A low-cost hybrid vision system for intelligent cruise control applications

Abstract

In recent years, automobiles have become increasingly computerized and varying degrees of intelligent control has been integrated into automotive systems. A natural extension of this trend is full intelligent and autonomous control of vehicle by onboard computer systems. This thesis presents the design, development, and construction of a low-cost, low-power vision system suitable for on-board automated vehicle systems such as intelligent cruise control. The apparatus leverages vision algorithms, simplified by a prescribed camera geometry, to compute depth maps in real-time, given the input from three imagers mounted on the vehicle. The early vision algorithms are implemented using Dr. David Martin's ADAP mixed signal array processor. The back-end algorithms are mplemented in software on PC for simplicity, but could easily be implemented in hardware in a later design. The final apparatus was able to compute depth maps at a rate of 24 frames per second, limited only by the interrupt latency of the PC executing the algorithms.