In this paper, we improve the single-vehicle 3D object detection models using
LiDAR by extending their capacity to process point cloud sequences instead of
individual point clouds. In this step, we extend our previous work on
rectification of the shadow effect in the concatenation of point clouds to
boost the detection accuracy of multi-frame detection models. Our extension
includes incorporating HD Map and distilling an Oracle model. Next, we further
increase the performance of single-vehicle perception using multi-agent
collaboration via Vehicle-to-everything (V2X) communication. We devise a simple
yet effective collaboration method that achieves better bandwidth-performance
tradeoffs than prior arts while minimizing changes made to single-vehicle
detection models and assumptions on inter-agent synchronization. Experiments on
the V2X-Sim dataset show that our collaboration method achieves 98% performance
of the early collaboration while consuming the equivalent amount of bandwidth
usage of late collaboration which is 0.03% of early collaboration. The code
will be released at this https URL

本文介绍一种新的通过点云序列检测车辆 3D 物体的方法，采用 LiDAR 传感器解决阴影效应与 HD 地图配合，并使用 V2X 通信技术提高车辆感知性能，同时优化带宽性能.