构建真实车联网通信下的多车协同自动驾驶数据集
CooperScene: Multi-Modal Cooperative Autonomy Benchmark with C-V2X Communication Characterization
- 三辆自动驾驶汽车+路侧单元，搭载多模传感器与商用通信设备
- 59,000帧中含34.4万3D目标，10Hz同步标注，厘米级定位精度
- 支持多智能体扩展性测试，适配真实道路场景的算法评估
蜂窝车联网（C-V2X）使车辆能在视距外实现协同感知、预测与规划。然而现有数据集常忽略真实部署中的复杂因素，如通信带宽限制及其动态变化、异构感知模态以及超越单个协作伙伴的可扩展性。本文提出CooperScene，一个高保真多智能体协同自动驾驶数据集，包含真实C-V2X通信特征。数据涵盖交叉口、高速公路匝道、停车场等多样场景，由三辆联网自动驾驶汽车（CAVs）和一个路侧单元（RSU）组成，均配备多模传感器及商用C-V2X通信无线电。所有场景以10 Hz频率进行全局一致的3D标注，共覆盖59,000帧、344,000个物体，依托紧密的传感器与智能体同步、厘米级定位与空间对齐、精确跨模态标定，以及符合3GPP标准的C-V2X通信。CooperScene为评估多智能体扩展性与真实可部署环境下的性能提供了严格基准。项目官网提供数据与评测平台：https://cisl.ucr.edu/CooperScene
原文摘要 · Abstract (English)
Cellular vehicle-to-everything (C-V2X) enables cooperative perception, prediction, and planning beyond the field of view of individual agents. However, existing datasets often overlook the complexities of real-world deployment, such as limited communication bandwidth and its dynamics, heterogeneous sensing modalities, and scalability beyond a single cooperative partner. In this paper, we introduce CooperScene, a high-fidelity cooperative autonomy dataset with real-world C-V2X communication characterization. The dataset is organized into diverse scenes, including intersections, highway ramps, and parking lots. These scenes involve three connected and autonomous vehicles (CAVs) and one infrastructure roadside unit (RSU), all equipped with multi-modal sensors and commercial off-the-shelf C-V2X communication radios. All scenes are annotated with globally consistent 3D labels at 10 Hz, totaling 344K objects across 59K frames, underpinned by tight sensor- and agent-synchronization, centimeter-level localization and spatial alignment, precise cross-modality calibration, and 3GPP-standard-compliant C-V2X communication. CooperScene establishes a rigorous benchmark for evaluating multi-agent scaling and actual performance in real-world deployable settings. Project website for data and benchmark: https://cisl.ucr.edu/CooperScene
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