| name | compact-latent-coordination-for-autonomous-vehicles-at-unsignalized-1 |
| description | Compact Latent Coordination for Autonomous Vehicles at Unsignalized Intersections |
| metadata | {"arxiv_id":"2607.21488","utility":1,"date_added":"2026-07-26"} |
Compact Latent Coordination for Autonomous Vehicles at Unsignalized Intersections
arXiv: 2607.21488
Published: 2026-07-23
Utility: 1.0
Summary
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....
Key Information
- Title: Compact Latent Coordination for Autonomous Vehicles at Unsignalized Intersections
- Authors: [Extract from entry]
- Primary Category: cs.LG
Potential Skill Application
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Reference
