Can object recognition theories explain visual search failures at junctions?

Martin Langham Applied Psychology Group Looking and failing to see, or gazing and failing to cognise appropriately? "Looked but failed to see error" refers to a set of circumstances where a driver accounts for an accident in the terms of failing to notice an approaching vehicle. Often thought as a prototypical form of such accidents are collisions between motorcycles and cars. Research into causality of motorcycle accidents tend to make two basic assumptions. Firstly, that the offending driver looks but then fails to see the motorcyclist. Secondly, that this failure can be explained in terms of the relative lack of conspicuity of the motorcyclist when compared to other road users. Are the physical properties of the approaching object the sole determiner of its successful detection, or does the psychological state of the driver have a role? This research therefore attempts to explain motorcycle accidents within the framework of psychological models of visual search, selective attention and expectancy. My work can be described as eclectic. My current research investigates how a driver through experience develops an efficient search strategy enabling them to emerge onto a main road. This research involves the measurement of driver eyemovements in a laboratory environment. Other ongoing research involves collaborative work with Sussex police investigating driver speed perception. Can object recognition theories explain visual search failures at junctions? Drivers' visual search at junctions can be very rapid. Models of human object recognition, such as Biederman's Recognition By Components (RBC) theory, provide a theoretical account for how we can rapidly comprehend the semantics of a scene, despite short display times. Can such theories account for failures in driver's search at junctions? Inspired by RBC theory, this laboratory study attempts to understand changes in search behaviour by drivers at differing levels of experience. Each subject watched video clips of an approaching vehicle in a traffic scene, as viewed from the perspective of a driver at an intersection. In one of the clips, the vehicle was replaced by an animated shape. This was either a vertical bar moving in the same way as a motorcycle; a horizontal bar moving in the same way as a car; or a pixelated "rippling" distortion. Drivers' decision times were recorded in response to differing search instructions: they were asked to search either for a motorcycle or for any motor vehicle. Experienced drivers - but not non-drivers or inexperienced drivers - treated the animated shapes as vehicles, provided the search instructions matched the shapes orientation. Thus if the shape was a vertical bar and the instructions were to search for a motorcycle, experienced drivers were more likely to believe that a vehicle was present than if they had been asked to search for any vehicle. An unexpected finding was that police traffic officers treated the "rippling" distortion as a cue to a vehicle's presence; possibly movement is all that is required by such highly trained drivers. We suggest that with experience, drivers may develop shorter search times at junctions and may extract from complex traffic scenes only a minimal amount of information, based on prior expectancies about what they are likely to see.

2 `Looking and Failing To See Error' The cost of experience? Abstract The Transport and Road Research Laboratory coined the phrase `looked but failed to see error' referring to a set of circumstances where a driver accounts for an accident in the terms of failing to see. A prototypical form of such accidents involves collisions between motorcycles and cars. Motorcycle accidents tend to involve another road user who often claims not to have seen them in time to avert a collision. Research into the causes of motorcycle accidents has tended to make two basic assumptions: firstly, that the offending driver actually looks but then fails to see the motorcyclist,. secondly, that this failure can be explained in terms of the relative lack of conspicuity of the motorcyclist. This laboratory experiment suggests an alternative explanation for such accidents. Experienced and inexperienced drivers viewed video tape clips of approaching traffic at intersections. Subjects' eye movements were recorded in response to different search instructions. Under the conditions of this experiment, experienced drivers appear to use 'pre-programmed' search patterns directed towards areas of the road environment which are informationally rich; there was little evidence of these in the eye-movements of inexperienced drivers. Experienced drivers appeared to start their search at a midpoint in the scene whist inexperienced drivers started their search nearby. One consequence of this was that experienced drivers took longer to detect motorcyclists who were nearby. (i.e. to the left of the initial point of fixation.) University of Sussex School of Cognitive and Computing Sciences An investigation of the role of vehicle conspicuity in the Looked but failed to see error in driving by Martin Paul Langham Thesis presented for Doctor of Philosophy

3 I hereby declare that this thesis has not been submitted either in the same or different form, to this or any other university for a degree Signature

