Driver behaviour and training. 2, [Based on the contributions for the Second International Conference in Driver Behaviour and Training (DB & T 2005) held in Edinburgh in November 2005] 0-7546-4430-8, 9780754644309

Table of contents :
Content: Contents: Driver Training and Education: Interactive scenario modelling for hazard perception in driver training, Abs Dumbuya, N. Reed, G. Rhys-Tyler, Q .J. Zhao and R.L. Wood
An analysis of the national driver improvement scheme by referral type, Ian Edwards
Assessment of driver training courses, Lee Martin, Catriona Rae and Steve Stradling
Driver education BSM driving instructor training programme, Susan McCormack
Should driver education include training against instinctive human reactions?, Anthony C. Hastings
Cars, sex, drugs and media: comparing modalities of road safety and public health messages, Anne Morphett and Zoe Sofoulis. Simulation and In-Vehicle Technology: Novice driver performance improvement with simulator training, R. Wade Allen, Marcia L. Cook and George D. Park
Truck and bus driver training, can simulation contribute?, Torbjorn Falkmer
The potential to enhance older drivers' critical driving skills through simulator-based advice, Jerry Wachtel, Matthew R.E. Romoser, Donald L. Fisher, Konstantin Sizov and Ronald Mourant
Microsimulation of traffic for safety study of in-vehicle intelligent transportation systems, Ata M. Khan, Akihira Fukutomi, Sarah J. Taylor and Jennifer M. Armstrong
Assessing drivers' level of trust in adaptive cruise control and their conceptual models of the system: implications for system design, Tara A. Kazi, Neville A. Stanton, Mark S. Young and D. A. Harrison. Young Driver Behaviour and Road Safety: Driver education - a difficult but possible safety measure, Nils Petter Gregersen
Identifying young driver subtypes: relationship to risky driving and crash involvement, Lisa Wundersitz and N. Burns
Development and first evaluation of a prediction model for risk of offences and accident involvement among young drivers, Antje Biermann, Eva-Maria Eick, Roland Brunken, Gunter Debus and Detlev Leutner
Assessment of a diary to study development of higher-order-skills during driving experience, Saskia de Craen and Divera A.M. Twisk
Young drivers' attitudes towards risks arising from hazardous driving behaviours, A. Ian Glendon
Prediction of driving accident risk in novice drivers in Ontario: the development of a screening instrument, Laurence Jerome and A. Segal
Seat-belt use by Spanish adolescents, Monica Cunill, M. Eugenia Gras, Mark J.M. Sullman and Montserrat Planes. Vulnerable Road Users: Designing powered two wheeler training to match rider goals, Paul Broughton
Understanding the increasing trend of motorcycle fatalities: rider error, driver error or training error?, Simon Labbett and Martin Langham
Driving at fifteen: assessment of moped rider training amongst teens, Patricia Antonio, Manuel Matos and Mario Horta
Vulnerable road user safety: social interaction on the road?, Ian Walker. Personality, Emotions and Driving: The transactional model of driver stress and fatigue and its implications for driver training, Gerald Matthews, Amanda K. Emo and Gregory J. Funke
A cross-cultural comparison of the driving anger scale, Mark J.M. Sullman, M. Eugenia Gras, Monica Cunill and Montserrat Planes
The effect of sensation-seeking on driver fatigue, Thomas Vohringer-Kuhnt, Katja Karrer and N. Schlienz
The use of group dynamics in a driver rehabilitation course, Ana Monica Dias and Silvino Indias Cordeiro. At-Work Road Safety: Factors influencing the behaviour of people who drive at work, Catriona Rae, Lee Martin and Steve Stradling
A qualitative analysis of company car driver road safety, Sarah Fletcher
Development of the police driver risk index, Julie Gandolfi and Lisa Dorn
Fatigue-related driver behaviour in untrained and professional drivers, Katja Karrer, Thomas Vohringer-Kuhnt, S. Briest and T. Baumgarten
Predictors of coach drivers' safety behaviour and health status, M. Anthony Machin
Comparing IT-based driver assessment results against self-reported and actual crash outcomes in a large motor vehicle fleet, Will Murray, Andy Cuerden and Phil Darby
Differential accident involvement of bus drivers, Anders E. af Wahlberg
The safety value of driver education in Nigeria: an assessment of professional driver behaviour, Innocent C. Ogwude and Chinonye Ugboma. Crash Analysis: The application of systems engineering techniques to the modelling of crash causation, Peter J. Hillard, D. Logan and B. Fildes
The application of accident script analysis to truck crashes, Mark J.M. Sullman
Non-linear methods for the identification of drivers at risk to cause accidents, Markus Sommer and Joachim Hausler. Driver Attention and Knowledge: Use of the d2 test of attention as a predictor of driving proficiency, Wendy Lord and Peter Clarke
Mental models and attentional processes in car driving, Rainer Hoger, Jessica Seidenstucker and Nicki Marquardt
What drivers don't know, S. David Leonard
Transfer of useful field of vision from team sports to driving skills in a simulated driving test, Rui Matos and Mario Godinho. Conclusion: Driver coaching: driving standards

Citation preview

DRIVER BEHAVIOUR AND TRAINING

Dedication

To my precious daddy with more love than you can imagine Lisa

Driver Behaviour and Training Volume II

Edited by LISA DORN Cranfield University, UK

First published 2005 by Ashgate Publishing Published 2016 by Routledge 2 Park Square, Milton Park, Abingdon, Oxon OX14 4RN 711 Third Avenue, New York, NY 10017, USA Routledge is an imprint of the Taylor & Francis Group, an informa business Copyright © 2005 Lisa Dorn Lisa Dorn has asserted her right under the Copyright, Designs and Patents Act, 1988, to be identified as the editor of this work.

