Blind Spot Warning System based on Vehicle Analysis in Stream Images by a Real-Time Self-Supervised Deep Learning Model

With the advent of intelligent systems, we are still facing a high number of fatal traffic accidents. Driver assistance systems can significantly reduce this rate. For example, when a driver uses a turn signal, driver assistance systems alert the object's presence in blind spot areas. Camera-based driver assistance systems for blind spots usually alert by detecting objects, including vehicles, in image frames. Based on a more dynamic dangerous situation classification for lane changing and turning to the sides, we propose an efficient blind-spot warning system that works with a single camera sensor for each side. Our contribution consists of two sections. First, we take a deeper look at classifying dangerous and safe situations in a dynamic environment with moving objects. Second, to distinguish dangerous situations from safe conditions, we install a pre-trained SOTA object detector to track vehicles in consecutive frames and then estimate the distances of tracked cars by a 6% mean percentage error rate. In addition, to detect objects in blind spots, the proposed system uses cars' relative velocity to warn dangerous situations. This classification process is not real-time. So, in the second section, we propose a tiny model as a driver assistance system for the blind spot that works in real-time. This tiny model feeds optical flow into CNN layers. This vision-based system uses self-supervised learning without the necessity of the labeled data. It shows 97% accuracy and can detect dangerous situations as a real-time system.