Using Markov Chains to Understand the Sequence of Drivers' Gaze Transitions During Lane-Changes in Automated Driving

This paper reports the results of a driving simulator study, which analyzed differences in drivers’ raw gaze transition patterns during different stages of a lane-change maneuver, measured during manual, partially and conditionally automated driving. To understand whether the different levels of automation affected behaviour, and particularly how visual attention was distributed during a lane-change maneuver, a Markov chains approach was used to compare gaze transitions between the different information sources available in the surrounding road and cockpit environment, for each of the three drives. Results showed that drivers initiated fewer safety-related inspections (for example to the wing mirrors) during partial automation, throughout the whole lane change maneuver, possibly because they were focusing on how to the transition of control from automation. Drivers in this condition also had a higher probability of checking the system’s HMI, to verify the automation’s status. In contrast, during conditional automation, the lack of a need for vehicle control by the driver resulted in more gaze transitions between information sources, and fewer gazes to locations where a potential hazard could be present, when compared to manual. Finally, drivers generally only deviated their gaze towards information related to aspects of vehicle control they were responsible for, which we conclude could make them susceptible to missing hazards during both routine and safety-critical take-overs.

[1]  David Crundall,et al.  Effects of experience and processing demands on visual information acquisition in drivers , 1998 .

[2]  Ronald R. Mourant,et al.  Strategies of Visual Search by Novice and Experienced Drivers , 1972 .

[3]  T. Victor.  Keeping Eye and Mind on the Road , 2005 .

[4]  Jean Underwood,et al.  Visual attention while driving: sequences of eye fixations made by experienced and novice drivers , 2003, Ergonomics.

[5]  P. G. Gipps,et al.  A MODEL FOR THE STRUCTURE OF LANE-CHANGING DECISIONS , 1986 .

[6]  Dot Hs,et al.  Analysis of Lane-Change Crashes and Near-Crashes , 2009 .

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

[8]  Raja Parasuraman,et al.  Humans and Automation: Use, Misuse, Disuse, Abuse , 1997, Hum. Factors.

[9]  Natasha Merat,et al.  Engaging in NDRTs affects drivers’ responses and glance patterns after silent automation failures , 2019, Transportation Research Part F: Traffic Psychology and Behaviour.

[10]  Louis Tijerina,et al.  Eye Glance Behavior of van and Passenger Car Drivers during Lane Change Decision Phase , 2005 .

[11]  Charles A. Green,et al.  The “Out-of-the-Loop” concept in automated driving: proposed definition, measures and implications , 2018, Cognition, Technology & Work.

[12]  L Tijerina,et al.  EXAMINATION OF LANE CHANGE CRASHES AND POTENTIAL IVHS COUNTERMEASURES. FINAL REPORT , 1993 .

[13]  Natasha Merat,et al.  The effect of varying levels of vehicle automation on drivers’ lane changing behaviour , 2018, PloS one.

[14]  Natasha Merat,et al.  Are you in the loop? Using gaze dispersion to understand driver visual attention during vehicle automation , 2017 .

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

[16]  E. Seneta.  Non-negative Matrices and Markov Chains , 2008 .

[17]  Lisanne Bainbridge,et al.  Ironies of automation , 1982, Autom..

[18]  Tatsuya Suzuki,et al.  Analyzing driver gaze behavior and consistency of decision making during automated driving , 2015, 2015 IEEE Intelligent Vehicles Symposium (IV).

[19]  Andrew Liu,et al.  CONTROL AND MONITORING DURING LANE CHANGES , 2001 .

[20]  Tomohiro Yamamura,et al.  Lane-Change Detection Using a Computational Driver Model , 2007, Hum. Factors.

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