mlsanityData to 14 Oct 2026

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

arXiv 2507.09311 ↗First posted 12 Jul 2025eess.SY cs.SYcited by 0

On the map: Deep reinforcement learning methods → RL for autonomous driving and traffic

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.

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.

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13 of 26 references.

References · 13

  1. 2018Addressing Function Approximation Error in Actor-Critic Methodscited by 2,417
  2. 2017Modeling Relational Data with Graph Convolutional Networkscited by 1,694
  3. 2014A Survey of Multi-Objective Sequential Decision-Makingcited by 242
  4. 2017Flow: A Modular Learning Framework for Mixed Autonomy Trafficcited by 73
  5. 2023Multi-Objective Reinforcement Learning Based on Decomposition: A Taxonomy and Frameworkcited by 31
  6. 2022Cooperative Behavior Planning for Automated Driving using Graph Neural Networkscited by 10
  7. 2022An Enhanced Graph Representation for Machine Learning Based Automatic Intersection Managementcited by 4
  8. 2023Automatic Intersection Management in Mixed Traffic Using Reinforcement Learning and Graph Neural Networkscited by 4
  9. 2024Efficient Sensors Selection for Traffic Flow Monitoring: An Overview of Model-Based Techniques leveraging Network Observabilitycited by 4
  10. 2022Visual Sensor Network Stimulation Model Identification via Gaussian Mixture Model and Deep Embedded Featurescited by 3
  11. 2024A Fairness-Oriented Reinforcement Learning Approach for the Operation and Control of Shared Micromobility Servicescited by 3
  12. 2024A Distributed Approach to Autonomous Intersection Management via Multi-Agent Reinforcement Learningcited by 1
1 more
  1. 2025Regulating Spatial Fairness in a Tripartite Micromobility Sharing System via Reinforcement Learningcited by 1

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  1. 2026Balancing Efficiency and Fairness in Traffic Light Control through Deep Reinforcement Learning
  2. 2025Multi-Objective Reinforcement Learning for Large-Scale Mixed Traffic Control
  3. 2026Constrained Multi-Objective Reinforcement Learning with Max-Min Criterion
  4. 2024Queue-based Eco-Driving at Roundabouts with Reinforcement Learning
  5. 2021A Reinforcement Learning Benchmark for Autonomous Driving in Intersection Scenarios
  6. 2025Optimizing Efficiency of Mixed Traffic through Reinforcement Learning: A Topology-Independent Approach and Benchmark
  7. 2025Preference-based Multi-Objective Reinforcement Learning
  8. 2025Multi-Objective Reinforcement Learning for Adaptable Personalized Autonomous Driving