Assisting Driver Sovereignty: A Fail-Safe Design Approach to Driver Distraction

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Assisting Driver Sovereignty

A Fail-Safe Design Approach to Driver Distraction

Arno van Gijssel

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All rights reserved. No part of this book may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying, recording or by any information storage and retrieval system, without permission from the author.

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Assisting Driver Sovereignty

A Fail-Safe Design Approach to Driver Distraction

Proefschrift

Ter verkrijging van de graad van doctor aan de Technische Universiteit Delft,

op gezag van de Rector Magnificusprof. ir. K.C.A.M. Luyben,

voorzitter van het College voor Promoties,

in het openbaar te verdedigen op woensdag 16 januari 2013 om 15:00 uur door Arno VAN GIJSSEL

Ingenieur industrieel ontwerpen,

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Dit proefschrift is goedgekeurd door de promotoren: Prof. dr. H. de Ridder

Prof. dr. H. H. C. M. Christiaans

Samenstelling promotiecommissie:

Rector Magnificus voorzitter

Prof. dr. H. de Ridder Technische Universiteit Delft, promotor

Prof. dr. H.H.C.M. Christiaans Universidade Técnica de Lisboa, promotor Prof. dr. P. Vink Technische Universiteit Delft

Prof. dr. ing. W.B. Verwey Technische Universiteit Twente Prof. dr.-Ing. L. Eckstein RWTH Aachen University Prof. dr. W.A. IJsselsteijn Technische Universiteit Eindhoven Dr. D. de Waard Rijksuniversiteit Groningen Prof. dr. E. Giaccardi Technische Universiteit Delft, reservelid

Het onderzoek binnen de context van dit proefschrift is uitgevoerd in, en gefinancierd door, het BMW Group Research and Innovation Center te München, Duitsland.

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Glossary of Terms and Acronyms

This glossary contains adopted definitions (with references), abbreviations and author interpretations of terms from the public domain or from within the specific context of this thesis.

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A

Action-driven attention Task-dependent, conscious attention selection, also referred to as top-down attention (Koch, 2004). Example: situation monitoring.

Active Safety The level of safety related to measures taken for accident prevention. It refers to systems such as intelligent speed adaptation, anti-lock braking system, electronic stability control, brake assist, traction control, seat belt pre-tensioning. These systems are active in the sense that they invoke an action to improve safety.

ADAS Advanced Driver Assistance Systems

Aftermarket In the automotive context it is the secondary market of the automotive industry, concerned with the manufacturing, distribution and installation of vehicle parts after the sale of the automobile by the original equipment manufacturer (OEM) to the consumer. In this thesis it is used in the context of OEM and aftermarket in-vehicle information systems.

Attention The ability to concentrate on a particular stimulus, event, or thought while excluding competing stimuli.

Anticipative driving Style of driving based on the projection of the status of perceived environmental elements over time. Precondition is driver situation awareness up to the third level of “projection” according to Endsley’s (1995) model of situation awareness.

Attention allocation In this context it is the allocation of driver information processing

resources to information sources (see also resource allocation).

C

Central executive Postulated to be responsible for the selection, initiation, and

termination of processing routines (e.g., encoding, storing, retrieving) in the working memory of the brain (Baddeley, 1986).

Center console Refers to the control-bearing surfaces in the center of the front of the

vehicle interior. It is the area beginning in the dashboard and continuing beneath it, and ending at the transmission tunnel which runs between the driver's and passenger's seats of many vehicles.

Contributing factors Any circumstance that leads up to or affects the outcome of a critical

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Cognition The mental faculty of knowing, which includes perceiving, recognizing, conceiving, judging, reasoning, and imagining (The American Heritage Medical Dictionary, 2007).

Cognitive capture Also known as cognitive tunneling, it is a phenomenon in which the

driver is focused on detail (e.g. the speedometer) and not on the comprehensive driving situation. It may lead to decreased situation awareness if peripheral perception is impaired.

Cognitive tunneling Same as cognitive capture. Not to confuse with visual tunneling which

does not necessarily involve attention focus on a specific information source.

F

Focal vision Sharp central vision, which is used for reading, driving, and any activity

where visual detail is of primary importance.

Foveal vision Same as focal vision.

G

Goal-driven attention Conscious form of attention selection, also known as top-down attention, driven by internal stimuli and aimed at achieving a targeted objective (Koch, 2004). Example: finding a destination on a map.

H

HFE Human Factors Engineering; Discipline of applying what is known

about human capabilities and limitations to the design of products, processes, systems, and work environments.

HMI Human-machine Interaction; Study, planning and design of the

interaction between humans and machines within an interaction environment.

HUD Head-up Display; Transparent display that presents information

without requiring the driver to look away from the forward roadway. The origin of the name stems from pilots being able to view information with heads "up" and looking forward, instead of angled down looking at lower instruments.

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Human Factor Physical or cognitive property of an individual or social behavior which is specific to humans and influences functioning of technological systems.

Human performance In this context it is referred to as the level on which the driving task is

accomplished in accordance with standards of accuracy, efficiency and safety.

I

iSense Working name for the concept approach, emphasizing the pursuit of an

intuitive, close-to-natural sensory and cognitive support system interface for anticipative situation monitoring.

Inattention to the Inattention due to a necessary acceptable driving task where the subject

Forward Roadway is required to shift attention away from the forward roadway such as

checking blind spots, center mirror, or instrument panel(Dingus et al.,

2006).

K

Knowledge-based level Knowledge-based information processing represents a more advanced

level of reasoning (Wirstad, 1988) in situations that are novel or unexpected. Since operators (drivers) need to form explicit goals based on their current analysis of the system, cognitive workload is typically greater than when using skill- or rule-based information processing.

L

LV Lead Vehicle. Vehicle preceding the subject vehicle (SV) in the same

lane.

LTM Long-term Memory; memory in which associations among items are

stored, as part of the theory of a dual-store memory model, posed by Miller (1956). It encodes information semantically for storage (Baddeley, 1966).

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Near-crash Any circumstance that requires a rapid, evasive maneuver by the subject vehicle or any other vehicle, pedestrian, cyclist, or animal in order to avoid a crash. A rapid evasive maneuver is defined as a steering, braking, accelerating, or any combination of control inputs that approaches the limits of the vehicle capabilities (Dingus et al., 2006).

O

OEM Original Equipment Manufacturer; a manufacturer of products or

components that are retailed under the manufacturer's brand name.

P

Parallel monitoring The process of perceiving situational information via multiple sensory

channels at the same time. In this context it refers to simultaneous information perception via the focal-visual and the peripheral visual senses.

Parallel processing Simultaneous information processing on different cognitive levels such

as the skill-based, rule-based and/or knowledge-based level (Rasmussen, 1983).

Passive Safety The level of safety related to measures taken for accident injury

mitigation. It refers to technologies such as the passenger safety cell, deformation zones, seat belts, air-bags, fuel pump kill switches. These measures are passive in the sense that they are designed for injury mitigation but not for accident prevention.

PDT Peripheral Detection Task; a quantitative method to measure peripheral

perception as a measure for driver workload.

Perception The process of attaining awareness or understanding of the

environment by organizing and interpreting sensory information (Pomerantz, 2003).

Peripheral vision Part of vision that occurs outside the center of gaze.

Primary task Task, related to driving, e.g. controlling the steering wheel, the

accelerator pedal, the brake pedal and which also includes support tasks such as monitoring the driving situation, control of window wipers control, gear shifting or navigation.

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R

Resource allocation Process of allocating sensory and cognitive resources among the various

tasks and information sources (see also attention allocation).

Risk The possibility of loss or injury (the Merriam-Webster dictionary).

RSME Rating Scale Mental Effort; a self-report measure for quantifying

subjective assessment of workload, developed by Zijlstra (1993).

Rule-based level Rule-based information processing is characterized by the use of rules

and procedures in a familiar work (e.g. driving) situation (Rasmussen, 1990). The rules can be a set of instructions acquired by the driver through experience or given by supervisors.

S

Secondary task Task, unrelated to the driving task, which requires subjects to divert attention from the primary driving task, e.g. when talking on the cell phone, with passengers, eating, etc.

Sensory processing Sensory processing is an encompassing term that refers to the way in which the central and peripheral nervous systems manage incoming sensory information from the peripheral sensory systems. The reception, modulation, integration, and organization of sensory stimuli, including the behavioral responses to sensory input, are all components of sensory processing (Miller & Lane, 2000).

Sequential monitoring The process of perceiving situational information via one sensory

channel at a time, for example, sequential focal visual situation monitoring.

