A Reinforcement Learning Benchmark for Autonomous Driving in Intersection Scenarios.
2021
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.
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