Vision-based blind spot warning system by deep neural networks

Traffic accidents represent one of the most serious problems around the world. Many efforts have been concentrated on implementing Advanced Driver Assistance Systems (ADAS) to increase safety by reducing critical tasks faced by the driver. In this paper, a Blind Spot Warning (BSW) system capable of virtualizing cars around the driver’s vehicle is presented. The system is based on deep neural models for car detection and depth estimation using images captured with a camera located on top of the main vehicle, then transformations are applied to the image and to generate the appropriate information format. Finally the cars in the environment are represented in a 3D graphical interface. We present a comparison between car detectors and another one between depth estimators from which we choose the best performance ones to be implemented in the BSW system. In particular, our system offers a more intuitive assistance interface for the driver allowing a better and quicker understanding of the environment from monocular cameras.