How to Design the Emergency of Takeover Warning and Takeover Scene to Make Drivers have Better Takeover Performance?

This study aims to explore the driver's takeover performance in different emergency takeover scenes and takeover warnings, and to respond to how to design the emergency degree of takeover warning. First, we designed 4 takeover events based on takeover scenes (accident scene - emergency, ramp scene - non-emergency) and takeover request time variables (5s- emergency, l0s- non-emergency). 42 drivers participated in the driving simulation experiment, and 8 evaluation indicators were obtained from four dimensions: control behavior, eye tracking, heart rate and vehicle performance. The results show that the driver's takeover performance is globally optimal in non-emergency scenes and non-emergency warnings. In non-emergency situations, drivers can reduce stress behavior and maintain good driving status. After receiving the non-emergency takeover warning, the driver's control and psychological performance are better in the emergency scene. This study can provide a reference for a comprehensive evaluation of takeover performance in different takeover scenes and warnings, and can also be applied to the human-machine interaction takeover warning design of automated vehicles.

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Publication Details

Published
2024-10-25
DOI
https://doi.org/10.1109/cvci63518.2024.10830188
Citations
1
Primary Topic
Human-Automation Interaction and Safety
Type
article
Field-Weighted Citation Impact
0.64
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How to Design the Emergency of Takeover Warning and Takeover Scene to Make Drivers have Better Takeover Performance?

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article

How to Design the Emergency of Takeover Warning and Takeover Scene to Make Drivers have Better Takeover Performance?

Xiaohua Zhao, Qiang Fu, Qiuyang Tang, Zhenlong Li, Haolin Chen, Zhi‐Hao Wang
article en
1 citations

Abstract

This study aims to explore the driver's takeover performance in different emergency takeover scenes and takeover warnings, and to respond to how to design the emergency degree of takeover warning. First, we designed 4 takeover events based on takeover scenes (accident scene - emergency, ramp scene - non-emergency) and takeover request time variables (5s- emergency, l0s- non-emergency). 42 drivers participated in the driving simulation experiment, and 8 evaluation indicators were obtained from four dimensions: control behavior, eye tracking, heart rate and vehicle performance. The results show that the driver's takeover performance is globally optimal in non-emergency scenes and non-emergency warnings. In non-emergency situations, drivers can reduce stress behavior and maintain good driving status. After receiving the non-emergency takeover warning, the driver's control and psychological performance are better in the emergency scene. This study can provide a reference for a comprehensive evaluation of takeover performance in different takeover scenes and warnings, and can also be applied to the human-machine interaction takeover warning design of automated vehicles.

Beijing University of Technology (CN), China Automotive Engineering Research Institute (CN)
Climate action
Openalex Percentile: Top 23%
Human-Automation Interaction and Safety
0.64
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