Driver behavior and workload in an on-road automated vehicle

Conference paper (2017)

Authors

J.C.J. Stapel Intelligent Vehicles - Mechanical, Maritime and Materials Engineering
F.A. Mullakkal-Babu Transport and Planning - CEG
R. Happee Intelligent Vehicles - Mechanical, Maritime and Materials Engineering
Research Group
Intelligent Vehicles (Mechanical, Maritime and Materials Engineering) (TU Delft)
Copyright: © 2017 J.C.J. Stapel, F.A. Mullakkal-Babu, R. Happee
Experience Automated Driving Workload On-road Underload
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Published Date

2017

Language

English

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Faculty

Mechanical, Maritime and Materials Engineering

Department

Cognitive Robotics

Research Group

Intelligent Vehicles

Abstract

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.