Perception is an essential task for self-driving cars, but most perception tasks are usually handled independently. We propose a unified neural network named DLT-Net to detect drivable areas, lane lines, and traffic objects simultaneously. These three tasks are most important for autonomous driving, especially when a high-definition map and accurate localization are unavailable. Instead of separating tasks in the decoder, we construct context tensors between sub-task decoders to share designate influence among tasks. Therefore, each task can benefit from others during multi-task learning. Experiments show that our model outperforms the conventional multi-task network in terms of the task-wise accuracy and the overall computational efficiency, in the challenging BDD dataset.


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    Titel :

    DLT-Net: Joint Detection of Drivable Areas, Lane Lines, and Traffic Objects


    Beteiligte:
    Qian, Yeqiang (Autor:in) / Dolan, John M. (Autor:in) / Yang, Ming (Autor:in)


    Erscheinungsdatum :

    2020-11-01


    Format / Umfang :

    2580323 byte




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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