The relationship between level of engagement in a non-driving task and driver response time when taking control of an au
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ORIGINAL ARTICLE
The relationship between level of engagement in a non‑driving task and driver response time when taking control of an automated vehicle Philippe Rauffet1 · Assaf Botzer2 · Christine Chauvin1 · Farida Saïd3 · Camille Tordet1 Received: 2 May 2019 / Accepted: 23 October 2019 © Springer-Verlag London Ltd., part of Springer Nature 2019
Abstract Drivers of conditionally automated vehicles may occasionally be required to take control of their vehicle due to system boundaries, but their performance in such cases might be impaired if they were engaged in non-driving tasks. In this study, we conducted an experiment in a driving simulator, where the non-driving task involved playing a video game. We tested whether, after a take-over request (TOR), driver behaviour can be predicted from measures of game engagement. A sample of 28 participants drove in two counterbalanced conditions—manual driving and automated driving—and needed to change lanes at a certain time in their trip following auditory and visual requests. In the automated condition, drivers could play an endless runner game and were instructed to deactivate the automated mode to change lanes when they received a TOR. We used the proportion of glance durations on the game and the time between game sessions as indicators of game engagement. Findings showed that drivers were highly engaged in the video game during the automated driving session (more than 70% of the time) and that the inspection of driving-related areas of interests was significantly altered by this engagement. Moreover, the two indicators of game engagement predicted drivers’ response times to the TOR. Our findings suggest that indices of game engagement might assist in setting better timing for TORs and therefore, that it might be beneficial to synchronize measures of game engagement consoles with automated vehicle decision algorithms. Keywords Autonomous driving · Perception · Decision-making · Level of engagement · Gaze behaviour
1 Introduction
* Philippe Rauffet philippe.rauffet@univ‑ubs.fr Assaf Botzer [email protected] Christine Chauvin christine.chauvin@univ‑ubs.fr Farida Saïd farida.said@univ‑ubs.fr Camille Tordet tordet@univ‑ubs.fr 1
Lab‑STICC UMR CNRS 6285, University of South Brittany, Lorient, France
2
Department of Industrial Engineering and Management, Ariel University, Ariel, Israel
3
LMBA UMR CNRS 6205, University of South Brittany, Lorient, France
Automated functions in vehicles such as adaptive cruise control (ACC) and lane keeping assistance have become increasingly prevalent due to their contribution to driver comfort and potential contribution to driver safety (Bishop 2000). To gain additional benefits from automation, the next step in vehicle control is expected to be conditionally automated vehicles. The SAE International standard (SAE International 2018) defines six levels of driving automation, from level 0 (full manual driving) to level 5 (full automation). At SAE level 3 of conditional automation, the vehicle most often operates automatic
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