4 Abstract Accidents involving motorcycles tend to occur at intersections where a car driver claims to have looked but failed to have seen them. Motorcycle accidents accounted for by the looked but failed to see error'(l.b.f.s.) are traditionally explained in terms of the motorcyclists relative lack of conspicuity compared to cars. After reviewing the literature and assessing the diverse methods available to investigate the L.B.F.S. error this thesis refines previous experimental methodologies. Evidence is then gathered from a police accident database, and by laboratory and field studies to support the view that poor physical conspicuity is not the only explanation for motorcycle L.B.F.S. accident. The theoretical stance taken is that driver expectancies may account for, at least in part, some of these accidents and that the functioning that human attentional systems during driving may aid the understanding of this error. Experimental chapters investigate the amount of time drivers search at junctions and consider if object recognition theories can account for possible detection failures of uncommon vehicles such as motorcycles. These studies are complemented by investigations of driver eye-movements in the laboratory which compare detection performance of novice, experienced and expert drivers. Results suggest that experienced drivers adapt through time to perform driving as possibly an automated process neglecting uncommon vehicles that may be encountered on the highway, even if they are conspicuous in sensory terms. To illustrate this, the final experimental chapter reports accidents involving conspicuous police vehicles which are hit by drivers who claim that they did not see them. Tentative conclusions drawn suggest that experience need not mean expertise and that the L.B.F.S. error may be a cost of experience.

5 Acknowledgements I gratefully acknowledge the support of my supervisor Dr. Graham Hole who has kept me on the straight and narrow during this thesis. I wish to thank the officers of the Sussex Constabulary for their patience as participants and for listening quietly at the quarterly reviews, in particular Colin O Neil, Albert Mariner and the accident statistics sections. I wish to thank the Institute of Traffic Accident Investigators for co-ordinating data collection. I gratefully thank Professor Mike Land for his support and allowing his name to be used on conference papers. I would also like to thank Professors Hancock and Sivak at the earlier stages of the work for directing me away from areas they had already investigated and to Professor Irving Biederman for his comments on Chapter 7. Thanks to the participants who took part in my experiments. Thanks to my editor Tricia Roussel who meant my dyslexia should not distract too much from the contents. Above all, thanks to Laura who allowed me to stop full time employment and investigate an idea from my undergraduate project.

6 Contents ABSTRACT 4 ACKNOWLEDGEMENTS 5 CONTENTS 6 LIST OF FIGURES 14 LIST OF TABLES 16 FOREWORD 17 1. GENERAL INTRODUCTION AND LITERATURE REVIEW 19 1.1 Introduction 19 1.1.1 The Looked but failed to see error 19 1.1.2 Two types of L.B.F.S. error 20 1.2 Motorcycles and the L.B.F.S. error 21 1.2.1 Accident Data 21 1.3 Current explanation of the L.B.F.S. motorcycle accident 23 1.3.1 A working definition of conspicuity 23 1.3.2 Previous Motorcycle conspicuity research 23 1.3.3 Other vulnerable road users 24 1.4 Reasons to doubt the conspicuity hypothesis as the sole explanation - the need for research 24 1.4.1 A simple problem with physical conspicuity 24 1.4.2 Detection and accident potential 25 1.4.3 Late detection is not just a problem for motorcycles. 25 1.4.4 Conspicuity enhancers do not guarantee detection 25 1.5 Alternative explanations of the L.B.F.S. error 27 1.5.1 Blockages in the visual field 27 1.5.2 Judgement errors 27

7 1.5.3 Speed Estimation and Time to Collision 28 1.6 The position so far 28 1.7 A more sophisticated understanding of the L.B.F.S. error. 29 1.7.1 Is conspicuity solely determined by signal-to-noise ratios? 29 1.7.2 Expectancy and awareness of drivers 30 1.8 Towards a more sophisticated understanding of human perception 31 1.8.1 Cognitive style 32 1.8.2 Vision 32 1.8.3 Vision direct or constructed 32 1.8.4 Central versus peripheral vision. 33 1.8.5 Periphery filtering and motorcycle accidents? 33 1.8.6 Driver eye-movements 34 1.8.7 Attention 36 1.8.8 Conclusion 40 1.8.9 Experience and expertise 41 1.9 Motorcycle accidents considered in relation to an information processing model 42 1.9.1 Model introduction 42 1.9.2 What happens at each stage? 44 1.10 Conclusion 45 1.10.1 Issues in methodology 46 2. MEASURING THE RARE EVENT: ISSUES IN METHODOLOGY, AND MEASUREMENT 47 2.1 Introduction. 47 2.1.1 Converging methods. 47 2.1.2 Methodology: Applied and Basic Research 47 2.2 Accident Statistics 48 2.2.1 Overview 48 2.2.2 Sources and techniques 48 2.2.3 Accident data and conspicuity 49 2.2.4 Culpability 50 2.2.5 Sources of accident data 50 2.2.6 Accuracy in databases 51 2.2.7 Post accident interviews and forensic studies 51 2.2.8 Conclusion accident and forensic data 51

