Transportation is a vastly developed research field that tries to optimize humans' safety in everyday life. Recently, autonomous vehicles are being thoroughly developed by a lot of companies such as Uber[1] and Tesla[2], which will improve safety on the roads. These vehicles are using artificial intelligence to correctly interact with their environment. First, we developed a Java program called “ARIBAN” which provides algorithms for recognition and classification of traffic signs with Multi-Layer Perceptron Neural Networks (MLPNN). Second, we use back propagation algorithm in a supervised manner to establish the network. Third, the neural networks are trained and used with a large amount of traffic signs. Finally, the post-processing combines the results to make a recognition decision. Eventually, we have tested our trained network with more than 62 types of traffic signs. Experimental results have demonstrated the effectiveness of the proposed system. Furthermore, the proposed system was deployed within an architecture for autonomous driving.


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    Title :

    Traffic Sign Recognition Using Neural Networks Useful for Autonomous Vehicles


    Contributors:


    Publication date :

    2019-12-01


    Size :

    739790 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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