All rights reserved. No part of this book may be reprinted or reproduced or utilised in any form or by any electronic, mechanical, or other means, now known or hereafter invented, including photocopying and recording, or in any information storage or retrieval system, without permission in writing from the publishers. Notice: Product or corporate names may be trademarks or registered trademarks, and are used only for identification and explanation without intent to infringe. British Library Cataloguing in Publication Data International Conference on Driver Behaviour and Training (2nd : 2005 : Edinburgh, Scotland) Driver behaviour and training Vol. 2. - (Human factors in road and rail transport) 1.Motor vehicle drivers - Training of - Congresses 2.Motor vehicle drivers - Attitudes - Congresses 3.Motor vehicle driving - Congresses I.Title II.Dorn, Lisa 629.2’83 Library of Congress Cataloging-in-Publication Data International Conference on Driver Behaviour and Training (1st : 2003 : Stratford-upon-Avon, England) Driver behaviour and training / edited by Lisa Dorn. p. cm. Includes bibliographical references and index. ISBN 0 7546 4430 8 1. Traffic safety--Congresses. 2.Automobile drivers--Congresses. 3. Automobiles--Safety appliances--Congresses. 2.Automobile driver education--Congresses. I. Dorn, Lisa. II.Title. HE5614.I553 2003 363.12'5--dc22 ISBN 9780754644309 (hbk)

2003058287

Contents

List of Figures and Tables Acknowledgements Preface

xi xix xxi

Part 1 Driver Training and Education 1

Interactive Scenario Modelling for Hazard Perception in Driver Training Abs Dumbuya, N. Reed, G. Rhys-Tyler, Q J. Zhao and R.L. Wood

2

An Analysis of the National Driver Improvement Scheme by Referral Type Ian Edwards

19

3

Assessment of Driver Training Courses Lee Martin, Catriona Rae and Steve Stradling

33

4

Driver Education BSM Driving Instructor Training Programme Susan McCormack

47

5

Should Driver Education Include Training Against Instinctive Human Reactions? Anthony C. Hastings

55

6

Cars, Sex, Drugs and Media: Comparing Modalities of Road Safety and Public Health Messages Anne Morphett and Zoë Sofoulis

61

3

Part 2 Simulation and In-Vehicle Technology 7

Novice Driver Performance Improvement with Simulator Training R. Wade Allen, Marcia L. Cook and George D. Park

81

8

Truck and Bus Driver Training, Can Simulation Contribute? Torbjörn Falkmer

93

vi

Driver Behaviour and Training – Volume II

9

The Potential to Enhance Older Drivers’ Critical Driving Skills Through Simulator-Based Advice Jerry Wachtel, Matthew R.E. Romoser, Donald L. Fisher, Konstantin Sizov and Ronald Mourant

105

10

Microsimulation of Traffic for Safety Study of In-Vehicle Intelligent Transportation Systems Ata M. Khan, Akihira Fukutomi, Sarah J. Taylor and Jennifer M. Armstrong

121

11

Assessing Drivers’ Level of Trust in Adaptive Cruise Control and Their Conceptual Models of the System: Implications for System Design Tara A. Kazi, Neville A. Stanton, Mark S. Young and D A. Harrison

133

Part 3 Young Driver Behaviour and Road Safety 12

Driver Education – A Difficult but Possible Safety Measure Nils Petter Gregersen

145

13

Identifying Young Driver Subtypes: Relationship to Risky Driving and Crash Involvement Lisa Wundersitz and N. Burns

155

14

Development and First Evaluation of a Prediction Model for Risk of Offences and Accident Involvement Among Young Drivers Antje Biermann, Eva-Maria Eick, Roland Brünken, Günter Debus and Detlev Leutner

169

15

Assessment of a Diary to Study Development of HigherOrder-Skills During Driving Experience Saskia de Craen and Divera A.M. Twisk

179

16

Young Drivers’ Attitudes Towards Risks Arising from Hazardous Driving Behaviours A. Ian Glendon

193

17

Prediction of Driving Accident Risk in Novice Drivers in Ontario: The Development of a Screening Instrument Laurence Jerome and A. Segal

207

Contents

18

Seat-Belt Use by Spanish Adolescents Monica Cunill, M. Eugenia Gras, Mark J.M. Sullman and Montserrat Planes

vii

223

Part 4 Vulnerable Road Users 19

Designing Powered Two Wheeler Training to Match Rider Goals Paul Broughton

233

20

Understanding the Increasing Trend of Motorcycle Fatalities: Rider Error, Driver Error or Training Error? Simon Labbett and Martin Langham

243

21

Driving at Fifteen: Assessment of Moped Rider Training Amongst Teens Patricia António, Manuel Matos and Mario Horta

253

22

Vulnerable Road User Safety: Social Interaction on the Road? Ian Walker

261

Part 5 Personality, Emotions and Driving 23

The Transactional Model of Driver Stress and Fatigue and its Implications for Driver Training Gerald Matthews, Amanda K. Emo and Gregory J. Funke

273

24

A Cross-Cultural Comparison of the Driving Anger Scale Mark J.M. Sullman, M. Eugenia Gras, Monica Cunill and Montserrat Planes

287

25

The Effect of Sensation-Seeking on Driver Fatigue Thomas Vöhringer-Kuhnt, Katja Karrer and N. Schlienz

299

26

The Use of Group Dynamics in a Driver Rehabilitation Course Ana Mónica Dias and Silvino Índias Cordeiro

309

Part 6 At-Work Road Safety 27

Factors Influencing the Behaviour of People Who Drive at Work Catriona Rae, Lee Martin and Steve Stradling

319

viii

Driver Behaviour and Training – Volume II

28

A Qualitative Analysis of Company Car Driver Road Safety Sarah Fletcher

327

29

Development of the Police Driver Risk Index Julie Gandolfi and Lisa Dorn

337

30

Fatigue-Related Driver Behaviour in Untrained and Professional Drivers Katja Karrer, Thomas Vöhringer-Kuhnt, S. Briest and T. Baumgarten

