汽车工程 ›› 2024, Vol. 46 ›› Issue (5): 795-804.doi: 10.19562/j.chinasae.qcgc.2024.05.006

• • 上一篇    

面向人机共驾模式下驾驶人接管过程的视觉转移特性研究

李梦凡1,冯忠祥2(),张卫华2,李靖宇1   

  1. 1.合肥工业大学土木与水利工程学院,合肥 230000
    2.合肥工业大学汽车与交通工程学院,合肥 230000
  • 收稿日期:2023-10-10 修回日期:2023-12-06 出版日期:2024-05-25 发布日期:2024-05-17
  • 通讯作者: 冯忠祥 E-mail:fzx@hfut.edu.cn

Study on Driver's Visual Transfer Characteristics During the Takeover Process of Human-Computer Co-driving Mode

Mengfan Li1,Zhongxiang Feng2(),Weihua Zhang2,Jingyu Li1   

  1. 1.School of Civil and Hydraulic Engineering,Hefei University of Technology,Hefei 230000
    2.School of Automobile and Traffic Engineering,Hefei University of Technology,Hefei 230000
  • Received:2023-10-10 Revised:2023-12-06 Online:2024-05-25 Published:2024-05-17
  • Contact: Zhongxiang Feng E-mail:fzx@hfut.edu.cn

摘要:

当在L3级自动驾驶系统操作期间,驾驶人无须时刻监管车辆,往往会处于脱离驾驶任务的状态,并可能从事各种与驾驶无关的任务。当自动驾驶系统遇到突发状况并发出接管请求时,驾驶人是否能安全及时地进行人机交互操作并顺利接管车辆是目前L3级自动驾驶所面临的重要问题。本文针对驾驶人接管操作过程,根据道路线形的差异性,设计了5种不同道路场景进行L3级自动驾驶接管试验,分析了驾驶人在不同道路场景下的注视熵,构建了驾驶人接管行为的马尔可夫链注视模型,探讨了自动驾驶人机交互过程中驾驶人的视觉转移特性。结果表明,驾驶人在接管操作期间表现出明显的注视行为规律,重点关注道路前方、次任务区域和人机交互区域。随着道路曲率半径的减小,驾驶人的视觉特性呈现出明显的变化,对道路前方和右侧区域的关注增加,而对次任务区域的关注减少。结果可为自动驾驶人机界面的优化研究提供科学依据,从而提高驾驶员接管绩效和驾驶安全。

关键词: 自动驾驶, 人机交互, 人机接管, 注视特性, 马尔可夫链

Abstract:

When the driver is not required to supervise the vehicle at all times during the L3 autonomous system operation, he or she is often removed from the driving task and may be engaged in a variety of non-driving related tasks. When the autonomous driving system encounters unexpected situation and sends a takeover request, whether the driver can safely and timely conduct human-machine interaction and take over the vehicle is an important issue for L3 autonomous driving. In this paper, five different road scenarios are designed for L3 autonomous driving takeover experiments based on the difference of road alignment, the entropy of driver's gaze in different road scenarios is analyzed, a Markov chain gaze model of driver's takeover behavior is constructed, and the visual transfer characteristics of the driver during human-computer interaction in the intelligent cockpit are explored. The results show that the driver shows obvious staring behavior in the process of take over, with a focus on the road ahead, sub task areas and human-machine interaction areas. With the decrease of road curvature radius, the driver's staring behavior changes obviously, and the driver's attention to the front and right side of the road increases, while the attention to the sub-mission area decreases. The results can provide a scientific basis for the optimization of the human-machine interface for autonomous driving, thereby improving driver takeover performance and driving safety.

Key words: autonomous driving, human-computer interaction, human-machine takeover, gaze characteristics, Markov chain