ing at that the requirements advanced driving assistance system for vehicle forward looking depth of field information, this paper proposes a scene depth estimation method based on monocular vision under the framework of unsupervised learning. In order to reduce the influence of forward looking targets with diverse sizes on the depth estimation results, the proposed method uses a pyramid structure to preprocess the input image. In the training process, the depth estimation problem is transformed into an image reconstruction problem, and a new loss function is designed using binocular images instead of the true depth label, which solves the problem that the depth data of the real scene is difficult to obtain. The size of disparity map and original input image is unified, which improves the hole phenomenon in depth map and improves the accuracy of scene depth estimation. The quantitative and qualitative comparison results on the KITTI and Make3D datasets show that the proposed method can obtain high accuracy absolute depth of field data and has good generalization ability, Experimental results in real road scenes show that the proposed method can obtain pixel level depth of field information from a single vehicle forward looking image.