349

31

Predictors of Coach Drivers’ Safety Behaviour and Health Status M. Anthony Machin

359

32

Comparing IT-Based Driver Assessment Results Against Self-Reported and Actual Crash Outcomes in a Large Motor Vehicle Fleet Will Murray, Andy Cuerden and Phil Darby

373

33

Differential Accident Involvement of Bus Drivers Anders E. af Wåhlberg

383

34

The Safety Value of Driver Education in Nigeria: An Assessment of Professional Driver Behaviour Innocent C. Ogwude and Chinonye Ugboma

395

Part 7 Crash Analysis 35

The Application of Systems Engineering Techniques to the Modelling of Crash Causation Peter J. Hillard, D. Logan and B. Fildes

407

36

The Application of Accident Script Analysis to Truck Crashes Mark J.M. Sullman

417

37

Non-Linear Methods for the Identification of Drivers at Risk to Cause Accidents Markus Sommer and Joachim Häusler

425

Part 8 Driver Attention and Knowledge 38

Use of the d2 Test of Attention as a Predictor of Driving Proficiency Wendy Lord and Peter Clarke

437

Contents

ix

39

Mental Models and Attentional Processes in Car Driving Rainer Höger, Jessica Seidenstücker and Nicki Marquardt

443

40

What Drivers Don’t Know S. David Leonard

451

41

Transfer of Useful Field of Vision from Team Sports to Driving Skills in a Simulated Driving Test Rui Matos and Màrio Godinho

459

Conclusion Driver Coaching: Driving Standards Higher Lisa Dorn Index

471 481

List of Figures and Tables Figures 1.1 1.2 1.3a 1.3b 1.4 1.5 2.1 6.1 6.2 6.3 6.4 6.5 6.6 6.7 6.8 7.1 7.2 7.3 7.4 7.5 7.6 7.7 8.1 8.2 8.3 8.4 8.5 9.1 9.2 9.3

Basic Components of Driving Components View of ST-SIM Sample Image from Drivers’ Viewpoint in 3D Scene Sample Image from Driver Vision Model Comparison of Driving Profiles for Real and Simulated Drivers Qualitative and Quantitative Visualization of the Driving Scenario Ajzen (1988) Theory of Planned Behaviour Random Breath Tests Easter Seat-Belts Prevent Disease Action for AIDS Protect Your ‘Luv’ Dead Guys Condoman You Lose Simulator Configurations Example Orientation Power Point Slides for Novice Driver Training Typical Roadway Scenes in Training Scenarios Average Total Number of Excessive Control Instances per Subject Average Total Number of Errors per Subject Average Speed and Time-to-Collision Speed vs Accuracy Trade-off Registered Vehicle Types per Country as a Percentage of the Total Number of Road Vehicles (OECD, 1996) Crash Involvement vs Occurrence of Swedish Light Trucks Crash Injury Severity with Respect to Different Categories of Vehicle Occupants Number of Crashes in Norway in 1993, with Respect to Years of Truck or Bus Driver Experience Truck Driver Training Programme Curriculum Hours with Respect to Different Training ‘Arenas’, Offered by 24 US Private Training Schools and the Average Crashes Involving Drivers 55+ University of Massachusetts Driving Simulator DriveSquare Simulator

5 9 10 10 13 13 21 65 66 69 70 71 72 73 74 84 84 85 87 88 89 90 94 95 96 98 99 106 108 109

xii

9.4 9.5 9.6 9.7 9.8 9.9 10.1 10.2 10.3 12.1 14.1 14.2 14.3 15.1 15.2 16.1 16.2 16.3 19.1 19.2 19.3 21.1 22.1 23.1 23.2 25.1 25.2 25.3 25.4 29.1 30.1 30.2 30.3 31.1

Driver Behaviour and Training – Volume II

Y Intersection Scenario Red Flags per Scenario Response to Advice Red Flags per Scenario Response to Advice Change in Pattern of Responses Components of ITS Major Components of a Microsimulator Simulation for Safety Study of In-Vehicle ITS Four Cornerstones of Driver Education Prediction Model Pair-Wise Correlations for the Male Subgroup (N=72; Controlled for Drivers’ Licence Holding Time) Pair-Wise Correlations for the Female Subgroup (N=181; Controlled for Drivers’ Licence Holding Time) Percentage of Situations where the Young Driver was Responsible for the Situation Percentage of Situations in which the Drivers’ Behaviour was Appropriate Sub-Themes within the Speeding Theme Sub-Themes within the Alcohol/Other Drugs Theme Sub-Themes within the Fatigue/Tiredness Theme Illustration of Hierarchical Levels of Driver Behaviour Edzell Track Flow FJC Adolescents over the Past Five Years throughout the Country Drivers’ Decision Times by Bicyclist’s Portrayed Action A Generic Transactional Model of Driver Stress and Fatigue Patterning of Subjective State Change Response to Three Real World Drives and One Simulated Drive Frequency of Ratings vs Sensation-Seeking (Day) Frequency of Ratings vs Sensation-Seeking (Night) Sensation-Seeking vs Occurrence of Micro Sleep Events during the Night Drive Sensation-Seeking vs Occurrence of Warnings during the Drive Scree Plot for DSI Factor Analysis Driving Simulator Differences in Mean Length of Micro-Sleep Event or DWA between Untrained Drivers and Remaining Sample [s] Differences in Number of Micro-Sleep Events or DWA between Untrained Drivers and Remaining Sample Proposed Model of Relationships between Drivers’ Perceptions of Supervisor’s Leadership Style, Dimensions of Safety Climate, Safety Outcomes and Health Status

110 113 113 115 116 117 124 126 128 152 171 173 174 188 188 195 199 201 234 235 237 254 263 274 276 303 304 304 306 342 351 357 357 362

