# Vision-Based Distance Measurement in Advanced Driving Assistance Systems
## 7 Conclusions  
The distance information between the target vehicle or pedestrian and the subject vehicle plays a very important role in ADAS. Therefore, this paper firstly proposed a semi-supervised depth map estimation algorithm, and then combined it with the Mask-RCNN instance segmentation algorithm to propose different distance measurement methods for target pedestrians and target vehicles. The depth map estimation algorithm in this paper used the left and right views of binocular vision and sparse depth ground truth to pretrain an encoding-decoding network. In the process of depth estimation, we used the known camera focal length, baseline length of training samples, and the pretrained deep model to compute the absolute depth map of a single input image. On the basis of the estimated depth map and the pixel-level classification results of Mask-RCNN for the pedestrian target, this paper proposed a distance measurement method that calculates the average of the depth values corresponding to all pixels whose depth values are in the peak range of the target region depth histogram. For the vehicle target, this paper proposed a distance measurement method which first fits a plane using  
Figure 11: Ground truths (GT) of the distance and corresponding estimated values of all targets in Figure 10 using the proposed architecture (unit: m).  
Figure 12: The results of depth map estimation in the overexposed area and shadowy area. (**a**) Overexposed area. (**b**) Shadowy area. (**c**) Depth map of (**a**). (**d**) Depth map of (**b**).  
RANSAC, then projects all the pixels from the target to this plane, and finally uses the minimum depth value of these projected points to calculate the distance to the target vehicle. Extensive tests using a public dataset were conducted to assess the results of depth map estimation and real experiments were performed to evaluate the results of the distance measurements. The experimental results using the public dataset proved the superior performance of the proposed depth map estimation method, and the experimental results in real road scenarios confirmed the effectiveness of the distance measurement methods.  
Since the accuracy of the proposed distance measurement results depends to some extent on the results of instance segmentation, we plan to combine the depth map and the shape of the target to improve the location precision of masks obtained by instance segmentation and further improve the accuracy of distance measurement. Additionally, in bad visibility conditions caused by illumination, gas particles, dust, fog, etc., the proposed method using images from the visible light sensor does not achieve satisfactory results. Therefore, our research group is studying a completely new method that uses infrared images for distance estimation for ADAS in cases of low visibility.  
[PERSON] and [PERSON] conceived the research. [PERSON], [PERSON] and [PERSON] developed and implemented the software. [PERSON] and [PERSON] wrote the paper. [PERSON], [PERSON], [PERSON] and [PERSON] analyzed the results. All authors have read and agreed to the published version of the manuscript.  
This work was partially supported by the National Natural Science Foundation of China (No.61673211, No.U1633105) and by the Fundamental Research Funds for the Central Universities of China (No.NS2020049).  
The authors declare they have no conflicts of interest.