DMAVA: Distributed Multi-Autonomous Vehicle Architecture Using Autoware

TL;DR

DMAVA is a novel distributed simulation architecture enabling multiple autonomous vehicles to operate concurrently across different hosts with coordinated behavior, leveraging ROS 2, Autoware, and Zenoh for high-fidelity multi-vehicle autonomous driving simulation.

Contribution

The paper introduces DMAVA, a distributed multi-vehicle simulation system that supports independent autonomy stacks across multiple hosts with integrated communication, enabling scalable and reliable multi-vehicle autonomous driving experiments.

Findings

Stable localization across hosts

Reliable inter-host communication

Consistent control in distributed setup

Abstract

Simulating and validating coordination among multiple autonomous vehicles remains challenging, as many existing simulation architectures are limited to single-vehicle operation or rely on centralized control. This paper presents the Distributed Multi-Autonomous Vehicle Architecture (DMAVA), a simulation architecture that enables concurrent execution of multiple independent vehicle autonomy stacks distributed across multiple physical hosts within a shared simulation environment. Each vehicle operates its own complete autonomous driving stack while maintaining coordinated behavior through a data-centric communication layer. The proposed system integrates ROS 2 Humble, Autoware Universe, AWSIM Labs, and Zenoh to support high data accuracy and controllability during multi-vehicle simulation, enabling consistent perception, planning, and control behavior under distributed execution. Experiments conducted on multiple-host configurations demonstrate stable localization, reliable inter-host communication, and consistent closed-loop control under distributed execution. DMAVA also serves as a foundation for Multi-Vehicle Autonomous Valet Parking, demonstrating its extensibility toward higher-level cooperative autonomy. Demo videos and source code are available at: https://github.com/zubxxr/distributed-multi-autonomous-vehicle-architecture.