List of Figures and Tables

31.2 32.1 32.2 32.3 33.1

33.2 33.3

33.4 33.5

37.1 37.2 37.3 39.1 39.2 39.3 41.1 41.2 41.3 41.4 41.5

Revised Model of Relationships Between Supervisor’s Transformational Leadership Style, Organizational Safety Climate, Safety Behaviour and Indices of Health Status Comparison of RoadRISK Score against Self-Reported Crashes Average Assessment Scores by Crash History Average Assessment Score by Crashes in Past Three Years The Relative Risk for each Age Band, Calculated as their Percent Prevalence in Accidents Divided by their Mean (of five years) Prevalence in the Population, for the Time Period 1999-2003 The Mean Raw Number of Accidents Plotted against Hours Worked during the Years 1999-2003, with 95% Confidence Intervals. N=552 Bus Driver Accidents as the Total Number and per 1000 Hours Worked for the period 1999-2003, Plotted against Years of Employment at the Bus Company, with 95% Confidence Intervals (N=552) The Association Between Age and Accidents, in Mean Raw Numbers and per 1000 Hours Worked, with 95% Confidence Intervals (N=552) For the Changes in Accident Liability from the First to the Fifth Year of Driving of Drivers Starting Work between 1994-1999 and doing so for at least Five Years Afterwards (N=107). Repeated Measurements ANOVA is significant at p25 years)

27

56.00

7.15

36.11

6.64

Total

60

42.23

14.78

23.02

13.89

Table 1.2b Demographics of Participants, Grouped by Age Years Since licence acquisition

Age Age grouping

N

Mean

SD

Mean

SD

Younger (18-42)

31

29.94

7.47

11.77

7.37

Older (43-68)

29

55.38

7.35

35.03

7.65

Total

60

42.23

14.78

23.02

13.89

Interactive Scenario Modelling for Hazard Perception in Driver Training

11

The scenario was set up for motorway driving. In the task, participants were instructed to drive along the motorway as they normally would in light ambient traffic conditions. At 20.58 km (20,580 m) into the trial there was an obstruction vehicle parked in the fend-in position in lane one of the motorway. At a distance of approximately one mile (1609.344 m) before the obstruction vehicle, the ambient traffic was removed from the driving environment so that participants were not impeded when making the avoidance manoeuvre. Participants had to move from lane one into lane two or lane three of the motorway to avoid the obstruction vehicle. Data was recorded at twenty Hz over the course of the trial. Parameters recorded were time, current lane, distance through the trial, lateral distance, steering wheel angle, accelerator and brake pedal depression and the speed of the driven vehicle. Scenario Configuration within ST-SIM ST-SIM currently runs on a standard PC in a series of time steps. In this comparison, scenario configuration in ST-SIM was based on the experimental data obtained from the TRL driving simulator. A sample of twenty-five data sets were selected from the sixty drivers and organised into the same groups of novice (four male and five female samples), experienced (five male and five female samples), and veteran (three male and three female samples). The driver’s lane changing manoeuvre was simulated along with lane changing behaviour, taking into account the need to prevent collision with an obstruction vehicle parked in the fend-in position in lane one. The set up of parameters in ST-SIM is shown below: TS: Simulation Time Step (0.06 s) TL: Simulation Time Length (4.5 s) STS: Sample Time Steps from experimental data (selection based on the experimental scenario and data) X: Initial Abscissa (0m for driven vehicle, 80 m for obstruction vehicle) Y: Initial Lateral Distance to road centre (m) Spd Dir: Initial Speed Direction (deg) SEL: Speed Error Limitation under which the driver takes no action, defined as the percentage of current preferred speed (percent) APL: Angle Percept Limitation below which driver will not execute manoeuvre, defined as the percentage of current horizontal vision angle (deg) FR: Foreseen Range (ie the foreseen distance for driver’s steering angle decision) HL: Headway Limit, the driver’s safe distance to the front driver, defined by the percentage of pixels occupied by the front vehicle in the driver vision model pixel map SYL: Safe Yaw Limitation, the yaw velocity below which driver will feel safe (deg/s) PreSpeed: Preferred Speed (mph)

12

Driver Behaviour and Training – Volume II

The SEL, APL, FR, and HL are set up based on the driver’s decision rule weights in the decision model as shown in Table 1.3. Table 1.3 Driver Rule Weights Used to Configure the Decision Model Driver Group

Rule 1

Rule 2

Rule 3

Rule 4

Rule 5

SEL

APL

FR

HL

Novice Experienced Veteran

0.3 0.6 0.7

0.8 0.7 0.6

0.8 0.5 0.3

0.3 0.5 0.6

0.6 0.7 0.8

5 2 0.5

0.1 0.05 0.01

0.8 0.85 0.9

0.02 0.01 0.01

Rule no 1 2 3 4 5

Description Slow down Speed up Maintain preferred speed Maintain preferred lane Maintain preferred forward distance.

Results and Discussion Figure 1.4 presents selected examples drawn from ST-SIM simulation runs and TRL simulator results. The results compare the behaviour of real drivers and simulated drivers for the three categories of driving experience – novice, experienced and veteran. As a simple demonstration, we provide the results for two novices, two experienced and two veterans. The driving profiles show the lateral positions of the drivers as they travelled in the northbound direction of the motorway. Both the real and simulated drivers were able to make an avoidance manoeuvre by moving from lane one, lane two and lane three to avoid the obstruction vehicle. For comparison of actual and simulated veteran drivers agreement is good, with slightly greater variation between simulated and actual driving for one of the experienced drivers. However, there is a noticeable difference in simulated and actual behaviour of the novice drivers. This is contributed to by three factors: the actual novice driving is relatively erratic; fine tuning of driver character in ST-SIM is currently a demanding task; and the current vehicle model in ST-SIM lacks some of the detailed inertial and frictional effects found in the steering and suspension of real vehicles. Therefore, creating a driver character in ST-SIM to match real vehicle behaviour implicitly involves some compensation for this. However, it is important to note that ST-SIM provides a framework in which the realism of the component models can be developed in the future. Figure 1.5a provides qualitative visualization of one instant in the scenario, as seen from the driver’s viewpoint, when the real driver’s car has moved to lane two to avoid the parked car on the left (lane one). Figure 1.5b shows the pixel map generated in the driver vision model within ST-SIM at the same instant.

