3D object detection plays a fundamental role in enabling autonomous driving,
which is regarded as the significant key to unlocking the bottleneck of
contemporary transportation systems from the perspectives of safety, mobility,
and sustainability. Most of the state-of-the-art (SOTA) 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 \textit{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 phases: 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 the SOTA
single-LiDAR-based 3D object detection methods with respect to both accuracy
and range by a large margin.

提出了在自动驾驶中利用多雷达协同感知的 PillarGrid 方法，包括点云预处理、柱体分割、多传感器特征融合、基于 CNN 的物体检测等阶段，实验证明该方法能够显著提高 3D 物体检测的精度和范围。