Driver Take-Over Reaction in Autonomous Vehicles with Rotatable Seats

A new concept in the interior design of autonomous vehicles is rotatable or swivelling seats that allow people sitting in the front row to rotate their seats and face backwards. In the current study, we used a take-over request task conducted in a fixed-based driving simulator to compare two conditions, driver front-facing and rear-facing. Thirty-six adult drivers participated in the experiment using a within-subject design with take-over time budget varied. Take-over reaction time, remaining action time, crash, situation awareness and trust in automation were measured. Repeated measures ANOVA and Generalized Linear Mixed Model were conducted to analyze the results. The results showed that the rear-facing configuration led to longer take-over reaction time (on average 1.56 s longer than front-facing, p < 0.001), but it caused drivers to intervene faster after they turned back their seat in comparison to the traditional front-facing configuration. Situation awareness in both front-facing and rear-facing autonomous driving conditions were significantly lower (p < 0.001) than the manual driving condition, but there was no significant difference between the two autonomous driving conditions (p = 1.000). There was no significant difference of automation trust between front-facing and rear-facing conditions (p = 0.166). The current study showed that in a fixed-based simulator representing a conditionally autonomous car, when using the rear-facing driver seat configuration (where participants rotated the seat by themselves), participants had longer take-over reaction time overall due to physical turning, but they intervened faster after they turned back their seat for take-over response in comparison to the traditional front-facing seat configuration. This behavioral change might be at the cost of reduced take-over response quality. Crash rate was not significantly different in the current laboratory study (overall the average rate of crash was 11%). A limitation of the current study is that the driving simulator does not support other measures of take-over request (TOR) quality such as minimal time to collision and maximum magnitude of acceleration. Based on the current study, future studies are needed to further examine the effect of rotatable seat configurations with more detailed analysis of both TOR speed and quality measures as well as in real world driving conditions for better understanding of their safety implications.

[1]  Nengchao Lyu,et al.  Modeling Driver Take-Over Reaction Time and Emergency Response Time using an Integrated Cognitive Architecture , 2019, Transportation Research Record: Journal of the Transportation Research Board.

[2]  Ann M. Bisantz,et al.  Assessment of operator trust in and utilization of automated decision-aids under different framing conditions , 2001 .

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

[4]  Riender Happee,et al.  Modeling take-over performance in level 3 conditionally automated vehicles. , 2017, Accident; analysis and prevention.

[5]  Kathrin Zeeb,et al.  What determines the take-over time? An integrated model approach of driver take-over after automated driving. , 2015, Accident; analysis and prevention.

[6]  Xin Jin,et al.  Occupant kinematics and biomechanics with rotatable seat in autonomous vehicle collision: a preliminary concept and strategy , 2018 .

[7]  Changxu Wu,et al.  The Effects of Lead Time of Take-Over Request and Nondriving Tasks on Taking-Over Control of Automated Vehicles , 2018, IEEE Transactions on Human-Machine Systems.

[8]  R. M. Taylor,et al.  Multi-Modal Cockpit Warnings: Pictures. Words. or Both? , 1992 .

[9]  G. Weltman,et al.  Perceptual Narrowing during Simulated Pressure-Chamber Exposure , 1971, Human factors.

[10]  Klaus Bengler,et al.  How does relaxing posture influence take-over performance in an automated vehicle? , 2018, Proceedings of the Human Factors and Ergonomics Society Annual Meeting.

[11]  Wayne A. Wickelgren,et al.  Speed-accuracy tradeoff and information processing dynamics , 1977 .

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

[13]  Mica R. Endsley,et al.  Situation awareness global assessment technique (SAGAT) , 1988, Proceedings of the IEEE 1988 National Aerospace and Electronics Conference.

[14]  J. Dusek,et al.  Impact of Incremental Increases in Cognitive Workload on Physiological Arousal and Performance in Young Adult Drivers , 2009 .

[15]  Taxonomy and definitions for terms related to driving automation systems for on-road motor vehicles , 2022 .

[16]  Katarina Bohman,et al.  Seating positions and activities in highly automated cars: a qualitative study of future automated driving scenarios , 2017 .

[17]  Moritz Körber,et al.  Introduction matters: Manipulating trust in automation and reliance in automated driving. , 2018, Applied ergonomics.

[18]  W. Spirduso.  Reaction and movement time as a function of age and physical activity level. , 1975, Journal of gerontology.

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

[20]  Neville A. Stanton,et al.  Take-Over Time in Highly Automated Vehicles , 2018, Driver Reactions to Automated Vehicles.

[21]  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 .

[22]  Riender Happee,et al.  Take-over performance in evasive manoeuvres. , 2017, Accident; analysis and prevention.

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

[24]  Edgar Erdfelder,et al.  G*Power 3: A flexible statistical power analysis program for the social, behavioral, and biomedical sciences , 2007, Behavior research methods.

[25]  I. C. MariAnne Karlsson,et al.  Setting the stage for autonomous cars: a pilot study of future autonomous driving experiences , 2015 .

[26]  George Mason.  Situation Awareness, Mental Workload, and Trust in Automation:Viable, Empirically Supported Cognitive Engineering Constructs , 2011 .

[27]  Philipp Wintersberger,et al.  Traffic Augmentation as a Means to Increase Trust in Automated Driving Systems , 2017, CHItaly.