CRLLK: Constrained Reinforcement Learning for Lane Keeping in Autonomous Driving

Xinwei Gao, Arambam James Singh, Gangadhar Royyuru, Michael Yuhas, Arvind Easwaran
3/28/2025
cs.LGcs.RO

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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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Cite this paper

@article{gao2025crllk,
  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},
  year   = {2025},
  eprint = {2503.22248},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2503.22248v1}
}

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