Driver behavior and workload in an on-road automated vehicle

Driver mental underload is an important concern in the operational safety of automated driving. In this study, workload was evaluated subjectively (NASA RTLX) and objectively (auditory detection-response task) on Dutch public highways (~150km) in a Tesla Model S comparing manual and supervised automated driving with moderators automation experience and traffic complexity. Participants (N=16) were either automationinexperienced drivers or automation-experienced Tesla owners. Complexity ranged from an engaging environment with a road geometry stimulating continuous traffic interaction, and a monotonic environment with lower traffic density and a simple road geometry. Perceived and objective workload increased with traffic complexity. Automation use reduced perceived workload in both environments for automation-experienced drivers, but not for inexperienced drivers. However, the DRT did not reveal a reduced attentional demand with automation. This suggests that attentive monitoring requires a similar attentional demand as manual driving. The findings highlight the relevance of using system-experienced participants and the relevance of on-road testing for behavioral validity.

[1]  N A Stanton,et al.  What's skill got to do with it? Vehicle automation and driver mental workload , 2007, Ergonomics.

[2]  Charles A. Green,et al.  Human factors evaluation of Level 2 and Level 3 automated driving concepts , 2015 .

[3]  Samantha Jamson,et al.  Temporal fluctuations in driving demand: the effect of traffic complexity on subjective measures of workload and driving performance , 2014 .

[4]  Riender Happee,et al.  Advantages and Disadvantages of Driving Simulators: A Discussion , 2012 .

[5]  Catherine Berthelon,et al.  Mental workload and driving , 2014, Front. Psychol..

[6]  Annika F.L. Larsson,et al.  Learning from experience: familiarity with ACC and responding to a cut-in situation in automated driving , 2014 .

[7]  Donald L. Fisher,et al.  Experienced drivers are quicker to achieve situation awareness than inexperienced drivers in situations of transfer of control within a Level 3 autonomous environment , 2016 .

[8]  N A Stanton,et al.  Transition to manual: Comparing simulator with on-road control transitions. , 2017, Accident; analysis and prevention.

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

[10]  Martin Krampell.  Accelerated Behavioural Adaptation through Targeted Training Programs : the Case of Highly Automated Driving , 2016 .

[11]  J. Krems,et al.  Learning and development of trust, acceptance and the mental model of ACC. A longitudinal on-road study , 2015 .

[12]  Katja Kircher,et al.  Vehicle-based studies of driving in the real world: the hard truth? , 2013, Accident; analysis and prevention.

[13]  A. Riener.  Assessment of Simulator Fidelity and Validity in Simulator and On-the-road Studies , 2011 .

[14]  Jordan Navarro,et al.  Obstacle avoidance under automated steering: Impact on driving and gaze behaviours , 2016 .

[15]  Edward M. Hitchcock,et al.  Active and passive fatigue in simulated driving: discriminating styles of workload regulation and their safety impacts. , 2013, Journal of experimental psychology. Applied.

[16]  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 .

[17]  Victoria A Banks,et al.  Keep the driver in control: Automating automobiles of the future. , 2016, Applied ergonomics.

[18]  Toshiya Hirose,et al.  Driving Characteristics of Drivers in a State of Low Alertness when an Autonomous System Changes from Autonomous Driving to Manual Driving , 2015 .

[19]  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.

[20]  Colin G. Drury,et al.  Foundations for an Empirically Determined Scale of Trust in Automated Systems , 2000 .

[21]  Joost C. F. de Winter,et al.  Acclimatizing to automation: Driver workload and stress during partially automated car following in real traffic , 2019, Transportation Research Part F: Traffic Psychology and Behaviour.

[22]  Haneen Farah,et al.  Towards Real-Time, Nonintrusive Estimation of Driver Workload in a Simulated Environment , 2017 .

[23]  Klaus Bengler,et al.  Taking Over Control From Highly Automated Vehicles in Complex Traffic Situations , 2016, Hum. Factors.

[24]  Marika Hoedemaeker,et al.  Driver behavior in an emergency situation in the Automated Highway System , 1999 .

[25]  Alexandra Neukum,et al.  Secondary task engagement and vehicle automation - comparing the effects of different automation levels in an on-road experiment , 2016 .

[26]  Carina Fors,et al.  Sleepy driving on the real road and in the simulator--A comparison. , 2013, Accident; analysis and prevention.

[27]  Miguel Leitão,et al.  Reaction to a Critical Situation during Driving with Adaptive Cruise Control for Users and Non-Users of the System , 2015 .

[28]  Josef F. Krems,et al.  Keep Your Scanners Peeled , 2016, Hum. Factors.

[29]  Kan Zhang,et al.  The Measurement of Driver's Mental Workload: A Simulation-Based Study , 2007 .