Situation assessment The process of achieving, acquiring, or maintaining Situation

Awareness (Endsley, 1995).

Situation awareness The current status and projection of perceived environmental elements,

specifically involving environmental elements critical to HMI decision-making (Endsley, 1995).

Situation monitoring Perception of environmental elements with respect to time and/or space

and the comprehension of their meaning (Endsley, 1995).

Skill-based level Skill-based information processing requires very little or no conscious

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operators (e.g. drivers) to free up cognitive resources, which can then be used for higher cognitive functions like problem solving (Wickens & Hollands, 2000).

Stimulus-driven attention Automatic form of selective attention, also known as bottom-up attention, that only depends on intrinsic qualities in the input (Koch, 2004). The more salient a location or object in the image, the more likely it will be noticed.

STM Short-term Memory; is the capacity for holding a small amount of

information in mind in an active, readily available state for a short

period of time, as part of the theory of a dual-store memory model

posed by Miller (1956). A commonly-cited memory capacity is 7 ± 2 elements. In contrast, long-term memory indefinitely stores a seemingly unlimited amount of information.

SV Subject Vehicle. The vehicle controlled by the test person taking part in

an experiment session.

T

TTC Time-To-Collision. The time it will take until a collision will occur, in

case no preventive action is taken.

V

Visual tunneling Phenomenon in which the peripheral visual field of view is severely

constricted, while retaining central vision. It can be caused by high cognitive workload or stress resulting in reduced variance of eye glance directions and reduced peripheral visual perception. Not to confuse with cognitive tunneling or cognitive capture which can, but do not forcedly, go hand in hand.

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Index / Tables and Figures

Figures

FIGURE 1.1: ROAD SAFETY TRENDS IN EU-27(CARE DATABASE,2011) ... 2

FIGURE 1.2: PERCENTAGE OF EVENTS WITH ATTENTIVE AND INATTENTIVE DRIVERS, AS A FUNCTION OF EVENT

SEVERITY (DINGUS ET AL.,2006). ... 5

FIGURE 1.3: PERCENTAGE OF RUN-OF-ROAD (ROR) EVENTS IN WHICH EACH CONTRIBUTING FACTOR WAS

IDENTIFIED (MCLAUGHLIN ET AL.,2009).AN EVENT CAN BE A CRASH, A NEAR-CRASH OR A CRITICAL

INCIDENT.MULTIPLE FACTORS CAN CONTRIBUTE TO A SINGLE EVENT. ... 6

FIGURE 1.4: EXAMPLE OF A CENTER CONSOLE-LOCATED TOUCH SCREEN INTERFACE, REQUIRING HAND-EYE

COORDINATION AND POTENTIALLY COMPROMISING SAFETY DUE TO EYE GLANCES AWAY FROM THE

FORWARD ROADWAY. ... 10

FIGURE 1.5: SCHEMATIC REPRESENTATION OF THE THESIS STRUCTURE (C=‘CHAPTER’,S=‘SECTION’).IT

ILLUSTRATES HOW CONCEPT DIVERGENCE STARTS WITH THE GENERATION OF SOLUTION

APPROACHES AND INTENSIFIES BEFORE CHANGING INTO CONCEPT CONVERGENCE.THEREAFTER,

SELECTED CONCEPTS ARE EXPLORED AND TESTED AGAINST A BENCHMARK AND CONTROL INTERFACE.

... 12

FIGURE 2.1: TYPES OF DISTRACTION AND INATTENTION AS ACCIDENT-CONTRIBUTING FACTOR IN RUN-OF-ROAD

(ROR) TYPE ACCIDENTS BY MCLAUGHLIN ET AL.(2009), IN PERCENTAGE OF ALL DISTRACTION AND

INATTENTION-RELATED ACCIDENTS. ... 15

FIGURE 2.2: SIMPLIFIED REPRESENTATION OF A DRIVER-VEHICLE INTERACTION MODEL. ... 18

FIGURE 2.3: AVIATION VS. AUTOMOTIVE HMI: DIFFERENCES IN OPERATOR, SYSTEM AND ENVIRONMENT

CHARACTERISTICS AFFECTING OPERATOR-SYSTEM INTERACTION.DERIVED FROM GISH AND STAPLIN

(1995). ... 19

FIGURE 2.4: A MODEL FOR DRIVER-VEHICLE INTERACTION (VAN GIJSSEL,2006), COMPILED FROM THE

INFORMATION PROCESSING MODEL BY WICKENS AND HOLLAND (2000) AND AN INTERACTION

ENVIRONMENT CLASSIFICATION DERIVED FROM ENDSLEY (1999). ... 20

FIGURE 2.5: IMPRESSION OF THE AREA COVERED BY DIRECT VISUAL CONTACT (SECTIONS A TO D).THE GRAY

AREAS ARE BLOCKED FROM VIEW BUT CAN PARTLY BE MONITORED VIA REAR VIEW MIRRORS. ... 22

FIGURE 2.6: HIGH COGNITIVE WORKLOAD MAY REDUCE THE PERIPHERAL PERCEPTION AND EYE GLANCE

VARIABILITY (VISUAL TUNNELING)... 23

FIGURE 2.7: A MODEL FOR DRIVER INFORMATION PROCESSING (AS A SUBSECTION OF FIGURE 2.4), ADAPTED

FROM WICKENS AND HOLLAND (2000). ... 24

FIGURE 2.8: A MODEL OF THE WORKING MEMORY BY BADDELEY (2000) ... 25

FIGURE 2.9: THE COMPONENT STRUCTURE OF WICKENS’ MULTIPLE-RESOURCE MODEL OF ATTENTION (1984) . 26

FIGURE 2.10: SIMPLE MODEL FOR THREE LEVELS OF PERFORMANCE BY SKILLED HUMAN OPERATORS (RASMUSSEN,

1983). ... 28

FIGURE 2.11: MAPPING OF RASMUSSEN’S LEVELS OF INFORMATION PROCESSING ONTO THE DRIVER-VEHICLE

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FIGURE 2.12: A SELECTION OF COMMERCIAL STATE-OF-THE-ART ADVANCED DRIVER ASSISTANCE SYSTEMS,

HORIZONTALLY PROJECTED ON A TIME-TO-ACCIDENT SCALE. ... 30

FIGURE 2.13: VEHICLE INTERACTION COMPLEXITY IN THE PAST AND FUTURE OBJECTIVE BY BMWAG(DASHED

LINE)... 32

FIGURE 2.14: BESIDES THE CONVENTIONAL TECHNOLOGIES SUCH AS RADIO OR SATELLITE COMMUNICATION, NEW

TECHNOLOGIES SUCH AS CAR-TO-CAR AND CAR-TO-INFRASTRUCTURE BECOME AVAILABLE FOR THE

ACQUISITION OF SITUATIONAL INFORMATION. ... 36

FIGURE 2.15: A SIMPLIFIED ILLUSTRATION OF DISTRACTION OCCURRENCE FROM THE RESOURCE DEMAND

PERSPECTIVE.RD STANDS FOR ‘RESOURCE DEMAND’. ... 37

FIGURE 2.16: A MODEL FOR DRIVER INATTENTION. ... 37

FIGURE 2.17: EARLY STAGE INFORMATION DISPLAY, ALLOWING FOR ANTICIPATING COMPLEXITY INCREASE AND

THEREBY REDUCING HIGH WORKLOAD, DISCOMFORT BY INTRUSIVE WARNING DISPLAY. ... 42

FIGURE 2.18: THE RADAR SCREEN AS AN INTERFACE CONCEPT OF UTMOST SIMPLE BUT INTRIGUING CONTENT

DISPLAY. ... 43

FIGURE 2.19: CONCENTRATION OF MONITORING FEEDBACK IN THE FORWARD FIELD OF VIEW (RIGHT IMAGE) TO

ANTICIPATE REDUCED PERIPHERAL VISUAL PERCEPTION (VISUAL TUNNELING, LEFT) IN SITUATIONS OF

INCREASED WORKLOAD OR STRESS. ... 43

FIGURE 2.20: PARALLEL MONITORING OF THE ROAD CENTER AREA AND PERIPHERAL SIGNAL-BASED ADAS

INFORMATION (TOP) VERSUS SEQUENTIAL MONITORING OF SYMBOL-BASED INFORMATION

REQUIRING GLANCE DEVIATIONS (BOTTOM). ... 44

FIGURE 3.1: THE LOW-FIDELITY, SEMI-FUNCTIONAL CONCEPT PROTOTYPE AS USED IN THE CONCEPT

DEVELOPMENT PHASE.THIS EXAMPLE SHOWS THE MSCI CONCEPT (SECTION 3.7) LOCATED IN THE

HUD AND THE HISCI CONCEPT (SECTION 3.8) IN THE INSTRUMENT CLUSTER. ... 50

FIGURE 3.2: THE SIMULATION INTERFACE CONTROL PANEL CREATED FOR MANUAL SIMULATION OF SITUATION

COMPLEXITY PARAMETERS. ... 51

FIGURE 3.3: AN INFORMATION MODEL FOR ISENSE DRIVING SITUATION ASSESSMENT AND EXPECTANCY. ... 53

FIGURE 3.4: MODERN DRIVER-ORIENTED VEHICLE INTERFACE (BMWAG) WITH DRIVING TASK-RELATED

FUNCTIONALITY (LEFT) SEPARATED FROM SECONDARY TASK RELATED FUNCTIONALITY (RIGHT, E.G.

