Feature-based lateral position estimation of surrounding vehicles using stereo vision

Abstract

Driver behavior Prediction has become an important topic in the recent development of Advanced Driver Assistance Systems (ADAS). To predict future behavior and potential risks associated with surrounding vehicles, their lateral position information is required. However, existing computer vision algorithms tend to either focus on longitudinal measurements or provide lateral position information with limited performance for limited scenes (i.e no viewpoint change and occlusion). In this paper, feature-based lateral position estimation algorithm is proposed using stereo vision and provides lateral position regardless of viewpoint change and occlusion by extracting a pixel-wise feature. In the preprocessing step, v-disparity from stereo depth map is calculated and used for ground detection. Then, vehicle candidates are created based on image thresholding and filtering, removing the ground portion from the camera image. These generated candidates are verified as vehicles by using deep convolutional neural network. In order to track and estimate the lateral position of the detected vehicles, speeded up robust feature (SURF) points are matched in consecutive image frames, and the feature point is projected onto the ground; defined as the grounded feature point. Finally, inverse perspective mapping (IPM) is applied on the original image to estimate the lateral position of the grounded feature point. The proposed algorithm successfully detects a feature point of neighboring vehicle and estimates its lateral position by tracking the grounded feature point. For testing the algorithm, the datasets in a highway and an urban setting are used and provide zero mean error and 0.25m standard deviation error in lateral position estimation.