这论文用安全滤波器和课程学习搞车辆控制，在CarSim上验证了，比传统方法省调参还更安全。
该论文提出一种结合基于物理的预测性安全滤波器的课程学习控制器，用于车辆运动与动力学控制。方法旨在缓解传统方法参数标定繁重的问题，同时提升学习控制的安全性和鲁棒性。在Python-CarSim平台上进行了验证，结果显示该方法在多种机动场景下具有更好的改进效果和可扩展性。
Predictive safety filter enhanced curriculum learning control for efficient vehicle dynamics controller
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.