# Collective Autonomous Vehicle Routing Needs Social Awareness for Coexistence with Human Drivers

arXiv: https://arxiv.org/abs/2610.13439 · HTML: https://mlsanity.com/paper/2610.13439/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Cooperative and V2X driving, one of 9 directions in End-to-end autonomous driving.
- Builds on 10 of its 39 references that are in the map.
- First posted on arXiv 9 Oct 2026.

## Placed in

- End-to-end autonomous driving / [Cooperative and V2X driving](https://mlsanity.com/driving/cooperative-and-v2x-driving/)

## Builds on (most cited first, 10 of 10)

- [PettingZoo: Gym for Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2009.14471/) (2020)
- [A Policy Gradient Algorithm for Learning to Learn in Multiagent Reinforcement Learning](https://mlsanity.com/paper/2011.00382/) (2020)
- [Collective eXplainable AI: Explaining Cooperative Strategies and Agent Contribution in Multiagent Reinforcement Learning with Shapley Values](https://mlsanity.com/paper/2110.01307/) (2021)
- [Learning How to Dynamically Route Autonomous Vehicles on Shared Roads](https://mlsanity.com/paper/1909.03664/) (2019)
- [InTAS -- The Ingolstadt Traffic Scenario for SUMO](https://mlsanity.com/paper/2011.11995/) (2020)
- [Understanding Individual Agent Importance in Multi-Agent System via Counterfactual Reasoning](https://mlsanity.com/paper/2412.15619/) (2024)
- [URB -- Urban Routing Benchmark for RL-equipped Connected Autonomous Vehicles](https://mlsanity.com/paper/2505.17734/) (2025)
- [RouteRL: Multi-agent reinforcement learning framework for urban route choice with autonomous vehicles](https://mlsanity.com/paper/2502.20065/) (2025)
- [Impact of Collective Behaviors of Autonomous Vehicles on Urban Traffic Dynamics: A Multi-Agent Reinforcement Learning Approach](https://mlsanity.com/paper/2509.22216/) (2025)
- [Autonomous vehicles need social awareness to find optima in multi-agent reinforcement learning routing games](https://mlsanity.com/paper/2510.11410/) (2025)

## Abstract (arXiv)

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.