Multi-Agent Embodied Autonomous Driving: From V2X Information Exchange to Shared World Models
This survey examines the shift from isolated vehicle intelligence to multi-agent embodied systems in autonomous driving, focusing on Shared World Models (SWMs) as predictive cross-agent representations. Reviewing over 380 publications, it covers V2X communication, collaborative perception, inter-agent cognition, cooperative planning, end-to-end cooperative driving, and simulation engines. The study finds evaluation concentrated in simulation and offline protocols, with foundation-model-based coordination lacking real-time safety guarantees. Key research priorities include verifiable shared-state maintenance, robust intent and plan alignment, and safe coordinated action under communication and latency constraints.
[2606.13840] Multi-Agent Embodied Autonomous Driving: From V2X Information Exchange to Shared World Models
[Submitted on 11 Jun 2026]
Title:Multi-Agent Embodied Autonomous Driving: From V2X Information Exchange to Shared World Models
View a PDF of the paper titled Multi-Agent Embodied Autonomous Driving: From V2X Information Exchange to Shared World Models, by Senkang Hu and 5 other authors
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Abstract:Autonomous driving is shifting from isolated vehicle intelligence toward multi-agent embodied systems that share perception, infer intent, and coordinate action under uncertainty. This survey examines this transition through the lens of Shared World Models (SWMs): predictive cross-agent representations maintained across vehicles, infrastructure, and other traffic participants. We review more than 380 publications spanning vehicle-to-everything (V2X) communication, collaborative perception, inter-agent cognition, cooperative planning, end-to-end cooperative driving, and simulation and data engines for closed-loop validation. The organizing question is how exchanged observations become aligned state, intent-aware interaction, and coordinated downstream action. Across the surveyed literature, evaluation remains concentrated in simulation, curated benchmarks, and offline protocols. Foundation-model-based coordination also lacks verified real-time safety guarantees in open traffic. These gaps motivate key research priorities for multi-agent embodied autonomous driving (MAEAD): verifiable shared-state maintenance, robust intent and plan alignment, and safe coordinated action under communication, latency, and deployment constraints.
Subjects:
Robotics (cs.RO); Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2606.13840 [cs.RO]
(or arXiv:2606.13840v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2606.13840
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Senkang Hu [view email] [v1] Thu, 11 Jun 2026 19:10:13 UTC (3,303 KB)
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