In emerging semi-autonomous vehicles, accurate environmental perception with advanced driver assistance systems (ADAS) is critical to achieving safety and performance goals. Enabling robust perception for vehicles with ADAS requires solving multiple complex problems related to the selection and placement of sensors, object detection, and sensor fusion. Current methods address these problems in isolation, which leads to inefficient solutions. We present PASTA, a novel framework for global co-optimization of deep learning and sensing for ADAS-based vehicle perception. Experimental results with the Audi-TT and BMW-Minicooper vehicles show how PASTA can intelligently traverse the perception design space to find robust, vehicle-specific solutions.


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    Titel :

    Machine Learning Based Perception Architecture Design for Semi-autonomous Vehicles


    Beteiligte:
    Kukkala, Vipin Kumar (Herausgeber:in) / Pasricha, Sudeep (Herausgeber:in) / Dey, Joydeep (Autor:in) / Pasricha, Sudeep (Autor:in)


    Erscheinungsdatum :

    02.09.2023


    Format / Umfang :

    22 pages




    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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