While convolutional neural networks (CNNs) have revolutionized ground-vehicle autonomy in the last decade, this class of algorithms requires large, truth-labeled data sets to be trained. The process of collecting and labeling training data is tedious, time-consuming, expensive, and error-prone. In order to automate this process, an automated method for training CNNs with simulated data was developed. This method utilizes physics-based simulation of sensors, along with automated truth labeling, to improve the speed and accuracy of training data acquisition for both camera and LIDAR sensors. This framework is enabled by the MSU Autonomous Vehicle Simulator (MAVS), a physics-based sensor simulator for ground vehicle robotics that includes high-fidelity simulations of LIDAR, cameras, and other sensors.


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    Titel :

    Training of Neural Networks with Automated Labeling of Simulated Sensor Data


    Weitere Titelangaben:

    Sae Technical Papers


    Beteiligte:
    Goodin, Chris (Autor:in) / Hudson, Christopher (Autor:in) / Carruth, Daniel (Autor:in) / Dabbiru, Lalitha (Autor:in) / Doude, Matthew (Autor:in) / Sharma, Suvash (Autor:in)

    Kongress:

    WCX SAE World Congress Experience ; 2019



    Erscheinungsdatum :

    02.04.2019




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Print


    Sprache :

    Englisch




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