Predictive safety filter enhanced curriculum learning control for efficient vehicle dynamics controller

• URL: http://arxiv.org/abs/2608.09653v1
• Date: Mon, 10 Aug 2026 14:28:44 GMT
• Title: Predictive safety filter enhanced curriculum learning control for efficient vehicle dynamics controller
• Abstract summary: This work aims to develop a curriculum learning controller enhanced with physics-based predictive safety filter.<n>The validation is conducted with the Python-CarSim platform, demonstrating better improvements and scalability under various maneuvers.
• Score: 12.523329808080279
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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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