An FPGA-based CollisionWarning System Using Hybrid Approach

Abstract

In this paper, we propose an FPGA-based collision warning system for advanced automobile driver assistance systems or autonomous moving robots. The system consists of three function blocks: coarse edge detection using a resistive-fuse network, moving-object detection inspired by neuronal propagation in the hippocampus, and danger evaluation and collision warning using fuzzy inference. The first two functions are implemented in FPGAs. The system can detect moving objects with a speed range of 3-192 km/h with a sampling period of 30 ms for an input image of 320 x 256 pixels, and can output a warning against dangerous regions in the input image.