Modelling Driver Behaviour in Automotive Environments: Critical Issues in Driver Interactions with Intelligent Transport Systems

1846286174......Page 1
Modelling Driver\rBehaviourin Automotive\rEnvironments......Page 3
Copyright Page......Page 4
Table of Contents......Page 5
Editorial......Page 8
List of Contributors......Page 12
I\rInternational Projects and Actions on\rDriver Modelling......Page 15
1.1 Introduction......Page 16
1.2.1 Michon\'s Hierarchical Control Model......Page 17
1.2.2 The GADGET-Matrix: Integrating Hierarchical Control Models and Motivational Models of Driver Behaviour......Page 18
1.2.3 DRIVABILITY Model......Page 19
1.3 Driver Behaviour Adaptation Models and Their Relation to ADAS......Page 22
1.3.2 Locus of Control......Page 24
1.3.3 Risk Homeostasis......Page 25
1.3.5 Threat Avoidance......Page 26
1.3.7 Behavioural Adaptation Formula......Page 27
1.4.2.1 Use of Seat Belts......Page 28
1.4.2.2 Use of Motorcycle Helmet......Page 30
1.4.2.4 Antilock Braking Systems......Page 32
1.4.3 Driver Models Use for ADAS Design and Impact Assessment......Page 33
1.5 Conclusions......Page 35
References......Page 36
2.1 Introduction......Page 39
2.2.1.2 Driver Model Architecture and Implementations......Page 40
2.2.1.3 Calibration and Validation......Page 41
2.2.2.1 In-Vehicle Information System - Jon Hankey......Page 42
2.2.2.2 ACT-R Driver Model- Dario Salvucci......Page 43
2.2.2.3 Optimal Control Model - Richard van der Horst......Page 45
2.2.2.4 ACME......Page 47
2.2.2.5 Fuzzy Logic Based Motorway Simulation......Page 48
2.3.1 Understanding Models\' Scope......Page 49
2.3.2 Driver Model Toolbox......Page 51
2.4 Towards a Comprehensive Model of Driving......Page 52
2.5 Conclusions......Page 53
References......Page 55
3.1 Introduction......Page 56
3.2.1 Aims of the Project......Page 57
3.2.4 Detection of Non-Normative Behaviour......Page 58
3.2.5.1 Estimation of Driver \'s Mental Tension......Page 59
3.3.1 Aims of the Project......Page 60
3.3.2 Adaptive Function Allocation Between Drivers and Automation......Page 63
3.3.3 Decision Authority and the Levels of Automation......Page 64
3.3.4 Model-Based Evaluation of Levels of Automation......Page 65
3.3.4.2 Driver\'s Response to an Alert......Page 66
3.4 Concluding Remarks......Page 67
References......Page 69
II\rConceptual Framework and\rModelling Architectures......Page 71
4.1 Introduction......Page 72
4.2.2 Coherent Description ofExpected Behavioural Effects of Driver Support Functions......Page 73
4.3.1 Manual Control Models......Page 74
4.3.3 Motivational Models......Page 75
4.3.4 Safety Margins......Page 76
4.4 A Conceptual Framework......Page 77
4.4.2 Dynamical Representation of Driver Behaviour......Page 78
4.4.3 The Contextual Control Model (COCOM)......Page 79
4.4.4 The Extended Control Model (ECOM)......Page 81
4.5.1.1 Support for Tracking......Page 83
4.5.1.4 Support for Targeting......Page 84
4.5.2 Characterising Behavioural Effects of Driver Support Functions......Page 85
4.5.2.1 Behavioural Adaptation to Driving Support Functions......Page 86
4.5.2.2 Effects of Multitasking While Driving......Page 87
4.5.3 Driver Behaviour and Accident Risk......Page 89
4.6 Discussion and Conclusions......Page 91
References......Page 92
5.2 A Cognitive View of Driving......Page 96
5.3 Human Abilities......Page 97
5.5 Hierarchical Control Models......Page 98
5.6 Control Theory......Page 100
5.7 Adaptive Control Models......Page 102
5.8 Cognition in Control......Page 104
5.9 Goals for Control......Page 106
