自动驾驶车辆集体路径规划引入边际成本奖励，减少对人类驾驶车辆的干扰
Collective Autonomous Vehicle Routing Needs Social Awareness for Coexistence with Human Drivers
自动驾驶车抢路会让人类司机变慢，这篇论文在 MARL 奖励里加了个边际成本项，实验里人类司机受影响从 1.7% 降到 1.1%，做交通仿真的可以看看。
论文用多智能体强化学习（MARL）研究混合交通中的 AV 路径选择，在奖励中加入边际成本——即通过反事实模拟衡量一辆 AV 对其他车辆行驶时间的影响。在 Ingolstadt 网络上，40% 渗透率的自私 AV 路径规划会让其他车辆行驶时间增加 1.7%，加入社会奖励后降至 1.1%，同时 AV 自身和整体系统性能也得到改善。该效应在 Ingolstadt 与 Provins 两城及不同渗透率下一致，但社会权重过高时系统性能反而无法达标。为降低计算量，论文只对路径选择影响最大的 AV 计算边际成本，以显著更低的反事实模拟次数获得相近性能。
Collective Autonomous Vehicle Routing Needs Social Awareness for Coexistence with Human Drivers
Collective Autonomous Vehicle (AV) route choices in real-world traffic networks can create externalities for Human-Driven Vehicles (HDVs), as routes that benefit AVs may increase travel times for other road users. To address this, we introduce a social component, the marginal cost, to the selfish travel time AV reward. Marginal cost captures an AV's impact on other road users by comparing their travel times with those in a counterfactual simulation in which the AV is removed. We study AV route choice in mixed-autonomy traffic settings using Multi-Agent Reinforcement Learning (MARL), where AVs learn routing policies through repeated interactions with the traffic environment. Experiments on the real-world networks of Ingolstadt and Provins show that incorporating marginal cost into the AV reward not only limits the increase in HDV travel times and shortens AV travel times relative to selfish AV routing, but also improves overall system performance. In Ingolstadt, selfish routing of 40\% AVs increases the travel times of other vehicles by 1.7\%, and this drops to 1.1\% when the social reward is included. Interestingly, not only does overall system performance improve, but the AVs are also better off when acting socially.This effect is consistent across tested cities, penetration rates, and replications, yet when the weight of the social component in the reward increases, the system fails to reach the desired performance. For a more scalable approach to social AV routing in mixed-autonomy traffic, we compute the marginal cost only for the AVs whose route choices most affect the traffic system. This reduces the number of counterfactual simulations and achieves similar performance at a significantly lower computational cost. Our results suggest that AV routing should go beyond selfish rewards and, for the benefit of all traffic participants, include a social component in its objective.