Optimizing Trajectories for Highway Driving with Offline Reinforcement Learning
This work addresses the problem of trajectory optimization for autonomous vehicles on highways, offering an incremental improvement by integrating rule-based safety with learning-based generalization.
The paper tackles the challenge of generating feasible, smooth, and efficient trajectories for autonomous highway driving by combining rule-based and learning-based approaches, resulting in an offline reinforcement learning agent that outperforms four other agents in simulations, achieving velocities close to the desired velocity.
Implementing an autonomous vehicle that is able to output feasible, smooth and efficient trajectories is a long-standing challenge. Several approaches have been considered, roughly falling under two categories: rule-based and learning-based approaches. The rule-based approaches, while guaranteeing safety and feasibility, fall short when it comes to long-term planning and generalization. The learning-based approaches are able to account for long-term planning and generalization to unseen situations, but may fail to achieve smoothness, safety and the feasibility which rule-based approaches ensure. Hence, combining the two approaches is an evident step towards yielding the best compromise out of both. We propose a Reinforcement Learning-based approach, which learns target trajectory parameters for fully autonomous driving on highways. The trained agent outputs continuous trajectory parameters based on which a feasible polynomial-based trajectory is generated and executed. We compare the performance of our agent against four other highway driving agents. The experiments are conducted in the Sumo simulator, taking into consideration various realistic, dynamically changing highway scenarios, including surrounding vehicles with different driver behaviors. We demonstrate that our offline trained agent, with randomly collected data, learns to drive smoothly, achieving velocities as close as possible to the desired velocity, while outperforming the other agents. Code, training data and details available at: https://nrgit.informatik.uni-freiburg. de/branka.mirchevska/offline-rl-tp.