models of Driver Behaviour;18
6.1.2.3;1.2.3 DRIVABILITY Model;19
6.1.3;1.3 Driver Behaviour Adaptation Models and Their Relation to ADAS;22
6.1.3.1;1.3.1 Automaticity;24
6.1.3.2;1.3.2 Locus of Control;24
6.1.3.3;1.3.3 Risk Homeostasis;25
6.1.3.4;1.3.4 Risk Compensation;26
6.1.3.5;1.3.5 Threat Avoidance;26
6.1.3.6;1.3.6 Utility Maximisation;27
6.1.3.7;1.3.7 Behavioural Adaptation Formula;27
6.1.4;1.4 Use of Driver Behaviour Models in EU and International Projects;28
6.1.4.1;1.4.1 Driver Models Use for Driver Training and Assessment;28
6.1.4.2;1.4.2 Evaluation ofDriver Models' Use for Safety Aids;28
6.1.4.2.1;1.4.2.1 Use of Seat Belts;28
6.1.4.2.2;1.4.2.2 Use of Motorcycle Helmet;30
6.1.4.2.3;1.4.2.3 Studded Tyres;32
6.1.4.2.4;1.4.2.4 Antilock Braking Systems;32
6.1.4.3;1.4.3 Driver Models Use for ADAS Design and Impact Assessment;33
6.1.5;1.5 Conclusions;35
6.1.6;References;36
6.2;2 TRB Workshop on Driver Models: A Step Towards a Comprehensive Model of Driving?;39
6.2.1;2.1 Introduction;39
6.2.2;2.2 Workshop Presentation and Speakers' Contribution;40
6.2.2.1;2.2.1 Workshop Content;40
6.2.2.1.1;2.2.1.1 Driver Model Purpose and Application;40
6.2.2.1.2;2.2.1.2 Driver Model Architecture and Implementations;40
6.2.2.1.3;2.2.1.3 Calibration and Validation;41
6.2.2.2;2.2.2 Summaries ofthe Speakers' Contributions;42
6.2.2.2.1;2.2.2.1 In-Vehicle Information System - Jon Hankey;42
6.2.2.2.2;2.2.2.2 ACT-R Driver Model- Dario Salvucci;43
6.2.2.2.3;2.2.2.3 Optimal Control Model - Richard van der Horst;45
6.2.2.2.4;2.2.2.4 ACME;47
6.2.2.2.5;2.2.2.5 Fuzzy Logic Based Motorway Simulation;48
6.2.3;2.3 Synthesis of Presented Models;49
6.2.3.1;2.3.1 Understanding Models' Scope;49
6.2.3.2;2.3.2 Driver Model Toolbox;51
6.2.4;2.4 Towards a Comprehensive Model of Driving;52
6.2.5;2.5 Conclusions;53
6.2.6;References;55
6.3;3 Towards Monitoring and Modelling for Situation-Adaptive Driver Assist Systems;56
6.3.1;3.1 Introduction;56
6.3.2;3.2 Behaviour-Based Human Environment Creation Technology Project;57
6.3.2.1;3.2.1 Aims of the Project;57
6.3.2.2;3.2.2 Measurement of Driving Behaviour;58
6.3.2.3;3.2.3 Driving Behaviour Modelling;58
6.3.2.4;3.2.4 Detection of Non-Normative Behaviour;58
6.3.2.5;3.2.5 Estimation of Driver's State;59
6.3.2.5.1;3.2.5.1 Estimation of Driver 's Mental Tension;59
6.3.2.5.2;3.2.5.2 Estimation of Driver's Fatigue;60
6.3.3;3.3 Situation and Intention Recognition for Risk Finding and Avoidance Project;60
6.3.3.1;3.3.1 Aims of the Project;60
6.3.3.2;3.3.2 Adaptive Function Allocation Between Drivers and Automation;63
6.3.3.3;3.3.3 Decision Authority and the Levels of Automation;64
6.3.3.4;3.3.4 Model-Based Evaluation of Levels of Automation;65
6.3.3.4.1;3.3.4.1 Drivers' Psychological States and Their Transitions;66
6.3.3.4.2;3.3.4.2 Driver's Response to an Alert;66
6.3.3.4.3;3.3.4.3 Evaluation of Efficacy of Levels of Automation;67
6.3.4;3.4 Concluding Remarks;67
6.3.5;References;69
7;II Conceptual Framework and Modelling Architectures;71
7.1;4 A General Conceptual Framework for Modelling Behavioural Effects of Driver Support Functions;72
7.1.1;4.1 Introduction;72
7.1.2;4.2 Intended Application Areas and Requirements;73
