Correctly understanding surrounding environments is a fundamental capability for autonomous driving. Semantic forecasting of bird-eye-view (BEV) maps can provide semantic perception information in advance, which is important for environment understanding. Currently, the research works on combining semantic forecasting and semantic BEV map generation is limited. Most existing work focuses on individual tasks only. In this work, we attempt to forecast semantic BEV maps in an end-to-end framework for future front-view (FV) images. To this end, we predict depth distributions and context features for FV input images and then forecast depth-context features for the future. The depth-context features are finally converted to the future semantic BEV maps. We conduct ablation studies and create baselines for evaluation and comparison. The results demonstrate that our network achieves superior performance.


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    Title :

    Forecasting Semantic Bird-Eye-View Maps for Autonomous Driving


    Contributors:


    Publication date :

    2024-06-02


    Size :

    1292193 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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