5.10 Time and Time Again......Page 108
5.11 Multiple Layers of Control......Page 109
5.12 Joystick Controlled Cars - An Example......Page 111
5.13 Summary and Conclusion......Page 112
References......Page 113
6.1 Introduction......Page 116
6.3.1 Task Models......Page 117
6.3.3 Production Models......Page 118
6.4 Motivational Models......Page 120
6.5.1 What Type of Model Is Required?......Page 123
6.5.2 Grouping the Factors......Page 124
6.5.3 A Proposed Structure......Page 126
6.5.4 Verifying the Model......Page 127
6.6 Developing an Online Model......Page 128
References......Page 130
III\rLearning and Behavioural Adaptation......Page 132
7.1 Introduction......Page 133
7.2 Methodological Issues......Page 135
7.3 Experimental Examples......Page 136
7.3.1 Evaluation of a Multimodal HMI......Page 137
7.3.2 Destination Entry While Driving......Page 139
7.3.3 Evaluation of Driver Assistance Systems......Page 140
7.4 Solutions......Page 141
7.5 Conclusions......Page 142
References......Page 143
8.2 Expected Risk Reductions from New Technology on the Road......Page 145
8.3.3 Opportunities for New Errors......Page 146
8.4.1 Direct Changes in Behaviour......Page 147
8.4.2 A Word of Caution About Working with Risk Measures in Traffic Safety Studies......Page 149
8.4.3 A Piece of Empirical Evidence from Seat Belt Accident Statistics......Page 150
8.4.4 Higher-Order Forms ofAdaptation......Page 151
8.5.2 Lane-Keeping Performance and Risk......Page 152
8.5.3 Car-Following and Risk......Page 153
8.8 An Afterthought......Page 154
References......Page 155
9.1 Introduction......Page 157
9.2 \'Behavioural Adaptation\' in Road Safety Research......Page 158
9.3 Behavioural Adaptation to Advanced Driver Support Systems......Page 159
9.3.1 The Diversity of Behavioural Changes Studied and Observed......Page 160
9.3.2 The Importance of the Situational Context and the Interactive Dimension of Driving......Page 162
9.3.3 The Potential Differential Impact of Driver Support Systems......Page 163
9.3.4 Learning to Drive with New Driver Support Systems......Page 165
9.4 Behavioural Adaptation in the AIDE Project......Page 167
References......Page 168
IV\rModelling Motivation and\rPsychological Mechanisms......Page 172
10.2.1 Behaviour Analysis......Page 173
10.2.2 The Theory of Planned Behaviour......Page 175
10.2.3 Risk Homeostasis Theory......Page 177
10.2.4 The Task-Capability Interface Model......Page 179
10.2.4.1 The Determination of Task-Difficulty Level: Task-Difficulty Homeostasis......Page 182
10.2.4.2 The Representation of Task-Difficulty......Page 185
10.2.5 The Somatic-Marker Hypothesis......Page 187
10.2.5.1 Predictions and Speculations from the Somatic-Marker Hypothesis......Page 189
10.3 Conclusions......Page 191
References......Page 193
11.1 Introduction......Page 197
11.2 Emotional Tension and \'Risk Monitor\'......Page 198
11.3 Safety Margins and Safety Zone......Page 199
11.4 Available Time, Workload and Multilevel Task Control......Page 201
11.5 Safety Margins, Affordances and Skills......Page 204
11.6 Towards Unifying Emotional Conceptsin Routine Driving......Page 206
11.6.1 Safety Margins - To Control and Survive......Page 207
11.6.3 Rule Following - ToAvoid Sanctions......Page 208
11.7 Comfort Through Satisficing......Page 209
11.8 \'Go to the Road\': Need of On-Road Research......Page 211
References......Page 212
12.2 Defining Motivation......Page 216
12.3 Motivational Aspects in Driver Behaviour Models......Page 217
