Are you in the loop? Using gaze dispersion to understand driver visual attention during vehicle automation

This driving simulator study, conducted as part of the EC-funded AdaptIVe project, assessed drivers’ visual attention distribution during automation and on approach to a critical event, and examined whether such attention changes following repeated exposure to an impending collision. Measures of drivers’ horizontal and vertical gaze dispersion during both conventional and automated (SAE Level 2) driving were compared on approach to such critical events. Using a between-participant design, 60 drivers (15 in each group) experienced automation with one of four screen manipulations: (1) no manipulation, (2) manipulation by light fog, (3) manipulation by heavy fog, and (4) manipulation by heavy fog with a secondary task, which were used to induce varying levels of engagement with the driving task. Results showed that, during automation, drivers’ horizontal gaze was generally more dispersed than that observed during manual driving. Drivers clearly looked around more when their view of the driving scene was completely blocked by an opaque screen in the heavy fog condition. By contrast, horizontal gaze dispersion was (unsurprisingly) more concentrated when drivers performed a visual secondary task, which was overlaid on the opaque screen. However, once the manipulations ceased and an uncertainty alert captured drivers’ attention towards an impending incident, a similar gaze pattern was found for all drivers, with no carry-over effects observed after the screen manipulations. Results showed that drivers’ understanding of the automated system increased as time progressed, and that scenarios that encourage driver gaze towards the road centre are more likely to increase situation awareness during high levels of automation.

[1]  Klaus Bengler,et al.  “Take over!” How long does it take to get the driver back into the loop? , 2013 .

[2]  Natasha Merat,et al.  Driver Inattention During Vehicle Automation: How Does Driver Engagement Affect Resumption Of Control? , 2015 .

[3]  Natasha Merat,et al.  How do Drivers Behave in a Highly Automated Car , 2017 .

[4]  William B. Rouse,et al.  Human Detection and Diagnosis of System Failures , 1981 .

[5]  Kathrin Zeeb,et al.  What determines the take-over time? An integrated model approach of driver take-over after automated driving. , 2015, Accident; analysis and prevention.

[6]  Mark S. Young,et al.  Vehicle automation and driving performance , 1998 .

[7]  M. Posner,et al.  Orienting of Attention* , 1980, The Quarterly journal of experimental psychology.

[8]  Neil D. Lerner,et al.  Mind Wandering While Driving , 2015 .

[9]  Andy Field,et al.  Discovering statistics using SPSS: and sex and drugs and rock 'n' roll, 3rd Edition , 2009 .

[10]  Renwick E. Curry,et al.  Flight-deck automation: promises and problems , 1980 .

[11]  Leo Gugerty.  Situational Awareness in Driving , 2011 .

[12]  Niklas Strand,et al.  Semi-automated versus highly automated driving in critical situations caused by automation failures , 2014 .

[13]  Natasha Merat,et al.  Control Task Substitution in Semiautomated Driving , 2012, Hum. Factors.

[14]  L. Bainbridge.  Ironies of Automation , 1982 .

[15]  James P Foley.  Now You See It, Now You Don't: Visual Occlusion as a Surrogate Distraction Measurement Technique , 2009 .

[16]  D. Lafond,et al.  Cognitive shadow: A policy capturing tool to support naturalistic decision making , 2013, 2013 IEEE International Multi-Disciplinary Conference on Cognitive Methods in Situation Awareness and Decision Support (CogSIMA).

[17]  K. Bengler,et al.  Vigilance Decrement and Passive Fatigue Caused by Monotony in Automated Driving , 2015 .

[18]  Natasha Merat,et al.  Engaging with Highly Automated Driving: To be or Not to be in the Loop? , 2017 .

[19]  Klaus Bengler,et al.  Requirements for cooperative vehicle guidance , 2013, 16th International IEEE Conference on Intelligent Transportation Systems (ITSC 2013).

[20]  Harry W. Orlady,et al.  Flight deck automation: Promises and realities , 1989 .

[21]  Johan Engström,et al.  Effects of visual and cognitive load in real and simulated motorway driving , 2005 .

[22]  Ying Wang,et al.  The sensitivity of different methodologies for characterizing drivers’ gaze concentration under increased cognitive demand , 2014, Transportation Research Part F: Traffic Psychology and Behaviour.

[23]  Christina M. Rudin-Brown,et al.  BEHAVIOURAL ADAPTATION TO ADAPTIVE CRUISE CONTROL (ACC): IMPLICATIONS FOR PREVENTIVE STRATEGIES , 2004 .

[24]  John W. Senders,et al.  THE ATTENTIONAL DEMAND OF AUTOMOBILE DRIVING , 1967 .

[25]  J. Gregory Trafton,et al.  Situation Awareness Recovery , 2014, Hum. Factors.

[26]  D A Norman,et al.  The 'problem' with automation: inappropriate feedback and interaction, not 'over-automation'. , 1990, Philosophical transactions of the Royal Society of London. Series B, Biological sciences.

[27]  Natasha Merat,et al.  Highly Automated Driving, Secondary Task Performance, and Driver State , 2012, Hum. Factors.

[28]  David J. Cole,et al.  Neuromuscular dynamics in the driver–vehicle system , 2006 .

[29]  Nidhi Kalra,et al.  Autonomous Vehicle Technology: A Guide for Policymakers , 2014 .

[30]  Kevin W. Williams.  Designing for Situation Awareness: An Approach to User-Centered Design by Mica R. Endsley, Betty Bolté. & Debra G. Jones 2003. 333 pages. $120.00 (hardcover), $54.95 (paper) New York Taylor & Francis ISBN 0-748-40966-1 (hardcover) ISBN 0-748-40967-X (paper) , 2004 .

[31]  F. McKenna,et al.  Drivers' hazard perception ability: Situation awareness on the road , 2004 .

[32]  Farid Kianifard.  Applied Linear Regression Models , 1984 .

[33]  Mark Vollrath,et al.  Improving the Driver–Automation Interaction , 2013, Hum. Factors.

[34]  Natasha Merat,et al.  Behavioural changes in drivers experiencing highly-automated vehicle control in varying traffic conditions , 2013 .

[35]  Michael H. Kutner.  Applied Linear Statistical Models , 1974 .

[36]  Natasha Merat,et al.  Transition to manual: driver behaviour when resuming control from a highly automated vehicle , 2014 .

[37]  Mica R. Endsley,et al.  The Out-of-the-Loop Performance Problem and Level of Control in Automation , 1995, Hum. Factors.

[38]  Göran Falkman,et al.  Presenting system uncertainty in automotive UIs for supporting trust calibration in autonomous driving , 2013, AutomotiveUI.

[39]  Neville A. Stanton,et al.  Effects of adaptive cruise control and highly automated driving on workload and situation awareness: A review of the empirical evidence , 2014 .

[40]  Natasha Merat,et al.  Were they in the loop during automated driving? Links between visual attention and crash potential , 2016, Injury Prevention.

[41]  B. Tabachnick,et al.  Using Multivariate Statistics , 1983 .

[42]  Mica R. Endsley,et al.  Designing for Situation Awareness : An Approach to User-Centered Design , 2003 .

[43]  Mica R. Endsley,et al.  Designing for Situation Awareness : An Approach to User-Centered Design , 2003 .

[44]  Klaus Bengler,et al.  Towards an H-Mode for highly automated vehicles: driving with side sticks , 2009, AutomotiveUI.