Drivers’ Ability to Engage in a Non-Driving Related Task While in Automated Driving Mode in Real Traffic

Engaging in non-driving related tasks (NDRTs) while driving can be considered distracting and safety detrimental. However, with the introduction of highly automated driving systems that relieve drivers from driving, more NDRTs will be feasible. In fact, many car manufacturers emphasize that one of the main advantages with automated cars is that it “frees up time” for other activities while on the move. This paper investigates how well drivers are able to engage in an NDRT while in automated driving mode (i.e., SAE Level 4) in real traffic, via a Wizard of Oz platform. The NDRT was designed to be visually and cognitively demanding and require manual interaction. The results show that the drivers’ attention to a great extent shifted from the road ahead towards the NDRT. Participants could perform the NDRT equally well as when in an office (e.g. correct answers, time to completion), showing that the performance did not deteriorate when in the automated vehicle. Yet, many participants indicated that they noted and reacted to environmental changes and sudden changes in vehicle motion. Participants were also surprised by their own ability to, with ease, disconnect from driving. The presented study extends previous research by identifying that drivers to a high extent are able to engage in a NDRT while in automated mode in real traffic. This is promising for future of automated cars ability to “free up time” and enable drivers to engage in non-driving related activities.

[1]  J. C. Flanagan Psychological Bulletin THE CRITICAL INCIDENT TECHNIQUE , 2022 .

[2]  Johan Engström,et al.  Sensitivity of eye-movement measures to in-vehicle task difficulty , 2005 .

[3]  Karel Brookhuis,et al.  HMI and Safety-Related Driver Performance , 2004 .

[4]  Gijsbert Stoet,et al.  PsyToolkit: A Novel Web-Based Method for Running Online Questionnaires and Reaction-Time Experiments , 2017 .

[5]  Nadja Schömig,et al.  Secondary task engagement and disengagement in the context of highly automated driving , 2018, Transportation Research Part F: Traffic Psychology and Behaviour.

[6]  Bryan Reimer,et al.  Revisiting Radio Tuning: A Secondary Analysis Comparing Glance Behavior Across Five Vehicles , 2017 .

[7]  Lewis L. Chuang,et al.  Workshop on The Mobile Office , 2018, AutomotiveUI.

[8]  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 .

[9]  Sarah Pink,et al.  Building Collaborative Test Practices: Design Ethnography and WOz in Autonomous Driving Research , 2018, IxD&A.

[10]  Katja Kircher,et al.  Issues related to the driver distraction detection algorithm AttenD , 2009 .

[11]  Peter A. Hancock,et al.  Workload and Performance: Associations, Insensitivities, and Dissociations , 2018, Hum. Factors.

[12]  Marco Dozza,et al.  A Reference Model for Driver Attention in Automation: Glance Behavior Changes During Lateral and Longitudinal Assistance , 2019, IEEE Transactions on Intelligent Transportation Systems.

[13]  David R. Large,et al.  A Longitudinal Simulator Study to Explore Drivers' Behaviour in Level 3 Automated Vehicles , 2019, AutomotiveUI.

[14]  Nadja Schömig,et al.  Anticipatory and control processes in the interaction with secondary tasks while driving , 2011 .

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

[16]  Marco Dozza,et al.  Driving context influences drivers' decision to engage in visual-manual phone tasks: Evidence from a naturalistic driving study. , 2015, Journal of safety research.

[17]  Yong Gu Ji,et al.  How we can measure the non-driving-task engagement in automated driving: Comparing flow experience and workload. , 2018, Applied ergonomics.

[18]  S. Engeser,et al.  Flow, performance and moderators of challenge-skill balance , 2008 .

[19]  Joel Johansson,et al.  Automation Expectation Mismatch: Incorrect Prediction Despite Eyes on Threat and Hands on Wheel , 2018, Hum. Factors.

[20]  L. Cooper Mental rotation of random two-dimensional shapes , 1975, Cognitive Psychology.

[21]  Annie Rydström,et al.  Drivers Quickly Trust Autonomous Cars , 2018 .

[22]  Tobias Hecht,et al.  Non-driving Related Activities in Automated Driving - An Online Survey Investigating User Needs , 2019, IHSED.

[23]  Stephen M. Casner,et al.  The challenges of partially automated driving , 2016, Commun. ACM.

[24]  R. Goonetilleke,et al.  Simplified subjective workload assessment technique , 2001, Ergonomics.

[25]  R. Shepard,et al.  Mental Rotation of Three-Dimensional Objects , 1971, Science.

[26]  Philipp Wintersberger,et al.  Where we come from and where we are going: a review of automated driving studies , 2019, AutomotiveUI.

[27]  Klaus Bengler,et al.  How long does it take to relax? Observation of driver behavior during real-world conditionally automated driving , 2019 .

[28]  Alexandra Neukum,et al.  A Review of Non-driving-related Tasks Used in Studies on Automated Driving , 2017 .

[29]  O. Carsten,et al.  Do drivers self-regulate their engagement in secondary tasks at intersections? An examination based on naturalistic driving data. , 2020, Accident; analysis and prevention.

[30]  Jonas Andersson,et al.  Evaluating interactions with non-existing automated vehicles: three Wizard of Oz approaches , 2016, 2016 IEEE Intelligent Vehicles Symposium (IV).

[31]  Bernhard Wandtner,et al.  The role of self-regulation in the context of driver distraction: A simulator study , 2016, Traffic injury prevention.

[32]  Daniel Lüddecke,et al.  Retrospective and Future Automotive Infotainment Systems—100 Years of User Interface Evolution , 2017 .

[33]  Bastian Pfleging,et al.  Investigating user needs for non-driving-related activities during automated driving , 2016, MUM.

[34]  Shamsi T. Iqbal,et al.  Interrupted by my car? Implications of interruption and interleaving research for automated vehicles , 2019, Int. J. Hum. Comput. Stud..

[35]  Anthony Gerald King,et al.  Autonomous Driving - A Practical Roadmap , 2010 .

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

[37]  John G. Gaspar,et al.  The Effect of Partial Automation on Driver Attention: A Naturalistic Driving Study , 2019, Hum. Factors.

[38]  Gijsbert Stoet,et al.  PsyToolkit: A software package for programming psychological experiments using Linux , 2010, Behavior research methods.