With the fast development of driving automation technologies, user psychological acceptance of driving automation has become one of the major obstacles to the adoption of the driving automation technology. The most basic function of a passenger car is to transport passengers or drivers to their destinations safely and comfortably. Thus, the design of the driving automation should not just guarantee the safety of vehicle operation but also ensure occupant subjective level of comfort. Hence this paper proposes a local path planning algorithm for obstacle avoidance with occupant subjective feelings considered. Firstly, turning and obstacle avoidance conditions are designed, and four classifiers in machine learning are used to respectively establish subjective and objective evaluation models that link the objective vehicle dynamics parameters and occupant subjective confidence. Then, two potential fields are established based on the artificial potential field, reflecting the psychological feeling of drivers on obstacles and road boundaries. Accordingly, a path planning algorithm and a path tracking algorithm are designed respectively based on model predictive control, and the psychological safety boundary and the optimal classifier are used as part of cost functions. Finally, co-simulations of MATLAB/Simulink and CarSim are carried out. The results confirm the effectiveness of the proposed control algorithm, which can avoid obstacles satisfactorily and improve the psychological feeling of occupants effectively.

翻译：随着驾驶自动化技术的快速发展，用户对驾驶自动化的心理接受度已成为该技术推广的主要障碍之一。乘用车最基本的功能是安全舒适地将乘客或驾驶员送达目的地。因此，驾驶自动化设计不仅需要保证车辆运行安全，还应确保乘员主观舒适度。为此，本文提出一种考虑乘员主观感受的避障局部路径规划算法。首先，设计转向与避障工况，利用机器学习中的四种分类器分别建立联系车辆客观动力学参数与乘员主观信心的主客观评价模型。然后，基于人工势场法构建两个势场，分别反映驾驶员对障碍物和道路边界的心理感受。据此，基于模型预测控制分别设计路径规划算法与路径跟踪算法，并将心理安全边界和最优分类器作为代价函数的组成部分。最后，开展MATLAB/Simulink与CarSim联合仿真。结果验证了所提控制算法的有效性，该算法能够良好地完成避障任务，并有效改善乘员心理感受。