CRLLK: Constrained Reinforcement Learning for Lane Keeping in Autonomous Driving

Xinwei Gao, Arambam James Singh, Gangadhar Royyuru, Michael Yuhas, Arvind Easwaran

Abstract: 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.