首个面向卡车的协同感知数据集，解决大车盲区与遮挡难题。
TruckV2X: A Truck-Centered Perception Dataset
- 以卡车为中心构建多模态、多智能体协同感知数据集
- 包含激光雷达与摄像头，覆盖牵引车、挂车、智能车与路侧单元
- 适合研究重载车辆协同感知与自动驾驶系统部署
自动驾驶卡车可显著提升安全性和降低成本，但因车身庞大及挂车动态运动，面临独特感知挑战，如广泛盲区和遮挡问题，影响卡车自身及其它道路使用者的感知能力。为此，协同感知成为有前景的解决方案。然而，现有数据集多聚焦轻型车辆交互，或缺乏重型车辆场景下的多智能体配置。为填补这一空白，本文提出 TruckV2X——首个大规模卡车中心的协同感知数据集，涵盖多模态传感（激光雷达与摄像头）和多智能体协作（牵引车、挂车、智能网联汽车CAVs、路侧单元RSUs）。我们进一步分析卡车对协同感知需求的影响，建立性能基准，并提出重型车辆感知的研究优先方向。该数据集为开发具备更强遮挡处理能力的协同感知系统提供基础，加速多智能体自动驾驶卡车系统的落地。数据集已公开于 https://huggingface.co/datasets/XieTenghu1/TruckV2X。
原文摘要 · Abstract (English)
Autonomous trucking offers significant benefits, such as improved safety and reduced costs, but faces unique perception challenges due to trucks' large size and dynamic trailer movements. These challenges include extensive blind spots and occlusions that hinder the truck's perception and the capabilities of other road users. To address these limitations, cooperative perception emerges as a promising solution. However, existing datasets predominantly feature light vehicle interactions or lack multi-agent configurations for heavy-duty vehicle scenarios. To bridge this gap, we introduce TruckV2X, the first large-scale truck-centered cooperative perception dataset featuring multi-modal sensing (LiDAR and cameras) and multi-agent cooperation (tractors, trailers, CAVs, and RSUs). We further investigate how trucks influence collaborative perception needs, establishing performance benchmarks while suggesting research priorities for heavy vehicle perception. The dataset provides a foundation for developing cooperative perception systems with enhanced occlusion handling capabilities, and accelerates the deployment of multi-agent autonomous trucking systems. The TruckV2X dataset is available at https://huggingface.co/datasets/XieTenghu1/TruckV2X.
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