COMFORT, ENTERTAINMENT). ... 59

FIGURE 3.5: RELEVANCE OF HMI STANDARDS AND PRINCIPLES IN THE VEHICLE DEVELOPMENT PROCESS

(ECKSTEIN &VAN GIJSSEL,2006). ... 63

FIGURE 3.6: OVERVIEW OF THE TWO INTEGRATED ISENSE ADAS INTERFACE CONCEPTS MSCI AND HISCI,

SUBJECTED TO EXPLORATION AND VALIDATION IN THIS RESEARCH. ... 64

FIGURE 3.7: ANALOG TO THE REAL-WORLD VEHICLE SURROUNDING AREA (LEFT), THE MSCI INTERFACE OFFERS

INFORMATION ON A METAPHOR-BASED LAYOUT, REPRESENTING THE VEHICLE AND ENVIRONMENT

STATUS AND ENCODED BY DIRECTION AND COMPLEXITY. ... 65

FIGURE 3.8: THE MSCI DISPLAY CONCEPT DESIGN PATTERN.ANALOGOUS TO THE VEHICLE-SURROUNDING AREA,

NUMBERS 101 TO 803 REPRESENT DIFFERENT SECTORS AS WELL AS COMPLEXITY LEVELS. ... 66

FIGURE 3.9: EXAMPLES OF THE MSCI SYSTEM STATUS FEEDBACK ON DIFFERENT LEVELS OF ABSTRACTION: NO

VEHICLE REPRESENTATION (LEFT), ABSTRACT VEHICLE REPRESENTATION (MIDDLE) AND MORE

REALISTIC REPRESENTATION (RIGHT). ... 68

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FIGURE 3.11: EXAMPLES OF THE MSCI SYSTEM FEEDBACK WITH STATUS FEEDBACK OF AUTOMATION SYSTEMS SUCH

AS DISTANCE KEEPING (LEFT), LANE KEEPING (MIDDLE) OR TRANSVERSE AND LONGITUDINAL

CONTROL (RIGHT). ... 68

FIGURE 3.12: EXAMPLES OF THE MSCI SYSTEM FEEDBACK: DISPLAY OF SIGNALS REPRESENTING DIFFERENT LEVELS

OF SITUATION COMPLEXITY.SIGNAL INTRUSIVENESS RANGES FROM LOW (FADE-IN, SMALL SURFACE,

LEFT IMAGE) TO HIGH (NO FADE-IN, BIG SURFACE, CLOSER TO CENTER, DYNAMIC DISPLAY, ACOUSTIC

COMPONENT, RIGHT IMAGE). ... 69

FIGURE 3.13: EXAMPLES OF THE MSCI SYSTEM FEEDBACK: DISPLAY OF SYMBOL-BASED INFORMATION REGARDING

VEHICLE STATUS CHANGES SUCH AS SEATBELT NOT FASTENED (LEFT), THE PARKING BRAKE IS

ACTIVATED (MIDDLE). ... 69

FIGURE 3.14: EXAMPLE OF MSCI SYSTEM STATUS CONFIGURATION.SECTORS ARE PRESELECTED BY A ROTARY

CONTROL AND SWITCHED ON AND OFF BY PUSHING (LEFT).THE RIGHT IMAGE SHOWS A RENDERING

OF A TOGGLE PUSH BUTTON CONTROL FOR SWITCHING GROUPED SECTOR LEVELS (PATENT NO.DE

102007029032,2007). ... 70

FIGURE 3.15: EXAMPLES OF THE MSCI CONCEPT INTERFACE: DETAILED STATUS FEEDBACK DURING SYSTEM

CONFIGURATION (INCLUDED IN PATENT NO.DE102007029034,2007). ... 70

FIGURE 3.16: IMPRESSION OF HOW PARK DISTANCE FEEDBACK CAN BE INTEGRATED INTO THE MSCI INTERFACE,

SHOWING DIFFERENT LEVELS OF PROXIMITY TO SURROUNDING OBJECTS OR VEHICLES. ... 70

FIGURE 3.17: THE STRUCTURAL HISCI CONCEPT DESIGN. ... 72

FIGURE 3.18: EXAMPLES OF HISCI SIGN AND SIGNAL STATES BY INCREASING SITUATION COMPLEXITY FROM LEFT

TO RIGHT. ... 73

FIGURE 3.19: EXAMPLES OF HISCI SIGN AND SIGNAL STATES FOR MEDIUM TO HIGHLY CRITICAL SITUATIONS (LEFT

AND MIDDLE) AND A LOW CRITICALITY SITUATION WITH COMPLEMENTARY SYMBOL-BASED CAUSAL

FACTOR INFORMATION (RIGHT). ... 73

FIGURE 3.20: EXAMPLES OF HISCI SIGN AND SIGNAL STATES: SEPARATE DISPLAY OF CURRENT AND PREDICTED

SITUATION COMPLEXITY (LEFT), DESIGN FOR TENDENCIES, E.G. HOLLOW SHAPE FOR A DOWNWARD

COMPLEXITY TREND (MIDDLE) AND A VARIABLE INDICATOR MAXIMUM BASED ON DRIVER STATE

MEASUREMENT (E.G. SLEEPINESS, HEART RATE VARIABILITY). ... 73

FIGURE 3.21: IMPRESSION OF THE HISCI CONCEPT AS DISPLAYED IN THE HUD(TOP) OR THE INSTRUMENT

CLUSTER (BOTTOM). ... 74

FIGURE 3.22: STATE-OF-ART ADAS(LEFT) AND MSCI AND HISCI, AS A FUNCTION OF INTEGRATION DEGREE. .. 76

FIGURE 4.1: THE VEHICLE MOCK-UP AS A PLATFORM FOR CONCEPT SIMULATION (LEFT).THE HEAD-UP DISPLAY IS

SIMULATED WITH A PROJECTOR SETUP (MIDDLE AND RIGHT) IN AN ATTEMPT FOR MAXIMUM

FLEXIBILITY IN CONTENT DISPLAY. ... 80

FIGURE 4.2: THE DRIVING SIMULATOR FIELD OF VIEW (APPROXIMATELY 270).FRONT AND SIDE VIEW

PROJECTIONS ARE COMPLEMENTED WITH LCD SCREENS PROVIDING REAR VIEWS... 81

FIGURE 4.3: THE DRIVING SIMULATOR ROUTE MAP CONSISTING OF AN INTERSTATE LOOP (BLACK), A RURAL ROAD

NETWORK (GREEN) AND AN URBAN DISTRICT (RED). ... 83

FIGURE 4.4: THE TECHNICAL INTEGRATION OF THE ISENSE AND L6 SIMULATOR PCS, THE VEHICLE MOCK-UP AND

THE DRIVING SIMULATOR.MFS STANDS FOR MULTIFUNCTIONAL STEERING WHEEL. ... 84

FIGURE 4.5: THE INTERSTATE DRIVING SCENARIO (AB) INCLUDES 8 LOCATION-BOUND COMPLEXITY-INDUCING

EVENTS. ... 88

FIGURE 4.6: THE RURAL DRIVING SCENARIO (LS) INCLUDES 12 LOCATION-BOUND COMPLEXITY-INDUCING

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FIGURE 4.7: THE URBAN DISTRICT SCENARIO (SB) INCLUDES 17 LOCATION-BOUND COMPLEXITY-INDUCING

