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.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

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


    Contributors:


    Publication date :

    2022-10-08


    Size :

    538654 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Automatic Background Filtering for Cooperative Perception Using Roadside LiDAR

    Liu, Jianqi / Zhao, Jianguo / Guo, Junfeng et al. | IEEE | 2024


    Cyber Mobility Mirror: A Deep Learning-Based Real-World Object Perception Platform Using Roadside LiDAR

    Bai, Zhengwei / Nayak, Saswat P. / Zhao, Xuanpeng et al. | IEEE | 2023


    Automatic Background Construction and Object Detection Based on Roadside LiDAR

    Zhang, Zhenyao / Zheng, Jianying / Xu, Hao et al. | IEEE | 2020