Interactive Scenario Modelling for Hazard Perception in Driver Training

13

Figure 1.4 Comparison of Driving Profiles for Real and Simulated Drivers

Figure 1.5a Rendered View

Figure 1.5b Pixel Map (or binary image)

Figure 1.5 Qualitative and Quantitative Visualization of the Driving Scenario Interactive Scenario Modelling for Hazard Perception The study reported in this paper has demonstrated an aspect of Hazard Perception (HP) involving avoidance action. Hazardous situations can be dynamic involving other road users such as vehicles or pedestrians, or may simply include static or environmental features. Various studies have shown the need for evaluation of hazard perception. For example, research studies have shown correlation of hazard

14

Driver Behaviour and Training – Volume II

perception skills and potential for crashes, especially for inexperienced drivers (Grayson and Sexton, 2002; Quimby and Watts, 1981 and Maycock et al, 1991). Other studies have also demonstrated that hazard perception skills can be improved through training (McKenna and Crick, 1994) and this research evidence has led to the hazard perception test administered by the UK Driving Standard Agency (DSA). The DSA paper (Wedge, 2002) lists some of the competencies linked to HP as: effective scanning to enable early clues to be recognized; anticipation and planning; safe separation distance; and correct use of speed. Hazard perception training typically involves the use of video recording (clips) of either planned (staged events) or unplanned (opportunistic) hazardous driving scenarios. The subject responds by pressing a button within a set time period after they have identified the hazard. Responding to the DSA’s Hazard Perception Test, BSM has exploited computer game technology to produce a commercial tool for improving hazard perception skills. The training package called MAP – Mind Alertness Programme (McCormack, 2003), is targeted at learner drivers to help improve cognitive skills such as reaction time, visual scanning, risk avoiding, hand-eye co-ordination etc. The results presented here show that ST-SIM is capable of performing some aspects of hazard perception, eg simulated driver agents are able to identify parked cars as a hazard and consequently decide and apply preventative driving action, eg lane-changing behaviour, to avoid collision with the parked vehicle. In the context of hazard perception, the paper has demonstrated the potential of using ST-SIM as a comprehensive modelling tool for practitioners, engineers, scientists and decision-makers working in many commercial aspects of transport. Current Limitations ST-SIM is still under development and as such has current limitations which are briefly commented upon here: •

•

•

Driver steering behaviour is based on the analysis of pixel map output from the vision model. However, the driver’s brake/accelerate behaviour is not fully implemented, currently relying only on output from the driver decision-making model, with linear interpolation between driver’s anticipated speed and manoeuvre time. One solution to this is to use nonlinear interpolation functions for the brake/accelerate behaviour used; The Vehicle Control System is currently not fully developed; the vehicle’s braking, steering and acceleration models should simulate the vehicle dynamics’ response to the driver’s control behaviour at a more realistic level (eg time delay between driver’s steering behaviour and vehicle’s yaw response and lateral speed change, or the delay between brake/accelerate behaviour and vehicle’s speed change etc); Driver perception of vehicle forces has not yet been implemented.

Interactive Scenario Modelling for Hazard Perception in Driver Training

15

Acknowledgement The authors would like to thank Sue Burton and Toby Philpott of TRL for their help in providing us with the simulator data. The authors are also grateful for ongoing funding and support provided by the UK Highways Agency through the Vehicle Safety and Research Centre (VSRC) at Loughborough University, in the recent development of the ST-SIM project. In addition, the authors would like to thank Pete Thomas and Julian Hill of VSRC for their support in validating some aspects of ST-SIM. The views expressed in this paper belong to the authors and are not necessarily those of TRL, HA or Loughborough University. References Allen, R.W. Park, G. Rosenthal, T.J. and Aponso, B.M. (2004), ‘A process for developing scenarios for driving simulations’, IMAGE 2004 Conference, Arizona, Paper No 632. Allen, R.W. Rosenthal, T.J. and Hogue, J.R. (1996), ‘Modelling and Simulation of Driver/Vehicle Interaction’, International Congress & Exposition, SAE Paper 960177, Detroit, MI, pp. 26-29. Allen, R.W. Rosenthal T.J. and Park, G. (2003), ‘Scenarios produced by procedural methods for driving research, assessment and training applications’, Driving Simulation Conference, North America (DSC-NA), Michigan, Paper No 621. Brackstone, M. and McDonald, M. (2000), ‘Car following: an historical review’, Transportation Research Part F, vol 2, no 4, pp. 181-196. Champion, A. Mandiau, R. Kolski, C. Heidet, A. and Kemeny A. (1999), ‘Traffic generation with the SCANeR II simulator: towards a multi-agent architecture’, Driving Simulation Conference, DSC’99 Paris, France, pp. 311-324. Drew, D.R. (1968), ‘Traffic Flow Theory and Control’, (New York: McGraw-Hill). Dumbuya, A.D. and Wood, R.L. (2001), ‘A software model of visual perception as part of emergent behaviour in a synthetic driving simulator’, International Conference on Vision in Vehicles IX, Brisbane, Australia, (in press). Dumbuya, A.D. and Wood, R.L. (2003), ‘Visual perception modelling for intelligent virtual driver agents in synthetic driving simulation’, Journal of Experimental and Theoretical Artificial Intelligence (JETAI), vol 15, no 1, pp. 73-102. Dumbuya, A.D. Wood, R.L. and Thomas, P. (2002a), ‘A computational model of visual information processing: mechanisms underlying intelligent driver behaviour’, 33rd European Conference on Mathematical Psychology, Bremen, Germany, p. 20. Dumbuya, A.D. Wood, R.L. Gordon, T.J. and Thomas, P. (2002b), ‘An agent-based traffic simulation framework to model intelligent virtual driver behaviour’, Driving Simulation Conference (DSC’02), Paris, pp. 363-373. Gordon, T.J. Best, M.C. and Dixon, P.J. (2002), ‘An automated driver based on convergent vector fields’, Proceedings, of the Institution of Mechanical Engineers: Part D, Automobile Engineering vol 216, D4, pp. 329-347. Grayson, G.B. and Sexton, B.F. (2002), ‘The development of hazard perception testing’. (TRL Research Report 558). Transport Research Laboratory: Crowthorne, Berkshire, England. Iversen, H. and Rundmo, T. (2002), ‘Personality, risky driving and accident involvement among Norwegian drivers’, Personality and Individual Differences, vol 33, pp. 12511263.