8 2.2.9 Accident data in this thesis 51 2.3 Laboratory studies 52 2.3.1 Overview and case example 52 2.3.2 Laboratory studies in general 54 2.3.3 New technology, Video and film. 55 2.3.4 Driving simulators. 55 2.3.5 Laboratory Measures 55 2.3.6 Alternative measures 57 2.3.7 Experimental design 59 2.3.8 Laboratory studies in this thesis 60 2.4 Field Studies 60 2.4.1 Overview 60 2.4.2 Gap acceptance 60 2.4.3 Other methods used in the field 62 2.4.4 Observation studies 62 2.4.5 Conclusion. 62 2.5 Conclusion 63 3. WHAT CAN ACCIDENT STATISTICS TELL US ABOUT THE L.B.F.S. ERROR? 65 3.1 Introduction 65 3.1.1 Relevance to previous accident database studies 65 3.2 Method 66 3.2.1 Period of statistical knowledge. 66 3.2.2 Where do the data come from? 66 3.2.3 The contents of these statistical databases 66 3.3 Methods of data analysis 67 3.3.1 Strada Analysis by accident cause code 67 3.3.2 Geo-location software search 67 3.3.3 Method of Detailed Analysis 67 3.4 Results and Discussion 68 3.4.1 Do Motorcycle accidents still occur? 68 3.4.2 Motorcycles are uncommon in traffic 69 3.4.3 Motorcycle accidents involve another road user 70 3.4.4 Accidents happen at intersections 71

9 3.4.5 Accidents occur when the vehicle is close to home 73 3.4.6 The Offending Driver 73 3.4.7 Conspicuity enhancers 75 3.4.8 Pre-accident speed and Time to collision 77 3.5 Problems with accident statistics - quality, collection methods, biases, and sources 77 3.5.2 Use of Fatal Accident Files 78 3.5.3 The accident scenario is dependent on the source of data 78 3.5.4 Snapshot of Social Trends 79 3.6 General Discussion 79 3.6.1 Use of accident statistics as evidence of the L.B.F.S. error 79 3.7 Conclusion 80 4. REACTION TIME AND TIME TO DETECT. HOW LONG DO DRIVERS SEARCH AT A JUNCTION? 82 4.1 Introduction 82 4.1.1 Visual Search 82 4.1.2 How long do drivers search at a junction? 83 4.1.3 Display time from previous motorcycle studies 83 4.1.4 Deriving display times from typical reaction times 84 4.1.5 Intersections and searching 85 4.1.6 Issues in Methodology 86 4.2 Method 86 4.2.1 Procedure 86 4.2.2 Selection of the site 87 4.3 Results 87 4.3.1 Analysis and calibration 87 4.3.2 Inter-rater reliability 88 4.3.3 Treatment of data 88 4.3.4 Search Time 89 4.3.5 Distance from the junction at which drivers looked and searched for hazards 89 4.3.6 Age of the driver 89 4.4 Discussion 90 4.4.1 Stimulus display in the laboratory 90 4.4.2 Rapid visual search - attention for objects or spatial locations 91 4.4.3 Problems 92

10 4.4.4 Improvements 92 4.5 Conclusion 93 5. EXPERIMENTAL DESIGN - INSTRUCTIONS TO PARTICIPANTS. EXPECTANCY AND VISUAL SEARCH. 94 5.1 Introduction 94 5.1.1 Theories of Search, based on Laboratory Research. 94 5.1.2 Search for motorcycles 95 5.1.3 Cues to location by instructions to participants 95 5.1.4 Cues for the what to look for 96 5.1.5 Conceptions of Visual Search in Applied Research 97 5.1.6 Expectancy 97 5.1.7 Issues in Design. 98 5.1.8 Implementation 98 5.2 Experiment 1 - Repeated Measures 99 5.2.1 Method 99 5.2.2 Results 100 5.2.3 Discussion 103 5.3 Experiment 2 - Independent Measures 103 5.3.1 Method 103 5.3.2 Results 105 5.3.3 Treatment of data. 105 5.3.4 Discussion 106 5.4 Overall Discussion 106 5.4.1 Issues in conspicuity. 106 5.4.2 Errors 107 5.4.3 Reaction time - laboratory and real world. 107 5.4.4 Measures 107 5.4.5 Expectancy 109 5.4.6 Improvements for Future Work 109 5.4.7 What Next 110 5.5 Conclusion 110 6. WHERE DO DRIVERS LOOK AT INTERSECTIONS? 111 6.1 Introduction. 111

