Compact Latent Coordination for Autonomous Vehicles at Unsignalized Intersections

• URL: http://arxiv.org/abs/2607.21488v1
• Date: Thu, 23 Jul 2026 16:28:35 GMT
• Title: Compact Latent Coordination for Autonomous Vehicles at Unsignalized Intersections
• Abstract summary: Coordinating autonomous vehicles at unsignalized intersections remains a critical challenge for multi-agent reinforcement learning systems.<n>We propose Master-Agent Proto-plan System (MAPS), a hierarchical deep reinforcement learning architecture in which a central Master agent generates a compact, continuous embedding.<n>MAPS achieves collision-free navigation while significantly reducing average travel time, outperforming state-of-the-art baselines.
• Score: 6.990371004201033
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Coordinating autonomous vehicles at unsignalized intersections remains a critical challenge for multi-agent reinforcement learning (MARL) systems, which typically struggle with combinatorial action spaces, reliance on privileged information, or rigid agent designs. We propose Master-Agent Proto-plan System (MAPS), a hierarchical deep reinforcement learning (DRL) architecture in which a centralized Master agent generates a compact, continuous embedding, denoted as proto-plan, that encodes a global coordination strategy. Decentralized Worker agents integrate this embedding with local observations to execute vehicle-specific control, decoupling strategic intent from tactical execution and enabling independent optimization of each module. As a proof-of-concept evaluation of this coordination mechanism, we test MAPS across 72 intersection configurations in HighwayEnv. MAPS achieves collision-free navigation while significantly reducing average travel time, outperforming state-of-the-art baselines. The learned proto-plans further exhibit robust generalization: a system trained with three agents achieves a 94% success rate when deployed zero-shot to five-agent scenarios, confirming that proto-plan-based hierarchical learning provides a promising framework for multi-vehicle coordination.

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