7.1.2.1;4.2.1 Functional Characterisation of Driver Support Functions;73
7.1.2.2;4.2.2 Coherent Description ofExpected Behavioural Effects of Driver Support Functions;73
7.1.2.3;4.2.3 Conceptualising Relations Between Behavioural Effects and Road Safety;74
7.1.2.4;4.2.4 Specific Requirements;74
7.1.3;4.3 Existing Models of Driver Behaviour;74
7.1.3.1;4.3.1 Manual Control Models;74
7.1.3.2;4.3.2 Information Processing Models;75
7.1.3.3;4.3.3 Motivational Models;75
7.1.3.4;4.3.4 Safety Margins;76
7.1.3.5;4.3.5 Hierarchical Models;77
7.1.4;4.4 A Conceptual Framework;77
7.1.4.1;4.4.1 Driver Behaviour as Goal-Directed Activity;78
7.1.4.2;4.4.2 Dynamical Representation of Driver Behaviour;78
7.1.4.3;4.4.3 The Contextual Control Model (COCOM);79
7.1.4.4;4.4.4 The Extended Control Model (ECOM);81
7.1.5;4.5 Application;83
7.1.5.1;4.5.1 Characterising Driver Support Functions;83
7.1.5.1.1;4.5.1.1 Support for Tracking;83
7.1.5.1.2;4.5.1.2 Support for Regulating;84
7.1.5.1.3;4.5.1.3 Support for Monitoring;84
7.1.5.1.4;4.5.1.4 Support for Targeting;84
7.1.5.1.5;4.5.1.5 Non-Driving-Related Functions;85
7.1.5.1.6;4.5.1.6 Workload Management Functions;85
7.1.5.2;4.5.2 Characterising Behavioural Effects of Driver Support Functions;85
7.1.5.2.1;4.5.2.1 Behavioural Adaptation to Driving Support Functions;86
7.1.5.2.2;4.5.2.2 Effects of Multitasking While Driving;87
7.1.5.3;4.5.3 Driver Behaviour and Accident Risk;89
7.1.6;4.6 Discussion and Conclusions;91
7.1.7;References;92
7.2;5 Modelling the Driver in Control;96
7.2.1;5.1 Introduction;96
7.2.2;5.2 A Cognitive View of Driving;96
7.2.3;5.3 Human Abilities;97
7.2.4;5.4 Classifying Driver Behaviour Models;98
7.2.5;5.5 Hierarchical Control Models;98
7.2.6;5.6 Control Theory;100
7.2.7;5.7 Adaptive Control Models;102
7.2.8;5.8 Cognition in Control;104
7.2.9;5.9 Goals for Control;106
7.2.10;5.10 Time and Time Again;108
7.2.11;5.11 Multiple Layers of Control;109
7.2.12;5.12 Joystick Controlled Cars - An Example;111
7.2.13;5.13 Summary and Conclusion;112
7.2.14;References;113
7.3;6 From Driver Models to Modelling the Driver: What Do We Really Need to Know About the Driver?;116
7.3.1;6.1 Introduction;116
7.3.2;6.2 A Typology of Models;117
7.3.3;6.3 Descriptive Models;117
7.3.3.1;6.3.1 Task Models;117
7.3.3.2;6.3.2 Adaptive Control Models;118
7.3.3.3;6.3.3 Production Models;118
7.3.4;6.4 Motivational Models;120
7.3.5;6.5 Towards a Real-Time Model of the Driver;123
7.3.5.1;6.5.1 What Type of Model Is Required?;123
7.3.5.2;6.5.2 Grouping the Factors;124
7.3.5.3;6.5.3 A Proposed Structure;126
7.3.5.4;6.5.4 Verifying the Model;127
7.3.6;6.6 Developing an Online Model;128
7.3.7;6.7 Conclusions;130
7.3.8;References;130
8;III Learning and Behavioural Adaptation;132
8.1;7 Subject Testing for Evaluation of Driver Information Systems and Driver Assistance Systems - Learning Effects and Methodological Solutions;133
8.1.1;SUMMARY;133
8.1.2;7.1 Introduction;133
8.1.3;7.2 Methodological Issues;135
8.1.4;7.3 Experimental Examples;136
8.1.4.1;7.3.1 Evaluation of a Multimodal HMI;137
8.1.4.2;7.3.2 Destination Entry While Driving;139
8.1.4.3;7.3.3 Evaluation of Driver Assistance Systems;140
8.1.5;7.4 Solutions;141
8.1.6;7.5 Conclusions;142
