# Multi-Agent Embodied Autonomous Driving (MAEAD): From V2X Information Exchange to Shared World Models

arXiv: https://arxiv.org/abs/2606.13840 · HTML: https://mlsanity.com/paper/2606.13840/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in World models for autonomous driving, one of 11 directions in World models.
- Builds on 50 of its 406 references that are in the map.
- First posted on arXiv 11 Jun 2026.

## Placed in

- World models / [World models for autonomous driving](https://mlsanity.com/wm/world-models-for-autonomous-driving/)
- End-to-end autonomous driving / [Cooperative and V2X driving](https://mlsanity.com/driving/cooperative-and-v2x-driving/)

## Builds on (most cited first, 30 of 50)

- [GAIA-1: A Generative World Model for Autonomous Driving](https://mlsanity.com/paper/2309.17080/) (2023)
- [DriveDreamer: Towards Real-world-driven World Models for Autonomous Driving](https://mlsanity.com/paper/2309.09777/) (2023)
- [LanguageMPC: Large Language Models as Decision Makers for Autonomous Driving](https://mlsanity.com/paper/2310.03026/) (2023)
- [GAIA-2: A Controllable Multi-View Generative World Model for Autonomous Driving](https://mlsanity.com/paper/2503.20523/) (2025)
- [Senna: Bridging Large Vision-Language Models and End-to-End Autonomous Driving](https://mlsanity.com/paper/2410.22313/) (2024)
- [Diffusion-Based Planning for Autonomous Driving with Flexible Guidance](https://mlsanity.com/paper/2501.15564/) (2025)
- [Epona: Autoregressive Diffusion World Model for Autonomous Driving](https://mlsanity.com/paper/2506.24113/) (2025)
- [COMBO: Compositional World Models for Embodied Multi-Agent Cooperation](https://mlsanity.com/paper/2404.10775/) (2024)
- [DriveWorld-VLA: Unified Latent-Space World Modeling with Vision-Language-Action for Autonomous Driving](https://mlsanity.com/paper/2602.06521/) (2026)
- [LangCoop: Collaborative Driving with Language](https://mlsanity.com/paper/2504.13406/) (2025)
- [DrivingGen: A Comprehensive Benchmark for Generative Video World Models in Autonomous Driving](https://mlsanity.com/paper/2601.01528/) (2026)
- [MiLA: Multi-view Intensive-fidelity Long-term Video Generation World Model for Autonomous Driving](https://mlsanity.com/paper/2503.15875/) (2025)
- [Latent Chain-of-Thought World Modeling for End-to-End Driving](https://mlsanity.com/paper/2512.10226/) (2025)
- [V2X-UniPool: Unifying Multimodal Perception and Knowledge Reasoning for Autonomous Driving](https://mlsanity.com/paper/2506.02580/) (2025)
- [Bridging Scene Generation and Planning: Driving with World Model via Unifying Vision and Motion Representation](https://mlsanity.com/paper/2603.14948/) (2026)
- [CoWorld-VLA: Thinking in a Multi-Expert World Model for Autonomous Driving](https://mlsanity.com/paper/2605.10426/) (2026)
- [Uni-World VLA: Interleaved World Modeling and Planning for Autonomous Driving](https://mlsanity.com/paper/2603.27287/) (2026)
- [Learning Vision-Language-Action World Models for Autonomous Driving](https://mlsanity.com/paper/2604.09059/) (2026)
- [ScenicNL: Generating Probabilistic Scenario Programs from Natural Language](https://mlsanity.com/paper/2405.03709/) (2024)
- [ShareVerse: Multi-Agent Consistent Video Generation for Shared World Modeling](https://mlsanity.com/paper/2603.02697/) (2026)
- [UniDriveDreamer: A Single-Stage Multimodal World Model for Autonomous Driving](https://mlsanity.com/paper/2602.02002/) (2026)
- [Emergent Road Rules In Multi-Agent Driving Environments](https://mlsanity.com/paper/2011.10753/) (2020)
- [We Need to Talk: Identifying and Overcoming Communication-Critical Scenarios for Self-Driving](https://mlsanity.com/paper/2305.04352/) (2023)
- [X-World: Controllable Ego-Centric Multi-Camera World Models for Scalable End-to-End Driving](https://mlsanity.com/paper/2603.19979/) (2026)
- [Multi-Agent Reinforcement Learning Guided by Signal Temporal Logic Specifications](https://mlsanity.com/paper/2306.06808/) (2023)
- [CP-Guard+: A New Paradigm for Malicious Agent Detection and Defense in Collaborative Perception](https://mlsanity.com/paper/2502.07807/) (2025)
- [Shared Spatial Memory Through Predictive Coding](https://mlsanity.com/paper/2511.04235/) (2025)
- [Learning to Drive in New Cities Without Human Demonstrations](https://mlsanity.com/paper/2602.15891/) (2026)
- [Graph World Models: Concepts, Taxonomy, and Future Directions](https://mlsanity.com/paper/2604.27895/) (2026)
- [M3CAD: Towards Generic Cooperative Autonomous Driving Benchmark](https://mlsanity.com/paper/2505.06746/) (2025)

## Abstract (arXiv)

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 approximately 400 publications covering 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 verifiable 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 and computing constraints in real-world deployment. We maintain an open-source project to continuously track the latest developments at https://github.com/dl-m9/Multi-Agent-Embodied-Autonomous-Driving.