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作者:

Hu, Dunli (Hu, Dunli.) | Feng, Xiaofan (Feng, Xiaofan.) | Zhao, Xiaohua (Zhao, Xiaohua.) | Li, Haijian (Li, Haijian.) | Ma, Jianming (Ma, Jianming.) | Fu, Qiang (Fu, Qiang.)

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SSCI

摘要:

Connected vehicle technology relying on Human Machine Interface (HMI) achieve a dominant position in the overall safety improvement. However, the impact of HMI on the driver's visual attention cannot be ignored, especially on the accident-prone foggy freeway. The objective of this paper is to evaluate the level of distraction caused by HMI in data analysis of drivers' visual characteristics and to establish a generic evaluation methodology. A connected vehicle test platform has been established based on the driving simulator, in which visibility was set to the level of heavy fog and the technical condition was set in two conditions (with or without HMI). Measurement of driving behavior parameters include frequency of fixations and saccades and the proportion of fixation. The researchers compared and analyzed the driver's visual characteristics and the degree of distraction in a combination of indices based on the AttenD algorithm, setting two technical conditions in a heavy fog. Drivers suffering more visual distraction and interference with HMI may have an impact on the driver's driving safety. The results provide a generic approach to evaluate the HMI of a connected vehicle system and a safety assessment methodology for the connected vehicle system.

关键词:

AttenD connected vehicle distraction heavy fog human machine interface visual characteristics

作者机构:

  • [ 1 ] [Hu, Dunli]Beijing Key Lab Field Bus Technol & Automat, Beijing, Peoples R China
  • [ 2 ] [Feng, Xiaofan]Beijing Key Lab Field Bus Technol & Automat, Beijing, Peoples R China
  • [ 3 ] [Hu, Dunli]North China Univ Technol, Coll Elect & Control Engn, Beijing, Peoples R China
  • [ 4 ] [Feng, Xiaofan]North China Univ Technol, Coll Elect & Control Engn, Beijing, Peoples R China
  • [ 5 ] [Zhao, Xiaohua]Beijing Collaborat Innovat Ctr Metropolitan Trans, Beijing 100124, Peoples R China
  • [ 6 ] [Li, Haijian]Beijing Collaborat Innovat Ctr Metropolitan Trans, Beijing 100124, Peoples R China
  • [ 7 ] [Zhao, Xiaohua]Beijing Univ Technol, Coll Metropolitan Transportat, Beijing, Peoples R China
  • [ 8 ] [Li, Haijian]Beijing Univ Technol, Coll Metropolitan Transportat, Beijing, Peoples R China
  • [ 9 ] [Fu, Qiang]Beijing Univ Technol, Coll Metropolitan Transportat, Beijing, Peoples R China
  • [ 10 ] [Ma, Jianming]Texas Dept Transportat, Austin, TX USA

通讯作者信息:

  • [Zhao, Xiaohua]Beijing Collaborat Innovat Ctr Metropolitan Trans, Beijing 100124, Peoples R China

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来源 :

JOURNAL OF TRANSPORTATION SAFETY & SECURITY

ISSN: 1943-9962

年份: 2020

JCR分区:3

被引次数:

WoS核心集被引频次: 13

SCOPUS被引频次: 12

ESI高被引论文在榜: 0 展开所有

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