DMAVA: Distributed Multi-Autonomous Vehicle Architecture Using Autoware

• URL: http://arxiv.org/abs/2601.16336v1
• Date: Thu, 22 Jan 2026 21:45:14 GMT
• Title: DMAVA: Distributed Multi-Autonomous Vehicle Architecture Using Autoware
• Abstract summary: This paper presents a Distributed Multi-AV Architecture (DMAVA) that enables synchronized, real-time autonomous driving simulation across multiple physical hosts.<n>The proposed system integrates ROS 2 Humble, Autoware Universe, AWSIM Labs, and Zenoh to support concurrent execution of multiple Autoware stacks within a shared Unity-based environment.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Simulating and validating coordination among multiple autonomous vehicles (AVs) is a challenging task as most existing simulation architectures are limited to single-vehicle operation or rely on centralized control. This paper presents a Distributed Multi-AV Architecture (DMAVA) that enables synchronized, real-time autonomous driving simulation across multiple physical hosts. Each vehicle runs its own complete AV stack and operates independently from other AVs. The vehicles in the simulation maintain synchronized coordination through a low-latency data-centric communication layer. The proposed system integrates ROS 2 Humble, Autoware Universe, AWSIM Labs, and Zenoh to support concurrent execution of multiple Autoware stacks within a shared Unity-based environment. Experiments conducted on multiple-host configurations demonstrate stable localization, reliable inter-host communication, and fully synchronized closed-loop control. The 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.