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Lawton, R. (1998), ‘Not working to rule: Understanding procedural violations at work’, Safety Science, vol 28, pp. 77-95. Lieberman, E. and Rathi, A.K. (1997), ‘Traffic simulation’, Traffic Flow Theory: A State-ofthe-Art Report, Transport Research Board Special Report 165, (Virginia: TurnerFairbank Highway Research Centre), pp. 10-23. Maycock, G. Lockwood, C. and Lester, J. (1991), ‘The accident liability of car drivers’. (TRL Research Report 315). Transport Research Laboratory: Crowthorne, Berkshire, England. McCormack, S. (2003), ‘Recent developments in driver training’, 68th Road Safety Congress, Safer Driving Reducing Risks, Crashes and Casualties, Blackpool, pp. 1-10 McKenna, F.P. and Crick, J.L. (1994), ‘Hazard perception in drivers: a methodology for testing and training’, (TRL Contractor Report 313). Transport Research Laboratory: Crowthorne, Berkshire, England. McKnight, A.J. and Adams, B.B. (1970a), ‘Driver education task analysis. Volume I: Task descriptions’, Alexandria, VA: Human Resources Research Organisation, Final Report, (Contract No FH 11-7336). McKnight, A.J. and Adams, B.B. (1970b), ‘Driver education task analysis. Volume II: Task analysis methods’, Alexandria, VA: Human Resources Research Organisation, Final Report, (Contract No FH 11-7336). McKnight, A.J. and Hundt, A.G. (1971), ‘Driver education task analysis. Volume III: Instructional Objectives’, Alexandria, VA: Human Resources Research Organisation, Final Report, (Contract No. FH 11-7336). Michon, J.A. (1985), ‘A Critical view of driver behaviour models: What do we know, what should we do?’, in L. Evans and R. Schwing, (eds), Human Behaviour and Traffic Safety, (London: Plenum), pp. 516-520. Papelis, Y. (1996), ‘Graphical authoring of complex scenarios using high level coordinators’, Workshop on Scenario and Traffic Generation in Driving Simulation, Orlando, USA, pp. 3-10. Parkes, A.M. (2005), ‘Improved realism and improved utility of driving simulators: are they mutually exclusive?’, HUMANIST Workshop, Brno, (in press). Pursula, M. (1999), ‘Simulation of traffic systems – an overview of’, Journal of Geographic Information and Decision Analysis, vol 3, no 1, pp. 1-8. Quimby, A.R. and Watts, G.R. (1981), ‘Human factors and driving performance’. (TRL Research Report 1004). Transport Research Laboratory: Crowthorne, Berkshire, England. Rothery, R.W. (1997), ‘Car following models’, Traffic Flow Theory, (Transportation Research Board Special Report 165, Chapter 4). Ulleberg, P. (2002), ‘Personality subtypes of young drivers. Relationship to risk-taking preferences, accident involvement, and response to a traffic safety campaign’. Transportation Research Part F, vol 4, pp. 279-297. Wedge, T. (2002), ‘The future of driver training and testing in Great Britain’, 67th Road Safety Congress, Safer Driving – the Road to Success, Stratford upon Avon, Warwickshire, pp. 1-16. Wicky, C. Printant, P. Le Coadou, F. and McCormack, S. (2001), ‘Faros driving simulators for training: concepts, syllabus and validation’, Driving Simulation Conference, Sophia Antipolis, France, pp. 1-12. Wood, R.L. and Arnold, J.E. (1997), ‘An automata based simulation of co-operative decision making’, in R.A. Adey, G. Rzevski and R. Teti (eds) Applications of Artificial Intelligence in Engineering, (Southampton, UK: Computational Mechanics Publications), pp. 105-109.

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Wood, R.L. Dumbuya, A.D. Hill, J.R. Thomas, P. and Zhao, Q. (2003), ‘Simulation of driver, vehicle and environmental aspects of crash initiation - a new method to improve integrated safety effectiveness’, 18th International Conference on the Enhanced Safety of Vehicles, (ESV), Nagoya, Japan, Paper No 356.

