arXiv cs.AI· Anastasia Psarou, Mikolaj Rams, Blazej Torbus, Rafal Kucharski·· 6 天前AI 评分22
集体自动驾驶车辆路由需具备社会意识才能与人类驾驶员共存
Collective Autonomous Vehicle Routing Needs Social Awareness for Coexistence with Human Drivers
AI 导读
研究将边际成本引入自动驾驶车辆（AV）的自私行驶时间奖励，用多智能体强化学习（MARL）在 Ingolstadt 和 Provins 真实路网中研究混合自动驾驶场景下的路由选择。
正文 · 原文
Abstract: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 this http URL 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.
| Subjects: | Multiagent Systems (cs.MA); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2610.13439 [cs.MA] |
| (or arXiv:2610.13439v1 [cs.MA] for this version) | |
| https://doi.org/10.48550/arXiv.2610.13439 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Anastasia Psarou [view email]
[v1]
Fri, 9 Oct 2026 11:37:47 UTC (729 KB)
来源：arXiv cs.AI · arxiv.org