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Author:

Yao, Ying (Yao, Ying.) | Zhao, Xiaohua (Zhao, Xiaohua.) | Feng, Xiaofan (Feng, Xiaofan.) | Rong, Jian (Rong, Jian.) (Scholars:荣建)

Indexed by:

SSCI EI Scopus SCIE

Abstract:

Risk assessment of in-vehicle devices is an important part of the study of distracted driving. In this study, a visual distraction assessment method was formulated based on the AttenD algorithm. Driving simulator experiments were conducted with five secondary tasks including navigation, tuning the radio, replying to a text message, replying to a voice message, and making a phone call. Drivers' general visual and visual characteristics in the process of performing different secondary tasks were observed. An assessment method of secondary tasks based on the AttenD algorithm is proposed, and the degree of visual distraction of drivers with different experience and ages under different secondary tasks was evaluated and ranked. The results show that a significant difference exists among visual features under different secondary tasks; drivers' experience and age and secondary tasks had an interaction effect on visual features. This assessment method lays the foundation for the visual-manual standard development of in-vehicle information systems.

Keyword:

Visualization Accidents Computer crashes Vehicles Urban transportation distracted driving Task analysis visual features distraction assessment Mobile handsets AttenD algorithm Licenses

Author Community:

  • [ 1 ] [Yao, Ying]Beijing Univ Technol, Coll Metropolitan Transportat, Beijing Key Lab Traff Engn, Beijing 100124, Peoples R China
  • [ 2 ] [Zhao, Xiaohua]Beijing Univ Technol, Coll Metropolitan Transportat, Beijing Key Lab Traff Engn, Beijing 100124, Peoples R China
  • [ 3 ] [Rong, Jian]Beijing Univ Technol, Coll Metropolitan Transportat, Beijing Key Lab Traff Engn, Beijing 100124, Peoples R China
  • [ 4 ] [Feng, Xiaofan]North China Univ Technol, Beijing 100144, Peoples R China

Reprint Author's Address:

  • [Zhao, Xiaohua]Beijing Univ Technol, Coll Metropolitan Transportat, Beijing Key Lab Traff Engn, Beijing 100124, Peoples R China

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Source :

IEEE ACCESS

ISSN: 2169-3536

Year: 2020

Volume: 8

Page: 136108-136118

3 . 9 0 0

JCR@2022

Cited Count:

WoS CC Cited Count: 11

SCOPUS Cited Count: 11

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 3

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