A Reinforcement Learning Benchmark for Autonomous Driving in Intersection Scenarios
This work addresses the need for standardized evaluation in autonomous driving research, though it is incremental as it builds on existing methods by creating a new benchmark.
The authors tackled the lack of a comprehensive framework for testing reinforcement learning methods in autonomous driving at intersections by proposing RL-CIS, a benchmark that includes various baseline algorithms to provide a fair training and testing platform.
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.