# A Fairness-Oriented Multi-Objective Reinforcement Learning approach for Autonomous Intersection Management

arXiv: https://arxiv.org/abs/2507.09311 · HTML: https://mlsanity.com/paper/2507.09311/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in RL for autonomous driving and traffic, one of 15 directions in Deep reinforcement learning methods.
- Builds on 13 of its 26 references that are in the map.
- First posted on arXiv 12 Jul 2025.

## Placed in

- Deep reinforcement learning methods / [RL for autonomous driving and traffic](https://mlsanity.com/deeprl/rl-for-autonomous-driving-and-traffic/)

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

- [Addressing Function Approximation Error in Actor-Critic Methods](https://mlsanity.com/paper/1802.09477/) (2018)
- [Modeling Relational Data with Graph Convolutional Networks](https://mlsanity.com/paper/1703.06103/) (2017)
- [A Survey of Multi-Objective Sequential Decision-Making](https://mlsanity.com/paper/1402.0590/) (2014)
- [Flow: A Modular Learning Framework for Mixed Autonomy Traffic](https://mlsanity.com/paper/1710.05465/) (2017)
- [Multi-Objective Reinforcement Learning Based on Decomposition: A Taxonomy and Framework](https://mlsanity.com/paper/2311.12495/) (2023)
- [Cooperative Behavior Planning for Automated Driving using Graph Neural Networks](https://mlsanity.com/paper/2202.11376/) (2022)
- [An Enhanced Graph Representation for Machine Learning Based Automatic Intersection Management](https://mlsanity.com/paper/2207.08655/) (2022)
- [Automatic Intersection Management in Mixed Traffic Using Reinforcement Learning and Graph Neural Networks](https://mlsanity.com/paper/2301.12717/) (2023)
- [Efficient Sensors Selection for Traffic Flow Monitoring: An Overview of Model-Based Techniques leveraging Network Observability](https://mlsanity.com/paper/2404.08588/) (2024)
- [Visual Sensor Network Stimulation Model Identification via Gaussian Mixture Model and Deep Embedded Features](https://mlsanity.com/paper/2201.06804/) (2022)
- [A Fairness-Oriented Reinforcement Learning Approach for the Operation and Control of Shared Micromobility Services](https://mlsanity.com/paper/2403.15780/) (2024)
- [A Distributed Approach to Autonomous Intersection Management via Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2405.08655/) (2024)
- [Regulating Spatial Fairness in a Tripartite Micromobility Sharing System via Reinforcement Learning](https://mlsanity.com/paper/2504.02597/) (2025)

## Abstract (arXiv)

This study introduces a novel multi-objective reinforcement learning (MORL) approach for autonomous intersection management, aiming to balance traffic efficiency and environmental sustainability across electric and internal combustion vehicles. The proposed method utilizes MORL to identify Pareto-optimal policies, with a post-hoc fairness criterion guiding the selection of the final policy. Simulation results in a complex intersection scenario demonstrate the approach's effectiveness in optimizing traffic efficiency and emissions reduction while ensuring fairness across vehicle categories. We believe that this criterion can lay the foundation for ensuring equitable service, while fostering safe, efficient, and sustainable practices in smart urban mobility.