PillarGrid: Deep Learning-Based Cooperative Perception for 3D Object Detection from Onboard-Roadside LiDAR

3D object detection plays a fundamental role in enabling driving automation, which is regarded as a significant leap forward for contemporary transportation systems from the perspectives of safety, mobility, and sustainability. Most of the state-of-the-art object detection methods from point clouds are developed based on a single onboard LiDAR, whose performance will be inevitably limited by the range and occlusion, especially in dense traffic scenarios. In this paper, we propose PillarGrid, a novel cooperative perception method fusing information from multiple 3D LiDARs (both on-board and roadside), to enhance the situation awareness for connected and automated vehicles (CAVs). PillarGrid consists of four main components: 1) cooperative preprocessing of point clouds, 2) pillar-wise voxelization and feature extraction, 3) grid-wise deep fusion of features from multiple sensors, and 4) convolutional neural network (CNN)-based augmented 3D object detection. A novel cooperative perception platform is developed for model training and testing. Extensive experimentation shows that PillarGrid outperforms other single-LiDAR-based 3D object detection methods concerning both accuracy and range by a large margin.