Classification of Automated Lane-Change Styles by Modeling and Analyzing Truck Driver Behavior: A Driving Simulator Study

DOI PDF 研究データあり 参考文献43件 オープンアクセス
  • Zheng Wang
    Institute of Industrial Science, The University of Tokyo, Tokyo, Japan
  • Muhua Guan
    Institute of Industrial Science, The University of Tokyo, Tokyo, Japan
  • Jin Lan
    Institute of Industrial Science, The University of Tokyo, Tokyo, Japan
  • Bo Yang
    Institute of Industrial Science, The University of Tokyo, Tokyo, Japan
  • Tsutomu Kaizuka
    Department of Mechanical Science and Engineering, Kogakuin University, Tokyo, Japan
  • Junichi Taki
    Vehicle Experiment Department, Hino Motors Ltd., Tokyo, Japan
  • Kimihiko Nakano
    Institute of Industrial Science, The University of Tokyo, Tokyo, Japan

説明

Lane change is a highly demanding driving task. A number of traffic accidents are induced by erroneous maneuvers. An automated lane-change system has the potential to reduce the driver workload and improve driving safety. A challenge is to improve the driver acceptance of the automated system. From the perspective of human factors, an automated system with different styles would improve user acceptance, because drivers could drive with different styles in different driving scenarios. This paper proposes a method to design different lane-change styles for automated driving by analyzing and modeling truck-driver behavior. A truck driving simulator experiment with 12 participants was conducted to identify the driver-model parameters. The lane change styles were classified into three types: aggressive, medium, and conservative. The proposed automated lane-change system was evaluated by another truck driving simulator experiment with the same 12 participants. Moreover, the effects of different lane-change decisionmaking styles on the driver experience and acceptance were evaluated from the perspectives of both the ego truck and surrounding vehicles. The evaluation results demonstrate that different lane-change decisionmaking styles can be distinguished by drivers. Overall, the three styles were evaluated by the human drivers as being safe and reliable. The main contribution of this study is that it provides the insights into the design of an automated driving system with different driving styles. Furthermore, these observations can be applied to commercial automated trucks.

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