城市与机器学习社区需合作，确保自动驾驶车辆路由公平高效。
Collaboration Between the City and Machine Learning Community is Crucial to Efficient Autonomous Vehicles Routing
- 通过仿真模拟人车交互，发现多智能体强化学习难收敛且训练耗时。
- 真实道路测试易引发交通紊乱，增加碳排放并导致行为非平稳性。
- 呼吁城建部门与算法研究者共建监管标准，避免系统性风险。
自动驾驶车辆（AV）可能采用多智能体强化学习（MARL）实现路径协同优化，但可能扰乱交通网络，导致人类驾驶员通行时间延长。本文通过仿真人类司机与自动驾驶车辆的互动，发现标准MARL算法在简化和复杂交通网络中均难以收敛至最优解，或需极长训练周期。该问题因缺乏对人类驾驶行为的准确建模而加剧；完全依赖模拟训练不可靠，而现实道路测试则可能破坏城市交通系统，带来碳排放上升等外部成本，并因人类驾驶员对自动驾驶行为的不可预测适应，引入非平稳性。本文主张，城市管理部门必须与机器学习研究社区协作，对车企提出的路由算法进行监测与评估，以推动公平、高效的系统级路由算法及监管标准制定。
原文摘要 · Abstract (English)
Autonomous vehicles (AVs), possibly using Multi-Agent Reinforcement Learning (MARL) for simultaneous route optimization, may destabilize traffic networks, with human drivers potentially experiencing longer travel times. We study this interaction by simulating human drivers and AVs. Our experiments with standard MARL algorithms reveal that, both in simplified and complex networks, policies often fail to converge to an optimal solution or require long training periods. This problem is amplified by the fact that we cannot rely entirely on simulated training, as there are no accurate models of human routing behavior. At the same time, real-world training in cities risks destabilizing urban traffic systems, increasing externalities, such as $CO_2$ emissions, and introducing non-stationarity as human drivers will adapt unpredictably to AV behaviors. In this position paper, we argue that city authorities must collaborate with the ML community to monitor and critically evaluate the routing algorithms proposed by car companies toward fair and system-efficient routing algorithms and regulatory standards.
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