With the remarkable development of the practicality of deep learning and the ultra - high - speed information transmission rate of 5G communication technology, autonomous driving is becoming a key technology that affects future industries. Sensors are crucial for perceiving the external world in autonomous driving systems, and their performance influences the safety of autonomous vehicles. In this study, we deeply explore the multi - sensor fusion technology of IMU, LiDAR and GNSS for autonomous driving. A fusion architecture and algorithm are designed based on the Extended Kalman Filter. The predicted position state curve of the vehicle after incorporating the machine learning model is discussed. It is concluded that the sensor fusion technology after integrating the Random Forest model algorithm can more precisely predict the position state of the vehicle.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Research on Multi - Sensor Fusion Technology of IMU LiDAR and GNSS for Autonomous Driving


    Beteiligte:
    Cheng, Zexiang (Autor:in)


    Erscheinungsdatum :

    21.03.2025


    Format / Umfang :

    1356114 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    AUTONOMOUS DRIVING LIDAR TECHNOLOGY

    WANG PANQU / WANG YU / ZHAO XIANGCHEN et al. | Europäisches Patentamt | 2023

    Freier Zugriff

    Adaptive Sensor Fusion of Camera, GNSS and IMU for Autonomous Driving Navigation

    Ren, Weining / Jiang, Kun / Chen, Xinxin et al. | IEEE | 2020


    Multi-sensor fusion for autonomous driving

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


    Research on the Application of Multi-Sensor Fusion in Autonomous Driving

    Li, Wu / Xie, Zongzhi / He, Pengfei | IEEE | 2025


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

    Wang, Xuan / Li, Kaiqiang / Chehri, Abdellah | IEEE | 2024