An Intelligent Rider Assistance System Using Machine Learning for Two Wheel Vehicles
Jisha Mariyam John1, Hariharan R L2
1Jisha Mariyam John , Department of Computer Science and Engineering, Providence College of Engineering ,Chengannur, Kerala, India.
2Hariharan R L, Department of Computer Science and Engineering, Providence College of Engineering ,Chengannur, Kerala, India.
Manuscript received on July 20, 2019. | Revised Manuscript received on August 10, 2019. | Manuscript published on August 30, 2019. | PP: 1361-1366 | Volume-8 Issue-6, August 2019. | Retrieval Number: F8337088619/2019©BEIESP | DOI: 10.35940/ijeat.F83370.88619
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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: As far as a rider is concerned, safety is the first priority while driving a vehicle. There have been significant changes over the years in the design of four wheeler in terms of security. During the design of vehicles manufacturers are incorporating many modifications to the existing model so that the security is more. But when the safety of motorbikes is concerned this is not true, as there exits some blind spots which are not visible to the rider through the mirrors. Blind spots are the rare quarter areas towards the rear of the vehicle on both sides, vehicles in the contiguous paths of the road may fall into these vulnerable sides and driver may not be able to see them using only the mirrors. In this paper we have introduced a new approach using machine learning tools to detect the obstacles and to alert rider and there by reducing the risk of collision. This system could reduce significant amount of risk that the rider is going to face because of the obstacles which are not visible through the mirrors.
Keywords: Obstacle Detection, Ordroid, YOLO, Machine Learning, Road Safety.