共驾型智能汽车控制权限转移算法研究

doi:10.3901/JME.2023.16.275

摘要/Abstract

摘要： 人机共驾型智能汽车中驾驶员和自动控制器共享车辆的控制权。其中，人类驾驶员能够更好地适应未知复杂环境，自动控制器在已知环境中具有更高的控制精度，车辆控制权限在两者之间转移可实现“1+1＞2”的控制效果。提出一种考虑驾驶员个性化操纵偏好的权限转移控制策略。首先，采用模型预测控制方法设计车辆控制器，根据驾驶员在视觉预瞄、反馈控制、比例增益、大脑和神经肌肉延迟的个性化操纵特性对所设计的控制器进行拟人化改进，以减少权限转移过程中的人机冲突。其次，提出一种基于样条曲线方法的柔性化权限转移策略，并在权限转移策略中考虑驾驶员的个性化预瞄时间和反应时间约束，使其能够符合驾驶员的操纵偏好。最后，将提出的柔性化权限转移策略与常见的阶跃式和渐进式两种权限转移方法进行对比。驾驶员在环试验结果表明，相比于常见的两种方法，采用设计的权限转移策略，使得新手驾驶员的路径跟踪精度分别提高33.8%和32.4%，权限转移过程中的驾驶舒适性分别提高50.6%和45.8%；熟练驾驶员的路径跟踪精度分别提高42%和33.3%，驾驶舒适性分别提高57.8%和48%。

关键词: 人机协同驾驶, 控制权限转移, 拟人化控制器, 模型预测控制

Abstract: In an intelligent vehicle of human-machine cooperative driving, the driver and the automatic controller share the control authority of the vehicle. Among them, the human driver can better adapt to the unknown environment, and the automatic controller has higher control accuracy in the known environment. Thus, the vehicle control authority can be transferred between them to achieve the control effect of “1+1＞2”. An authority transfer strategy is proposed, which takes into account the driver’s individual handling preferences. Firstly, a model predictive control（MPC） method is used to design the vehicle controller, and the designed controller is anthropomorphically improved according to the driver’s individual handling characteristics in visual preview, feedback control,proportional gain, brain response delay and neuromuscular system. Then the conflict of human-machine in the process of authority transfer can be reduced. Secondly, a flexible authority transfer strategy based on the spline curve method is proposed, which is constrained by the driver’s personalized preview time and reaction time. Then, the proposed strategy can be more in line with the driver’s handling preferences. Finally, the proposed strategy of authority transfer is compared with two commonly used authority transfer methods, step and gradual. The results of the driver-in-the-loop experiments show that, compared with the two commonly used methods, the path tracking accuracy of the unexperienced driver is improved by 33.8% and 32.4% through using the proposed authority transfer strategy, and the driving comfort during the process of authority transfer is improved by 50.6% and 45.8%,respectively. The path tracking accuracy of the experienced driver is improved by 42% and 33.3%, and the driving stability is improved by 57.8% and 48%, respectively.

Key words: human-machine cooperative driving, control authority transfer, human-like controller, model predictive control

中图分类号:

U463

严永俊, 林中盛, 王金湘, 方振伍, 汪䶮, 殷国栋. 共驾型智能汽车控制权限转移算法研究[J]. 机械工程学报, 2023, 59(16): 275-287.

YAN Yongjun, LIN Zhongsheng, WANG Jinxiang, FANG Zhenwu, WANG Yan, YIN Guodong. Research on Control Authority Transfer Algorithm for the Intelligent Vehicle with Human-machine Cooperative Driving[J]. Journal of Mechanical Engineering, 2023, 59(16): 275-287.

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