A Reinforcement Learning Benchmark for Autonomous Driving in Intersection Scenarios

TL;DR

This paper introduces RL-CIS, a comprehensive benchmark framework for training and testing reinforcement learning agents in complex urban intersection scenarios, addressing the need for realistic and fair evaluation environments.

Contribution

The paper presents a novel benchmark framework, RL-CIS, specifically designed for RL-based autonomous driving in intersection scenarios, including diverse baselines for fair comparison.

Findings

Established a comprehensive intersection scenario benchmark

Deployed various RL algorithms as baselines

Facilitated fair testing and comparison of RL methods

Abstract

In recent years, control under urban intersection scenarios becomes an emerging research topic. In such scenarios, the autonomous vehicle confronts complicated situations since it must deal with the interaction with social vehicles timely while obeying the traffic rules. Generally, the autonomous vehicle is supposed to avoid collisions while pursuing better efficiency. The existing work fails to provide a framework that emphasizes the integrity of the scenarios while being able to deploy and test reinforcement learning(RL) methods. Specifically, we propose a benchmark for training and testing RL-based autonomous driving agents in complex intersection scenarios, which is called RL-CIS. Then, a set of baselines are deployed consists of various algorithms. The test benchmark and baselines are to provide a fair and comprehensive training and testing platform for the study of RL for autonomous driving in the intersection scenario, advancing the progress of RL-based methods for intersection autonomous driving control. The code of our proposed framework can be found at https://github.com/liuyuqi123/ComplexUrbanScenarios.