Implementing IoT and Data Analytics To Overcome “Vehicles Danger”
Garima Sachdeva1, Rishabh Verma2, Deepak Chahal3, Latika Kharb4

1Garima Sachdeva, Student(MCA), Jagan Institute Of Management Studies, Delhi, India.
2Rishabh Verma, Student(MCA), Jagan Institute Of Management Studies, Delhi, India.
3Deepak Chahal, Professor, Jagan Institute Of Management Studies, Delhi, India.
4Latika Kharb, Professor, Jagan Institute Of Management Studies, Delhi, India.
Manuscript received on 24 August 2019. | Revised Manuscript received on 06 September 2019. | Manuscript published on 30 September 2019. | PP: 4298-4304 | Volume-8 Issue-11, September 2019. | Retrieval Number: K21530981119/2019©BEIESP | DOI: 10.35940/ijitee.K2153.0981119
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Abstract: In the fast pacing world, we commune from one place to another or one city to another with the means of vehicles. In recent years, data shows the worse conditions of the roads in Delhi with rapidly increasing of accidents. Roads in Delhi are more accident prone which creates more jeopardy to survive in Delhi for everyone. This problem persist from last few years due to various factors like open sewage, speed breakers, damaged roads which leads to loss of life of manhood and ecosystem. Since India is a developing nation there is a constant demand for good quality infrastructure, transportation and services. But since India is a vast country with quite a sizeable population this problem still has not yet addressed in totality. By using proposed methodology, for reducing the risk in the life of manhood as well as ecosystem by detecting all the factors for accidents and then improvement can be assured at the level of road safety management effectively and efficiently. Through this methodology, we can resolve these major issues by using Computer Vision, vision detector and Machine Learning so that drivers can easily aware of what ahead of them and act alertly before accident actually happens. Models are preinstalled on vehicles for safety purposes of the people, by using vision detector driver can acknowledge the obstruction ahead the car. Using the divergent and advanced technologies, live screening of the objects or obstacles ahead the vehicle will be visible to the driver for better ease and safe drive. Driver will get the notification from articulate assistant in any language by detecting the data ahead the car whilst specific range. Simple commands would comprise of “Speed Breaker ahead, slow down”.
Keywords: Speed breakers, damaged roads, transportation and services, Computer Vision, Machine Learning.
Scope of the Article: Machine Learning