Cooperative Takeover Method for Automated Vehicles Based on Indirect Shared Control

被引：0
|
作者：
Wu C.-Z. [1 ,2 ]
Wu H.-R. [1 ,2 ]
Lyu N.-C. [1 ,2 ]
机构：
[1] Intelligent Transportation Systems Research Center, Wuhan University of Technology, Wuhan
[2] National Engineering Research Center for Transportation Safety of Ministry of Education, Wuhan University of Technology, Wuhan
来源：
Zhongguo Gonglu Xuebao/China Journal of Highway and Transport | 2022年 / 35卷 / 03期
基金：
中国国家自然科学基金;
关键词：
Automated driving; Automotive engineering; Control transition; Human factor engineering; Human-vehicle collaboration; Indirect shared control;
D O I：
10.19721/j.cnki.1001-7372.2022.03.009
中图分类号：
学科分类号：
摘要：
The transfer of control from automatic to manual driving has become a popular research topic in the field of intelligent vehicles. In this study, an intelligent vehicle cooperative takeover method based on indirect shared control is proposed to improve the safety and stability problems caused by the lack of cognition and operational ability when the driver takes over control of the vehicle from non-driving related tasks based on a summary of previous studies. First, it was determined that the application scenario of the cooperative takeover method is the vehicle that needs to be taken over with a damaged global perception but good control resources. The takeover scenario is forward obstacle avoidance with tight time. Second, a local path-planning algorithm was designed using a sigmoid function. On the basis of meeting the constraints of collision-free and lateral stability, the target trajectory was planned with minimization of the curve slope as the optimization objective. Third, vehicle dynamics and kinematics models were established. A model predictive control algorithm was adopted to minimize the error in tracking the target trajectory and steering angle, and the control output of the automatic system was calculated. Simultaneously, the reciprocal of time-to-collision and lateral acceleration were utilized as the characterization indices of vehicle longitudinal and lateral driving risk, and a control weight distribution model that can be dynamically adjusted according to driving risk was designed to facilitate a smooth control transition. Finally, real drivers were invited as participants, and their non-driving-related task states under automated driving conditions were simulated. The driving simulator experiment verified the improvement effect of the designed cooperative takeover method on the safety, stability performance, workload, and other physiological indicators of the driver takeover process. Based on the results, it can be stated that the cooperative takeover method could significantly improve safety and stability performance after takeover, improve the workload mutation phenomenon of drivers, and significantly improve system acceptance. © 2022, Editorial Department of China Journal of Highway and Transport. All right reserved.
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页码：101 / 114
页数：13
相关论文
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