Practical speed measurement for an intelligent vehicle based on double Radon transform in urban traffic scenarios

Abstract

An on-board vision system is recognized as a promising tool for vehicle early warning and monitoring. Timely accurate estimation of vehicle speed is critical in allowing the on-board vision system to calculate the vehicle location, plan a driving path, and apply emergency brakes to avoid accidents. However, the scene images captured by the vision system always suffer from global motion blur, which causes great difficulty in precisely estimating vehicle speed. While extensive efforts have been focused on blurred image restoration and real-time driving speed estimation in highway scenarios, very limited work has addressed urban scenarios in which the vehicle speed is often less than 40 km h−1. In order to bridge this research gap, this study proposes a new method for real-time vehicle speed estimation. Firstly, the spectrum characteristics of blurred images at low vehicle speeds were investigated to determine the relationship between the direction and spacing of the spectrogram and vehicle motion parameters. Then, the blur-direction and blur-scale of the vehicle motion were analyzed by double Radon transform to develop a speed estimation model. Experimental evaluation results demonstrate that the proposed method was able to estimate vehicle speed in urban scenarios without updating the hardware of existing on-board vision systems. The estimation error was below 7.13% and the calculation efficiency of a single frame was 30 ms, both of which meet the practical application requirements of intelligent vehicles.