12.4 Behavioural Adaptation and Risk Compensation......Page 218
12.5 Wilde\'s Risk Homeostasis Theory (RHT)......Page 219
12.5.1 Target Risk or Target Feeling?......Page 222
12.6 Effects of ABS: An Illustrative Example of ITS......Page 223
12.7 Issues Raised by the Example of ABS: The Relevance for ITS......Page 226
12.8 Adaptation - Mismatch Between Technology and Human Capability......Page 227
12.9 ITS Technology May Enhance As Well As Reduce the Window of Opportunities......Page 228
12.10 Damasio and the Somatic Marker Hypothesis......Page 229
12.11 The Monitor Model......Page 232
12.12 The Monitor Model and Prediction of Effects of ITS......Page 235
12.13 Summary and Conclusions......Page 237
References......Page 238
V\rModelling Risk and Errors......Page 241
13.1 Introduction......Page 242
13.2 The Driving Task......Page 243
13.3.1 Time-to-Line Crossing (TLC)......Page 245
13.3.2 Lateral Distance When Passing......Page 246
13.4.1 Time-to-Collision (ITC)......Page 247
13.4.2 Time-to-Intersection (TTl)......Page 255
13.4.3 Time-to-Stop-Line (ITS)......Page 256
References......Page 257
14.2 Situation Awareness......Page 260
14.2.1 An Algorithmic Description of Situation Awareness......Page 261
14.2.1.1 The Construction of the Situation Model: Comprehending the Situation......Page 262
14.2.1.2 Selection of Actions and the Control of Behaviour......Page 264
14.3 Errors and the Comprehension Based-Model of Situation Awareness......Page 265
14.4.1 A Measurement Procedure: Context-Dependent Choice Reaction Task......Page 267
14.4.2 Evaluation of the Context-Dependent Choice Reaction Task......Page 269
14.5 Conclusions......Page 270
References......Page 271
15.1 Slips, Lapses and Mistakes......Page 273
15.2 Errors and Violations......Page 274
15.4 The DBQ and Road Traffic Accidents......Page 275
15.5 Aggressive Violations......Page 278
15.6 Anger-Provoking Situations......Page 279
References......Page 280
VI\rControl Theory Models of\rDriver Behaviour......Page 282
16.2.1 The Tustin-Model: Linear Part + Remnant......Page 283
16.2.2 Laboratory Research, Stochastic Input, Quasi-Linear Model......Page 285
16.2.3 A Holistic Approach: The Crossover Model......Page 286
16.2.4 Nonlinear Approaches: Improved Reproduction of Measured Behaviour......Page 287
16.3 Driver Models for Vehicle Design......Page 289
16.4 Summary and Future Prospects......Page 295
References......Page 296
17.1 Introduction......Page 299
17.2 Basic Crossover Model of the Human Operator......Page 300
17.3 Model for Driver Steering Control......Page 302
17.3.1 Equivalent Single-Loop System for Steering Control......Page 304
17.4 Model for Speed Control with Accelerator Pedal......Page 305
17.5.1 Driving Simulator Measurements......Page 308
17.5.1.1 Steering Control......Page 309
17.5.1.2 Speed Control......Page 310
17.6 Example Directional Control Application......Page 312
References......Page 316
VII\rSimulation of Driver Behaviour......Page 318
18.1 Introduction: A Brief Historical Overview on Driver Modelling......Page 319
18.2.1 Cognitive Architecture ofCOSMODRIVE......Page 321
18.2.2 The Tactical Module......Page 323
18.2.2.1 Driving Frames: A Framework for Modelling Mental Models......Page 324
18.2.2 .2 Architecture of the Tactical Module......Page 328
18.2.2.3 The Blackboards of the Tactical Module......Page 329
18.2.2.4 The Knowledge Bases (KB) of the Tactical Module......Page 330
18.2.2.5 The Cognitive Processes of the Tactical Module......Page 331
18.2.2.5.3 The Tactical Representations Generator Process......Page 332
