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Author:

Chen, H. (Chen, H..) | Zhao, X. (Zhao, X..) | Li, Z. (Li, Z..) | Fu, Q. (Fu, Q..) | Wang, Q. (Wang, Q..) | Zhao, L. (Zhao, L..)

Indexed by:

EI Scopus SCIE

Abstract:

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. IEEE

Keyword:

Cameras Situation awareness Takeover performance Decision making Correlation Intelligent vehicles Fog zone Behavioral sciences Non-driving-related tasks Automated vehicles Task analysis Takeover behavior mode Driving simulator Vehicles

Author Community:

  • [ 1 ] [Chen H.]Beijing Key Laboratory of Traffic Engineering, Beijing University of Technology, Beijing, China
  • [ 2 ] [Zhao X.]Beijing Key Laboratory of Traffic Engineering, Beijing University of Technology, Beijing, China
  • [ 3 ] [Li Z.]Beijing Key Laboratory of Traffic Engineering, Beijing University of Technology, Beijing, China
  • [ 4 ] [Fu Q.]Beijing Key Laboratory of Traffic Engineering, Beijing University of Technology, Beijing, China
  • [ 5 ] [Wang Q.]Research Institute for Road Safety of MPS, Beijing, China
  • [ 6 ] [Zhao L.]Research Institute for Road Safety of MPS, Beijing, China

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Source :

IEEE Transactions on Intelligent Vehicles

ISSN: 2379-8858

Year: 2023

Issue: 2

Volume: 9

Page: 1-15

8 . 2 0 0

JCR@2022

Cited Count:

WoS CC Cited Count: 0

SCOPUS Cited Count: 7

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 7

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