# Vision-Based Distance Measurement in Advanced Driving Assistance Systems
## 6 Experiments and Results
### Distance Measurement of the Target
#### 6.3.3 Distance Measurement on the KITTI Dataset  
In this section, we provide several distance measurement results using the proposed method, which combines semi-supervised depth estimation, Mask RCNN, and pedestrian and vehicle distance measurement capabilities. Figure 10 shows several forward-looking images from the KITTI dataset, the red rectangles represent the targets, i.e., pedestrians (P), trucks (T), vans (V), and cars (C). The true distances of these targets and the corresponding estimated results are shown in Figure 11. In Figure 11, there are 28 targets, containing 3 pedestrians, 1 truck, 3 vans, and 21 cars. The average distance error rate of the 28 targets was 5.56%, in which the average distance error rate of the pedestrians was 4.02%, and the vehicles' average error rate was 5.74%. Compared with the measurement results using images captured by our camera, the accuracy of the distance measurements using the KITTI dataset was obviously higher. This is mainly because our model for depth map estimation used KITTI images as training samples. It is worth noting that the average processing time using the proposed distance measurement method for each image was 1.824 s, of which the online running time for the depth map estimation was 0.085 s, the time required for MASK-RCNN as 1.682 s, and computational time of distance measurement was 0.057 s. Therefore, in order to improve the real-time performance of the proposed method, it is necessary to improve the computational efficiency of the instance segmentation algorithm.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \multirow{2}{*}{**Distance** **(Ground Truth)**} & \multicolumn{2}{c}{**Average Depth**} & \multicolumn{2}{c}{**Method of Depth**} & \multirow{2}{*}{**Ours**} \\  & \multicolumn{2}{c}{**Value**} & \multicolumn{2}{c}{**Average**} & & \\ \cline{2-2} \cline{5-7}  & **Car** & **SUV** & **Car** & **SUV** & **Car** & **SUV** \\ \hline
2.5 m & 4.7 & 2.9 & 5.4 & 2.7 & **2.9** & **2.4** \\
5 m & 7.6 & 6.8 & 5.8 & 5.3 & **5.0** & **5.0** \\
7.5 m & 11.1 & 8.2 & **7.6** & 7.1 & 7.3 & **7.4** \\
10 m & **10.6** & 8.8 & 8.5 & 7.7 & 9.2 & **9.3** \\
12.5 m & 14.3 & **9.9** & 10.4 & 9.5 & **10.8** & **9.9** \\ \hline \end{tabular}
\end{table}
Table 3: Results of the vehicle distance measurement. (The best result in each row and each type of vehicle is shown in bold and italics).  
Figure 10: Several examples from the KITTI dataset for distance measurement. Images (**a**)–(**j**) are the forward-looking images from the KITTI dataset which show the bounding boxes and categories.  
We also tested the performance of depth map estimation when the target brightness changed due to the angle of light irradiation. In Figure 12a, as a result of the reflection of light, the brightness values of some areas on the rear windshield of the vehicle in the red rectangle are too large. Conversely, the rear of the vehicle in the red rectangle in Figure 12b is in shadow. Figure 12c,d shows the corresponding depth maps of Figure 12a,b, respectively. From these two depth maps, we can observe that the depth values of pixels in the overexposed areas and shadowy areas have not changed significantly. Therefore, in a daytime road environment, the direction and intensity of light had little effect on the results of depth map estimation and distance measurement using the proposed method.