EVENTS. ... 90

FIGURE 4.8: EXAMPLES FOR THE DISPLAY OF SYMBOLIC INFORMATION IN ADDITION TO SIGNAL-BASED AND SIGN

-BASED DISPLAY: PARK ASSISTANCE INFORMATION IN MSCI(LEFT) OR SCHOOL ZONE INFORMATION IN

HISCI(RIGHT). ... 90

FIGURE 4.9: EXAMPLE OF VIDEO CAMERA PERSPECTIVES FOR POST-ACQUISITION OBSERVATIONS: THE SIMULATOR

VIEW (TOP LEFT), THE SUBJECT VIEW (TOP RIGHT).THE LEFT IMAGE SHOWS THE MSCI CONCEPT

DISPLAYED IN THE HUD AND THE RIGHT IMAGE THE HISCI CONCEPT IN THE INSTRUMENT

CLUSTER). ... 91

FIGURE 4.10: THE EXPERIMENTAL DESIGN FOR SUBJECTIVE EVALUATION OF THE HEDONIC, PRAGMATIC AND

ATTRACTIVENESS CONCEPT QUALITY. ... 91

FIGURE 4.11: COMMENT CLASS FREQUENCIES GROUPED BY CONCEPT (MSCI AND HISCI) AND RUN TYPE

(INTERSTATE, RURAL AND URBAN). ... 93

FIGURE 4.12: QUESTIONNAIRE RESULTS:5-POINT LIKERT SCALE MEAN SCORES (N=13).ALL STATEMENT PAIRS ARE

POLARIZED TO NEGATIVE (LEFT) AND POSITIVE (RIGHT).THE FINAL TWO STATEMENT PAIRS WERE

CONSIDERED NOT APPLICABLE FOR HISCI SINCE THESE ADDRESS SYSTEM CONFIGURATION FEEDBACK

WHICH WAS NOT INCLUDED IN THE HISCI CONCEPT. ... 96

FIGURE 4.13: EXEMPLARY CREATIVE GRAPHICAL IMPROVEMENT SUGGESTIONS BY SUBJECTS, AS A FORM OF

COOPERATIVE DESIGN. ... 100

FIGURE 4.14: GRAPHICAL REPRESENTATION OF THE ATTRAKDIFF RESULTS FOR BOTH MSCI AND HISCI CONCEPTS

(N=13). ... 102

FIGURE 4.15: THE ATTRAKDIFF RESULTS FOR EACH 7-ITEM QUALITY CATEGORY, AND STANDARD DEVIATION. ... 103

FIGURE 4.16: MSCI CONCEPT STATES REPRESENTING SOME OF THE MORE FREQUENT AND IMPORTANT SITUATIONS

OF INCREASED COMPLEXITY. ... 111

FIGURE 4.17: THE INFORMATION-BASED VERSUS COMMAND-BASED ADAS APPROACH.THE RIGHT IMAGE SHOWS

THE RESPECTIVE INTERFACE STATUSES FOR A VEHICLE-IN-PROXIMITY SITUATION. ... 112

FIGURE 4.18: THE EXPERIMENTAL DESIGN FOR REPEATED SUBJECTIVE EVALUATION OF THE HEDONIC, PRAGMATIC

AND ATTRACTIVENESS QUALITIES AND DRIVING PERFORMANCE MEASUREMENT. ... 114

FIGURE 4.19: MEAN SCORES PLOTS FOR SESSIONS A1(N=13) AND A2(N=14). ... 117

FIGURE 4.20: MEAN ATTRAKDIFF SCORES PLOT FOR BOTH SESSIONS A1(N=13) AND A2(N=14). ... 118

FIGURE 4.21: MEAN CATEGORY SCORES AND STANDARD DEVIATION FOR SESSION A2MSCI. ... 119

FIGURE 4.22: EXEMPLARY PLOT OF RECORDED DRIVING PERFORMANCE PARAMETERS: SUBJECT VEHICLE VELOCITY,

LEAD-VEHICLE VELOCITY, DISTANCE-TO-LEAD VEHICLE AND LANE ID. ... 121

FIGURE 4.23: EXEMPLARY PLOT OF RECORDED DRIVING PERFORMANCE PARAMETERS: BRAKE INTENSITY AND

SUBJECT VEHICLE ACCELERATION.NOTE THAT THE ACCELERATION VALUE HAS AN OFFSET OF

APPROXIMATELY 0.34 M/S2_{. ... 121}

FIGURE 4.24: MEAN MAXIMUM DECELERATION AND STANDARD ERROR MEAN, FOR VLV=30 KM/H AND FOR SINGLE

AND DUAL TASK SITUATIONS. ... 122

FIGURE 4.25: MEAN DECELERATION (M/S2₎_{AND STANDARD ERROR MEAN}_,_{MEASURED DURING BRAKE SEQUENCES}_,

FOR VLV=30 KM/H AND FOR SINGLE AND DUAL TASK SITUATIONS (N=14). ... 123

FIGURE 4.26: MEAN DISTANCE TO LEAD VEHICLE AT BRAKING INITIATION AND STANDARD ERROR MEAN (N=14),

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FIGURE 5.2: BENCHMARK INTERFACE “L6” WITH BRAKE ASSISTANCE (RED CAR SYMBOL), LANE KEEPING

ASSISTANCE (YELLOW POINTERS) AND LANE-CHANGE ASSISTANCE (REAR VIEW MIRROR)

INFORMATION. ... 131

FIGURE 5.3: THE EXPERIMENTAL DESIGN FOR SESSION B EXPERIMENTS. ... 132

FIGURE 5.4: LANE CHANGE INFORMATION DISPLAY IN THE BENCHMARK INTERFACE (SIDE MIRROR DISPLAY, LEFT

IMAGE), LANE KEEPING INFORMATION IN THE HUD(MIDDLE) AND THE ISENSE MSCI EQUIVALENT

(RIGHT). ... 133

FIGURE 5.5: RESPONSE RATES PER RESPONSE CATEGORY, FOR BOTH THE ISENSE MSCI AND L6 BENCHMARK

CONCEPTS. ... 136

FIGURE 5.6: D-PRIME SCORES PER SUBJECT (N=27) AND INTERFACE. ... 137

FIGURE 5.7: MEAN PROPORTION OF HITS, AS A FUNCTION OF THE CUMULATED PROPORTION OF FALSE POSITIVES

AND ‘UNAWARE-MISSED’, AS A MEASURE FOR INTERFACE PERCEIVABILITY. ... 138

FIGURE 5.8: RATING SCALE MENTAL EFFORT (RSME) MEANS WITH STANDARD DEVIATION (N=27). ... 139

FIGURE 5.9: EXEMPLARY TIME-EVENT PLOT (BOTTOM IMAGE) FROM MANUALLY SCORED MIRROR GLANCE EVENTS

FOR SUBJECT ID TP03.THE RELATIVELY HIGH EVENT DENSITY FOR THE BENCHMARK INTERFACE

(BOTTOM ROW) INDICATES HIGH GLANCE RATES.AS INDICATORS FOR MIRROR GLANCES HEAD AND

EYE MOVEMENTS ARE USED. ... 140

FIGURE 5.10: MEAN RESPONSE TIME AND STANDARD DEVIATION PER INTERFACE TYPE (N=25). ... 145

FIGURE 5.11: MEAN HIT RATE AND STANDARD DEVIATION PER INTERFACE TYPE (N=25). ... 146

FIGURE 5.12: MEAN RESPONSE TIME AND STANDARD ERROR MEAN PER INTERFACE TYPE AND SIGNAL POSITION

(N=25). ... 146

FIGURE 5.13: MEAN AND STANDARD ERROR MEAN OF OBJECT AND OBJECT IDENTIFIER RECALL FREQUENCIES PER

INTERFACE TYPE (N=15). ... 152

Tables

TABLE 2.1 A CLASSIFICATION OF THE SENSES (MATHER,2008). ... 21

TABLE 2.2 CATEGORIES OF DRIVER-VEHICLE INTERACTION ENVIRONMENTS.ADAPTED FROM ENDSLEY (1999).

