构建大规模多传感器多智能体数据集，支持自动驾驶系统训练与协同
Scaling Datasets for Multi-Sensor, Multi-Agent, and Multi-Domain Learning in Autonomous Systems
- 基于AVstack和CARLA构建模块化数据生成流水线
- 生成可用于地面、空中及基础设施系统的TB级真值标注数据
- 适合自动驾驶感知融合与多智能体协作研究者使用
现有数据集无法支撑多智能体、多传感器或多领域自主系统中的大规模学习，而多样性与协同性至关重要。本文提出一种模块化数据生成流程，利用AVstack框架和CARLA模拟器，为地面、空中及基础设施系统生成TB级、带真值标注的数据。该流程支持单/多智能体配置与灵活传感器组合，可在复杂条件下实现可控实验。代表性感知与融合研究证明，生成的数据可有效支持特定应用训练与协作式自主系统开发。
原文摘要 · Abstract (English)
Existing datasets cannot support large-scale learning in multi-agent, multi-sensor, or multi-domain autonomy, where diversity and coordination are essential. We present a modular dataset generation pipeline that creates terabyte-scale, ground-truth-labeled data for ground, aerial, and infrastructure-based systems using the AVstack framework and CARLA simulator. Supporting single- and multi-agent configurations with flexible sensor suites, the pipeline enables controllable experimentation across challenging conditions. Representative perception and fusion studies show how generated data can support application-specific training and collaborative autonomy.
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