CRLLK: Constrained Reinforcement Learning for Lane Keeping in Autonomous Driving
This work addresses the need for automated tuning in autonomous driving systems, though it appears incremental as it builds on existing constrained RL methods.
The paper tackled the problem of scenario-specific weight tuning in lane keeping for autonomous driving by formulating it as a constrained reinforcement learning problem, resulting in improved efficiency and reliability compared to traditional RL, with real-world demonstrations validating its practical value.
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.