A LiDAR-based driving perception system is crucial for autonomous driving, as it provides real-time, accurate, and robust perception results that significantly enhance driving safety. This article proposes a LiDAR-based panoptic driving perception algorithm, named LiDAR-PDP, which can simultaneously perform free-space segmentation, static object semantic segmentation, and moving object instance segmentation. This article constructs an efficient two-branch 2-D convolution neural network (CNN) backbone that consists of a spatial feature branch and a motion feature branch to extract fine-grained spatial-temporal features from sequential point cloud data. For the spatial feature branch, we designed a compound sampling convolution module (CSCM) that can learn 3-D spatial features with a more general geometric modeling capacity. Additionally, this article designs parallel feature pyramid networks (P-FPNs) that can aggregate multiscale features in both range-dependent and size-dependent manners. We evaluate LiDAR-PDP on SemanticKITTI and NuScenes. Our method outperforms other comparison methods that were modified from the state-of-the-art point cloud panoptic segmentation methods on both datasets, thereby demonstrating the effectiveness of the proposed components in LiDAR-PDP. Moreover, our lightweight LiDAR-PDP achieves outstanding accuracy and real-time performance on a vehicular and edge computing platform. The LiDAR-PDP would improve the perception ability of autonomous vehicles with limited computing resources.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    LiDAR-PDP: A LiDAR-Based Panoptic Dynamic Driving Environment Perception Algorithm


    Contributors:
    Wang, Hai (author) / Li, Jianguo (author) / Cai, Yingfeng (author) / Chen, Long (author) / Li, Yicheng (author)


    Publication date :

    2025-02-01


    Size :

    5854705 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



    Location-Guided LiDAR-Based Panoptic Segmentation for Autonomous Driving

    Xian, Guozeng / Ji, Changyun / Zhou, Lin et al. | IEEE | 2023


    Panoptic Based Camera and Lidar Fusion for Distance Estimation in Autonomous Driving Vehicles

    P, Aparna M / Thayyil Ravi, Arunkrishna / Jose, Edwin et al. | SAE Technical Papers | 2022


    Panoptic Based Camera and Lidar Fusion for Distance Estimation in Autonomous Driving Vehicles

    Jose, Edwin / P, Aparna M / Patil, Mrinalini et al. | British Library Conference Proceedings | 2022


    Panoptic Based Camera and Lidar Fusion for Distance Estimation in Autonomous Driving Vehicles

    Jose, Edwin / P, Aparna M / Patil, Mrinalini et al. | British Library Conference Proceedings | 2022


    MtpNet: Multi-Task Panoptic Driving Perception Network

    Li, Zheng / Yuan, Xiaohui / Sun, Bifan et al. | IEEE | 2025