Autonomous Driving Strategies at Intersections: Scenarios, State-of-the-Art, and Future Outlooks
This is an incremental review paper for researchers in intelligent transportation systems.
This paper reviews state-of-the-art autonomous driving strategies for complex intersection scenarios, summarizing common scenario types, simulation platforms, datasets, and categorizing existing approaches while identifying their limitations and proposing future research directions.
Due to the complex and dynamic character of intersection scenarios, the autonomous driving strategy at intersections has been a difficult problem and a hot point in the research of intelligent transportation systems in recent years. This paper gives a brief summary of state-of-the-art autonomous driving strategies at intersections. Firstly, we enumerate and analyze common types of intersection scenarios, corresponding simulation platforms, as well as related datasets. Secondly, by reviewing previous studies, we have summarized characteristics of existing autonomous driving strategies and classified them into several categories. Finally, we point out problems of the existing autonomous driving strategies and put forward several valuable research outlooks.