Data Driven Vehicular Heterogeneity based Intelligent Collision Avoidance System for IoV

Abstract

The Internet of Vehicles (IoV) is becoming a trending field of research for researchers. Its implementation in the real world is increasing day by day to facilitate drivers in driving and provide safe road conditions to travelers to be safe from accidents. Many research studies on IoV focused on the issue of collision avoidance between vehicles on the road. Companies like the world’s biggest car company TESLA use IoV and certain machine learning algorithms to make their cars autonomous. Keeping in view the importance of collision avoidance issues between vehicles on the road. Our proposed work is to perform an analysis of real-life vehicular data based on the characteristics of different types of vehicles moving on the roads. Due to the heterogeneousness of the types of vehicles (cars, buses), their parameters are also different. From the vehicular data based on this heterogeneousness, we intended to find out the different parameters’ roles for the collision avoidance system. Using the capabilities of machine learning techniques on the gathered data, a framework for the collision avoidance system is suggested for vehicles on the roads. Performance analysis of the framework for predicting the accident well before the time for the drivers is presented for better road conditions.