Abstract: Due to the change of vehicle steering attitude caused by road conditions and driver's intention during driving, location information of detected vehicles relative to the host vehicle is also changed. Aiming at the problem that the method of monocular vision ranging ignores changes in attitude angle in the process of driving, this paper presents a monocular vision ranging model based on inverse perspective mapping (IPM) of variable parameters and road vanishing point detection, which achieves a real-time measurement of longitudinal and horizontal distance during vehicle relative movement by taking advantage of location information of vehicle detection so that it can locate and detect the vehicle on the ground plane as well as provide a good environment perception for advanced driver assistance system (ADAS) and intelligent vehicle system. Firstly, owing to the relationship between changes in attitude angle and the coordinates of road vanishing point, the yaw angle and pitch angle of vehicle motion are calculated in real time through the algorithm for road vanishing point detection, which is based on texture orientation estimation. The algorithm, which possesses a better robustness under different light and road conditions, estimates dominant texture orientation of pixels according to joint activities and confidence measure of Gabor filter with 4 directions, and vanishing point candidates are confirmed by the modified locally adaptive soft voting and particle filter tracking algorithm. On account of the yaw angle which leads to a certain degree of rotation in the top view of IPM and the existence of the pitch angle which leaves the top view of IPM unable to restore the parallel relationship of the top view of actual road, IPM of variable parameters based on the coordinate of road vanishing point is used to compensate for the pitch angle to eliminate the influence of inverse perspective distortion, thereby restoring the parallel relationship of road plane and measuring longitudinal distance between the detected vehicle and the host vehicle using calibrated longitudinal scale factor. Then a modeling analysis of the yaw angle of vehicle motion during the process of IPM is made and the effects of the shape and size of the detected vehicle on ranging model are considered. When the horizontal axis in the lower-right bounding box of detected vehicle is less than half of the number of horizontal pixels in the imaging plane, the detected vehicle would be on the left of the host vehicle and its longitudinal and horizontal distance are calculated in accordance with the coordinate in the lower-right bounding box, while the horizontal axis in the lower-left bounding box of detected vehicle is greater than half of the number of horizontal pixels in the imaging plane, the detected vehicle would be on the right of the host vehicle and its longitudinal and horizontal distance are calculated in accordance with the coordinate in the lower-left bounding box; otherwise, it would be directly in front of the host vehicle with the horizontal distance being zero, and its longitudinal distance is calculated in accordance with the coordinate in the middle base of bounding box. Finally, the vehicle ranging model on the basis of location information of vehicle detection is established to consider compensating for attitude angle. The feasibility and effectiveness of this method are analyzed from 2 groups of contrast experiments on different road environments and ranging methods, and the results show that the proposed ranging model can effectively measure the distance of detected vehicles within about 70 m in the longitudinal direction and 4 m in the horizontal direction, having a measurement error of less than 5%; and the better the road environment, the smaller the error; the ranging error of a good flat road is within 3%, and the average processing speed of this algorithm reaches 40 frames/s.