The results of monocular depth estimation are no satisfactory in the automatic driving scenario. The combination of radar and camera for depth estimation is a feasible solution to the problem of depth estimation in similar scenes. The radar-camera pixel depth association model establishes a reliable correlation between radar depth and camera pixel. In this paper, a new depth estimation model named Deep-PDANet based on RC-PDA is proposed, which increases the depth and width of the network and alleviates the problem of network degradation through residual structure. Convolution kernels of different sizes are selected in the basic units to further improve the ability to extract global information while taking into account the extraction of information from a single pixel. The convergence speed and learning ability of the network are improved by the training strategy of multi-weight loss function in stages. In this paper, comparison experiments and ablation study were performed on the NuScenes dataset, and the accuracy of the multidimensional model was improved over the baseline model, which exceeded the existing excellent algorithms.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Deep-PDANet: Camera-Radar Fusion for Depth Estimation in Autonomous Driving Scenarios


    Additional title:

    Sae Technical Papers


    Contributors:
    Zheng, Lianqing (author) / Ai, Wenjin (author) / Ma, Zhixiong (author)

    Conference:

    SAE 2023 Intelligent and Connected Vehicles Symposium ; 2023



    Publication date :

    2023-12-20




    Type of media :

    Conference paper


    Type of material :

    Print


    Language :

    English




    KDepthNet: Mono-Camera Based Depth Estimation for Autonomous Driving

    Dargahi, Javad / Zadeh, Mehrdad / Tavakolian, Niloofar et al. | SAE Technical Papers | 2022


    KDepthNet: Mono-Camera Based Depth Estimation for Autonomous Driving

    Tavakolian, Niloofar / Fekri, Pedram / Zadeh, Mehrdad et al. | British Library Conference Proceedings | 2022


    LIDAR and Monocular Camera Fusion: On-road Depth Completion for Autonomous Driving

    Fu, Chen / Mertz, Christoph / Dolan, John M. | IEEE | 2019


    Camera-Radar Fusion for 3-D Depth Reconstruction

    Niesen, Urs / Unnikrishnan, Jayakrishnan | IEEE | 2020


    FILTERING AUTONOMOUS DRIVING SIMULATION SCENARIOS

    IYRA ALESSANDRO / RANGASWAMY VIVEK | European Patent Office | 2024

    Free access