... 34

TABLE 2.3 RESEARCH QUESTIONS AS FORMULATED FOR THE ISENSE APPROACH EXPLORATION. ... 46

TABLE 3.1 PARAMETERS USED FOR SITUATION COMPLEXITY ASSESSMENT AND ISENSE INTERFACE CONTROL:

DRIVER STATE, VEHICLE STATE AND GEOGRAPHICAL ENVIRONMENT STATE. ... 56

TABLE 3.2 DESIGN CONSTRAINTS FOR ADAS INFORMATION ENCODING AND LOCATION.HUD=HEAD-UP

DISPLAY,CID=CENTRAL INFORMATION DISPLAY.TASK LEVELS DERIVED FROM JANSSEN (1979) AND

MICHON (1985). ... 60

TABLE 3.3 THE MSCI AND HISCIADAS CONCEPTS ACCORDING TO ISENSE PRINCIPLES CONFORMITY (SECTION

2.8.1) ... 75

TABLE 4.1 PARTICIPANTS (N=13) LEVEL OF EXPERIENCE WITH ADVANCED DRIVER ASSISTANCE SYSTEMS

(ADAS), PER ADAS TYPE. ... 86

TABLE 4.2 COMMENT FREQUENCIES FROM THE THINK-ALOUD TRANSCRIPTIONS PER CATEGORY, EVENT TYPE

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TABLE 4.3 LIKERT SCALE DESCRIPTIVE STATISTICS FOR 18 STATEMENT PAIRS (LAST TWO DO NOT APPLY TO

HISCI). ... 97

TABLE 4.4 EXEMPLARY CHI SQUARE RESULTS FOR QUESTION 1 ON INTUITIVE USE FOR ISENSE VARIANT MSCI.

THE ‘GOODNESS OF FIT’ IS TESTED FOR THE GROUPED CATEGORIES ‘NEGATIVE’(≤0) AND ‘POSITIVE’

(>0). ... 97

TABLE 4.5 CHI SQUARE RESULTS FOR BOTH ISENSE VARIANTS FOR THE GROUPED CATEGORIES ‘NEGATIVE’(≤0)

AND ‘POSITIVE’(>0).RESULTS ARE SIGNIFICANT IF P<.05(*). ... 98

TABLE 4.6 WILCOXON SIGNED-RANK TEST ON 16LIKERT ITEMS (N=13, CRITICAL Z-VALUE =1.96).

SIGNIFICANCE AT P <.05(*). ... 99

TABLE 4.7 OPEN QUESTION RESPONSES REGARDING STRENGTHS, WEAKNESSES AND IMPROVEMENT POTENTIAL

OF BOTH MSCI AND HISCI CONCEPTS. ... 100

TABLE 4.8 RESULTS OF THE ONE-SAMPLE T-TEST ON QUALITY CATEGORY RESULTS (TEST VALUE =0). ... 103

TABLE 4.9 CHI SQUARE RESULTS FOR BOTH MSCI AND HISCI PER QUALITY CATEGORY.HIGHLIGHTED P

-VALUES INDICATE SIGNIFICANCE (P<.05) ... 103

TABLE 4.10 PAIRED T-TEST RESULTS FOR RELATIVE ATTRAKDIFF PERFORMANCE. ... 104

TABLE 4.11 BINOMIAL TEST RESULTS FOR QUESTION 1 REGARDING DISPLAY LOCATION PREFERENCE FOR MSCI

AND HISCI. ... 105

TABLE 4.12 SUBJECTS RATIONALE ON DISPLAY LOCATION PREFERENCE. ... 105

TABLE 4.13 BINOMIAL TEST RESULTS FOR FORCED-CHOICE QUESTIONS 2,3 AND 4.SIGNIFICANCE AT P<.05. . 106

TABLE 4.14 PRAGMATIC QUALITY ITEMS WITH A MEAN SCORE <1 AND THE APPLIED DESIGN PARAMETER

IMPROVEMENTS.THE NUMBERS IN THE FIRST COLUMN REFER TO THE ORIGINAL LIKERT ITEM

NUMBERS AS USED IN FIGURE 4.12. ... 110

TABLE 4.15 SESSION A2 COMMENT FREQUENCIES FROM THE THINK-ALOUD PROTOCOL TRANSCRIPTIONS PER

EVENT, FOR MSCI(N=14). ... 116

TABLE 4.16 CHI SQUARE RESULTS FOR POSITIVE (>0) VERSUS NEGATIVE + NEUTRAL (≤0) SCORES IN SESSION A2

WITH THE REDESIGNED MSCI INTERFACE.SIGNIFICANCE AT P<.05*... 117

TABLE 4.17 ONE-SAMPLE T-TEST RESULTS FOR QUALITY CATEGORIES FOR MSCI IN SESSION A2(SIGNIFICANCE AT

P<0.05). ... 119

TABLE 4.18 CHI-SQUARE RESULTS FOR QUALITY CATEGORIES FOR MSCI IN SESSION A2(SIGNIFICANCE AT

P<0.05). ... 119

TABLE 4.19 PAIRED T-TEST RESULTS FOR COMPARISON OF THE MSCI SCORES IN SESSIONS A1 AND A2 PER

QUALITY CATEGORY. ... 120

TABLE 4.20 MEAN VALUES, STANDARD DEVIATION AND STANDARD ERROR FOR MAX, MEAN DECELERATION AND

DISTANCE TO LEAD VEHICLE AT BRAKING INITIATION. ... 122

TABLE 4.21 ESTIMATES OF FIXED EFFECTS FOR MAXIMUM DECELERATION. ... 123

TABLE 4.22 ESTIMATES OF FIXED EFFECTS FOR MEAN DECELERATION. ... 123

TABLE 4.23 ESTIMATES OF FIXED EFFECTS FOR MEAN DISTANCE TO LEAD VEHICLE AT BRAKING INITIATION. .... 124

TABLE 5.1 PROGRAMMED ORDER OF LEAD VEHICLE (LV) APPEARANCE IN SIMULATOR RUNS, WITH PREDEFINED

TRIGGER INSTANCES A1 TO F2. ... 134

TABLE 5.2 RECALL TRIGGER CONDITIONS FOR LANE CHANGE SITUATION AWARENESS MEASUREMENTS IN

SIMULATOR RUNS 3 AND 4.THE LANE CHANGE ASSISTANCE (LCA) STATE CAN BE ‘ON’(I.E. VEHICLE

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TABLE 5.5 NUMBER AND RATE OF MIRROR GLANCES (LEFT + CENTER MIRROR), FOR INTERVALS IN WHICH THE

SUBJECT VEHICLE IS ON THE MOST RIGHT LANE (N=10). ... 140

TABLE 5.6 MIRROR GLANCES PER 6-SECOND PRE-LANE CHANGE INTERVAL, PER INTERFACE CONCEPT MSCI AND

L6(N=10)... 141

TABLE 5.7 PDT SIGNAL POSITION COORDINATES. ... 144

TABLE 5.8 SITUATIONAL OBJECTS, OBJECT IDENTIFIERS AND LOCATION PER DRIVING SCENARIO A AND B, FOR

TEST SITUATIONS 1 AND 2. ... 150

TABLE 5.9 SITUATIONAL OBJECTS, OBJECT IDENTIFIERS AND LOCATION PER DRIVING SCENARIO A AND B, FOR

SITUATIONS 3 AND 4, AND THE SUMMED OBJECTS AND IDENTIFIERS IN TOTAL AND PER SCENARIO AND

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CHAPTER 1

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1.1 The Quest for a Safer Journey

Traffic accidents are a negative side effect of the universal and economical desire for mobility. Although on a steady decline over the past decades (Figure 1. 1, European Union Care Database, 2011), the year 2009 saw the alarming numbers of 34.817 fatalities and 1.565.151 injured resulting from in total 1.190.448 recorded accidents in the 27 European Union member states alone.

Figure 1. 1: Road safety trends in EU-27 (Care Database, 2011)

Despite the steady decline, traffic accidents cause more death and injury in our communities than wars, acts of terrorism, and disasters put together, and they are the leading cause of posttraumatic stress disorders in the general population (Blanchard & Hickling, 1997). Apart from this human tragedy, road accidents result in enormous economic costs accumulating to about 200 billion Euros a year in the European Union alone, corresponding with 2% of GDP (European Commission, 2006).

Whenever societal acceptance is decreasing at a faster rate than the accident occurrences, there will be incentives to set in to place improvement measures and research programs. Authorities set ambitious goals for increased traffic safety. Quantitative targets are used in national road safety strategies and programs which are mostly focused on eliminating death and serious injury resulting from road traffic accidents. Targets on higher political levels (e.g. the European Union pursued reducing fatalities by 50% between 2001 and 2010) cannot be achieved without effective road safety efforts on all levels of government (Koornstra, 2003). Since the 1970s, national targets have been used in road safety strategies in Europe with Finland setting the first target to reduce deaths by 50% by the end of the 1970s (Peltola, 2003). In the meantime, some EU countries, such as the Scandinavian countries and The Netherlands, share the long-term objective to make the traffic system intrinsically safe. Sweden seeks the elimination of all death and serious injury from

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Measures to achieve these safety targets need to be set in to place by a joint effort of governmental institutions and private sector institutions. As an example, the European Commission takes an effort by initiating research programs aimed at cooperation between academic institutes and the industries. Lower level authorities are improving the safety of the infrastructure, for instance by applying innovative road surface materials such as porous asphalt layers. These have better water drainage characteristics to reduce aquaplaning, improve visibility and reduce brake distances in rainy weather conditions. Another example is the replacement of conventional intersections by roundabouts that reduce vehicle velocities while at the same time improving traffic flow. Governments are also attempting to invoke safer driver behavior by adopting stricter regulations, raising standards for driver qualifications and intensified law enforcement.

