Application of machine learning techniques in vehicle collision detection

Abstract

Vehicle accidents are still happening daily even with the existing technological support provided. This can be due to limitations of the technology and/or human error. Using a newer technology, the vehicle-to-everything communication, the aim is to use machine learning techniques to make predictions on GPS data, in order to provide an early collision warning system. With such a system in place, drivers would be alerted if a collision might happen several seconds prior and be mentally prepared for the potential threat. The algorithms explored in this study is the multi-layered perceptron classifier, random forest and Tabnet.