Resolve Highway Conflict in Multi-Autonomous Vehicle Controls with Local State Attention
On the map: End-to-end autonomous driving → Cooperative and V2X driving
Abstract (arXiv)
In mixed-traffic environments, autonomous vehicles must adapt to human-controlled vehicles and other unusual driving situations. This setting can be framed as a multi-agent reinforcement learning (MARL) environment with full cooperative reward among the autonomous vehicles. While methods such as Multi-agent Proximal Policy Optimization can be effective in training MARL tasks, they often fail to resolve local conflict between agents and are unable to generalize to stochastic events. In this paper, we propose a Local State Attention module to assist the input state representation. By relying on the self-attention operator, the module is expected to compress the essential information of nearby agents to resolve the conflict in traffic situations. Utilizing a simulated highway merging scenario with the priority vehicle as the unexpected event, our approach is able to prioritize other vehicles' information to manage the merging process. The results demonstrate significant improvements in merging efficiency compared to popular baselines, especially in high-density traffic settings.
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 16 of its 27 references that are in the map. First posted on arXiv 13 Jun 2025.
Builds on
16 of 27 references.
References · 16
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4 more
- 2020Deep Implicit Coordination Graphs for Multi-agent Reinforcement Learningcited by 60
- 2021Settling the Variance of Multi-Agent Policy Gradientscited by 45
- 2021Deep Multi-agent Reinforcement Learning for Highway On-Ramp Merging in Mixed Trafficcited by 41
- 2022Augmenting Reinforcement Learning with Transformer-based Scene Representation Learning for Decision-making of Autonomous Drivingcited by 24
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- 2025Z-Merge: Multi-Agent Reinforcement Learning for On-Ramp Merging with Zone-Specific V2X Traffic Information ↗
- 2021Multi-agent Reinforcement Learning for Cooperative Lane Changing of Connected and Autonomous Vehicles in Mixed Traffic
- 2024Cooperative Decision-Making for CAVs at Unsignalized Intersections: A MARL Approach with Attention and Hierarchical Game Priors
- 2023Connected and Automated Vehicles in Mixed-Traffic: Learning Human Driver Behavior for Effective On-Ramp Merging
- 2026Partial Attention in Deep Reinforcement Learning for Safe Multi-Agent Control ↗
- 2021Learning to Robustly Negotiate Bi-Directional Lane Usage in High-Conflict Driving Scenarios
- 2024A Systematic Study of Multi-Agent Deep Reinforcement Learning for Safe and Robust Autonomous Highway Ramp Entry
- 2024A Distributed Approach to Autonomous Intersection Management via Multi-Agent Reinforcement Learning