Chapter 2

An Analysis of the National Driver Improvement Scheme by Referral Type Ian Edwards Kirklees Metropolitan Council, UK

Introduction Road deaths have remained stubbornly static for many years, at approximately 3500 a year. In 2000 the UK Government published a ten-year plan for road safety entitled ‘Tomorrow’s Roads – Safer For Everyone’. This set some ambitious targets; a forty percent reduction in the number of road deaths and serious injuries, a fifty percent reduction in children killed or seriously injured and a ten percent reduction in slight injury accidents. The same document advocated the wider use of retraining rather than punishment of drivers as one way to achieve these aims. The National Driver Improvement Scheme is a driver retraining scheme based on this type of educational approach. Background and History of the National Driver Improvement Scheme (NDIS) The NDIS is a court deviation scheme offered to drivers, by a participating police force, as an alternative to prosecution under Section 3 of the Road Traffic Act 1988. This Section of the Act is commonly referred to as driving without ‘Due Care’ and prosecution is generally for a minor accident (referred to in this chapter as an accident) or an observed act (referred to in this chapter as an incident). In 1991 Devon and Cornwell Constabulary and Devon County Council delivered the first NDIS course. Today the scheme is operated by most police forces in the United Kingdom. The scheme is open to all full licence holders and in the case of powered two wheel users (eg motorbikes and mopeds) also to provisional licence holders. The term driver used in this document applies to all the above road user categories.

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Course Content The delivery of the scheme is outlined in the Association of National Driver Improvements Scheme Providers (ANDISP) ‘Guidelines for Instructors’. This document identifies the schemes aims as improving a driver’s: • • •

Skills; Attitudes; Behaviour.

The course is a mix of theory and in-car practical driver training, delivered over one and a half days. The first morning covers most of the theory models with the remaining two half-day sessions focusing on practical driving training. The theory element of the course is split into three modules; these are: ‘In the Driving Seat’, ‘Hazard Recognition’ and ‘Crash Investigation’. The practical driving element of the course is less prescriptive than the theory element to allow the instructor to tailor the tuition to the needs of the client. This session is generally delivered on a ratio of one instructor to three clients. Theoretical Background to the Scheme Parker and Stradling (2001) have suggested that drivers learn and develop their driving in three distinct phases: technical mastery, reading the road and the expressive phase. It is in this final phase of development where drivers will start to commit violations. A violation is when a driver engages in behaviour that they know to be unsafe, possibly to gain a short-term advantage. Reason (1993 cited by Burgess, 2004) identified four types of driver violation, which are: Exceptional Violation: These happen when a driver is in a new situation, tend not to follow any planning and are generally not a routine element of a driving style. Situational Violation: These occur when the driver is placed under pressure by a situation or task that they feel is beyond their control. Optimising Violation: Here the driver’s aim is not safe driving but is to gain a thrill or gain the ‘upper hand’ over another driver. Routine Violation: This is where a driving pattern has become so ingrained that violation has become the driver’s normal behaviour, such as excessive or inappropriate speed and close-following. The NDIS attempts to reduce the occurrences of violations by addressing the skills, attitude and therefore, hopefully, the behaviour of the course participants.

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Attitude and Behaviour The NDIS syllabus aims to improve attitude in the hope that a positive change in attitude will encourage a long-term change in driving behaviour. This approach is in line with Ajzen’s (1988) Theory of Planned Behaviour (TPB), an outline of which is shown in Figure 2.1.

Figure 2.1 Ajzen (1988) Theory of Planned Behaviour However, the role of attitude is not this simple. Attitudes are not constant and can be influenced by many issues but, most importantly, context. These may include our mood, goals and the time and motivation we have in considering our attitude. Role of Motivation and Goal Setting Research (for an overview see Bohner and Wanke, 2002) has indicated that how we construct our attitudes will depend on the goals we are seeking. For example, a driver may believe that speed enforcement on the road where she/he lives should be strengthened by the introduction of a speed camera. However, when late for work she/he may feel that it is OK to speed to satisfy her/his goal of getting to work on time. Whilst this may only be a temporary shift of attitude, it may become a permanent feature if rewarded by success with no negative consequences, eg gets to work safely, in less time, without being caught and without having an accident.

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In other words, our behaviour has a substantial impact on the way we construct our attitudes. However, a group of theories generally grouped together under the heading of persuasion theory would indicate that attitude change could take place, depending on several variables. These include the strength of the message and the message source. Previous and Current Evaluation Burgess (Unpublished) in 1998 carried out an evaluation of the model developed by Devon County Council’s Road Safety Unit. This evaluation identified that attending an NDIS course had a significant effect on participants’ attitudes and self-reported behaviour over the three-month post-course period. However, participants who chose to engage in inappropriate behaviour pre-course still continued to do so after course completion, although the frequency of this behaviour decreased. Aims of the Research The aim of this research was to identify the types of accident and incident that led to referral and identify the outcomes of scheme attendance by accident/incident type. Although drivers can only be referred to the scheme under Section 3 of the Road Traffic Act (1988), this section has a wide definition and covers a variety of accident and incident types. It was anticipated that by correlating the different types of incident/accident with the course outcomes some conclusions might be drawn as to the appropriateness of the NDIS syllabus. Methodology Sample All the participants in the research attended a National Driver Improvement Scheme delivered by Kirklees Metropolitan Council. Kirklees Metropolitan Council provides the scheme on behalf of both the South and West Yorkshire Police Forces at two centres located in Barnsley and Huddersfield. The data used in this survey was collected over a period from 1 May 2004 to 25 July 2004. Of the 150 NDIS participants contacted, ninety-nine (N= 99) completed at least one of the measures, giving a return rate of sixty-six percent.

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Based upon the total number of t1 questionnaires returned (N=81) the sample consisted of 80.2 percent (sixty-five participants) males and 19.8 percent (sixteen participants) female. The mean age of the participants was 42.31, with the youngest being eighteen and the oldest being eighty-two; (SD = 17.069). An independent sample t-test showed no significant differences in sample by age and gender. Driving Experience Participants who had been driving for less than four years accounted for 13.8 percent of referrals, a percentage that is substantially less than previous evaluations have shown. Burgess’ (Unpublished work, 1998) evaluation of the scheme found that twenty-five percent of course participants had held a licence for less than two years, with twenty percent falling into the seventeen to twenty-one year age group, compared to only 9.88 percent in this study. Just over 27.5 percent of clients attending a course had been awarded points in the previous three years. Measures Two quantitative measures were used in the evaluation, a questionnaire and an observation measure. Questionnaire Measure A questionnaire was sent to the participants two weeks prior to attending the course. The participants handed in the completed questionnaire on initial arrival of the first day of the course. Immediately post-course, a second questionnaire was issued and completed prior to the participant leaving at the end of the second day. The questionnaires used incorporated the following: • • • • • • • •

Demographic information; A section on the specific incident/accident that had led to referral to the Scheme; Questions on driving history and involvement in adverse traffic events; A shortened version of the Driver Behaviour Questionnaire (DBQ) (Parker et al, 1995); The Driver Attitude Questionnaire (DAQ) (Parker et al, 1996); Drugs and Driving; Questions on the client’s own views of the National Driver Improvement Scheme and the referral system; Thrill and Adventure seeking subscale of the Zuckerman (1983) Sensation Seeking Scale.

