As the forward-looking depth information plays a considerable role in advanced driving assistance systems, in this paper, we first propose a method of depth map estimation based on semi-supervised learning, which uses the left and right views of binocular vision and sparse depth values as inputs to train a deep learning network with an encoding–decoding structure. Compared with unsupervised networks without sparse depth labels, the proposed semi-supervised network improves the estimation accuracy of depth maps. Secondly, this paper combines the estimated depth map with the results of instance segmentation to measure the distance between the subject vehicle and the target vehicle or pedestrian. Specifically, for measuring the distance between the subject vehicle and a pedestrian, this paper proposes a depth histogram-based method that calculates the average depth values of all pixels whose depth values are in the peak range of the depth histogram of this pedestrian. To measure the distance between the subject vehicle and the target vehicle, this paper proposes a method that first fits a 3-D plane based on the locations of target points in the camera body coordinate using RANSAC (RANdom SAmple Consensus), it then projects all the pixels of the target to this plane, and finally uses the minimum depth value of these projected points to calculate the distance to the target vehicle. The results of the quantitative and qualitative comparisons on the KITTI dataset show that the proposed method can effectively estimate depth maps. The experimental results in real road scenarios and the KITTI dataset confirm the accuracy of the proposed distance measurement methods.
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