Driver behavior and workload in an on-road automated vehicle

Driver behavior and workload in an on-road automated vehicle

Stapel, J.C.J. (TU Delft Intelligent Vehicles)
Mullakkal-Babu, F.A. (TU Delft Transport and Planning)
Happee, R. (TU Delft Intelligent Vehicles)

2017

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.

Automated Driving
On-road
Workload
Experience
Underload

http://resolver.tudelft.nl/uuid:53b8c5eb-a1ad-43f4-b349-286e8dffd8f1

Proceedings Road Safety & Simulation International Conference 2017

RSS2017: Road Safety and Simulation International Conference 2017, 2017-10-17 → 2017-10-19, Grand Hotel Amrâth Kurhaus, The Hague, Netherlands

Paper no. 284

Institutional Repository

conference paper

© 2017 J.C.J. Stapel, F.A. Mullakkal-Babu, R. Happee