This paper presents DMV-AVP, a distributed simulation of Multi-Vehicle Autonomous Valet Parking (AVP). The system was implemented as an application of the Distributed Multi-Autonomous Vehicle Architecture (DMAVA) for synchronized multi-host execution. Most existing simulation approaches rely on centralized or non-distributed designs that constrain scalability and limit fully autonomous control. This work introduces two modules built on top of DMAVA: 1) the Multi-Vehicle AVP Coordination Framework, composed of AVP Managers and a per-vehicle AVP Node, is responsible for global parking state tracking, vehicle queuing, parking spot reservation, lifecycle coordination, and conflict resolution across multiple vehicles, and 2) the Unity-Integrated YOLOv5 Parking Spot Detection Module, that provides real-time, vision-based perception within AWSIM Labs. Both modules integrate seamlessly with DMAVA and extend it specifically for multi-vehicle AVP operation, supported by a Zenoh communication layer that ensures high data accuracy and controllability across hosts. Experiments conducted on two- and three-host configurations demonstrate consistent coordination, conflict-free parking behavior, and scalable performance across distributed Autoware instances. The results confirm that the proposed DMV-AVP supports cooperative AVP simulation and establishes a foundation for future real-world and hardware-in-the-loop validation. Demo videos and source code are available at: https://github.com/zubxxr/multi-vehicle-avp

翻译：本文提出DMV-AVP，一种分布式多车自主代客泊车（AVP）仿真系统。该系统作为分布式多自主车辆架构（DMAVA）的应用实现，支持多主机同步执行。现有仿真方案大多依赖集中式或非分布式设计，限制了可扩展性与全自主控制能力。本研究在DMAVA基础上引入两个模块：1）多车AVP协调框架，由AVP管理器与每车独立的AVP节点构成，负责全局泊车状态跟踪、车辆排队、车位预留、生命周期协调及多车间冲突消解；2）集成Unity的YOLOv5车位检测模块，在AWSIM Labs中提供基于视觉的实时感知。两模块均与DMAVA无缝集成，并通过Zenoh通信层保障跨主机数据高精度与可控性，专为多车AVP操作扩展功能。在双主机与三主机配置下的实验表明，分布式Autoware实例间能实现稳定协调、无冲突泊车行为及可扩展性能。结果证实所提DMV-AVP支持协同AVP仿真，为未来实车与硬件在环验证奠定基础。演示视频与源代码详见：https://github.com/zubxxr/multi-vehicle-avp