8.1.7;References;143
8.2;8 Modelling Driver's Risk Taking Behaviour;145
8.2.1;8.1 Introduction;145
8.2.2;8.2 Expected Risk Reductions from New Technology on the Road;145
8.2.3;8.3 Behaviour When Driving with Supports;146
8.2.3.1;8.3.1 The Importance of Plain Old Ergonomics;146
8.2.3.2;8.3.2 The Loss of Potentially Useful Skills;146
8.2.3.3;8.3.3 Opportunities for New Errors;146
8.2.3.4;8.3.4 Problematic Transitions;147
8.2.3.5;8.3.5 Risk and Risk Perception: My Risk and Yours;147
8.2.4;8.4 Behavioural Adaptation;147
8.2.4.1;8.4.1 Direct Changes in Behaviour;147
8.2.4.2;8.4.2 A Word of Caution About Working with Risk Measures in Traffic Safety Studies;149
8.2.4.3;8.4.3 A Piece of Empirical Evidence from Seat Belt Accident Statistics;150
8.2.4.4;8.4.4 Higher-Order Forms ofAdaptation;151
8.2.5;8.5 The Link Between Behaviour and Risk;152
8.2.5.1;8.5.1 Average Speed, Speed Variability and Risk;152
8.2.5.2;8.5.2 Lane-Keeping Performance and Risk;152
8.2.5.3;8.5.3 Car-Following and Risk;153
8.2.6;8.6 Countermeasures Against Behavioural Adaptation;154
8.2.6.1;8.6.1 Should There Be Any?;154
8.2.6.2;8.6.2 Incentive Schemes and Their Expected Results;154
8.2.7;8.7 Conclusions;154
8.2.8;8.8 An Afterthought;154
8.2.9;References;155
8.3;9 Dealing with Behavioural Adaptations to Advanced Driver Support Systems;157
8.3.1;9.1 Introduction;157
8.3.2;9.2 'Behavioural Adaptation' in Road Safety Research;158
8.3.3;9.3 Behavioural Adaptation to Advanced Driver Support Systems;159
8.3.3.1;9.3.1 The Diversity of Behavioural Changes Studied and Observed;160
8.3.3.2;9.3.2 The Importance of the Situational Context and the Interactive Dimension of Driving;162
8.3.3.3;9.3.3 The Potential Differential Impact of Driver Support Systems;163
8.3.3.4;9.3.4 Learning to Drive with New Driver Support Systems;165
8.3.4;9.4 Behavioural Adaptation in the AIDE Project;167
8.3.5;References;168
9;IV Modelling Motivation and Psychological Mechanisms;172
9.1;10 Motivational Determinants of Control in the Driving Task;173
9.1.1;10.1 Introduction;173
9.1.2;10.2 Understanding Speed Choice;173
9.1.2.1;10.2.1 Behaviour Analysis;173
9.1.2.2;10.2.2 The Theory of Planned Behaviour;175
9.1.2.3;10.2.3 Risk Homeostasis Theory;177
9.1.2.4;10.2.4 The Task-Capability Interface Model;179
9.1.2.4.1;10.2.4.1 The Determination of Task-Difficulty Level: Task-Difficulty Homeostasis;182
9.1.2.4.2;10.2.4.2 The Representation of Task-Difficulty;185
9.1.2.5;10.2.5 The Somatic-Marker Hypothesis;187
9.1.2.5.1;10.2.5.1 Predictions and Speculations from the Somatic-Marker Hypothesis;189
9.1.3;10.3 Conclusions;191
9.1.4;References;193
9.2;11 Towards Understanding Motivational and Emotional Factors in Driver Behaviour: Comfort Through Satisficing;197
9.2.1;11.1 Introduction;197
9.2.2;11.2 Emotional Tension and 'Risk Monitor';198
9.2.3;11.3 Safety Margins and Safety Zone;199
9.2.4;11.4 Available Time, Workload and Multilevel Task Control;201
9.2.5;11.5 Safety Margins, Affordances and Skills;204
9.2.6;11.6 Towards Unifying Emotional Conceptsin Routine Driving;206
9.2.6.1;11.6.1 Safety Margins - To Control and Survive;207
9.2.6.2;11.6.2 Vehicle/Road System - To Provide Smooth and Comfortable Travel;208
