Machine Learning Algorithm for Automotive Collision Avoidance

Automotive collision avoidance system is a measure of enhanced safety. Car collisions have claimed the lives of many, and the advancement of science and technology has made collision avoidance a reality. Traditionally, collision avoidance systems are designed with the aim to avoid rear end collision, but in this paper, we are going to look at the collision avoidance with respect to fast approaching automobiles from a blind turn, making use of the navigation system. Here, we reviewed two levels of probability for collision. The first case is with high probability of probable collision and another case is with high probability of imminent collision. If the probability of probable collision is high, the driver is warned and requested to control the speed of the car. If the probability of imminent collision is high, the driver is warned, and autonomous braking takes effect. To achieve this, they made use of Bayesian Network which is built for the two speeds, one for host automobile and another for the fast approaching automobile. The recommendation is to use Machine Learning (ML) models for better robust models to prevent collisions.