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作者:

Chen, Haolin (Chen, Haolin.) | Zhao, Xiaohua (Zhao, Xiaohua.) | Li, Zhenlong (Li, Zhenlong.) | Fu, Qiang (Fu, Qiang.) | Wang, Qiuhong (Wang, Qiuhong.) | Zhao, Libo (Zhao, Libo.)

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EI Scopus SCIE

摘要:

In automated driving, takeover behavior is crucial to safe transition of control. Based on this fact, the goal of the present study is to construct the driver's takeover behavior mode and verify its effectiveness. In the takeover process, we define the takeover behavior mode, which is the inherent mode of completing the takeover task, as the behavioral theoretical framework that drivers should follow when taking over. We conducted a driving simulation experiment to analyze driver takeover behavior in the fog zone, where non-driving-related tasks were external variables. First, we constructed the takeover behavior mode (situation awareness - decision and reaction - takeover performance). Second, we analyzed the characteristics of each stage and the correlation between each stage of the takeover behavior mode accordingly. The result shows that drivers' performance in each stage differed significantly under different non-driving-related tasks. For example, their saccade and gaze duration were longer on the entertainment task than on the work task, and the driver prioritized the longitudinal stability after taking control of the automated vehicle. Results show that there is a correlation between different stages of takeover behavior mode. In this study, we propose the takeover behavior mode, aiming to help beginners and researchers in this field understand the driver takeover process more intuitively and provide behavioral theoretical support for subsequent studies. In addition, the takeover behavior mode can provide support for training drivers in real-world driving situations.

关键词:

situation awareness Vehicles Correlation Task analysis Intelligent vehicles Automated vehicles Behavioral sciences non-driving-related tasks takeover behavior mode takeover performance fog zone Cameras Decision making driving simulator

作者机构:

  • [ 1 ] [Chen, Haolin]Beijing Univ Technol, Beijing Key Lab Traff Engn, Beijing 100124, Peoples R China
  • [ 2 ] [Zhao, Xiaohua]Beijing Univ Technol, Beijing Key Lab Traff Engn, Beijing 100124, Peoples R China
  • [ 3 ] [Li, Zhenlong]Beijing Univ Technol, Beijing Key Lab Traff Engn, Beijing 100124, Peoples R China
  • [ 4 ] [Fu, Qiang]Beijing Univ Technol, Beijing Key Lab Traff Engn, Beijing 100124, Peoples R China
  • [ 5 ] [Wang, Qiuhong]Res Inst Rd Safety MPS, Beijing 100062, Peoples R China
  • [ 6 ] [Zhao, Libo]Res Inst Rd Safety MPS, Beijing 100062, Peoples R China

通讯作者信息:

  • [Li, Zhenlong]Beijing Univ Technol, Beijing Key Lab Traff Engn, Beijing 100124, Peoples R China;;

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来源 :

IEEE TRANSACTIONS ON INTELLIGENT VEHICLES

ISSN: 2379-8858

年份: 2024

期: 2

卷: 9

页码: 4040-4054

8 . 2 0 0

JCR@2022

被引次数:

WoS核心集被引频次:

SCOPUS被引频次: 8

ESI高被引论文在榜: 0 展开所有

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