用安全过滤器增强课程学习，提升车辆动态控制的安全与效率
Predictive safety filter enhanced curriculum learning control for efficient vehicle dynamics controller
- 结合物理模型预测安全约束，动态调整学习过程
- 在多种驾驶工况下实现更优的稳定性和敏捷性表现
- 适合关注自动驾驶控制安全性的研究者与工程师
基于学习的控制在多个领域取得显著进展，但缺乏安全性保障。针对车辆运动与动力学控制中传统方法参数调校繁琐、学习方法难以保证安全的问题，本文提出一种融合物理驱动预测安全过滤器的课程学习控制器。该方法通过引入基于物理的实时安全约束，指导学习过程，提升控制系统的鲁棒性与效率。在Python-CarSim平台上的验证表明，该方法在多种驾驶工况下均表现出更好的性能提升与可扩展性，显著优于先前方法在稳定性与敏捷性方面的表现。
原文摘要 · Abstract (English)
Recent advances in learning-based control have enabled impressive achievements in solving complex control problems in various domains. However, since learning-based control may not be able to realize safety-guaranties, it is of great importance to enhance safety and robustness while maintaining good performances. Take vehicle motion \& dynamics control as an example, in order to overcome the pain points of traditional methods such as heavy parameter calibration effort and learning-based control to bring better performance and efficiency in stability \& agility over prior work for state-based vehicle control tasks, in this work, our method aims to develop a curriculum learning controller enhanced with physics-based predictive safety filter. The validation is conducted with the Python-CarSim platform, demonstrating better improvements and scalability under various maneuvers.
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