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.

翻译：自动驾驶系统中的车道保持任务需要针对不同目标进行场景特定的权重调优。本文将车道保持问题建模为一个约束强化学习问题，其中权重系数与策略一同自动学习，从而无需进行场景特定的调优。实验结果表明，我们的方法在效率和可靠性上均优于传统强化学习。此外，实际道路演示验证了该方法在现实世界自动驾驶中的实用价值。