The Research on Autonomous vehicles is profoundly increasing in popularity. Autonomous vehicles are widely preferred for its advanced technological implementation in Automatic emergency breaking systems and forward collision warning. Another important aspect of self-driving cars is that providing driving assistance hence ensuring a safe and flexible driving experience and preventing catastrophic accidents due to drivers’ negligence. Thus proposed a feasible neural network code for obstacle detection and train the model for distance predictions and automatic braking. The proposed system is designed by using Node MCU ESP8266 which receives the data from the external environment using sensors such as camera module and ultrasonic sensor. The Convolutional Neural Network (CNN) processes these data and provides the required breaking based on the distance from the obstacle detected. The camera module sends signals which are used for object classification which is performed by the trained Convoluted Neural Network model. According to the proposed system, the autonomous vehicle successfully predicts the safe driving distance with the trained CNN model in MATLAB R2022b.


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    Titel :

    Emergency Braking Distance Prediction in Autonomous Vehicles


    Beteiligte:
    Sharmila Devi, S. (Autor:in) / Shruthi, G. (Autor:in) / Harini, S.Sree (Autor:in)


    Erscheinungsdatum :

    17.05.2023


    Format / Umfang :

    1512822 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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