Lv Yukang, Chen Yi, Chen Ziguo, Fan Yuze, Tao Yongchao, Zhao Rui, Gao Fei
College of Automotive Engineering, Jilin University, Changchun 130025, China.
National Key Laboratory of Automotive Chassis Integration and Bionics, Jilin University, Changchun 130025, China.
Sensors (Basel). 2025 Jun 12;25(12):3695. doi: 10.3390/s25123695.
Path tracking is an essential task for autonomous driving (AD), for which controllers are designed to issue commands so that vehicles will follow the path of upper-level decision planning properly to ensure operational safety, comfort, and efficiency. Current path-tracking methods still face challenges in balancing tracking accuracy with computational overhead, and more critically, lack consideration for Motion Sickness (MS) mitigation. However, as AD applications divert occupants' attention to non-driving activities at varying degrees, MS in self-driving vehicles has been significantly exacerbated. This study presents a novel framework, the Hybrid Supervised-Reinforcement Learning (HSRL), designed to reduce passenger discomfort while achieving high-precision tracking performance with computational efficiency. The proposed HSRL employs expert data-guided supervised learning to rapidly optimize the path-tracking model, effectively mitigating the sample efficiency bottleneck inherent in pure Reinforcement Learning (RL). Simultaneously, the RL architecture integrates a passenger MS mechanism into a multi-objective reward function. This design enhances model robustness and control performance, achieving both high-precision tracking and passenger comfort optimization. Simulation experiments demonstrate that the HSRL significantly outperforms Proportional-Integral-Derivative (PID) and Model Predictive Control (MPC), achieving improved tracking accuracy and significantly reducing passengers' cumulative Motion Sickness Dose Value (MSDV) across several test scenarios.
路径跟踪是自动驾驶（AD）的一项基本任务，为此设计了控制器来发出指令，以使车辆能够正确地遵循上层决策规划的路径，从而确保运行安全、舒适和高效。当前的路径跟踪方法在平衡跟踪精度与计算开销方面仍然面临挑战，更关键的是，缺乏对减轻晕动病（MS）的考虑。然而，随着自动驾驶应用在不同程度上分散了乘客对驾驶活动的注意力，自动驾驶车辆中的晕动病问题已显著加剧。本研究提出了一种新颖的框架，即混合监督强化学习（HSRL），旨在在实现高精度跟踪性能和计算效率的同时，减少乘客的不适感。所提出的HSRL采用专家数据引导的监督学习来快速优化路径跟踪模型，有效缓解了纯强化学习（RL）中固有的样本效率瓶颈。同时，强化学习架构将乘客晕动病机制集成到多目标奖励函数中。这种设计增强了模型的鲁棒性和控制性能，实现了高精度跟踪和乘客舒适度优化。仿真实验表明，HSRL明显优于比例积分微分（PID）和模型预测控制（MPC），在多个测试场景中实现了更高的跟踪精度，并显著降低了乘客的累积晕动病剂量值（MSDV）。