11 6.1.1 Eye-movements 112 6.1.2 Driver scanning patterns 113 6.1.3 Driving and eye-movements. 114 6.1.4 Implementation 115 6.2 Method 115 6.2.1 Pilot Study 115 6.2.2 Design: 115 6.2.3 Participants: 116 6.2.4 Stimuli 116 6.2.5 Apparatus 117 6.2.6 Procedure 118 6.3 Results 119 6.3.1 Method of Initial analysis 119 6.3.2 Errors in detection 119 6.3.3 Treatment of results 119 6.3.4 Analysis of Search Patterns 120 6.3.5 Where do participants look? 121 6.3.6 Effects of experience EM and EV compared with IM and IV 121 6.4 Discussion 127 6.4.1 Patterns and eye-movement 127 6.4.2 Informationally Rich or Contour Complex? 128 6.4.3 Implications for the L.B.F.S. error 128 6.4.4 Do drivers search the entire field of view? 128 6.4.5 Do different types of junction elicit different types of search? 129 6.4.6 Effects of Instruction 130 6.4.7 Later replication 130 6.4.8 Problems 131 6.5 Conclusion 131 7. CAN OBJECT RECOGNITION THEORIES EXPLAIN THE L.B.F.S. ERROR? 133 7.1 Introduction 133 7.1.1 Scene and object recognition. 133 7.1.2 Low spatial frequencies and coarse descriptors 136 7.1.3 Recognition By Components 136 7.1.4 Implementation 137

12 7.2 Method 139 7.2.1 Design 139 7.2.2 Participants 139 7.2.3 Stimuli 139 7.2.4 Geon generation 139 7.2.5 Apparatus 142 7.2.6 Procedure 142 7.3 Results. 143 7.3.1 Treatment of Results. 143 7.3.2 Reaction time 143 7.3.3 Detection success 146 7.3.4 Detection of Deception 147 7.4 Discussion 149 7.4.1 Key findings 149 7.4.2 RBC, low spatial frequencies and rapid search 150 7.4.3 Problems and improvements 151 7.4.4 Ecological Validity and the L.B.F.S. error 151 7.4.5 Is the L.B.F.S. error a cost of experience? 151 7.4.6 Later replication 151 7.5 Conclusion 152 8. CAN THE L.B.F.S. ERROR INVOLVE HIGHLY CONSPICUOUS POLICE VEHICLES? THE POSSIBLE ROLE OF CONSPICUITY, VIGILANCE AND THE FALSE HYPOTHESIS. 153 8.1 Introduction 153 8.1.1 Visual size and the L.B.F.S. error 153 8.1.2 The Problem 154 8.1.3 What does vigilance research tell us about this? 154 8.1.4 What do vigilance failures by other transport operators tell us about this? 156 8.1.5 Experimental Design 157 8.2 Study 1 - Accident data 158 8.2.2 Method 158 8.2.3 Results 159 8.2.4 Discussion 163 8.3 Study 2 - Laboratory Investigation 164 8.3.1 Method 164

13 8.3.2 Results 169 8.3.3 Discussion. 170 8.4 General Discussion 170 8.4.1 Later Replication 171 8.4.2 Expectancies and the false hypothesis 171 8.4.3 The wrong type of conspicuity enhancer? 172 8.4.4 Alternative explanations 172 8.4.5 Further improvements, recommendations for further study 173 8.5 Overall Conclusion 173 9. CONCLUSION 174 9.1 The aim of this research 174 9.2 Key Findings 174 9.3 Limitations 178 9.3.1 The problem of generalisation 178 9.3.2 Age and experience 178 9.3.3 Eye-movements 178 9.4 Laboratory based Psychology 179 9.4.1 Visual Selective Attention - the Type 1 error 179 9.4.2 Vigilance and the Type 11 error 180 9.4.3 Methodology 180 9.5 Further Research 181 9.5.1 Methods, machines and larger samples 181 9.5.2 Folk Psychology - the need for communication 181 9.6 Final Remarks 182 10. REFERENCES 183