9.2.6.3;11.6.3 Rule Following - ToAvoid Sanctions;208
9.2.6.4;11.6.4 Good (or Expected) Progress of Trip -Mobility and Pace/Progress;209
9.2.7;11.7 Comfort Through Satisficing;209
9.2.8;11.8 'Go to the Road': Need of On-Road Research;211
9.2.9;References;212
9.3;12 Modelling Driver Behaviour on Basis of Emotions and Feelings: Intelligent Transport Systems and Behavioural Adaptations;216
9.3.1;12.1 Introduction;216
9.3.2;12.2 Defining Motivation;216
9.3.3;12.3 Motivational Aspects in Driver Behaviour Models;217
9.3.4;12.4 Behavioural Adaptation and Risk Compensation;218
9.3.5;12.5 Wilde's Risk Homeostasis Theory (RHT);219
9.3.5.1;12.5.1 Target Risk or Target Feeling?;222
9.3.6;12.6 Effects of ABS: An Illustrative Example of ITS;223
9.3.7;12.7 Issues Raised by the Example of ABS: The Relevance for ITS;226
9.3.8;12.8 Adaptation - Mismatch Between Technology and Human Capability;227
9.3.9;12.9 ITS Technology May Enhance As Well As Reduce the Window of Opportunities;228
9.3.10;12.10 Damasio and the Somatic Marker Hypothesis;229
9.3.11;12.11 The Monitor Model;232
9.3.12;12.12 The Monitor Model and Prediction of Effects of ITS;235
9.3.13;12.13 Summary and Conclusions;237
9.3.14;References;238
10;V Modelling Risk and Errors;241
10.1;13 Time-Related Measures for Modelling Risk in Driver Behaviour;242
10.1.1;13.1 Introduction;242
10.1.2;13.2 The Driving Task;243
10.1.3;13.3 Lateral Control;245
10.1.3.1;13.3.1 Time-to-Line Crossing (TLC);245
10.1.3.2;13.3.2 Lateral Distance When Passing;246
10.1.4;13.4 Longitudinal Control;247
10.1.4.1;13.4.1 Time-to-Collision (ITC);247
10.1.4.2;13.4.2 Time-to-Intersection (TTl);255
10.1.4.3;13.4.3 Time-to-Stop-Line (ITS);256
10.1.5;13.5 Conclusions;257
10.1.6;References;257
10.2;14 Situation Awareness and Driving: A Cognitive Model;260
10.2.1;14.1 Introduction;260
10.2.2;14.2 Situation Awareness;260
10.2.2.1;14.2.1 An Algorithmic Description of Situation Awareness;261
10.2.2.1.1;14.2.1.1 The Construction of the Situation Model: Comprehending the Situation;262
10.2.2.1.2;14.2.1.2 Selection of Actions and the Control of Behaviour;264
10.2.3;14.3 Errors and the Comprehension Based-Model of Situation Awareness;265
10.2.4;14.4 Situation Awareness and In-Vehicle Information System Tasks;267
10.2.4.1;14.4.1 A Measurement Procedure: Context-Dependent Choice Reaction Task;267
10.2.4.2;14.4.2 Evaluation of the Context-Dependent Choice Reaction Task;269
10.2.5;14.5 Conclusions;270
10.2.6;References;271
10.3;15 Driver Error and Crashes;273
10.3.1;15.1 Slips, Lapses and Mistakes;273
10.3.2;15.2 Errors and Violations;274
10.3.3;15.3 The Manchester Driver Behaviour Questionnaire;275
10.3.4;15.4 The DBQ and Road Traffic Accidents;275
10.3.5;15.5 Aggressive Violations;278
10.3.6;15.6 Anger-Provoking Situations;279
10.3.7;15.7 Conclusions;280
10.3.8;References;280
11;VI Control Theory Models of Driver Behaviour;282
11.1;16 Control Theory Models of the Driver;283
11.1.1;16.1 Introduction;283
11.1.2;16.2 Modelling Human Controlling Behaviour;283
11.1.2.1;16.2.1 The Tustin-Model: Linear Part + Remnant;283
11.1.2.2;16.2.2 Laboratory Research, Stochastic Input, Quasi-Linear Model;285
11.1.2.3;16.2.3 A Holistic Approach: The Crossover Model;286
