Deep Neural Network (DNN) can provide highly accurate recognition for trained data, however, the estimation error for untrained data cannot be controlled. This is an essential challenge of generalizability in machine learning, and it is difficult to solve fundamentally. To solve this problem, we embed the inherent knowledge of the model to be estimated into the training model and impose parameter constraints to prevent deviation from the model so that generalizability is improved. Also, we trained the model with self-supervised learning using Variational Auto Encoder (V AE) on the training data, and evaluated the reconstruction error for the input data to judge the reliability of the estimation results. If the reliability is judged to be low, we switch to the deductive model, which does not cause large errors. This will suppress the maximum error. In this paper, we applied this method to the shape estimation of parking vehicles using a millimeter-wave radar. A parametric model with a minimum number of parameters was designed to represent the vehicle shape, which was trained by Convolutional Neural Network (CNN) to estimate the shape. The estimation reliability is predicted using the model trained with self-supervised learning by V AE. When the reliability is judged to be low, the position error is corrected based on the reflection pattern of a millimeter-wave radar. We confirmed that a large error for untrained data can be suppressed using actual parking data.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Error mitigation for untrained data utilizing generative model in vehicle shape estimation with millimeter-wave radar by deep neural network


    Contributors:


    Publication date :

    2021-07-11


    Size :

    919626 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    ERROR MITIGATION FOR UNTRAINED DATA UTILIZING GENERATIVE MODEL IN VEHICLE SHAPE ESTIMATION WITH MILLIMETER-WAVE RADAR BY DEEP NEURAL NETWORK

    Akita, Tokihiko / Kyutoku, Haruya / Tanikawa, Ukyo et al. | British Library Conference Proceedings | 2021




    Road-Shape Recognition Using On-Vehicle Millimeter-wave Radar

    Miyake, Yasuyuki / Natsume, Kazuma / Hoshino, Koichi | IEEE | 2007


    Road-Shape Recognition Using On-Vehicle Millimeter-Wave Radar

    Miyake, Y. / Natsume, K. / Hoshino, K. et al. | British Library Conference Proceedings | 2007