Vision-Based Efficient Collision Avoidance Model Using Distance Measurement

Abstract

The Fourth industrial revolution (IR 4.0) saw the emergence of computer vision and artificial intelligence in creating smart imaging systems that can replace human vision and decision making especially to predict models for autonomous vehicles. In this context, advanced prediction of probable collision in real time scenario is an unsolved problem especially in the use of artificial intelligence and computer vision for autonomous vehicles. This research proposed an efficient collision avoidance model to avoid collision in real time scenario. Proposed model differs from other methods in a way that it does not require any other equipment like sensors for measuring distance between the vehicles. Proposed collision avoidance model estimates the relation between distance and size of the vehicle in real time scenario to generate an approximate notion of distance between the vehicles. Then, the ratio of distance between vehicles and size of the vehicle was used to depict vehicles that are in potentially dangerous positions for probable collision. Proposed collision avoidance model was experimented in the real-time traffic and experimental results showed that the model could detect vehicles in order to avoid the probable collisions efficiently. Proposed model is expected to be a possible tool in dealing with future demand of autonomous vehicles with the increase of 4IR technologies.