Design and implementation of a radar and camera-based obstacle classification system using machine-learning techniques

Road safety is an essential issue of modern life that must be tackled and resolved. Using AI technology to develop autonomous vehicles and driver-assistant systems is a promising approach to reduce accidents and preserve user's security. In this regard, obstacle detection and identification have been a topic of much concern for researchers over the last few years. In this paper, we propose an embedded system that operates on low-level, lightweight algorithms, based on two types of data, namely, radar signals and camera images with the purpose of identifying and classifying obstacles on the road. The proposed system has two major contributions. The first is the use of machine-learning methods alongside signal processing techniques to optimize the overall computing performance and efficiency. Then, the second contribution consists of the use of the dynamic reconfiguration feature using DSP48 instead of standard CLBs to improve surface usage. The overall system was developed on Xilinx Zedboard Zynq-7000 FPGA.