No abstract

The longitudinal distance between the vehicle and the forward vehicle, as well as the longitudinal distance between the vehicle and the opposite vehicle, is the main risk factor of overtaking behavior on two-way two-lane roads. Accurate measurement of these distances is the basis and key to automatic driving technology of two-way two-lane roads. In order to measure these longitudinal distances and improve the ranging accuracy, a vehicle distance measurement method of two-way two-lane roads based on monocular vision was proposed. Firstly, the vehicle detection model suitable for two-way two-lane roads was trained using YOLOv5s neural network. Secondly, aiming at the problem that the camera roll angle is not considered in the traditional geometric ranging method, the influence of the roll angle of the camera on ranging results using the traditional geometric ranging method was analyzed. In addition, the improved geometric ranging method considering the roll angle of the camera was proposed. Then, tests were conducted on a two-way two-lane road, and the results showed that the proposed method was effective. Compared with other methods, the improved geometric ranging method has higher ranging accuracy in this scene and can provide a reference for vision-based vehicle distance measurement in multi-lane scenes.