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Author:

Wu, Y. (Wu, Y..) | Zhao, Z. (Zhao, Z..) | Peng, Z. (Peng, Z..) | Rong, J. (Rong, J..)

Indexed by:

Scopus

Abstract:

To develop an effective warning strategy for driving fatigue on grassland highway, which is based on the prone points of driver fatigue during driving on grassland highway, drivers were divided into three types of stimulation preference (i. e., visual, auditory, and tactile) according to stress response theory and neuro-linguistic program theory. The existing typical anti-fatigue on-board equipment was referenced to determine the stimulation means and parameters for driving fatigue. A personalized anti-fatigue warning strategy was constructed to satisfy drivers' various preferences for stimulus selection. The effectiveness of this strategy was verified by driving simulator experiments. Results show that compared with other stimulus modes and blank control group, when the type of warning stimulus was preferred by drivers, the self-rating value of driving fatigue state, speed eigenvalue, acceleration eigenvalue, lateral offset eigenvalue and throttle power eigenvalue changed the least before and after the experiment. Drivers have the highest understanding and acceptance as well as the least disturbance of the preferred warning stimulus. The evaluation results by TOPSIS method indicated that different stimulus modes had different effects on alleviating driving fatigue. With the deepening of fatigue degree, drivers' preferred warning stimulus would alleviate driving fatigue more effectively. © 2023 Beijing University of Technology. All rights reserved.

Keyword:

grassland highway driving fatigue personalization anti-fatigue warning driving simulator TOPSIS comprehensive evaluation

Author Community:

  • [ 1 ] [Wu Y.]Faculty of Architecture and Transportation Engineering, Beijing University of Technology, Beijing, 100124, China
  • [ 2 ] [Zhao Z.]Faculty of Architecture and Transportation Engineering, Beijing University of Technology, Beijing, 100124, China
  • [ 3 ] [Peng Z.]Riou Traffic Safety Co., Ltd., Beijing, 100088, China
  • [ 4 ] [Rong J.]Faculty of Architecture and Transportation Engineering, Beijing University of Technology, Beijing, 100124, China

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Source :

Journal of Beijing University of Technology

ISSN: 0254-0037

Year: 2023

Issue: 8

Volume: 49

Page: 884-895

Cited Count:

WoS CC Cited Count:

SCOPUS Cited Count: 1

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 2

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