Related papers

• VIPS: Vehicle-Infrastructure Cooperative Planning Benchmark via Pseudo-Simulation [50.18051979020699]
  VIPS is a benchmark for cooperative autonomous driving in V2I settings based on pseudo-simulation.<n>CoS-V2X is a cooperative planning framework based on sparse representations.
  arXiv  Detail & Related papers  (2026-09-02T11:32:52Z)
• HERCULES: An Open-Source Simulation Framework for Heterogeneous Multi-Robot SLAM, Collaborative Perception, and Exploration [3.15872644487767]
  HERCULES is an open-source simulator and data-collection pipeline for heterogeneous multi-robot autonomy.<n>Built upon the Unreal Engine 5 (UE5)-based simulators AirSim and Cosys-AirSim, HERCULES resolves key architectural limitations of prior frameworks.<n>Our experiments in heterogeneous multi-robot SLAM, collaborative perception, and exploration, using both HERCULES-generated data and active closed-loop execution, demonstrate its utility for advancing heterogeneous multi-robot autonomy.
  arXiv  Detail & Related papers  (2026-06-22T01:51:40Z)
• CADET: A Modular Platform for Evaluating Distributed Cooperative Autonomy in Connected Autonomous Vehicles [3.544599494530976]
  CADET is a modular platform for systematic and reproducible evaluation of distributed cooperative autonomy systems.<n>We show that distributed deployment choices shape safety, with V2V intent packets outperforming cloud-based perception and RSU-assisted perception sustaining safety until overloaded by concurrent requests.
  arXiv  Detail & Related papers  (2026-06-02T15:13:13Z)
• Closed-Loop Hybrid Digital Twin Platform for Connected and Automated Vehicle Validation [8.373850248021927]
  This paper proposes a novel real-time hybrid digital twin platform for connected and automated vehicles (CAVs) validation.<n>Its core innovation lies in the tight coupling of a high-fidelity CARLA-SUMO co-simulation with a physical test site and vehicle via a low-latency Vehicle-to-Everything (V2X) communication link.<n> Detailed implementation includes using photogrammetry for full-scale asset reconstruction and a cloud-edge collaborative architecture for scalable, multi-user operation.
  arXiv  Detail & Related papers  (2026-05-19T07:44:59Z)
• OneDrive: Unified Multi-Paradigm Driving with Vision-Language-Action Models [69.2503510410147]
  We present a unified autonomous driving framework built upon a pretrained VLM.<n>We demonstrate that pretrained VLM attention exhibits strong transferability beyond pure language modeling.<n>Experiments on end-to-end autonomous driving benchmarks demonstrate state-of-the-art performance.
  arXiv  Detail & Related papers  (2026-04-20T07:50:00Z)
• Bench2Drive-VL: Benchmarks for Closed-Loop Autonomous Driving with Vision-Language Models [50.22099309218635]
  In autonomous driving, closed-loop evaluation has become widely recognized as a more reliable validation method than open-loop evaluation.<n>We present Bench2Drive-VL, an extension of Bench2Drive that brings closed-loop evaluation to VLM-based driving.
  arXiv  Detail & Related papers  (2026-04-01T11:38:46Z)
• From Code to Road: A Vehicle-in-the-Loop and Digital Twin-Based Framework for Central Car Server Testing in Autonomous Driving [12.14436913195805]
  This work presents a test framework for automotive software on the centralized E/E architecture.<n>The framework couples a physical test vehicle on a dynamometer test bench with its synchronized virtual counterpart in a simulation environment.
  arXiv  Detail & Related papers  (2026-03-05T15:23:28Z)
• DMV-AVP: Distributed Multi-Vehicle Autonomous Valet Parking using Autoware [0.0]
  The DMV-AVP System is a distributed simulation of Multi-Vehicle Autonomous Valet Parking (AVP)<n>The proposed system supports cooperative AVP simulation and establishes a foundation for future real-world and hardware-in-the-loop validation.
  arXiv  Detail & Related papers  (2026-01-22T21:24:04Z)
• Towards Intelligent Transportation with Pedestrians and Vehicles In-the-Loop: A Surveillance Video-Assisted Federated Digital Twin Framework [62.47416496137193]
  We propose a surveillance video assisted federated digital twin (SV-FDT) framework to empower ITSs with pedestrians and vehicles in-the-loop.<n>The architecture consists of three layers: (i) the end layer, which collects traffic surveillance videos from multiple sources; (ii) the edge layer, responsible for semantic segmentation-based visual understanding, twin agent-based interaction modeling, and local digital twin system (LDTS) creation in local regions; and (iii) the cloud layer, which integrates LDTSs across different regions to construct a global DT model in realtime.
  arXiv  Detail & Related papers  (2025-03-06T07:36:06Z)
• V2V-LLM: Vehicle-to-Vehicle Cooperative Autonomous Driving with Multi-Modal Large Language Models [31.537045261401666]
  We propose a novel problem setting that integrates a Multi-Modal Large Language Model into cooperative autonomous driving.<n>We also propose our baseline method Vehicle-to-Vehicle Multi-Modal Large Language Model (V2V-LLM)<n> Experimental results show that our proposed V2V-LLM can be a promising unified model architecture for performing various tasks in cooperative autonomous driving.
  arXiv  Detail & Related papers  (2025-02-14T08:05:41Z)
• WHALES: A Multi-Agent Scheduling Dataset for Enhanced Cooperation in Autonomous Driving [14.977628973132099]
  We introduce WHALES, the first large-scale V2X dataset explicitly designed to benchmark communication-aware agent scheduling and scalable cooperative perception.<n>WHALES features an average of 8.4 cooperative agents per scene and 2.01 million annotated 3D objects across diverse traffic scenarios.<n>It incorporates detailed communication metadata to emulate real-world communication bottlenecks, enabling rigorous evaluation of scheduling strategies.
  arXiv  Detail & Related papers  (2024-11-20T14:12:34Z)
• M2DA: Multi-Modal Fusion Transformer Incorporating Driver Attention for Autonomous Driving [11.36165122994834]
  We propose a Multi-Modal fusion transformer incorporating Driver Attention (M2DA) for autonomous driving. By incorporating driver attention, we empower the human-like scene understanding ability to autonomous vehicles to identify crucial areas precisely and ensure safety.
  arXiv  Detail & Related papers  (2024-03-19T08:54:52Z)
• Drive Anywhere: Generalizable End-to-end Autonomous Driving with Multi-modal Foundation Models [114.69732301904419]
  We present an approach to apply end-to-end open-set (any environment/scene) autonomous driving that is capable of providing driving decisions from representations queryable by image and text. Our approach demonstrates unparalleled results in diverse tests while achieving significantly greater robustness in out-of-distribution situations.
  arXiv  Detail & Related papers  (2023-10-26T17:56:35Z)
• UniM$^2$AE: Multi-modal Masked Autoencoders with Unified 3D Representation for 3D Perception in Autonomous Driving [47.590099762244535]
  Masked Autoencoders (MAE) play a pivotal role in learning potent representations, delivering outstanding results across various 3D perception tasks. This research delves into multi-modal Masked Autoencoders tailored for a unified representation space in autonomous driving. To intricately marry the semantics inherent in images with the geometric intricacies of LiDAR point clouds, we propose UniM$2$AE.
  arXiv  Detail & Related papers  (2023-08-21T02:13:40Z)
• Generative AI-empowered Simulation for Autonomous Driving in Vehicular Mixed Reality Metaverses [130.15554653948897]
  In vehicular mixed reality (MR) Metaverse, distance between physical and virtual entities can be overcome. Large-scale traffic and driving simulation via realistic data collection and fusion from the physical world is difficult and costly. We propose an autonomous driving architecture, where generative AI is leveraged to synthesize unlimited conditioned traffic and driving data in simulations.
  arXiv  Detail & Related papers  (2023-02-16T16:54:10Z)
• Generative AI-empowered Effective Physical-Virtual Synchronization in the Vehicular Metaverse [129.8037449161817]
  We propose a generative AI-empowered physical-virtual synchronization framework for the vehicular Metaverse. In virtual-to-physical synchronization, MARs customize diverse and personal AR recommendations via generative AI models based on user preferences.
  arXiv  Detail & Related papers  (2023-01-18T16:25:42Z)
• SMARTS: Scalable Multi-Agent Reinforcement Learning Training School for Autonomous Driving [96.50297622371457]
  Multi-agent interaction is a fundamental aspect of autonomous driving in the real world. Despite more than a decade of research and development, the problem of how to interact with diverse road users in diverse scenarios remains largely unsolved. We develop a dedicated simulation platform called SMARTS that generates diverse and competent driving interactions.
  arXiv  Detail & Related papers  (2020-10-19T18:26:10Z)
• MADRaS : Multi Agent Driving Simulator [15.451658979433667]
  We present MADRaS, an open-source multiagent driving simulator for use in the design and evaluation of motion planning algorithms for autonomous driving. MADRaS is built on TORCS, an open-source car-racing simulator.
  arXiv  Detail & Related papers  (2020-10-02T13:38:49Z)
• Towards Autonomous Driving: a Multi-Modal 360$^{\circ}$ Perception Proposal [87.11988786121447]
  This paper presents a framework for 3D object detection and tracking for autonomous vehicles. The solution, based on a novel sensor fusion configuration, provides accurate and reliable road environment detection. A variety of tests of the system, deployed in an autonomous vehicle, have successfully assessed the suitability of the proposed perception stack.
  arXiv  Detail & Related papers  (2020-08-21T20:36:21Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.