Intelligent Vehicle Control by Optimal Selection of Image Data

Abstract

This paper presents an algorithm for distance measurement and control between two moving vehicles using video input from a camera mounted on a follower car behind a preceding car. The idea is to keep the computational complexity bounded by switching between different levels of image quantization, using a pyramid decomposition of the image. The distance is related to the car size in the image, hence the number of pixels representing the car. In order to achieve global bounds on the error, the controller works coarsely when the vehicles are close and at finer range for more distant vehicles. Hence this paper presents a method for trading off accuracy vs. speed of computations in a specific vehicle following application.