The Effects of Takeover Request Modalities on Highly Automated Car Control Transitions
Overview
Authors
Affiliations
This study investigated the influences of takeover request (TOR) modalities on a drivers' takeover performance after they engaged in non-driving related (NDR) tasks in highly automated driving (HAD). Visual, vibrotactile, and auditory modalities were varied in the design of the experiment under four conditions: no-task, phone conversation, smartphone interaction, and video watching tasks. Driving simulator experiments were conducted to analyze the drivers' take-over performance by collecting data during the transition time of re-engaging control of the vehicle, the time taken to be on the loop, and time taken to be physically ready to drive. Data were gathered on the perceived usefulness, safety, satisfaction, and effectiveness for each TOR based on a self-reported questionnaire. Takeover and hands-on times varied considerably, as shown by high standard deviation values between modalities, especially for phone conversations and smartphone interaction tasks. Moreover, it was found that participants failed to take over control of the vehicle when they were given visual TORs for phone conversation and smartphone interaction tasks. The perceived safety and satisfaction varied for the NDR task. Results from the statistical analysis showed that the NDR task significantly influenced the takeover time, but there was no significant interaction effect between the TOR modalities and the NDR task. The results could potentially be applied to the design of safe and efficient transitions of highly controlled, automated driving, where drivers are enabled to engage in NDR tasks.
Getting back in the loop: Does autonomous driving duration affect driver's takeover performance?.
Portron A, Perrotte G, Ollier G, Bougard C, Bourdin C, Vercher J Heliyon. 2024; 10(3):e24112.
PMID: 38317989 PMC: 10839869. DOI: 10.1016/j.heliyon.2024.e24112.
Park Y, Ji J, Kang H Heliyon. 2024; 10(1):e23053.
PMID: 38173484 PMC: 10761363. DOI: 10.1016/j.heliyon.2023.e23053.
Influence of Multi-Modal Warning Interface on Takeover Efficiency of Autonomous High-Speed Train.
Jing C, Dai H, Yao X, Du D, Yu K, Yu D Int J Environ Res Public Health. 2023; 20(1).
PMID: 36612647 PMC: 9819043. DOI: 10.3390/ijerph20010322.
A Take-Over Performance Evaluation Model for Automated Vehicles from Automated to Manual Driving.
Yan L, Chen J, Wen C, Wan P, Peng L, Yu X Comput Intell Neurosci. 2022; 2022:3160449.
PMID: 35463280 PMC: 9033333. DOI: 10.1155/2022/3160449.
The Identification of Non-Driving Activities with Associated Implication on the Take-Over Process.
Yang L, Babayi Semiromi M, Xing Y, Lv C, Brighton J, Zhao Y Sensors (Basel). 2022; 22(1).
PMID: 35009582 PMC: 8747182. DOI: 10.3390/s22010042.