Intelligent Accident Prevention in VANETs
A. Mari Kirthima1, Rishabh Verma2, Chinmayi Rajashekar Hegde3, Arundhati S Shanbhag4
1A. Mari Kirthima, Assistant Professor, Department of CSE, B.M.S. Institute of Technology and Management, Bangalore, India.
2Rishabh Verma, Student, Department of CSE, B.M.S. Institute of Technology and Management, Bangalore, India.
3Chinmayi Rajashekar Hegde, Student, Department of CSE, B.M.S. Institute of Technology and Management, Bangalore, India.
4Arundhati S Shanbhag, Student, Department of CSE, B.M.S. Institute of Technology and Management, Bangalore, India.
Manuscript received on 12 March 2019 | Revised Manuscript received on 21 March 2019 | Manuscript published on 30 July 2019 | PP: 2401-2405 | Volume-8 Issue-2, July 2019 | Retrieval Number: B1805078219/19©BEIESP | DOI: 10.35940/ijrte.B1805.078219
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Accident prevention has always been an important issue for governments and car manufacturers across the world. Roughly 1.5 million people are killed in road accidents annually in India. The primary causes of accidents are broken and weathered roads, hazardous weather conditions, as well as human errors such as over speeding, distracted driving, and not following road safety rules. The traffic police work hard to enforce strict rules and maintain accident-free roads, but this hasn’t proven to be efficient. A vehicular ad hoc network (VANET), as the name says, is a network consisting of nodes. These nodes depict vehicles on the road. This project aims to use this technology with K-Nearest Neighbour Classifier (KNN) to create a prototype of a system which can notify drivers of an impending accident caused by forward collisions, rear collision etc., thus enabling them to take immediate action and prevent it.
Keywords: VANET, KNN, Accident Prevention
Scope of the Article: Artificial Intelligent Methods, Models, Techniques