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Observation Measure The observation measure used two still photographs, which contained several items of information that would be of use to a driver. One of the photographs was shown just before the beginning of the course and the other was shown at the end of the course, each for ten seconds. A PowerPoint projector was used to display the photos to all participants at the same time. Before the picture was shown the following message was displayed and read aloud by the presenter: A picture will be shown to you for ten seconds. Please look at it carefully and then write down all the information that would be useful to a driver. The participants were given a form to use to complete the exercise. On each course the picture order was reversed to counteract any order effect. Results Type of Incident/Accident that Led to Referral Based on all returned t1 questionnaires (N=81), most participants had been referred to the scheme as a result of an accident rather than an incident, with 97.5 percent of the referrals being for an accident. The majority of these, 73.9 percent, had been in collision with a substantial vehicle eg car, van, minibus, LGV, bus. The remaining 24.6 percent had been in collision with what road safety professionals would describe as a vulnerable road user. This term covers pedestrians, cyclists and powered two wheel vehicles. The largest percentage of referrals occurred on single carriageway roads, at 64.1 percent. Dual carriageway referrals accounted for 11.1 percent, motorways accounted for 6.4 percent and one-way systems for 2.6 percent. A further 12.8 percent of referrals occurred on roads where the participants were unable to specify the road type. The majority of referrals (61.8 percent) occurred on roads that carried a speed limit of thirty mph. A further 6.6 percent occurred on a road with a twenty mph speed limit. Collectively, these speed limits accounted for 68.4 percent of the accidents/incidents. A further 13.2 percent occurred in forty mph zones. It is important to remember that the course is aimed at minor offences and that high speed crashes may have resulted in serious injury and therefore may be outside of the criteria for referral. This could explain why the higher speed limits accounted for low levels of referral: fifty mph (5.3 percent), sixty mph (6.6 percent) and seventy mph (6.6 percent). A significant (p < .05) correlation between the age of drivers and the speed limit on which the accident/incident took place was identified (r = -.230, N = 76). This correlation indicated that the younger the driver, the higher the speed limit that applied to the road upon which the accident/incident took place.

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The largest single percentage (25.3 percent) of collisions occurred on what the participants regarded as straight roads. Not surprisingly junctions collectively, eg roundabouts, ‘T’ junctions and crossroads, account for a high percentage (55.7 percent) of the accident of the locations. Only five percent of the accidents occurred on bends with the remaining fourteen percent of referrals occurring at unspecified locations. Weather conditions do not appear to have played a major role in the majority of referrals, with 77.5 percent indicating the weather to be fine. However 61.7 percent of referrals took place in what the participants regarded as poor light conditions. Self-Assessment of Driving Ability Using a five-point scale, rating from very poor to very good, the participants were asked to rate their own driving ability. Pre-course, 72.8 percent rated their driving as ‘good’ or ‘very good’. This had reduced to sixty percent post-course. A comparison of pre- and post-course mean scores, based upon the number of paired t1 and t2 (N = 66) questionnaires, showed a significant reduction in the mean scores from 3.79 (SD = .691) pre-course to 3.44 (SD = .611). A paired sample ttest gave the following results SD = .712, t= 3.974, df =65, p=.0005. As a lower mean score shows a reduction in participants’ self-rating of driver ability, it appears that course attendance makes the participants more likely to rate their driving ability lower. Responsibility and Avoidability Pre- and post-course the participants were asked to rate how much responsibility they felt they had in the accident/incident that had led to referral and how much they felt they could have done to avoid the situation. Table 2.1 shows the results. Table 2.1 Pre- and Post-Course Scores for Responsibility and Avoidability Item (Pre-course) How responsible did the participant feel for the accident/incident (Post-course) How responsible did the participant feel for the accident/incident (Pre-course) How much more did the participant feel they could have done to avoid the accident/incident (Post-course) How much more did the participant feel they could have done to avoid the accident/incident

Mean 2.98

N 61

SD 1.284

3.61

61

1.307

3.05

62

1.453

3.84

62

1.308

In both responsibility and avoidability the post-course mean scores increased. This suggests that post-course the participants felt more responsible and believed

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that they could have done more to avoid the accident/incident that led to referral. A paired sample t-test showed that this finding was statistically significant for both variables (Tables 2.2 and 2.3). Table 2.2 Paired Sample T-Test Results for Pre- and Post-Course Responsibility Scores Pair

Mean SD

t=

df

Sig. (2-tailed)

(Pre-course) How responsible did the client feel for the accident/incident (Post-course) How responsible did the client feel for the accident/incident

-.62

.897 -5.421 60

.000

Table 2.3 Paired Sample T-Test Results for Pre- and Post-Course Avoidability Scores Pair

Mean SD

t=

df

Sig. (2-tailed)

(Pre-course) How much more did the client feel they could have done to avoid the accident/incident (Post-course) How much more did the client feel they could have done to avoid the accident/incident

-.79 1.203 -5.173 61

.000

Driver Behaviour Questionnaire (DBQ) A weak but significant ((r = .323, p