# A Reinforcement Learning Benchmark for Autonomous Driving in Intersection Scenarios

arXiv: https://arxiv.org/abs/2109.10557 · HTML: https://mlsanity.com/paper/2109.10557/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Placed in Reinforcement learning for driving, one of 9 directions in End-to-end autonomous driving.
- Builds on 13 of its 23 references that are in the map.
- First posted on arXiv 22 Sep 2021.

## Placed in

- End-to-end autonomous driving / [Reinforcement learning for driving](https://mlsanity.com/driving/reinforcement-learning-for-driving/)

## Builds on (most cited first, 13 of 13)

- [CARLA: An Open Urban Driving Simulator](https://mlsanity.com/paper/1711.03938/) (2017)
- [Addressing Function Approximation Error in Actor-Critic Methods](https://mlsanity.com/paper/1802.09477/) (2018)
- [Deep Reinforcement Learning for Autonomous Driving: A Survey](https://mlsanity.com/paper/2002.00444/) (2020)
- [A Survey of Autonomous Driving: Common Practices and Emerging Technologies](https://mlsanity.com/paper/1906.05113/) (2019)
- [A Survey of End-to-End Driving: Architectures and Training Methods](https://mlsanity.com/paper/2003.06404/) (2020)
- [SMARTS: Scalable Multi-Agent Reinforcement Learning Training School for Autonomous Driving](https://mlsanity.com/paper/2010.09776/) (2020)
- [Interpretable End-to-end Urban Autonomous Driving with Latent Deep Reinforcement Learning](https://mlsanity.com/paper/2001.08726/) (2020)
- [SUMMIT: A Simulator for Urban Driving in Massive Mixed Traffic](https://mlsanity.com/paper/1911.04074/) (2019)
- [Social Attention for Autonomous Decision-Making in Dense Traffic](https://mlsanity.com/paper/1911.12250/) (2019)
- [Lane Change Decision-making through Deep Reinforcement Learning with Rule-based Constraints](https://mlsanity.com/paper/1904.00231/) (2019)
- [BARK: Open Behavior Benchmarking in Multi-Agent Environments](https://mlsanity.com/paper/2003.02604/) (2020)
- [Reinforcement Learning and Deep Learning based Lateral Control for Autonomous Driving](https://mlsanity.com/paper/1810.12778/) (2018)
- [Learning Negotiating Behavior Between Cars in Intersections using Deep Q-Learning](https://mlsanity.com/paper/1810.10469/) (2018)

## Built on by (most cited first, 4 of 4)

- [LanguageMPC: Large Language Models as Decision Makers for Autonomous Driving](https://mlsanity.com/paper/2310.03026/) (2023)
- [Forging Vision Foundation Models for Autonomous Driving: Challenges, Methodologies, and Opportunities](https://mlsanity.com/paper/2401.08045/) (2024)
- [Towards Socially Responsive Autonomous Vehicles: A Reinforcement Learning Framework with Driving Priors and Coordination Awareness](https://mlsanity.com/paper/2309.09726/) (2023)
- [Urban Computing in the Era of Large Language Models](https://mlsanity.com/paper/2504.02009/) (2025)

## Abstract (arXiv)

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.