The automotive industry is taking responsibility by improving both the active as well as the passive safety of the mobility products it develops and markets. Improving passive safety means reducing crash-resulting injuries by introducing technologies such as airbags, safety belts and crumple zones. In the past few decades, increasing attention was given to accident prevention by enhancing active vehicle safety. An abundance of Advanced Driver Assistance Systems (ADAS) has been developed for longitudinal and transverse vehicle control such as systems for traction control, lane change assistance, lane departure warning and adaptive cruise control but also for enhanced driving strategies (e.g. in-vehicle navigation systems). Other active safety developments can be found in situation monitoring (e.g. adaptive light control, night vision) as well as driver state monitoring (e.g. driver drowsiness detection). Proper design and use provided, these systems may potentially contribute to the enhancement of traffic safety.

The starting point of the research in this thesis is the automotive industrial context. Subject of research is the role of driver-vehicle interaction and the driver distraction phenomenon as a causal factor in accident occurrence. More specifically, it is aimed at generating knowledge and innovation in active vehicle safety and driver assistance interfacing.

1.2 Accident Causation and Driver Distraction

When working towards the development of active safety systems, investigation of accident causation is key. In general, accidents and injuries are complex phenomena with multifactorial geneses and therefore need to be scientifically addressed with interdisciplinary approaches (Mazumdar, Sen, & Lahiri, 2007). Mostly, accident causation cannot be linked to a single causal factor. A run-of-road-type accident may be caused by any combination of factors such as poor general visibility, poorly visible road markings, a slippery road surface, bad weather conditions, a poorly maintained vehicle (worn tires) or an inattentive driver.

There is an abundance of descriptive accident statistics focusing on contextual parameters such as injuries and fatalities per region, accident type and victim gender. In-depth accident causation statistics, however, are far less standardized and rather scarce. Especially pre-crash data are indispensable for the analysis of effective countermeasures to prevent road crashes (Morris & Brace, 2006). An analysis conducted by the European Transport Safety Council (ETSC, 2001)

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identified that in all national road accident databases of EU-25 countries, there were major gaps including in-depth accident and injury causation. Several contributing factors can be identified:

 Limited public access to accident protocols due to personal confidentiality restrictions. Regulation maintaining the privacy of the individual have made it more difficult to collect data for safety research and policies (ETSC, 2001). The need to obtain an individual’s permission makes data collection less efficient as partially completed cases may have to be abandoned if permission is not forthcoming. Access to accident protocols is often restricted due to insurance policies.

 Limited independence of road accident investigation.

Insufficient independent road accident investigation is carried out at a national level, whether by independent research institutes, university departments or national accident investigation boards (ETSC, 2001).

 Protocol information is often incomplete or unreliable.

Databases containing accident data of private drivers, such as those of the police, hospitals and insurance companies, suffer from validity problems (e.g. Harris, 1990; Rosman & Knuiman, 1994).

 Accident reports are focused on deterministic fact finding.

Police-reported accident protocols are based on deterministic fact finding and are oriented at criminal offences and misconduct rather than psychological causation aspects (Otte, Pund, & Jaensch, 2009).

 Less severe accidents are under-reported.

Databases containing accident data of private drivers, such as those of the police, hospitals and insurance companies, suffer from under-reporting of less severe accidents (Hauer & Hakkert, 1988).

Even if accident statistics have to be interpreted in the light of the above limitations, an effort has been made by some governmental and academic institutions to gain insight in psychological aspects and driver conduct in accident causation processes (Otte et al., 2009, a.o.). A productive and in-depth research is the 100-Car Naturalistic Driving Study performed by Virginia Tech Transportation Institute (VTTI) for the National Highway Traffic Safety Administration (NHTSA). Data could be used in many causation analysis studies (Dingus et al., 2006, a.o.). One hundred drivers who commuted into or around the northern Virginia/Washington DC metropolitan area were recruited. They used either their own or leased vehicles. The sample was restricted to six vehicle types, due to instrumentation feasibility issues. The driver sample was selected to include disproportionate numbers of younger (18 to 25 years old) drivers and drivers with high annual mileage. This was intended to maximize the potential for recording crashes and near-crash events. Data were recorded over a 12- to 13-month period. All in all, there were 2 million vehicle

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The results from the evaluation of data by Dingus et al. (2006) suggests that distraction and inattention contribute to a great percentage of accident events. Figure 1. 2 shows the percentage of events with attentive and inattentive drivers, as a function of increasing event severity. This study indicates that in about 65% of the near-crashes and 78% of the crashes, the driver was inattentive to a certain degree. In this study, four distraction or inattention categories were distinguished: secondary or non-driving-related task execution, drowsiness, inattention to the forward roadway and nonspecific eye glances.

Figure 1. 2: Percentage of events with attentive and inattentive drivers, as a function of event severity (Dingus et al., 2006).

Olson, Hanowski, Hickman and Bocanegra (2009) performed a naturalistic driving study with commercial vehicles. It resulted in 4.452 safety-critical events (i.e. crashes, near-crashes, critical events and unintentional lane deviations) recorded from 203 drivers over a period of a year. In 71% of crashes and 46% of near-crashes, the driver appeared to have engaged in non-driving related tasks.

It is important to note that numbers tend to vary substantially depending on contextual factors such as accident type (e.g. head-tail or cross traffic), the type of infrastructure (e.g. straight road or intersection) or weather conditions (e.g. visibility, pavement condition or lane submersion). McLaughlin et al. (2009) have investigated contributing factors in run-off-road (ROR) type events (i.e. crashes, near-crashes and incidents) in which the subject vehicle departs the roadway. They found that in 40% of the events, distraction or inattention played a role as a contributing factor (Figure 1. 3). Note that in these results, ‘fatigue’ is represented by a separate category. McLaughlin et al. also found that in 47% of the ROR events, two or more factors were identified as a contributing factor in an event. Short following for instance may coincide with the driver being distracted by a non-driving-related task.

0 10 20 30 40 50 60 70 80 90

Critical Incident Near Crash Crash

Percent o f Events Event Severity Inattentive Attentive

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Figure 1. 3: Percentage of run-of-road (ROR) events in which each contributing factor was identified (McLaughlin et al., 2009). An event can be a crash, a near-crash or a critical incident. Multiple factors can contribute to a single event.

Despite the differences in results, there is broad consensus that driver distraction and inattention play a role in a vast share of accidents and critical incidents. Driver distraction as such has existed for as long as there are vehicles to drive. However, as more wireless communication and entertainment systems proliferate the vehicle market, it is possible that the rate of distraction-related crashes will escalate (Stutts, Reinfurt, Staplin, & Rodgman, 2001). Currently, drivers may converse by cell phone, read e-mail or access the Internet while driving. The increase of information-based technologies available to drivers and the range of in-vehicle activities on-board devices offer, gives rise to concern. Many of these are being adapted for in-vehicle use and have interfaces designed in accordance with industry guidelines such as the European Statement of Principles on Human-Machine Interface (ESoP, European Commission, 2000). However, especially after-market devices are often not well integrated into the vehicle interface. To make the Human-Machine Interaction (HMI) principles effective for traffic safety, it is crucial to ensure that all system types are developed to the same standards independent of their functionality and degree of integration (Eckstein & Van Gijssel, 2006).

Despite the distraction potential of in-vehicle information systems, it is important to stress that many of these, provided adequately designed and integrated, may actually enhance driving safety (Louwerse & Hoogendoorn, 2004, a.o.) by supporting the driver in decision-making and vehicle control. Navigation systems with text and voice information for instance can induce a safer driving performance comfortable compared to traditional navigation by paper maps (Lee & Cheng, 2008), presumably due to more hand-eye coordination involved in paper map reading.

0 5 10 15 20 25 30 35 40 45 Distraction/Inattention Roadway Boundaries Short Following Fatigue/Impairment Low Friction Encroaching Failure to Maintain Lane Low Speed Maneuvering Error Lead Vehicle Braking Late Route Selection

Percentage of Events (%)

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Considering above, it seems useful to look into some of the potential reasons for drivers to engage in non-driving-related activities:

 The rapid developments in information and communication technology.

