In the recent decade, it has become clear that two-wheelers are responsible for about 95% of the road accidents [12]. The apparent reason being due to negligence of wearing helmets, intoxicated driving, and speeding. The lack of immediate emergency services in response to such accidents results in the loss of life. As a result, driving two-wheeler vehicles has become more dangerous and unsafe. In light of this problem, this paper puts forth a solution to overcome a majority of this problem and thus make driving two-wheeler vehicles much safer. A safety device which is attached to the dashboard of the vehicle will scan for the driver wearing the helmet, and upon confirmation only would it start the engine upon ignition. The in-built accelerometer within the safety device will determine if the driver is intoxicated based on the vehicle movement pattern. This behavioral understanding is achieved by training the microprocessor within the device to differentiate normal vehicle movement from intoxicated vehicle movement through machine learning. The accelerometer is also capable of identifying any impact on the two-wheeler and hence, notify the smart system on board the vehicle to contact the nearest emergency centers for prompt action. The novelty of this work is that it is the first among the other works of its kind to ensure that the driver is wearing helmet and is not intoxicated during driving in single device.


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    Titel :

    Fool Proof Two-Wheeler Safety Device


    Weitere Titelangaben:

    Lect. Notes Electrical Eng.


    Beteiligte:
    Sengodan, Thangaprakash (Herausgeber:in) / Murugappan, M. (Herausgeber:in) / Misra, Sanjay (Herausgeber:in) / Siddharth, P. N. (Autor:in) / Panicker, C. T. Justus (Autor:in)

    Kongress:

    International Conference on Advances in Electrical and Computer Technologies ; 2020 October 28, 2020 - October 29, 2020



    Erscheinungsdatum :

    27.02.2021


    Format / Umfang :

    11 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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