11.1.2.4;16.2.4 Nonlinear Approaches: Improved Reproduction of Measured Behaviour;287
11.1.3;16.3 Driver Models for Vehicle Design;289
11.1.4;16.4 Summary and Future Prospects;295
11.1.5;References;296
11.2;17 Review of Control Theory Models for Directional and Speed Control;299
11.2.1;17.1 Introduction;299
11.2.2;17.2 Basic Crossover Model of the Human Operator;300
11.2.3;17.3 Model for Driver Steering Control;302
11.2.3.1;17.3.1 Equivalent Single-Loop System for Steering Control;304
11.2.4;17.4 Model for Speed Control with Accelerator Pedal;305
11.2.5;17.5 Experimental Data;308
11.2.5.1;17.5.1 Driving Simulator Measurements;308
11.2.5.1.1;17.5.1.1 Steering Control;309
11.2.5.1.2;17.5.1.2 Speed Control;310
11.2.5.2;17.5.2 Actual Vehicle Measurements;312
11.2.6;17.6 Example Directional Control Application;312
11.2.7;17.7 Discussion;316
11.2.8;References;316
12;VII Simulation of Driver Behaviour;318
12.1;18 Cognitive Modelling and Computational Simulation of Drivers Mental Activities;319
12.1.1;18.1 Introduction: A Brief Historical Overview on Driver Modelling;319
12.1.2;18.2 COSMODRIVE Model;321
12.1.2.1;18.2.1 Cognitive Architecture ofCOSMODRIVE;321
12.1.2.2;18.2.2 The Tactical Module;323
12.1.2.2.1;18.2.2.1 Driving Frames: A Framework for Modelling Mental Models;324
12.1.2.2.2;18.2.2 .2 Architecture of the Tactical Module;328
12.1.2.2.3;18.2.2.3 The Blackboards of the Tactical Module;329
12.1.2.2.4;18.2.2.4 The Knowledge Bases (KB) of the Tactical Module;330
12.1.2.2.5;18.2.2.5 The Cognitive Processes of the Tactical Module;331
12.1.2.2.5.1;18.2.2.5.1 Categorisation;332
12.1.2.2.5.2;18.2.2.5.2 The Place Recognition Process;332
12.1.2.2.5.3;18.2.2.5.3 The Tactical Representations Generator Process;332
12.1.2.2.5.4;18.2.2.5.4 The Anticipation Process;334
12.1.2.2.5.5;18.2.2.5.5 The Decision Process;335
12.1.3;18.3 Methodology to Study Driver's Situation Awareness;336
12.1.3.1;18.3.1 Main Hypothesis;336
12.1.3.2;18.3.2 Methodology;337
12.1.3.3;18.3.3 Main Results;337
12.1.3.4;18.3.4 Discussion and Conclusion Concerning Experimental Study of Drivers Situation Awareness;340
12.1.4;18.4 Some Experimental Results Simulation with Cosmodrive;341
12.1.5;18.5 Conclusion and Perspectives: From Behaviours to Mental Model;343
12.1.6;References;345
12.2;19 Simple Simulation of Driver Performance for Prediction and Design Analysis;348
12.2.1;19.1 Introduction;348
12.2.1.1;19.1.1 Modelling Human Behaviour in Modern Technology;348
12.2.1.2;19.1.2 Modelling Drivers in the Automotive Context;349
12.2.1.3;19.1.3 Use and Applications ofDriver Models;351
12.2.1.4;19.1.4 Content ofthe Paper;352
12.2.2;19.2 Simple Simulation of Driver Behaviour;352
12.2.2.1;19.2.1 Paradigm of Reference;352
12.2.2.2;19.2.2 Simulation Approach for Normative Behaviour;353
12.2.2.2.1;19.2.2.1 Task Analysis;353
12.2.2.2.2;19.2.2.2 Dynamic Logical Simulation of Tasks;354
12.2.2.3;19.2.3 Algorithms for Cognition, Behavioural Adaptation and Errors;356
12.2.2.3.1;19.2.3.1 Normative Driver Behaviour;358
12.2.2.3.2;19.2.3.2 Descriptive Driver Behaviour;359
12.2.2.3.3;19.2.3.3 Parameters and Measurable Variables;361
12.2.2.3.3.1;19.2.3.3.1 Task Demand;362
