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.

翻译：深度学习模型正日益成为自动驾驶系统流程的核心，然而其集成方式传统上遵循单一整体式设计，即感知、规划和控制均在单一车载计算机上执行。这种设计忽略了新兴的协同自主范式，即车辆通过车联网（V2X）连接与路侧单元、边缘服务器及云端智能进行交互。协同感知与控制虽能提升安全性与效率，但引入了系统层面的挑战：网络延迟、计算异构性以及多租户争用，这些均会严重影响实时决策。随着对大规模基础模型依赖性的增强，此类挑战进一步加剧，因为基础模型的规模要求必须部署于云端。我们提出了CADET（协同自主分布式实验工具包），这是一个模块化平台，用于在真实部署条件下系统化、可复现地评估分布式协同自主系统。CADET将自动驾驶系统流程解耦为可组合模块，这些模块能够灵活部署于车辆、基础设施以及边缘/云端。该框架集成了最先进的模型，融合了基于轨迹驱动的网络与工作负载仿真，并提供了同步的模型级、系统级与任务级测量手段。通过车-车与车-基础设施实验，我们证明分布式部署选择从根本上影响安全性：车-车意图数据包在性能上优于云端感知，而路侧单元辅助感知在过载并发请求之前仍能维持安全性。尽管CADET专为自动驾驶系统流程设计，但它也支持数据集驱动的实验，使系统与机器学习研究者能够在无需完整车辆仿真的情况下，基准测试分布式推理工作负载。CADET为开源项目，代码与演示详见https://nesl.github.io/cadet-web。