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arXiv · 2503.22248

CRLLK: Constrained Reinforcement Learning for Lane Keeping in Autonomous Driving

Xinwei Gao·Arambam James Singh·Gangadhar Royyuru·Michael Yuhas·Arvind Easwaran
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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.

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Xinwei Gao, Arambam James Singh, Gangadhar Royyuru, Michael Yuhas, Arvind Easwaran. 2025-03-28. CRLLK: Constrained Reinforcement Learning for Lane Keeping in Autonomous Driving. https://arxiv.org/abs/2503.22248

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