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@article{149609,
        author = {M.SHAKUNTHALA and S.RAHINI and P.REVATHI and G.ROHINI},
        title = {DRIVER DROWSINESS AND SAFETY ENHANCEMENT TECHINQUE IN VEHICLES USING MACHINE LEARNING},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {7},
        number = {1},
        pages = {62-68},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=149609},
        abstract = {Safety of life is the main priority while driving and travelling. There are many reasons for road accidents, but accidents due to driver fault and lack of safety in the vehicle comprises the maximum percentage. Driver drowsiness and safety enhancement system is one of the important domain in the road safety which involves continuous monitoring of fatigue condition and health parameters of the driver. However, the development of such system has many difficulties related to fast and proper recognition of a driver’s fatigue symptoms. One way to implement driver drowsiness detection systems is to use the vision-based approach. The proposed method uses Machine learning for monitoring the drowsiness of the driver by video processing and IOT enabled system for enhancing the safety in vehicles by detecting Heart beat rate, Temperature rate and oxygen level of the person inside the vehicle. If these parameters exceed from the threshold level an alert will be sent to the driver and also to their relatives via SMS.The fatigue detection system runs on MATLAB and is capable of detecting drowsiness of driversand based on the current situations an alert will be given to the driver using buzzer.},
        keywords = {Driver drowsiness, Safety, SVM, Sensor, Blinking, Sensor.},
        month = {},
        }