Abstract
Drivers of L3 automated vehicles (AVs) are not required to continuously monitor the AV system. However, they must be prepared to take over when requested. Therefore, it is necessary to design an in-vehicle environment that allows drivers to adapt their levels of preparedness to the likelihood of control transition. This study evaluates ambient in-vehicle lighting that continuously communicates the current level of AV reliability, specifically on how it could influence drivers’ take-over performance and mental workload (MW). We conducted an experiment in a driving simulator with 42 participants who experienced 10 take-over requests (TORs). The experimental group experienced a four-stage ambient light display that communicated the current level of AV reliability, which was not provided to the control group. The experimental group demonstrated better take-over performance, based on lower vehicle jerks. Notably, perceived MW did not differ between the groups, and the EEG indices of MW (frontal theta power, parietal alpha power, Task–Load Index) did not differ between the groups. These findings suggest that communicating the current level of reliability using ambient light might help drivers be better prepared for TORs and perform better without increasing their MW.
Funder
European Union's Horizon 2020 research and innovation program under the Marie Skłodowska-Curie grant agreement
Subject
Computer Networks and Communications,Computer Science Applications,Human-Computer Interaction,Neuroscience (miscellaneous)
Reference57 articles.
1. Definitions for Terms Related to On-Road Motor Vehicle Automated Driving Systems-J3016,2013
2. Getting Back Into the Loop: The Perceptual-Motor Determinants of Successful Transitions out of Automated Driving
3. Toward a Theory of Situation Awareness in Dynamic Systems
4. Effects of adaptive cruise control and highly automated driving on workload and situation awareness: A review of the empirical evidence
5. Feel the movement: Real motion influences responses to take-over requests in highly automated vehicles;Sadeghian Borojeni;Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems,2018
Cited by
7 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献