CRLLK：自动驾驶中车道保持的约束强化学习
CRLLK: Constrained Reinforcement Learning for Lane Keeping in Autonomous Driving
AI总结：
提出将车道保持建模为约束强化学习，自动学习权重系数，免去场景特定调整，实验证明其优于传统RL并具有实际应用价值。
摘要：
自动驾驶系统中的车道保持需要针对不同目标进行特定场景的权重调整。我们将车道保持问题建模为约束强化学习问题，其中权重系数与策略一同自动学习，从而无需针对特定场景进行调整。实验表明，我们的方法在效率和可靠性上优于传统强化学习。此外，真实世界的演示验证了其在真实自动驾驶中的实用价值。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
