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...
| Authors | Anastasia Psarou, Mikolaj Rams, Blazej Torbus et al. |
|---|---|
| Primary category | cs.MA |
| Published | 9 October 2026 |
| Last updated at source | 9 October 2026 |
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