CADET: A Modular Platform for Evaluating Distributed Cooperative Autonomy in Connected Autonomous Vehicles

2026-06-02

AI Summary

This work addresses the systemic challenges—such as network latency, computational heterogeneity, and multi-tenant contention—that hinder conventional monolithic autonomous driving architectures in V2X cooperative perception and control. The authors propose a modular distributed platform that decouples the autonomous driving stack into components flexibly deployable across vehicles, roadside units, and edge/cloud infrastructure. For the first time, the platform enables synchronized instrumentation at the model, system, and task levels. It supports trajectory-driven network and workload simulation, dataset-driven benchmarking of distributed inference, and end-to-end performance evaluation. Experiments demonstrate that vehicle-to-vehicle intent messages offer superior safety compared to cloud-based perception, and roadside assistance ensures safety when not overloaded. The platform is open-sourced to facilitate reproducible research in cooperative autonomous driving.

Abstract

Deep learning models are increasingly central to autonomous vehicle (AV) pipelines, yet their integration has traditionally followed a monolithic design where perception, planning, and control execute on a single onboard computer. This design overlooks the emerging paradigm of cooperative autonomy, where vehicles interact with roadside units (RSUs), edge servers, and cloud-hosted intelligence through vehicle-to-everything (V2X) connectivity. Cooperative perception and control improve safety and efficiency, but also introduce systems-level challenges: network latency, compute heterogeneity, and multi-tenant contention, all critically affect real-time decision-making. These challenges are further amplified by the increasing reliance on large foundation models, whose scale necessitates cloud deployment. We present CADET (Cooperative Autonomy through Distributed Experimentation Toolkit), a modular platform for systematic and reproducible evaluation of distributed cooperative autonomy systems under realistic deployment conditions. CADET decouples the AV stack into composable modules that can be flexibly deployed across vehicles, infrastructure, and edge/cloud tiers. The framework integrates state-of-the-art models, incorporates trace-driven network and workload emulation, and provides synchronized model-, system-, and task-level instrumentation. Through V2V and V2I experiments, we show that distributed deployment choices fundamentally shape safety, with V2V intent packets outperforming cloud-based perception and RSU-assisted perception sustaining safety until overloaded by concurrent requests. Although designed for AV pipelines, CADET also supports dataset-driven experimentation, enabling systems and ML researchers to benchmark distributed inference workloads independently of full vehicle simulation. CADET is open source, with code and demo available at https://nesl.github.io/cadet-web.

Problem

• distributed cooperative autonomy
• connected autonomous vehicles
• V2X
• system-level challenges
• real-time decision-making

Innovation

• distributed cooperative autonomy
• modular AV stack
• V2X-enabled perception
• trace-driven emulation
• edge-cloud deployment