Vision-based forward collision warning application for vehicles

Abstract

Driver Assistance Systems (DAS) have become an important part of vehicles, and there is a considerable amount of research in this area. Most accidents happen due to driver inattention caused by driver distraction and drowsiness. Driver Assistance Systems aim to minimize these conditions and increase road safety. Vision-based driver assistance plays a major role in DAS, where camera-based collision warning stands out as one of the most effective and accurate types. Our implementation is a collision warning system that utilizes a single monocular camera and performs 3D vehicle detection for better accuracy and performance. It is a low-cost, near real-time collision warning system that can be implemented on both new and old vehicles. For 2D vehicle detection, we employ YOLO, and then we estimate 3D bounding boxes based on the 2D bounding boxes. To track the vehicles, we use the Deep SORT algorithm. The application will generate a Birds Eye View (BEV) graph based on the 3D bounding box estimation. This BEV graph will represent a much more accurate position and orientation for vehicles in a 3D plane. Based on this data, the collision prediction algorithm will determine the possibility of a collision and output a warning signal. The collision prediction algorithm relies on the distance between the vehicle with the camera and other vehicles in each frame.