14 List of Figures FIGURE 1-1 THE THREE MOST COMMON FORMS OF MOTORCYCLE INTERSECTION ACCIDENTS.... 22 FIGURE 1-2 THE POSSIBLE STAGES OF AN INFORMATION PROCESSING MODEL OF A DRIVER AT A JUNCTION,... 43 FIGURE 3-1 NUMBER OF MOTORCYCLES REGISTERED AND NUMBER OF FATAL MOTORCYCLE ACCIDENTS... 68 FIGURE 3-2 ROAD TRAFFIC BY TYPE OF VEHICLE AND DISTANCE TRAVELLED... 69 FIGURE 3-3 INVOLVEMENT OF OTHER ROAD USERS IN MOTORCYCLE ACCIDENTS INVOLVING INJURY IN SUSSEX... 70 FIGURE 3-4 ACCIDENT INVOLVEMENT RATES RATE PER 100 MILLION VEHICLE KILOMETRES SOURCE CASUALITY REPORT... 71 FIGURE 3-5 UK ACCIDENT CAUSALITY BY VEHICLE TYPE AND MANOEUVRE (SOURCE CASUALITY REPORT 1996).... 72 FIGURE 3-6 PERCENTAGE OF ALL ACCIDENTS BY THE AGE OF THE OFFENDING DRIVER IN SUSSEX FOR INTERSECTION ACCIDENTS... 74 FIGURE 3-7 ACCIDENTS INVOLVING POLICE MOTORCYCLISTS ON GENERAL DUTIES... 76 FIGURE 4-1 PLAN OF THE SITE. WHERE THE OBSERVATIONAL STUDY TOOK PLACE... 86 FIGURE 5-1 REACTION TIME IN MILLISECONDS TO DETECT A MOTORCYCLE AT DIFFERENT DISTANCES FROM VIEWER, SEARCH INSTRUCTIONS AND LIGHTING CONDITION... 101 FIGURE 5-2 REDUCTION IN REACTION TIME COMPARING THE FIRST AND SUBSEQUENT TIMES THE SUBJECT SAW A VIDEO CLIP CONTAINING A MOTORCYCLE FOR PARTICIPANTS WHO SEARCHED SPECIFICALLY FOR A MOTORCYCLE OR ANY HAZARD.... 102 FIGURE 5-3 EFFECTS OF LIGHTING DISTANCE AND QUESTION ON REACTION TIMES... 105 FIGURE 6-1 SUSSEX MK3 EYE-TRACK EQUIPMENT... 118 FIGURE 6-2 MEAN NUMBER OF SACCADES TO EACH VIDEO CLIP FOR EXPERIENCED DRIVERS.... 119 FIGURE 6-3 MEAN NUMBER OF SACCADES TO EACH VIDEO CLIP FOR INEXPERIENCED DRIVERS.... 119 FIGURE 6-4 AN EXAMPLE OF ONE EM SUBJECT S VISUAL SEARCH PATTERN AT AN INTERSECTION. THE SHADED BOXES REPRESENT ALL POINTS WHERE THIS SUBJECT FIXATED FOR THAN TWO VIDEO FRAMES DURING THE ENTIRE TWO SECOND DISPLAY.... 121 FIGURE 6-5 AN EXAMPLE OF ON IM SUBJECT VISUAL SEARCH PATTERN AT AN INTERSECTION... THE SHADED BOXES REPRESENT ALL POINTS WHERE THIS SUBJECT FIXATED FOR THAN TWO VIDEO FRAMES DURING THE ENTIRE TWO SECOND DISPLAY.... 122 FIGURE 6-6 A SUMMARY OF EV SEARCH PATTERNS AT A T JUNCTION..... 122 FIGURE 6-7 SUMMARY OF IV SEARCH PATTERNS AT A T JUNCTION.... 123 FIGURE 6-8 A SUMMARY OF ALL EXPERIENCED DRIVERS FIXATION PATTERNS IN RESPONSE TO A MINI ROUNDABOUT..... 124 FIGURE 6-9 A SUMMARY OF ALL NOVICE DRIVERS FIXATION PATTERNS IN RESPONSE TO A MINI ROUNDABOUT..... 124

