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.
View on arXiv@article{gao2025_2503.22248,
title={ CRLLK: Constrained Reinforcement Learning for Lane Keeping in Autonomous Driving },
author={ Xinwei Gao and Arambam James Singh and Gangadhar Royyuru and Michael Yuhas and Arvind Easwaran },
journal={arXiv preprint arXiv:2503.22248},
year={ 2025 }
}