School of Automobile, Chang'an University, Xi'an 710064, China.
Sensors (Basel). 2020 Aug 26;20(17):4821. doi: 10.3390/s20174821.
Developing a human-like autonomous driving system has gained increasing amounts of attention from both technology companies and academic institutions, as it can improve the interpretability and acceptance of the autonomous system. Planning a safe and human-like obstacle avoidance trajectory is one of the critical issues for the development of autonomous vehicles (AVs). However, when designing automatic obstacle avoidance systems, few studies have focused on the obstacle avoidance characteristics of human drivers. This paper aims to develop an obstacle avoidance trajectory planning and trajectory tracking model for AVs that is consistent with the characteristics of human drivers' obstacle avoidance trajectory. Therefore, a modified artificial potential field (APF) model was established by adding a road boundary repulsive potential field and ameliorating the obstacle repulsive potential field based on the traditional APF model. The model predictive control (MPC) algorithm was combined with the APF model to make the planning model satisfy the kinematic constraints of the vehicle. In addition, a human driver's obstacle avoidance experiment was implemented based on a six-degree-of-freedom driving simulator equipped with multiple sensors to obtain the drivers' operation characteristics and provide a basis for parameter confirmation of the planning model. Then, a linear time-varying MPC algorithm was employed to construct the trajectory tracking model. Finally, a co-simulation model based on CarSim/Simulink was established for off-line simulation testing, and the results indicated that the proposed trajectory planning controller and the trajectory tracking controller were more human-like under the premise of ensuring the safety and comfort of the obstacle avoidance operation, providing a foundation for the development of AVs.
开发类人自动驾驶系统得到了科技公司和学术机构越来越多的关注，因为它可以提高自动驾驶系统的可解释性和可接受性。规划安全且类人的避障轨迹是自动驾驶汽车（AV）发展的关键问题之一。然而，在设计自动避障系统时，很少有研究关注人类驾驶员的避障特性。本文旨在为 AV 开发与人类驾驶员避障轨迹特征一致的避障轨迹规划和轨迹跟踪模型。因此，通过在传统的 APF 模型基础上添加道路边界排斥势场和改进障碍物排斥势场，建立了一种改进的人工势场（APF）模型。模型预测控制（MPC）算法与 APF 模型相结合，使规划模型满足车辆的运动学约束。此外，基于配备多个传感器的六自由度驾驶模拟器进行了人类驾驶员避障实验，以获得驾驶员的操作特性，并为规划模型的参数确认提供依据。然后，采用线性时变 MPC 算法构建轨迹跟踪模型。最后，基于 CarSim/Simulink 建立了离线仿真测试的协同仿真模型，结果表明，在保证避障操作的安全性和舒适性的前提下，所提出的轨迹规划控制器和轨迹跟踪控制器更具类人性，为 AV 的发展提供了基础。
