标题： 一种面向公平性的多目标强化学习方法用于自主交叉口管理 显示英文标题

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

摘要： 本研究引入了一种新颖的多目标强化学习（MORL）方法，用于自动驾驶交叉口管理，旨在平衡电动汽车和内燃机车辆的交通效率和环境可持续性。 所提出的方法利用MORL来识别帕累托最优策略，事后公平性标准指导最终策略的选择。 在复杂交叉口场景中的仿真结果表明，该方法在优化交通效率和减少排放的同时，确保了不同车辆类别之间的公平性。 我们认为，这一标准可以为确保公平服务奠定基础，同时促进智能城市交通中的安全、高效和可持续实践。 显示英文摘要

摘要： 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.