In this work, this paper discussed the maneuver of autonomous vehicles, the application of deep learning technology in autonomous driving and improvements in the recognition and training of autonomous driving. First, we discussed how the pure pursuit algorithm calculates the route to the destination. Then, for the maneuver of autonomous vehicles, we listed the use of fuzzy logic and actuator. For driving safety, we introduced eight factors that it depends on. To apply deep learning technology in autonomous driving, we introduced how deep learning is applied to autonomous driving by explaining the four main parts of the pipeline of autonomous driving: perception, localization, prediction and decision making. We also noted the KITTI dataset, popularly used in the deep learning academia. Finally, for improvements in the recognition and training of autonomous driving, we presented three ways: using neural networks, using more effective datasets, and virtual environments that simulate the real world.


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    Titel :

    A survey to applications of deep learning in autonomous driving


    Beteiligte:
    Feng, Jianyuan (Autor:in) / Zhang, Rongjian (Autor:in) / Liang, Jinming (Autor:in)

    Kongress:

    International Symposium on Artificial Intelligence Control and Application Technology (AICAT 2022) ; 2022 ; Hangzhou,China


    Erschienen in:

    Proc. SPIE ; 12305


    Erscheinungsdatum :

    23.08.2022





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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