12.2.2.3.3.2;19.2.3.3.2 Driver State;363
12.2.2.3.3.3;19.2.3.3.3 Situation Awareness;365
12.2.2.3.3.4;19.2.3.4 Intentions, Decision Making and Human Error;367
12.2.2.3.3.4.1;19.2.3.4.1 Intentions and Decision Making;368
12.2.2.3.3.4.2;19.2.3.4.2 Error Generation;369
12.2.2.4;19.2.4 Simulation of Control Actions;370
12.2.2.4.1;19.2.4.1 Normal Driving;371
12.2.2.4.2;19.2.4.2 Error in Control Actions;373
12.2.3;19.3 Sample Cases of Predictive DVE Interactions;375
12.2.3.1;19.3.1 Case 1;375
12.2.3.2;19.3.2 Case 2;377
12.2.4;19.4 Conclusions;378
12.2.5;References;378
13;VIII Simulation of Traffic and Real Situations;380
13.1;20 Real-Time Traffic and Environment Risk Estimation for the Optimisation of Human-Machine Interaction;381
13.1.1;20.1 Introduction;381
13.1.2;20.2 The AWAKE Use Case - Adaptation of a Driver Hypovigilance Warning System;382
13.1.2.1;20.2.1 AWAKE System Overview;382
13.1.2.2;20.2.2 Traffic Risk Estimation in AWAKE System;383
13.1.2.3;20.2.3 The Scenario-Assessment Unit;384
13.1.2.4;20.2.4 The Warning Strategies Unit;384
13.1.2.5;20.2.5 The Risk-Level Assessment Unit;385
13.1.3;20.3 The AIDE Use Case - Optimisation of the In-Vehicle Human-Machine Interaction;386
13.1.3.1;20.3.1 Overview;387
13.1.3.2;20.3.2 Architecture;388
13.1.3.2.1;20.3.2.1 Relevance to the AIDE Use Cases;388
13.1.3.2.2;20.3.2.2 Description of Environment;389
13.1.3.3;20.3.3 Algorithm for Risk Assessment;390
13.1.3.3.1;20.3.3.1 Rule-Based System Employed for TERA Algorithms;390
13.1.3.3.2;20.3.3.2 Main Traffic Risk Condition Detection;392
13.1.3.3.2.1;20.3.3.2.1 Risk of Frontal/(Lateral) Collision;393
13.1.3.3.2.2;20.3.3.2.2 Criteria of Assigning the Level of Risk;393
13.1.3.3.2.3;20.3.3.2.3 Risk of Lane/Road Departure;394
13.1.3.3.2.4;20.3.3.2.4 Risk of Approaching a Dangerous Curve Too Fast;395
13.1.3.4;20.3.4 Algorithmfor Estimating the Intention of the Driver;395
13.1.3.5;20.3.5 TERA Implementation;397
13.1.4;20.4 Conclusions;399
13.1.5;References;400
13.2;21 Present and Future of Simulation Traffic Models;402
13.2.1;21.1 Introduction;402
13.2.2;21.2 Traffic Simulator;403
13.2.2.1;21.2.1 General Overview: A Survey of Road Traffic Simulations;403
13.2.2.2;21.2.2 Types of Simulator;405
13.2.2.3;21.2.3 Case Studies of Traffic Simulator;407
13.2.2.4;21.2.4 Vehicle Model Properties;409
13.2.2.4.1;21.2.4.1 Perception Topics;411
13.2.2.4.2;21.2.4.2 Cognition Topics;412
13.2.2.4.3;21.2.4.3 Actuation/Control Topics;413
13.2.2.4.4;21.2.4.4 Implementation of Vehicle Model;413
13.2.2.5;21.2.5 Two Examples of Applications with Traffic Simulator;414
13.2.2.5.1;21.2.5.1 The University of Michigan Microscopic Traffic Simulator;415
13.2.2.5.2;21.2.5.2 The MECTRON-HMI Group at University of Modena and Reggio Emilia Driving Simulator used in Human factors and Human-Machine Interfaces Studies.;416
13.2.2.6;21.2.6 Integration of Driver, Vehicle and Environment in a Closed-Loop System: The AIDE Project;419
13.2.2.6.1;21.2.6.1 General DVE Architecture;420
13.2.2.6.2;21.2.6.2 Time Frame for DVE Model;421
13.2.3;21.3 Conclusions and Further Steps;422
13.2.3.1;21.3.1 Towards a Multi-Agent Approach;423
13.2.3.2;21.3.2 New Developments and Prospective;423
13.2.3.3;21.3.3 Open Points and Future Steps;424
