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 https://github.com/quan-dao/practical-collab-perception.

翻译：本文通过扩展单车辆激光雷达3D目标检测模型处理点云序列而非单帧点云的能力，对其进行了改进。在此步骤中，我们扩展了先前关于矫正点云拼接中阴影效应的工作，以提升多帧检测模型的准确率。该扩展包含融合高清地图及知识蒸馏专家模型。随后，我们通过车联网通信的多智能体协同机制，进一步提升了单车辆感知性能。我们设计了一种简单而有效的协同方法，在最小化对单车辆检测模型的改动及智能体间同步假设的前提下，实现了优于现有技术的带宽-性能折衷。在V2X-Sim数据集上的实验表明，本协同方法能达到早期协同98%的性能，同时消耗与后期协同相当的带宽（仅为早期协同的0.03%）。代码将发布在https://github.com/quan-dao/practical-collab-perception。