A Fairness-Oriented Multi-Objective Reinforcement Learning approach for Autonomous Intersection Management
Fuente:
arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Acceso en línea: | |
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| _version_ | 1866909958238371840 |
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| author | Cederle, Matteo Fabris, Marco Susto, Gian Antonio |
| author_facet | Cederle, Matteo Fabris, Marco Susto, Gian Antonio |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_09311 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Fairness-Oriented Multi-Objective Reinforcement Learning approach for Autonomous Intersection Management Cederle, Matteo Fabris, Marco Susto, Gian Antonio Systems and Control 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. |
| title | A Fairness-Oriented Multi-Objective Reinforcement Learning approach for Autonomous Intersection Management |
| topic | Systems and Control |
| url | https://arxiv.org/abs/2507.09311 |