15 FIGURE 6-10 THE FIXATION PATTERN OF TWO REPRESENTATIVES, ONE OF EACH CONDITION AT A ROUNDABOUT..... 125 FIGURE 6-11 THE FIXATION PATTERN OF AN EXPERIENCED DRIVER WHO HAS BEEN INSTRUCTED TO SEARCH FOR A MOTORCYCLE... 126 FIGURE 6-12 THE FIXATION PATTERN OF AN NOVICE DRIVER WHO HAS BEEN INSTRUCTED TO SEARCH FOR A MOTORCYCLE..... 126 FIGURE 7-1 RECOGNITION BY COMPONENTS (FROM BIEDERMAN 1987)... 136 FIGURE 7-2 THE VERTICAL GEON CLOSE TO THE JUNCTION AND REPRESENT ITS POSITION IN THE FINAL VIDEO FRAME 13 METRES AWAY FROM THE VIEWER... 140 FIGURE 7-3 THE HORIZONTAL GEON AT THE FIRST VIDEO FRAME APPROXIMATELY 75 METRES AWAY FROM THE VIEWER... 140 FIGURE 7-4 EXPERIENCED AND INEXPERIENCED DRIVERS MEAN REACTION TIMES TO DETECT THE MANIPULATED IMAGE... 144 FIGURE 7-5 PERCENTAGE OF YES AND NO RESPONSES FOR EXPERIENCED AND INEXPERIENCED DRIVERS ON SEEING THE MANIPULATED IMAGE... 146 FIGURE 7-6 SHOWS THE PERCENTAGE OF THOSE WHO DETECTED THE DECEPTION IN BOTH EXPERIENCE AND INEXPERIENCED DRIVERS... 148 FIGURE 8-1 THE REAR OF A POLICE MOTORWAY PATROL CAR,... 153 FIGURE 8-2 MOTORWAY ACCIDENTS INVOLVING POLICE CARS BY TIME OF DAY... 161 FIGURE 8-3 FREQUENCY OF ACCIDENTS BY THE AGE OF THE OFFENDING DRIVER FOR COLLISION WITH STATIONARY POLICE VEHICLES ON MOTORWAYS... 161 FIGURE 8-4 THE AGE OF THE OFFENDING DRIVER FOR CONSPICUOUS VEHICLE ACCIDENTS IN THE URBAN ENVIRONMENT... 162 FIGURE 8-5 PLAN OF ROLLING ROAD BLOCK SYSTEM USED TO COLLECT VIDEO STIMULUS MATERIAL... 164 FIGURE 8-6 PLAN OF SITE WHERE LABORATORY STIMULUS MATERIAL WAS OBTAINED... 166 FIGURE 8-7 EXAMPLE OF STIMULUS MATERIAL- AN ECHELON PARKED VEHICLE... 167

16 List of Tables TABLE 1-1 POSSIBLE CAUSES OF THE LOOKED BUT FAILED TO SEE ERROR FROM HILLS 1980 (P 185).... 20 TABLE 2-1 DIVERSE SOURCES IN DATA COLLECTION... 48 TABLE 2-2 WHAT DOES A KEY DEPRESSION MEAN?... 56 TABLE 3-1 PERCENTAGE OF ACCIDENT INVOLVEMENT BY JUNCTION TYPE... 71 TABLE 3-2 PERCENTAGE OF ACCIDENTS BY VEHICLE AND JUNCTION TYPE IN SUSSEX... 72 TABLE 3-3 A COMPARISON OF MOTORCYCLE MANOEUVRES WITH CAR MANOEUVRES FOR ACCIDENTS IN SUSSEX... 73 TABLE 3-4 PRIMARY CAUSE OF ACCIDENTS BY CAUSE CODE ANALYSIS OF INTERSECTION ACCIDENTS IN SUSSEX... 73 TABLE 3-5 SOURCES OF ACCIDENT DATA AND THE IMPRESSION THEY GIVE OF THE L.B.F.S. ERROR... 78 TABLE 4-1 DISPLAY TIME OF STIMULUS MATERIAL IN LABORATORY STUDIES EXAMINING ROAD SAFETY ISSUES.... 84 TABLE 4-2 THE NUMBER OF HEAD TURNS MADE BY DRIVERS AT THE OBSERVED INTERSECTIONS... 88 TABLE 4-3 THE AMOUNT OF TIME IN SECONDS LOOKING TO THE RIGHT.... 89 TABLE 4-4 PERCENTAGE OF DRIVERS WHO BEGAN SEARCHING AT GIVEN DISTANCES FROM THE JUNCTION... 89 TABLE 6-1 SUMMARY OF ALL SUBJECTS MEAN TOTAL DEGREES OF ARC FROM THE INITIAL FIXATION POINT WHEN SEARCHING... 120 TABLE 6-2 COMPARISON OF FOUR DRIVERS (TWO FROM EACH CONDITION) MEAN TOTAL DEGREES OF ARC... 120 TABLE 8-1 SUMMARY OF ACCIDENT DATA FROM THE NATIONAL SAMPLE OF MOTORWAY L.B.F.S. ERROR ACCIDENTS... 160 TABLE 8-2 DEFINITION OF CAUSE CODES... 160 TABLE 8-3 MEAN TIME TO DETECT THE STATIONARY POLICE-CAR IN THE FINAL VIDEO-CLIP:... 169 TABLE 8-4 MEAN TOTAL NUMBER OF KEY DEPRESSION FOR PARTICIPANTS FOR ALL SIX CLIPS... 169 TABLE 8-5 MEAN NUMBER OF FALSE POSITIVE RESPONSES TO EACH VIDEO CLIP (ALL PARTICIPANTS)... 170

