mlsanityData to 7 Oct 2026

Collaboration Between the City and Machine Learning Community is Crucial to Efficient Autonomous Vehicles Routing

arXiv 2502.13188 ↗First posted 18 Feb 2025cs.MA cs.LG cs.ROcited by 1

On the map: End-to-end autonomous driving → Cooperative and V2X driving

Abstract (arXiv)

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 CO2 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.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Placed in Cooperative and V2X driving, one of 9 directions in End-to-end autonomous driving. Builds on 21 of its 62 references that are in the map. First posted on arXiv 18 Feb 2025.

Builds on

21 of 62 references.

References · 21

  1. 2017Proximal Policy Optimization Algorithmscited by 14,441
  2. 2018Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actorcited by 4,635
  3. 2017Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environmentscited by 1,519
  4. 2018Soft Actor-Critic Algorithms and Applicationscited by 1,179
  5. 2018QMIX: Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learningcited by 697
  6. 2020AWAC: Accelerating Online Reinforcement Learning with Offline Datasetscited by 647
  7. 2017Value-Decomposition Networks For Cooperative Multi-Agent Learningcited by 490
  8. 2020Sim-to-Real Transfer in Deep Reinforcement Learning for Robotics: a Surveycited by 388
  9. 2020Is Independent Learning All You Need in the StarCraft Multi-Agent Challenge?cited by 281
  10. 2020PettingZoo: Gym for Multi-Agent Reinforcement Learningcited by 209
  11. 2020Benchmarking Multi-Agent Deep Reinforcement Learning Algorithms in Cooperative Taskscited by 207
  12. 2019MAVEN: Multi-Agent Variational Explorationcited by 181
9 more
  1. 2017Stabilising Experience Replay for Deep Multi-Agent Reinforcement Learningcited by 114
  2. 2023MAC-PO: Multi-Agent Experience Replay via Collective Priority Optimizationcited by 20
  3. 2019Learning How to Dynamically Route Autonomous Vehicles on Shared Roadscited by 12
  4. 2020InTAS -- The Ingolstadt Traffic Scenario for SUMOcited by 10
  5. 2019A Review on Energy, Environmental, and Sustainability Implications of Connected and Automated Vehiclescited by 7
  6. 2023Traffic4cast at NeurIPS 2022 -- Predict Dynamics along Graph Edges from Sparse Node Data: Whole City Traffic and ETA from Stationary Vehicle Detectorscited by 3
  7. 2023A Black-box Approach for Non-stationary Multi-agent Reinforcement Learningcited by 3
  8. 2023A Multiclass Simulation-Based Dynamic Traffic Assignment Model for Mixed Traffic Flow of Connected and Autonomous Vehicles and Human-Driven Vehiclescited by 2
  9. 2024Social impact of CAVs -- coexistence of machines and humans in the context of route choicecited by 2

Built on by

Cited by year

Citing papers per year: 2025: 1.

1 paper.

  1. 2025Autonomous vehicles need social awareness to find optima in multi-agent reinforcement learning routing gamescited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025Impact of Collective Behaviors of Autonomous Vehicles on Urban Traffic Dynamics: A Multi-Agent Reinforcement Learning Approach
  2. 2025URB -- Urban Routing Benchmark for RL-equipped Connected Autonomous Vehicles
  3. 2021Scalable Multiagent Driving Policies For Reducing Traffic Congestion
  4. 2025RouteRL: Multi-agent reinforcement learning framework for urban route choice with autonomous vehicles
  5. 2022Learning-based social coordination to improve safety and robustness of cooperative autonomous vehicles in mixed traffic
  6. 2024Multi-Agent Reinforcement Learning for Autonomous Driving: A Survey
  7. 2020A Survey on Autonomous Vehicle Control in the Era of Mixed-Autonomy: From Physics-Based to AI-Guided Driving Policy Learning
  8. 2026Multi-Agent Reinforcement Learning for Safe Autonomous Driving Under Pedestrian Behavioral Uncertainty