PREDICTION MODEL FOR POTENTIAL VEHICLES COLLISION

Abstract

The research subject is road crash accident nature and approaches for its preventions or predictions in the real-time using computer vision algorithms and usage of edge devices. The goal of this research is to create a model for prediction of potential vehicles collision, which works for real-time. The methodology used in the research is a combination of computer vision model TrafficCamNet_1.3 output with the math approaches to determine the possible vehicles collision. The exact math methods include calculation of cars’ movement projections and usage it for checks whether vehicles collision may occur or not. The experiments setup is based on the scenarios designed using BeamNG.tech, usage of Nvidia Jetson Orin Nano as a platform for running real-time classification and determination of possible road crash accidence. The main results of this research are outlining the exact time spent for having car stopped before crash, exact cars’ characteristics and case setup and the percentage of happened road crash accidents to determine the model robustness and ability to real life introducing. As a conclusion, this research reveals the facts, that model works for the cases, when cars did not exceed allowed speed limit on the particular road. With the allowed speed, driver will be able to be notified in time and will have enough time to stop the car, otherwise amount of time to react on the threat is being significantly reduced. As a model improvement, the usage of models’ ensemble with different training dataset sizes can be considered for early car classification on the image. The results of this research can be used for building the intelligent software system for the preventions of road traffic accidence on the defined as a dangerous road parts.