  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.
