Lane keeping in autonomous driving systems requires scenario-specific weight tuning for different objectives. We formulate lane-keeping as a constrained reinforcement learning problem, where weight coefficients are automatically learned along with the policy, eliminating the need for scenario-specific tuning. Empirically, our approach outperforms traditional RL in efficiency and reliability. Additionally, real-world demonstrations validate its practical value for real-world autonomous driving.
@inproceedings{gao2025crllk,title={CRLLK: Constrained Reinforcement Learning for Lane Keeping in Autonomous Driving},author={Gao, Xinwei and Singh, Arambam James and Royyuru, Gangadhar and Yuhas, Michael and Easwaran, Arvind},booktitle={Proceedings of the 24th International Conference on Autonomous Agents and Multiagent Systems},pages={3026--3028},year={2025},doi={10.65109/lfce1407}}
