A major traffic violation known as “reckless driving” is defined as driving with a deliberate disrespect for other people’s safety. Since there are too many unknowns in the scenario, reckless driving cannot be stopped. It is the driver’s obligation to drive safely; it is not under their control. Therefore, in order to address the issues around careless driving, we developed a method for determining whether or not a vehicle is being driven carelessly. Prior studies on the subject used the vehicle’s current speed and the presence of dents to identify the driver as careless. They neglect to take the vehicle’s trajectory into account. A car that is driving while intoxicated or that frequently changes lanes does not travel in a straight line. This trajectory can be examined to determine whether a car is being driven carelessly. With a CNN model, the vehicle’s trajectory may be examined. Without any pre-processing, analyzing the vehicle’s path would make the CNN model complex and time-consuming to train. In order to address this problem, we create a graph of the car’s trajectory and use it as input data to train a CNN model that determines whether or not the vehicle is being driven recklessly.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Enhancing Road Safety: Reckless Driver Detection via OpenCV in Simulated Environments


    Weitere Titelangaben:

    Communic.Comp.Inf.Science


    Beteiligte:
    Patil, Mukesh (Herausgeber:in) / Vyawahare, Vishwesh (Herausgeber:in) / Birajdar, Gajanan (Herausgeber:in) / Bhosale, Varun (Autor:in) / Shah, Jainam (Autor:in) / Doshi, Prem (Autor:in) / Mangrulkar, Ramchandra (Autor:in) / Williams, Idongesit (Autor:in)

    Kongress:

    International Conference on Intelligent Computing and Big Data Analytics ; 2024 ; Navi Mumbai, India June 14, 2024 - June 15, 2024



    Erscheinungsdatum :

    31.12.2024


    Format / Umfang :

    16 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




    Driver Drowsiness Detection System with OpenCV & Keras

    Srivastava, Mayank / Idrisi, Shoyab Alam / Gupta, Tushar | IEEE | 2021


    Driver Drowsiness Detection System with OpenCV and Keras

    Fathima, R Syed Ali / Keerthi, Kovi Venkata / Bhuvanesh, Kovuri Naga et al. | IEEE | 2024


    RECKLESS-VEHICLE REPORTING APPARATUS, RECKLESS-VEHICLE REPORTING PROGRAM PRODUCT, AND RECKLESS-VEHICLE REPORTING METHOD

    YAMASHIRO TAKAHISA / KUMABE SEIGOU | Europäisches Patentamt | 2016

    Freier Zugriff

    Real-Time Driver Drowsiness Detection Using Dlib And openCV

    Singh, Prashant / Chauhan, S P S / Rajesh, E. | IEEE | 2022


    A Real-Time Driver Drowsiness Detection Using OpenCV, DLib

    Bajaj, Srinidhi / Panchal, Leena / Patil, Saloni et al. | Springer Verlag | 2022