How Will Drivers Take Back Control in Automated Vehicles? A Driving Simulator Test of an Interleaving Framework

We explore the transfer of control from an automated vehicle to the driver. Based on data from N=19 participants who participated in a driving simulator experiment, we find evidence that the transfer of control often does not take place in one step. In other words, when the automated system requests the transfer of control back to the driver, the driver often does not simply stop the non-driving task. Rather, the transfer unfolds as a process of interleaving the non-driving and driving tasks. We also find that the process is moderated by the length of time available for the transfer of control: interleaving is more likely when more time is available. Our interface designs for automated vehicles must take these results into account so as to allow drivers to safely take back control from automation.

[1]  Michael Weber,et al.  Autonomous driving: investigating the feasibility of car-driver handover assistance , 2015, AutomotiveUI.

[2]  Shamsi T. Iqbal,et al.  Priming Drivers before Handover in Semi-Autonomous Cars , 2017, CHI.

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

[4]  Andrew L. Kun,et al.  Human-Machine Interaction for Vehicles: Review and Outlook , 2018, Found. Trends Hum. Comput. Interact..

[5]  John D. Lee,et al.  Trust in Automation: Designing for Appropriate Reliance , 2004, Hum. Factors.

[6]  David M. Neyens,et al.  Assessing drivers' response during automated driver support system failures with non-driving tasks. , 2017, Journal of safety research.

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

[8]  Zoe Falomir,et al.  Using Wearable Sensors to Detect Workload on Driving Simulated Scenarios , 2018, CCIA.

[9]  Wendy Ju,et al.  Emergency, Automation Off: Unstructured Transition Timing for Distracted Drivers of Automated Vehicles , 2015, 2015 IEEE 18th International Conference on Intelligent Transportation Systems.

[10]  Klaus Bengler,et al.  How Traffic Situations and Non-Driving Related Tasks Affect the Take-Over Quality in Highly Automated Driving , 2014 .

[11]  Niels Taatgen,et al.  What Makes Interruptions Disruptive?: A Process-Model Account of the Effects of the Problem State Bottleneck on Task Interruption and Resumption , 2015, CHI.

[12]  Andrew L. Kun,et al.  Calling while driving: an initial experiment with hololens , 2017 .

[13]  Neville A. Stanton,et al.  Takeover Time in Highly Automated Vehicles: Noncritical Transitions to and From Manual Control , 2017, Hum. Factors.

[14]  Andrew L. Kun,et al.  On the feasibility of using pupil diameter to estimate cognitive load changes for in-vehicle spoken dialogues , 2013, INTERSPEECH.

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

[16]  Deborah A Boehm-Davis,et al.  Reducing the disruptive effects of interruption: a cognitive framework for analysing the costs and benefits of intervention strategies. , 2009, Accident; analysis and prevention.

[17]  Duncan P. Brumby,et al.  Strategic Adaptation to Performance Objectives in a Dual-Task Setting , 2010, Cogn. Sci..

[18]  Joost C. F. de Winter,et al.  Determinants of take-over time from automated driving: A meta-analysis of 129 studies , 2019, Transportation Research Part F: Traffic Psychology and Behaviour.

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

[20]  Brian P. Bailey,et al.  Understanding changes in mental workload during execution of goal-directed tasks and its application for interruption management , 2008, TCHI.

[21]  Andrew L. Kun,et al.  Interactions between human–human multi-threaded dialogues and driving , 2012, Personal and Ubiquitous Computing.

[22]  Philipp Wintersberger,et al.  Let Me Finish before I Take Over: Towards Attention Aware Device Integration in Highly Automated Vehicles , 2018, AutomotiveUI.

[23]  Oscar Oviedo-Trespalacios,et al.  Getting away with texting: Behavioural adaptation of drivers engaging in visual-manual tasks while driving , 2018, Transportation Research Part A: Policy and Practice.

[24]  Gordon Fraser,et al.  Automatically testing self-driving cars with search-based procedural content generation , 2019, ISSTA.

[25]  Orit Shaer,et al.  Switching between augmented reality and a manual-visual task: a preliminary study , 2019, AutomotiveUI.