With the development of society, technological progress, and new needs, autonomous driving has become a trendy topic in smart cities. Due to technological limitations, autonomous driving is used mainly in limited and low-speed scenarios such as logistics and distribution, shared transport, unmanned retail, and other systems. On the other hand, the natural driving environment is complicated and unpredictable. As a result, to achieve all-weather and robust autonomous driving, the vehicle must precisely understand its environment. The self-driving cars are outfitted with a plethora of sensors to detect their environment. In order to provide researchers with a better understanding of the technical solutions for multi-sensor fusion, this paper provides a comprehensive review of multi-sensor fusion 3D object detection networks according to the fusion location, focusing on the most popular LiDAR and cameras currently in use. Furthermore, we describe the popular datasets and assessment metrics used for 3D object detection, as well as the problems and future prospects of 3D object detection in autonomous driving.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Multi-Sensor Fusion Technology for 3D Object Detection in Autonomous Driving: A Review


    Contributors:


    Publication date :

    2024-02-01


    Size :

    5865370 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



    A review of multi-sensor fusion 3D object detection for autonomous driving

    Zhao, Junwei / Li, Lixiang / Dai, Jun | SPIE | 2024


    Multi-sensor fusion 3D object detection for autonomous driving

    Alaba, Simegnew Yihunie / Ball, John E. | British Library Conference Proceedings | 2023


    Multi-sensor fusion for autonomous driving

    Zhang, Xinyu / Li, Jun / Li, Zhiwei et al. | TIBKAT | 2023


    Sensor fusion system for object recognition of autonomous driving system

    SUNG KWANG MO / KWAG SU JIN / LEE JONG KEUN et al. | European Patent Office | 2023

    Free access