17 Foreword Why does a car driver emerge from a junction and hit a motorcyclist, who was there to be seen, claiming that he looked but failed to see (L.B.F.S. error) the motorcyclist? The explanation up to now has been that motorcyclists present a particular problem in their detection by car drivers because, compared to cars, they are relatively inconspicuous. Previous research has taken the driver s statement that s/he looked but did not see literally. Because the driver claimed s/he did not see the motorcycle, and this was the only reason for the accident, the solution has been to make the motorcyclist as physically conspicuous as possible. However, some argue that physical conspicuity has very little to do with accident potential, as it is unlikely that any accident has a single exclusive cause. Several alternative explanations have been suggested together with a critique of previous methodology. Methodological weaknesses have included an over-reliance on accident statistics without thorough investigation of the facts, dubious laboratory methods that poorly reflect the task of a driver, and experimental designs which do not have the rigour of other psychological research investigating visual search and human attentional mechanisms. Limited alternative explanations to why drivers look but do not see suggest that the motorcycle may be seen by the car driver but, because of its rarity on the road, the car driver fails to recognise it as a motorised vehicle. Others point to the fact that motorcycles can appear in parts of the road environment where one would not expect to see a car, and have consequently argued that the car driver does not look in the appropriate place. Finally the driver may not see the motorcycle simply because it is small. This thesis investigates these three alternative explanations after modifying and improving previous methodology. The literature suggesting that physical conspicuity is the only answer to the L.B.F.S. problem is investigated and complemented with a review of human visual attention performance. A processing model is developed to understand how the limits of human attention may account for the error (Chapter 1). Whilst the single explanation for this type of accident is evident, diverse methods have been adopted and are reviewed (Chapter 2). One of these previous methods has been to cite evidence from local police accident databases to support the claim that drivers truly look but fail to see. The Sussex Police Accident Database is examined to substantiate previous claims and aid in the construction of laboratory stimulus materials (Chapter 3). A further critique of previous methodology is that of stimulus display time. An observational study of driver behaviour at intersections reports on the appropriate display time needed for the laboratory (Chapter 4). Previous research has typically used a repeated measures design showing film slides containing many motorcycles to the subject, whilst asking the subject to specifically search for a motorcycle. Motorcycles are however relatively rare on the road and drivers don t search just for motorcycles. Previous methodology is thought to be ecologically unsound (i.e. it poorly reflects the task of the driver) and Chapter 5 contrasts experimental designs and instructions given to participants. Alternative explanations for the L.B.F.S error indicate that a driver fails to recognise the motorcycle as a motorised vehicle. What is a driver expecting to see and how does s/he recognise vehicles for what they are in a dynamic

environment at a glance? Perhaps object recognition theories can help (Chapter 7). If an alternative explanation is that drivers are searching in the wrong place to successfully detect a motorcycle, then where do drivers look (Chapters 6)? Finally if motorcycles are not detected because of their small visual size, are large objects also involved in L.B.F.S accidents? Chapter 8 reviews cases of car drivers failing to see highly conspicuous stationary police motorway patrol cars. Chapter 9 provides a conclusion. 18