Vehicle interfaces nowadays may provide access to the internet, opening up a wide scale of functional applications. Mobile devices such as smartphones and similar devices provide a wide range of different communication and entertainment functions. According to data published by CTIA-The Wireless Association (CTIA, 2010), more than 1.5 trillion text messages were sent and received in 2009 in the US – amounting to about 5 billion messages daily and many of them at the wheel (AAA Foundation for Traffic Safety, 2009).

 The increasing popularity of online social networking(e.g. Facebook, Twitter).

A survey (Lenhart, Purcell, Smith, & Zickuhr, 2010) of 800 US adolescents and 2253 adults revealed a rise in online social networking among teens from 65% in 2006 to 73% in 2009 and among adults from 8% in 2005 up to 37% in 2009.

 Drivers generally tend to seek a minimum level of arousal.

If a driver is underchallenged or bored, this may result in a number of possible responses, including a search for stimulation or cognitive regression (Berlyne, 1960).

 Drivers may overestimate their abilities.

A survey (Tison, Chaudhary, & Cosgrove, 2011) of 6000 drivers representing all 50 United States revealed that half (54%) of all drivers who do talk on the phone while driving believe that doing so makes no difference on their driving performance. After all, despite the high occurrence of distraction-related accidents, an individual may get caught in an accident only once or a few times in a lifetime.

1.3 Problem Outline

Distraction and inattention will occur

The automotive industry is making efforts to reduce distraction involving in-vehicle information systems by committing themselves to standards of principles, criteria and validation methods for system and interface design (e.g. ESoP, 2000, JAMA, 2004 and AAM, 2006). Although these efforts may help reduce distraction (Eckstein & van Gijssel, 2006), they do not prevent the driver from engaging into non-driving related tasks. The use of cell phones while driving is one of the most intensively researched in the driver distraction context (Caird, Willness, Steel, & Scialfa, 2008; Horrey & Wickens, 2006). Studies have indicated an effect on driving performance such as response times (Hancock, Lesch, & Simmons, 2003). Brookhuis, De Vries, & De Waard (1991) found these effects to be largely dependent on the circumstances (e.g. traffic density). There are even indications that conversations, in some situations, may have an alerting effect (Mikkonen & Backman (1988). The approach of trying to discipline drivers in distraction conduct, such as prohibiting cell phone use while driving, may therefore conditionally improve safety. However,

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since such bans are restricted to a few types of observable distraction, it may be rather ineffective in the light of the wide range of observable as well as rather indiscernible distraction conduct such as day dreaming. As outlined in section 1.2, distraction plays a role in causation in a vast share of traffic accidents. Despite the high distraction-related accident frequencies, an individual driver may get caught in an accident only ones or a few times in a lifetime. This may potentially contribute to drivers overestimating their abilities while engaged in distraction conduct (Tison et al., 2011). Assuming that in by far most distraction conduct, there is no criminal intent, we may propose that the real problem lies not in the distraction conduct itself but, rather, in driver situation awareness and expectancy being impaired.

The ADAS potential for safety enhancement

The automotive industry is making an effort to enhance driver situation awareness by developing systems that support the driver in his situation monitoring tasks such as head-up displays, vehicle approach warnings, night vision or park distance feedback. Especially in the last decade we have seen a penetration of the automotive market by so called Advanced Driver Assistance Systems (ADAS). These mostly active safety systems assist the driver in controlling the vehicle, monitoring the situation and there is evidence that systems such as the head-up display may contribute to situation awareness and safety (Sakata, Okabayashi, Fukano, Hirose, & Ozone, 1987; Okabayashi, Sakata, Furukawa, & Hatada, 1990; Kiefer & Gellatly, 1996). Despite the potential contribution to safety, the design of ADAS systems has to deal with a biased technological viewpoint (van Waterschoot & van der Voort, 2009). During the development process, human factors professionals evaluate the ADAS through the behavioral impact or driving performance, however, the evaluation results do not translate to design suggestions. Neither do the evaluations relate to the (entire) driver-vehicle system.

An HMI designers vision on state-of-the-art ADAS interfaces

The biased technological viewpoint on ADAS design, as addressed in the previous paragraph, may explain why ADAS are still often based on a ‘one-sensor-one-indication’ system approach. Rasmussen stated in 1983 already: “in order to switch from the traditional one-sensor-one-indication technology to effective use of modern information technology for interface design, we have to consider human performance in an integrated way.” In the current era, the term ‘one-sensor-one-indication’ does not apply anymore in the technical sense seen the fact that modern ADAS make use of multiple sensors and smart data interpretation (Hsieh, Lian, & Hsu, 2007). However, from the HMI perspective, ADAS development can still be regarded as technology-biased by the sheer fact that the driver has to interact with multiple separate systems/features and that each of these address only a fragmental safety aspect of the overall driving task. The following presents an HMI-designers perspective on potential design flaws in state-of-the-art ADAS:

 ADAS are largely feature-based.

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system-www.honda.com) or Lane Departure Warning (BMW AG, www.bmw.com) exclusively address explicit driving situations and induce information display accordingly.

 ADAS visual interfaces are distributed across the vehicle interface.

ADAS information is often scattered over different physical interfaces such as the instrument cluster, HUD1_{, CID}2_{, IC}3_{or control interfaces and mirrors.}

 ADAS often lack uniformity in visual information ‘language’.

This presumably prevents the driver from learning from previously perceived information and thereby achieving more swift interpretation.

 ADAS visual information often requires high level cognitive processing.

Many of the ADAS provide symbolic information, which requires knowledge-based processing (Rasmussen, 1983) leading to longer processing times.

 ADAS information for vehicle control assistance is often restricted to medium and high levels of criticality.

Many advanced systems are aimed at accident prevention and act only if the situation has already reached a critical level (e.g. Lane Departure Warning, Forward Collision Warning). Due to intrusive information display, the driver may experience stress or discomfort which, from an HMI design perspective, should be avoided in the first place.

1.4 Thesis Approach

1.4.1 Multidisciplinary Research-to-Development Transition

As addressed in the problem outline (section 1.3), van Waterschoot & van der Voort (2009) describe how in the development process, human factors professionals evaluate the ADAS through the behavioral impact or driving performance but that the evaluation results do not often translate into design suggestions. An example of an interface solution that does not seem to conform perception ergonomics principles is presented in Figure 1. 4. It shows a touch screen interface in the center stack of a passenger car. Pitts et al. (2012) found that over 70% of the time taken to complete an in-vehicle touchscreen task can be spent looking away from the road. This potentially affects safety as the accident risk is correlated to the duration and frequency of glances away from the forward roadway (Wierwille, 1995). Other problems are the lack of tactile and kinesthetic feedback (Burnett & Porter, 2001). One may wonder why such presumed HMI mishaps do occur.

1_{Head-Up Display. Windscreen-projected information display, mostly used for display of driving task-related} information.

2_{Central Information Display. Mostly located in the vehicle center stack and predominantly used for secondary} information display.

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Figure 1. 4: Example of a center console-located touch screen interface, requiring hand-eye coordination and potentially compromising safety due to eye glances away from the forward roadway.

Waterschoot & van der Voort (2009) argue that the efficiency and safety of ADAS may increase if designers are informed about the consequences of design choices during the design process. Considering the mechanical and information design engineering aspects as well as market and focus group interests, the importance of a multidisciplinary development approach in this research is considered indispensable.

1.4.2 Empirical Research through Design

The term “research through design” is often used to refer to explorative design driven research (Zimmerman, Forlizzi, & Evenson, 2007). It focuses on the role of the product prototype as a tool for generation of design knowledge. It can vary in degrees of granularity, from paper-based concepts to fully functional prototypes, as a means to develop and validate design knowledge. The designer-researcher explores the prototype in a realistic user context and reflects on the design process with the actual user-interaction with the test prototype. This mostly iterative process helps to evolve the prototype as it is evolving towards a more realistic and fine-grained representation of real-world interaction. Keyson and Bruns (2009), however, stress the lack of formal manipulation, testing, and iterative development of design parameters often observed in typical research through design studies. They stress the need for an empirical approach.

In this thesis, research through design is applied throughout all concept development and testing phases but predominantly in the concept exploration phase (CHAPTER 4). An explicit attempt is made to identify referential design parameters to be tested and evolved throughout the iterative phases of concept generation, exploration and evolution.

