# Vision-Based Distance Measurement in Advanced Driving Assistance Systems
## 2 Related Work  
Generally speaking, stereo-vision methods are not suitable for distance estimation in ADAS. There are two reasons for this: firstly, these methods are very susceptible to errors in feature extraction and matching. Secondly, they can only achieve relatively sparse and local depth values. Therefore, it is difficult to compute the distances of multiple different targets at the same time through these depth values. Therefore, in this section, we mainly discuss the distance measurement methods based on monocular vision and the progress of the related key technologies. Currently, monocular-vision methods for distance estimation used in ADAS can be divided into two categories. The first category is based on the geometric relationship and camera imaging model. In these types of methods, several parameters from the camera (e.g., the azimuth and elevation angles of the camera) and the measured object (e.g., width of the target vehicle) need to be provided in advance. [PERSON] et al. used the geometric positional relationship of a vehicle in the camera coordinate system to construct the correspondence between the key points in the world coordinate system and the image coordinate system, and then established a ranging model to estimate the target vehicle distance [11]. [PERSON] et al. used the camera imaging model and the width of the target vehicle to estimate the distance to a moving vehicle that is far ahead [12]. The main disadvantage of such methods is that the accuracy of distance estimation depends heavily on the measurement accuracy of the parameters of the camera or the measured object. The second category involves constructing a regression model using machine learning. [PERSON] et al. trained a regression model using the correspondence between different positions in an image and their corresponding distances to complete distance estimation [13]. [PERSON] et al. used the target vehicle information to train a distance regression model for distance estimation [14]. The main disadvantage of these methods is that they have to collect a large number of training data with real distances. In the proposed method, the first and core task is to complete the depth map estimation. Traditional methods of vision-based depth map estimation are mostly realized using geometric constraints and handcrafted features (e.g., SIFT), such as Structure from Motion (SFM) [15]. The main disadvantage of these traditional methods is that they are very susceptible to errors in feature extraction and matching, and can only achieve relatively sparse and local depth maps. It is difficult to compute the distances of multiple different targets at the same time through these depth maps. In recent years, deep learning-based methods represented by convolutional neural networks (CNN) have been developed in various fields of computer vision [16], and several CNN-based approaches for depth map estimation have been studied [17; 18]. Depending on whether they use real depth data as the labels during the training process, these methods can be divided into three categories: supervised, semi-supervised, and unsupervised methods. Moreover, depth values obtained by vision-based methods can be divided into the absolute depth, which denotes the true depth value of each pixel in the camera coordinate system, and the relative depth, which indicates the relative distance relationship of different pixels in the image. As one of the representatives of the supervised methods, the Coarse-Fine method [19], proposed by [PERSON] et al., contains two CNNs of different scales: the coarse-scale CNN, used to estimate the globe depth of the input image; the fine-scale CNN, for optimizing the local details. On the basis of this method, [PERSON] et al. further proposed a multiscale network architecture [20], which can complete three tasks, including depth estimation, plan normal measurement, and semantic segmentation. [PERSON] et al.