18.2.2.5.4 The Anticipation Process......Page 334
18.2.2.5.5 The Decision Process......Page 335
18.3.1 Main Hypothesis......Page 336
18.3.3 Main Results......Page 337
18.3.4 Discussion and Conclusion Concerning Experimental Study of Drivers Situation Awareness......Page 340
18.4 Some Experimental Results Simulation with Cosmodrive......Page 341
18.5 Conclusion and Perspectives: From Behaviours to Mental Model......Page 343
References......Page 345
19.1.1 Modelling Human Behaviour in Modern Technology......Page 348
19.1.2 Modelling Drivers in the Automotive Context......Page 349
19.1.3 Use and Applications ofDriver Models......Page 351
19.2.1 Paradigm of Reference......Page 352
19.2.2.1 Task Analysis......Page 353
19.2.2.2 Dynamic Logical Simulation of Tasks......Page 354
19.2.3 Algorithms for Cognition, Behavioural Adaptation and Errors......Page 356
19.2.3.1 Normative Driver Behaviour......Page 358
19.2.3.2 Descriptive Driver Behaviour......Page 359
19.2.3.3 Parameters and Measurable Variables......Page 361
19.2.3.3.1 Task Demand......Page 362
19.2.3.3.2 Driver State......Page 363
19.2.3.3.3 Situation Awareness......Page 365
19.2.3.4 Intentions, Decision Making and Human Error......Page 367
19.2.3.4.1 Intentions and Decision Making......Page 368
19.2.3.4.2 Error Generation......Page 369
19.2.4 Simulation of Control Actions......Page 370
19.2.4.1 Normal Driving......Page 371
19.2.4.2 Error in Control Actions......Page 373
19.3.1 Case 1......Page 375
19.3.2 Case 2......Page 377
References......Page 378
VIII\rSimulation of Traffic\rand Real Situations......Page 380
20.1 Introduction......Page 381
20.2.1 AWAKE System Overview......Page 382
20.2.2 Traffic Risk Estimation in AWAKE System......Page 383
20.2.4 The Warning Strategies Unit......Page 384
20.2.5 The Risk-Level Assessment Unit......Page 385
20.3 The AIDE Use Case - Optimisation of the In-Vehicle Human-Machine Interaction......Page 386
20.3.1 Overview......Page 387
20.3.2.1 Relevance to the AIDE Use Cases......Page 388
20.3.2.2 Description of Environment......Page 389
20.3.3.1 Rule-Based System Employed for TERA Algorithms......Page 390
20.3.3.2 Main Traffic Risk Condition Detection......Page 392
20.3.3.2.2 Criteria of Assigning the Level of Risk......Page 393
20.3.3.2.3 Risk of Lane/Road Departure......Page 394
20.3.4 Algorithmfor Estimating the Intention of the Driver......Page 395
20.3.5 TERA Implementation......Page 397
20.4 Conclusions......Page 399
References......Page 400
21.1 Introduction......Page 402
21.2.1 General Overview: A Survey of Road Traffic Simulations......Page 403
21.2.2 Types of Simulator......Page 405
21.2.3 Case Studies of Traffic Simulator......Page 407
21.2.4 Vehicle Model Properties......Page 409
21.2.4.1 Perception Topics......Page 411
21.2.4.2 Cognition Topics......Page 412
21.2.4.4 Implementation of Vehicle Model......Page 413
21.2.5 Two Examples of Applications with Traffic Simulator......Page 414
21.2.5.1 The University of Michigan Microscopic Traffic Simulator......Page 415
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.......Page 416
21.2.6 Integration of Driver, Vehicle and Environment in a Closed-Loop System: The AIDE Project......Page 419
21.2.6.1 General DVE Architecture......Page 420
21.2.6.2 Time Frame for DVE Model......Page 421
21.3 Conclusions and Further Steps......Page 422
21.3.2 New Developments and Prospective......Page 423
21.3.3 Open Points and Future Steps......Page 424
References......Page 426
Index......Page 430