1.5 This Thesis

In previous sections, we have concluded that distraction and inattention cannot be prevented and that ADAS have a potential for improving driver situation awareness and safety improvement. We have however also concluded that ADAS are still largely feature-based and don’t yet consider

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ADAS interfaces, a challenge is found in the quest for a ‘fail-safe’ ADAS design approach to driver distraction.

The aim in this research is to design, develop, explore and validate integrated ADAS interface concepts that are more robust to distraction occurrences by enabling a more continued process of situation perception, assessment, awareness and expectancy. The high level objective with this approach is to enhance anticipative driving behavior to the benefit of driving safety and driver sovereignty.

The pursuit for a distraction mitigating ADAS interface starts (CHAPTER 2) with an examination of the driver distraction phenomenon including its role in accident causation. This is followed by a close look at human-machine interaction modeling and an outline of the solution approaches to be followed in this thesis. In CHAPTER 3, the first design iterations are described and illustrated, resulting in two structural interface concept approaches. The concept exploration phase (CHAPTER 4) is aimed at probing the potential of the iSense concept approaches, including a second design iteration phase. In the validation tests (CHAPTER 5), the concept performance is compared with that of a state-of-the-art benchmark ADAS interface and control situations. Discussion and a research outlook mark the end of this thesis (CHAPTER 6). Figure 1. 5 presents a schematic top-down representation of the thesis workflow with associated chapters.

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Figure 1. 5: Schematic representation of the thesis structure (C= ‘Chapter’, S= ‘Section’). It illustrates how concept divergence starts with the generation of solution approaches and intensifies before changing into concept convergence. Thereafter, selected concepts are explored and tested against a benchmark and control interface.

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CHAPTER 2 Distraction and Driver-Vehicle Interaction

The main objective in this research is to design, develop and explore integrated driver assistance interfaces, based on a comprehensive model for distraction prevention or mitigation. In the

following sections (2.1 – 2.6), the role of distraction as a causal factor in traffic accidents is

discussed in the light of driver information processing capabilities and limitations within the interaction environment. Sections 2.7 and 2.8 describe the solution approaches and the chapter ends with research questions (0) as a theoretical foundation for concept generation in the next chapter.

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2.1. Driver Distraction & Inattention: Definitions from Literature

Reading through distraction-related literature teaches us that there is no generally accepted definition for the term driver distraction (Trezise et al., 2006). In his comment on the European Statement of Principles (European Commission, 2000) on In-Vehicle HMI, Janssen (2000) poses this definition:

(A) „The capture of the driver’s attention by information that is irrelevant to the driving situation to a degree where insufficient attention is left for the primary task. “ Stutts et al. (2001) explicitly distinguish distraction from other forms of driver inattention and see it as a form of inattention in which a driver:

(B) “…is delayed in the recognition of information needed to safely accomplish the driving task because some event, activity, object, or person within or outside the vehicle compels or induces the driver’s shifting attention away from the driving task.” The International Organization for Standardization (ISO, www.iso.org) developed the following rudimentary definition (Pettitt, Burnett, & Stevens, 2005):

(C) “Distraction is attention given to a non-driving-related activity, typically to the detriment of driving performance”

McLaughlin et al. (2009) emphasize the role of the driving task itself as a potential cause of inattention to the forward roadway:

(D) “…inattention to the forward roadway due to a necessary and acceptable driving task where the subject is required to shift attention away from the forward roadway (e.g., checking blind spots, center mirror, or instrument panel)…”

The latter definition suggests that discussion and research on driver distraction may benefit from broadening the scope to include other types of inattention. The experts from the ITS Technical Task Force (2010) generally agreed that driver distraction involves some failure in attention selection including failures related to perceptual analysis, response selection, and response processing. It may involve erroneous selection of driving‐related (e.g. shoulder checking while a lead vehicle is stopping) as well as non‐driving‐related objects, events, and/or actions. However, the Task Force experts agreed that the term “distraction” should be saved for diversion of attention from to tasks/activities that are not critical for safe driving:

(E) “Driver distraction is the diversion of attention from activities critical for safe driving to a competing activity.”

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distraction while others do not. Hence, no conformity seems to exist for a comprehensive definition of the phenomenon.

In the light of the above, driver distraction is proposed as to be related to non-driving related activity (following definition E), but in the remainder of this thesis, the distraction problem scope is broadened to include other types of inattention. The next section will focus on these inattention types and in section 2.6, a generic definition of driver inattention (including distraction) is posed.

2.2. Types of Inattention Behavior (including Distraction)

In the previous section, inattention was proposed to be all activity leading attention away from activities critical for safe driving. Accident causation data can give insight in what such activity can be. As stated in the introduction chapter, reliable and in-depth accident causation data are difficult to obtain due to restricted availability, restricted access and limited level of detail in available data. Nevertheless, those available causal factors statistics can still give valuable insight in the types of distraction identified to play a role in accident causation.

Analyzing distraction-related events including self-report event protocols, McLaughlin et al. (2009) have distiguished 15 types of distraction and inattention in run-off-road (ROR) type accidents (Figure 2. 1). The major sources of distraction in ROR accidents from the 100-car study data found among these categories were distraction by outside events, interaction with build-in Original Equipment Manufacturer’s (OEM) systems (e.g. climate control), passengers or cell phones (Dingus et al, 2006)). Note that McLaughlin et al. (2009) identify driving tasks-induced inattention such as “glance to mirrors” (2%), “glance over shoulder” (2%), glance down (2%) and “outside-driving” (6%) as the dominant causal factor in 12% of distraction and inattention-related ROR-type accidents (Figure 2. 1).

Figure 2. 1: Types of distraction and inattention as accident-contributing factor in run-of-road (ROR) type accidents by McLaughlin et al. (2009), in percentage of all distraction and inattention-related accidents.

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The 15 types of distraction and inattention categories in Figure 2. 1 can all be assigned to one of four main categories of inattention (Gordon, 2005):

1) Distraction-related inattention (by in-vehicle and outside activities and events) 2) Driver-specific inattention (e.g. daydreaming, lost in thought)

3) Driving-related inattention (e.g. mirror or shoulder checking, sunstrike) 4) Driver state-related inattention - physical and emotional (e.g. fatigue or upset)

2.2.1. Distraction-related inattention

Inattention to the driving task caused by secondary task activity, to a degree were driving activity critical to safety is affected. This is the commonly known type of inattention and examples are interaction with entertainment devices (integrated in the vehicle interface or mobile), communication systems (integrated or mobile), smartphones, conversing with passengers, eating and many more (see also Figure 2. 1).

2.2.2. Driver-specific Inattention

Besides the rather obvious and observable distraction conduct, there are more complex and intangible inattention-inducing activities. Based on data from the 100-car naturalistic driving study, Dingus et al. (2006) found that in 6,7% of inattention-related crashes involving a lead vehicle, day-dreaming has been self-reported as a contributing factor. This phenomenon is mostly triggered by internal stimuli and usually there is no response output. Although daydreaming may seem a rather useless activity, Mueller and Dyer (1985) propose that there are important roles of daydreaming in human cognition such as: planning for the future (including decision-making), learning from successes and failures, processes of creativity and emotion regulation. Day-dreaming may become a problem if it keeps essential situational information from being perceived and processed by the driver.

2.2.3. Driving-related inattention

Driving-related inattention is related to any of the primary driving tasks. Three hierarchical levels of skill and control in the driving task are distinguished (Janssen, 1979; Michon, 1985):

 Operational (e.g. transverse or longitudinal vehicle control)  Tactical (i.e. maneuvering, e.g. overtaking, cornering)  Strategical (i.e. planning, e.g. navigation)

Assisting Driver Sovereignty: A Fail-Safe Design Approach to Driver Distraction

Assisting Driver Sovereignty

Assisting Driver Sovereignty

Contents

Glossary of Terms and Acronyms

A

C

F

G

H

I

K

L

O

P

R

S

T

V

Index / Tables and Figures

Figures

Tables

CHAPTER 1

1.1 The Quest for a Safer Journey

1.2 Accident Causation and Driver Distraction

1.3 Problem Outline

1.4 Thesis Approach

1.4.1

Multidisciplinary Research-to-Development Transition

1.4.2

Empirical Research through Design

1.5 This Thesis

CHAPTER 2

Distraction and Driver-Vehicle Interaction

2.1.

Driver Distraction & Inattention: Definitions from Literature

2.2.

Types of Inattention Behavior (including Distraction)

2.2.1.

Distraction-related inattention

2.2.2.

Driver-specific Inattention

2.2.3.

Driving-related inattention