13.2.3.4;References;426
14;Index;430

Leseproben

"V Modelling Risk and Errors 13 Time-Related Measures for Modelling Risk in Driver Behaviour (p. 234-236)

RICHARD VAN DER HORST

13.1 Introduction

Accident statistics have an important general safety monitoring function and form a basis for detecting specific traffic safety problems. However, the resulting information is inadequate for analysing and diagnosing, defining remedial measures and evaluating their effects. Systematic observations of driver behaviour, combined with knowledge of human information-processing capabilities and limitations, offer wider perspectives in understanding the causes of safety problems and modelling driver behaviour in both normal and critical situations.

Renewed interest results from the need to develop, test, assess and evaluate driver support systems in terms of drivers behaviour, performance and acceptance. The processes that result in near-accidents or traffic conflicts have much in common with the processes preceding actual collisions (Hyden, 1987); only the final outcome is different. The frequency of traffic conflicts is relatively high, and they offer a rich information source on causal relationships since the preceding process can be systematically observed. In this approach, traffic situations are ranked along a continuum ranging from normal situations, via conflicts to actual collisions.

A pyramidal representation of this continuum was introduced by Hyden (1987), clearly visualising the relative rate of occurrence of the different events (Fig. 13.1). The analysis of driver behaviour in critical encounters may not only offer a better understanding of the processes that ultimately result in accidents, but, perhaps even more efficient in the long run, also provide us with knowledge on drivers abilities of turning a critical situation into a controllable one.

A general conceptual description of the driving task as commonly used in traffic psychonomics, with time-to-line crossing (TLC) and time-to-collision (TTC) as a measure for describing the lateral and longitudinal driving task will be used to distinguish normal from critical behaviour. That may serve as realistic criterion settings for in-car warning systems such as forward collision warning and intersection collision avoidance warning systems.

13.2 The Driving Task

In the literature the task analy sis for driving a car is well documented. A frequently used conceptual model of the driving task consists of three hierarchically ordered levels, navigation, guidance and control (Allen et aI., 1971). In other publications these levels are also referred to as strategic, manoeuvring and control. Tasks at the navigat ion level refer to the activities related to planning and executing a trip from origin to destination. The need for processing information only occurs occasionally, with intervals ranging from a few minutes to hour s. The guidance level refer s to tasks deal ing with the interaction with both environment (roadway, traffic signs , traffic signals) and other road users. Activity is required rather frequently with intervals of a few seconds to a few minutes."