GRD Journals- Global Research and Development Journal for Engineering | Volume 6 | Issue 5 | April 2021 ISSN- 2455-5703
Driver Distraction, Alcohol and Obstacle Detection through Machine Learning: A Review Rupali Parte Professor Department of Computer Engineering Jaywantrao Sawant College of Engineering, Pune Kunal Patil UG Student Department of Computer Engineering Jaywantrao Sawant College of Engineering, Pune
Ajay Kulkarni UG Student Department of Computer Engineering Jaywantrao Sawant College of Engineering, Pune
Shubham Wadekar UG Student Department of Computer Engineering Jaywantrao Sawant College of Engineering, Pune
Rohit Sangamnerkar UG Student Department of Computer Engineering Jaywantrao Sawant College of Engineering, Pune
Abstract Driving is an inherently dangerous proposition as the vehicles are travelling at great speed which could lead to any minor inconsistencies or errors by the driver can lead to catastrophic results. To reduce such occurrences and provide a safe transport and travel for the users, there are carefully crafted rules and regulations that need to be abided. These rules are enforced by the traffic police and other regulatory authorities. But most of the time, the sheer number of vehicles on the road can overwhelm the authorities in their quest for compliance of the regulations. This leads to drunken driving and lethargic driver that is attempting to drive in that inebriated condition. This leads to unsafe conditions on the road that can lead to a mishap. There have been a multitude of approaches that are utilized for enabling the detection of drunkenness and distraction, but most of the approaches are either inaccurate or are highly intrusive. Therefore, this research proposes an effective technique for driver distraction along with alcohol and obstacle detection. The methodology employs the use of Region of Interest (ROI) in conjunction with Convolutional Neural Networks and Decision tree to provide highly accurate detection. This approach will be effectively outlined in the upcoming researches. Keywords- Convolutional Neural Networks and Decision Tree
I. INTRODUCTION Recent studies indicate that there has been an increase in road accidents which have been the cause of major fatalities. Road accidents are an unnecessary evil that has been taking countless lives across the world. The road accidents turn fatal due to the sheer speed of the vehicles along with the load of the heavy vehicles. The attentiveness of the driver too plays a major role in subverting an accident in an unpleasant scenario. Most of the fatal accidents happen due to the negligence of the driver which is one of the most dangerous situations that can lead to massive damage. The road fatalities in India are particularly problematic due to the very large population and the sub-standard quality of the roads. These conditions on the Indian streets are highly dangerous and have the potential to cause a lot of havoc to the distracted driver. The statistics have also indicated that there is also a prevalence of drunk driving which is extremely harmful to everyone. The inebriated driver has reduced reaction time that can impair the ability of the driver to react to a collision or an obstacle on the road. These conditions make it impossible for the driver to perform at the maximum potential that is necessary for the driver to conduct the various manoeuvres necessary for enabling safe and effective driving. There is a multitude of obstacles on the road especially in Indian cities that have roads that have an assortment of different obstacles that need to be managed effectively. The presence of any form of obstacles can veer the vehicle off track that can cause a collision in a different lane. This effect is compounded when the driver is drunk or distracted and encounters an obstacle on the street a little too late. This causes the driver to panic and try to avoid the obstacle at any cost. This leads to an erroneous judgment that can be detrimental to the drivers around. The panic could make the driver oversteer or apply too much course correction. This would immensely alter the course of the vehicle. The drunk or distracted driver would not be able to understand the feedback and judge the speed of the vehicle effectively. This would lead to the driver colliding into nearby cars and also reach oncoming traffic. This would be catastrophic as the other All rights reserved by www.grdjournals.com
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