Perception systems of autonomous vehicles are susceptible to occlusion, especially when examined from a vehicle-centric perspective. Such occlusion can lead to overlooked object detections, e.g., larger vehicles such as trucks or buses may create blind spots where cyclists or pedestrians could be obscured, accentuating the safety concerns associated with such perception system limitations. To mitigate these challenges, the vehicle-to-everything (V2X) paradigm suggests employing an infrastructure-side perception system (IPS) to complement autonomous vehicles with a broader perceptual scope. Nevertheless, the scarcity of real-world 3D infrastructure-side datasets constrains the advancement of V2X technologies. To bridge these gaps, this paper introduces a new 3D infrastructure-side collaborative perception dataset, abbreviated as inscope. Notably, InScope is the first dataset dedicated to addressing occlusion challenges by strategically deploying multiple-position Light Detection and Ranging (LiDAR) systems on the infrastructure side. Specifically, InScope encapsulates a 20-day capture duration with 303 tracking trajectories and 187,787 3D bounding boxes annotated by experts. Through analysis of benchmarks, four different benchmarks are presented for open traffic scenarios, including collaborative 3D object detection, multisource data fusion, data domain transfer, and 3D multiobject tracking tasks. Additionally, a new metric is designed to quantify the impact of occlusion, facilitating the evaluation of detection degradation ratios among various algorithms. The Experimental findings showcase the enhanced performance of leveraging InScope to assist in detecting and tracking 3D multiobjects in real-world scenarios, particularly in tracking obscured, small, and distant objects. The dataset and benchmarks are available at https://github.com/xf-zh/InScope.

翻译：自动驾驶车辆的感知系统易受遮挡影响，尤其在以车辆为中心的视角下考察时。此类遮挡可能导致目标漏检，例如卡车或公交车等大型车辆可能产生盲区，使骑行者或行人被遮蔽，从而加剧了此类感知系统局限所带来的安全隐患。为缓解这些挑战，车联网（V2X）范式建议采用路侧感知系统（IPS），以更广阔的感知范围对自动驾驶车辆进行补充。然而，真实世界三维路侧数据集的稀缺制约了V2X技术的发展。为弥补这些不足，本文提出了一种新型三维路侧协同感知数据集，简称为InScope。值得注意的是，InScope是首个通过在路侧战略性地部署多位置激光雷达（LiDAR）系统来专门应对遮挡挑战的数据集。具体而言，InScope包含为期20天的采集时长，涵盖303条跟踪轨迹和由专家标注的187,787个三维边界框。通过基准测试分析，本文为开放交通场景提出了四项不同的基准任务，包括协同三维目标检测、多源数据融合、数据域迁移以及三维多目标跟踪任务。此外，本文设计了一种新指标以量化遮挡的影响，便于评估不同算法间的检测性能衰减比率。实验结果表明，利用InScope能够显著提升真实场景中三维多目标的检测与跟踪性能，特别是在跟踪被遮挡、小型及远距离目标方面。数据集与基准测试资源可通过https://github.com/xf-zh/InScope获取。