Optimizing Trajectories for Highway Driving with Offline Reinforcement Learning

TL;DR

This paper presents an offline reinforcement learning approach for autonomous highway driving that learns to generate smooth, feasible, and efficient trajectories, outperforming other methods in realistic simulated scenarios.

Contribution

The paper introduces a novel offline RL method for trajectory planning in highway driving, combining learning-based flexibility with rule-based safety guarantees.

Findings

The offline RL agent achieves velocities close to desired targets.

It outperforms four other highway driving agents in simulations.

The approach demonstrates effective learning from randomly collected data.

Abstract

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.