# A Distributed Approach to Autonomous Intersection Management via Multi-Agent Reinforcement Learning

arXiv: https://arxiv.org/abs/2405.08655 · HTML: https://mlsanity.com/paper/2405.08655/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 45 references that are in the map.
- First posted on arXiv 14 May 2024.

## Builds on (most cited first, 12 of 12)

- [Deep Reinforcement Learning with Double Q-learning](https://mlsanity.com/paper/1509.06461/) (2015)
- [Prioritized Experience Replay](https://mlsanity.com/paper/1511.05952/) (2015)
- [Dueling Network Architectures for Deep Reinforcement Learning](https://mlsanity.com/paper/1511.06581/) (2015)
- [Flow: A Modular Learning Framework for Mixed Autonomy Traffic](https://mlsanity.com/paper/1710.05465/) (2017)
- [Reinforcement Learning for Mixed Autonomy Intersections](https://mlsanity.com/paper/2111.04686/) (2021)
- [An Introduction to Multi-Agent Reinforcement Learning and Review of its Application to Autonomous Mobility](https://mlsanity.com/paper/2203.07676/) (2022)
- [A Fleet of Miniature Cars for Experiments in Cooperative Driving](https://mlsanity.com/paper/1902.06133/) (2019)
- [Cooperative Behavior Planning for Automated Driving using Graph Neural Networks](https://mlsanity.com/paper/2202.11376/) (2022)
- [Potential Game-Based Decision-Making for Autonomous Driving](https://mlsanity.com/paper/2201.06157/) (2022)
- [A Review on Energy, Environmental, and Sustainability Implications of Connected and Automated Vehicles](https://mlsanity.com/paper/1901.10581/) (2019)
- [A Survey of Generative AI for Intelligent Transportation Systems: Road Transportation Perspective](https://mlsanity.com/paper/2312.08248/) (2023)
- [An Enhanced Graph Representation for Machine Learning Based Automatic Intersection Management](https://mlsanity.com/paper/2207.08655/) (2022)

## Built on by (most cited first, 1 of 1)

- [A Fairness-Oriented Multi-Objective Reinforcement Learning approach for Autonomous Intersection Management](https://mlsanity.com/paper/2507.09311/) (2025)

## Abstract (arXiv)

Autonomous intersection management (AIM) poses significant challenges due to the intricate nature of real-world traffic scenarios and the need for a highly expensive centralised server in charge of simultaneously controlling all the vehicles. This study addresses such issues by proposing a novel distributed approach to AIM utilizing multi-agent reinforcement learning (MARL). We show that by leveraging the 3D surround view technology for advanced assistance systems, autonomous vehicles can accurately navigate intersection scenarios without needing any centralised controller. The contributions of this paper thus include a MARL-based algorithm for the autonomous management of a 4-way intersection and also the introduction of a new strategy called prioritised scenario replay for improved training efficacy. We validate our approach as an innovative alternative to conventional centralised AIM techniques, ensuring the full reproducibility of our results. Specifically, experiments conducted in virtual environments using the SMARTS platform highlight its superiority over benchmarks across various metrics.