This paper presents the implementation of a driving assistance algorithm based on semantic segmentation. The proposed implementation uses a convolutional neural network architecture known as U-Net to perform the image segmentation of traffic scenes taken by the self-driving car during the navigation, the segmented image gives to every pixel a specific class. The driving assistance algorithm uses the data retrieved from the semantic segmentation to perform an evaluation of the environment and provide the results to the self-driving car to help it make a decision. The evaluation of the algorithm is based on the frequency of the pixels of each class, and on an equation that calculates the importance weight of a pixel with its own specific position and its respective class. Experimental results are presented to evaluate the feasibility of the proposed implementation.


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    Title :

    Driving assistance algorithm for self-driving cars based on semantic segmentation


    Contributors:

    Conference:

    Optics and Photonics for Information Processing XVI ; 2022 ; San Diego,California,United States


    Published in:

    Proc. SPIE ; 12225


    Publication date :

    2022-10-03





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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