Autonomous vehicles generate large volumes of data for applications such as fleet monitoring, model retraining, and high-definition map updates. Existing studies often rely on generic traffic traces, which do not capture the characteristics of autonomous driving workloads. This paper proposes a system-level workload modeling framework for vehicle-to-cloud data. We classify offloaded data into three types: telemetry, event-driven fleet learning, and high-definition map updates, while we model their generation using a parameterized formulation based on empirical data. Using a real-world mobility trace from Munich, we analyze the resulting workloads over time and space. The results show that workload scales with vehicle penetration, exhibits temporal structure and spatial imbalance across access points, and is distinguished from baseline traffic models.

翻译：自动驾驶车辆为车队监控、模型再训练和高精地图更新等应用生成大量数据。现有研究常依赖通用流量轨迹，未能捕捉自动驾驶工作负载的特性。本文提出一种面向车到云数据的系统级工作负载建模框架。我们将卸载数据分为三类：遥测数据、事件驱动的车队学习数据和高精地图更新数据，并基于经验数据通过参数化公式对其生成过程进行建模。利用来自慕尼黑的真实移动轨迹，我们分析了工作负载在时空维度的分布特征。结果表明，该工作负载随车辆渗透率增长，具有时间结构性和跨接入点的空间不均衡性，且与基准流量模型存在显著差异。