19 1. General introduction and literature review 1.1 Introduction In terms of human evolution driving is a relatively recent activity. Yet, after comparatively basic training, we can manoeuvre a motor vehicle at speed with apparent ease. However, crashes do occur and accident statistics show that in the EU as a whole there are some 45,000 fatalities per year (ETSC 1997). Since the mid 1950s the understanding of motor vehicle accidents has taken a multi-disciplinary approach involving engineers, physiologists and police officers. In more recent times the understanding of driver error has received interest from psychologists (e.g. Shinar 1985). Although driving a motor vehicle is a familiar task, it often tests our perceptual and cognitive abilities to the limits of human performance, and for this reason the skills required to drive a motor vehicle have attracted attention from psychologists. In this thesis by examining driving skills we can test current psychological theories in a dynamic environment. It is often claimed that 90% of the information necessary for driving comes from our visual system (Cummings 1964, Hartmann 1970, Moore 1969). Although Sivak (1996) claims that vision is important, the attribution of an exact percentage is, at best, doubtful. The majority of psychological research investigating driver error has focused on the perceptual abilities of the driver. This thesis examines the process of visual attention - the selection of a part of the available information for greater processing - considered vital in driving (Hills 1980) which depends on many factors. In broad terms these are the nature of the scene and the expectations, needs and goals of the observer (Yantis 1996). The relative importance of each one of these factors has been a difficult and contentious issue (Hills 1980). This thesis considers one particular suspected failure of the human perceptual system - the looked but failed to see (L.B.F.S.) error. 1.1.1 The Looked but failed to see error The looked but failed to see error or looked but did not see (Sabey and Staughton 1975, Staughton and Storie 1977) refers to a set of circumstances where a driver accounts for an accident in terms of failing to detect another road user in time to avoid a collision. The explanation of looking and failing to see (L.B.F.S) also implies that the other vehicle was there to be seen by the offending driver. The term L.B.F.S. error is used by Sabey to refer to accidents in which, during the post accident interview, the driver of the offending vehicle claimed not to have detected the other road user before the accident occurred. Sabey et al. s (1975) teams found that 44% of 2036 accidents appeared to have been produced by a perceptual error and in their judgement post accident interviews revealed that distraction and looked but failed to see errors were the most common form of perceptual errors made by drivers. Cairney and Catchpole (1995) estimate that 69-80% of all intersection accidents are failures by one driver to see another until it is too late. Seeing without perceiving (Dahlstedt 1986) or looking but they are not seeing (Rensink, Oreagan and Clark 1997) is not a problem exclusive to the UK. Dahlstedt (1986) from German research claims that looking without seeing is a common phenomenon with obvious potentially fatal consequences, especially in

20 road traffic manoeuvres. Rumar (1990) describes late detection or L.B.F.S. error as a common problem accounting for the majority of multi-vehicle accidents throughout Europe. A detection error is the basic cause, claims Rumar, because without detection no further processing of information, or decision processes can take place. Rumar identified two important causes of the late detection error: A lapse of cognitive expectation, illustrated by the failure to scan for a particular class of road user, or a failure to look in the appropriate direction. A difficulty with perceptual thresholds, illustrated by the failure to discern the relevant stimuli in lower levels of ambient illumination or in situations where vehicles approach in the peripheral visual field. In a review of driver vision and vehicle visibility Hills (1980) describes the L.B.F.S. error as a problem of the misjudgement of speed and distance and incorrect interpretation of information by the driver. He points out that an individual vehicle accident is: not normally due to one single cause but, rather is the result of a combination of causes (p184). Wulf, Hancock and Rahimi (1989) also conclude that there are many variables which may affect an accident, some of these only occurring in interaction. The number of factors or variables would at first appear to be numerous. McKnight (1972) for example claims to have identified some 1300 independent variables that can affect driver behaviour and accident potential. Hills (1980) presents a table (Table 1-1) of the possible reasons for the looked but failed to see error, many of which are discussed in this thesis. Table 1-1 Possible causes of the looked but failed to see error From Hills 1980 (p 185). 1 Impaired driver vision 2 Visual and perceptual limitations of the normal driver Limitations of visual search / peripheral vision Limitations of contrast sensitivity, acuity Need for target conspicuity Limitations of human attention Expectancy Problems of interpretation 3 Physical restrictions to visibility and conspicuity Reduced visibility due to night time glare Restricted visibility due to own vehicle or passenger Interposed traffic Obstruction due to road geometry and roadside furniture, etc. 4 Interaction between causes 1.1.2 Two types of L.B.F.S. error The term looked but failed to see is used in two principal contexts where the operator fails to detect an object or hazard. The first and more widespread, is when a driver of a car, after rapidly searching for other traffic, emerges into a main road and fails to detect another road user. The second, is typified by pilots and train operators who fail to notice an object which has been visible for some time. In the case of train drivers these are warning signals or other trains (Edkins and Pollock, 1997, Leibowitz, 1983) and in aviation accidents, it is the pilot s failure to detect other aircraft, (reviews in Hurst and Hurst 1982, O'Hare, Roscoe, Vette and Young, 1990). (See Sections 1.8.7.5 & 8.1 for elaboration).