School of Automotive Studies, Tongji University, Shanghai 201804, China.
SAIC Motor Corporation Limited, Shanghai 201800, China.
Sensors (Basel). 2019 Sep 16;19(18):3996. doi: 10.3390/s19183996.
According to the existing mainstream automatic parking system (APS), a parking path is first planned based on the parking slot detected by the sensors. Subsequently, the path tracking module guides the vehicle to track the planned parking path. However, since the vehicle is non-linear dynamic, path tracking error inevitably occurs, leading to inclination and deviation of the parking. Accordingly, in this paper, a reinforcement learning-based end-to-end parking algorithm is proposed to achieve automatic parking. The vehicle can continuously learn and accumulate experience from numerous parking attempts and then learn the command of the optimal steering wheel angle at different parking slots. Based on this end-to-end parking, errors caused by path tracking can be avoided. Moreover, to ensure that the parking slot can be obtained continuously in the process of learning, a parking slot tracking algorithm is proposed based on the combination of vision and vehicle chassis information. Furthermore, given that the learning network output is hard to converge, and it is easy to fall into local optimum during the parking process, several reinforcement learning training methods in terms of parking conditions are developed. Lastly, by the real vehicle test, it is proved that using the proposed method can achieve a better parking attitude than using the path planning and path tracking-based method.
根据现有的主流自动泊车系统 (APS)，首先基于传感器检测到的车位规划泊车路径。随后，路径跟踪模块引导车辆跟踪规划的泊车路径。然而，由于车辆是非线性动态的，路径跟踪误差不可避免，导致泊车倾斜和偏离。因此，本文提出了一种基于强化学习的端到端泊车算法，以实现自动泊车。车辆可以从无数次泊车尝试中不断学习和积累经验，然后学习在不同车位最佳转向角度的命令。基于这种端到端泊车，可以避免路径跟踪产生的误差。此外，为了确保在学习过程中能够连续获得车位，提出了一种基于视觉和车辆底盘信息相结合的车位跟踪算法。此外，由于学习网络输出难以收敛，并且在泊车过程中容易陷入局部最优，因此针对泊车条件开发了几种强化学习训练方法。最后，通过实车测试，证明了使用所提出的方法可以实现比基于路径规划和路径跟踪的方法更好的泊车姿态。
