Deep learning based robust forward collision warning system with range prediction

Abstract

Forward collision warning (FCW) system is an advanced driver assistance safety system that continually monitors the ego vehicle’s position, predicts a collision and update it to the driver. It is challenging as it requires to detect and track moving vehicles on-road. In this paper, a novel monocular vision-based FCW system with range prediction is proposed. Firstly, the on-road moving vehicles are detected using a pre-trained YOLO (You only look once) model by setting it as a regression problem to predict the bounding boxes and its class probabilities. The detected vehicles are then assigned a unique ID using Hungarian algorithm and is tracked using Kalman filter. Secondly, the distance between the detected vehicle and the ego-vehicle is calculated by mapping the image into a two-dimensional orthogonal top-down view using inverse perspective mapping (IPM). Road boundary detection is carried out to obtain the calibration points for IPM. Quantitative evaluation carried out on recorded video clips in traffic environment shows that the proposed FCW system effectively estimate the inter-vehicle distance and its relative velocity to predict a collision with great accuracy.