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.

翻译：自动驾驶正从孤立的单车智能向多智能体具身系统转变，这类系统能在不确定环境下共享感知、推断意图并协调行动。本综述以共享世界模型（SWMs）为视角审视这一转变：即跨车辆、基础设施及其他交通参与者维护的预测性智能体间表征。我们回顾了超过380篇文献，涵盖车联万物（V2X）通信、协同感知、智能体间认知、协同规划、端到端协同驾驶，以及用于闭环验证的仿真与数据引擎。核心研究问题在于：交换的观测信息如何转化为对齐的状态、感知意图的交互以及协调的下游行动。纵观所调研文献，评估仍集中于仿真环境、精选基准测试及离线协议。基于基础模型的协调方案在开放交通场景中也缺乏经过验证的实时安全保障。这些空白为多智能体具身自动驾驶（MAEAD）确立了关键研究优先级：可验证的共享状态维护、鲁棒的意图与计划对齐，以及在通信、时延与部署约束下实现安全的协调行动。