# An Intelligent Measurement Method and System for Vehicle Passing Angles
## 4 Experiment  
The experiment was conducted in a parking lot, and the hardware system used in the experiment is shown in Figure 7.  
Figure 1 shows the experimental process, where a wheeled robot is programmed to move underneath the stationary vehicle at a constant speed. During this motion, the robot acquires and builds point clouds, enabling the calculation of the passing angles. The first step in the experiment is the selection of segmentation methods. We conducted experiments using frames from the LIADR to evaluate the effectiveness of segmentation using sphere and plane models to determine suitable models and coefficients. As shown in Figure 8a,b, the segmentation results demonstrate that both circular and planar shapes can successfully segment ground point clouds. In Figure 8, it can be observed that the outermost laser line in Figure 8a appears to be more complete compared to Figure 8b. Furthermore, the segmentation results are also influenced by the choice of coefficients. Figure 8c,d show the results of over-segmentation, where points belonging to the vehicle are influenced as ground points due to the selection of inappropriate coefficients. To minimize the proportion of ground points without over-segmentation, we extracted frames from five regions of the vehicle for analysis. These five regions are located at the front wheels, the rear wheels and the front, middle and rear of the vehicle. Analyzing the frames selected from these five regions can provide a more generalized representation of the vehicle. Here, we segmented these frames using different segmentation coefficients and models, and the segmentation process was repeated five times. The results were then averaged and are shown in Figures 9 and 10. Figure 8: Segmentation results by models. (**a**) The result of the plane model, (**b**) the result of the sphere mode, (**c**) the over-segmentation results of the plane model, (**d**) the over-segmentation results of the sphere model. Figure 7: Experimental hardware system. The relationship between the number of points and the segmentation coefficient is shown in Figure 9. Based on objective evaluation, we observed that although there are differences in the distribution of ground points segmented by plane and sphere models, shown in Figure 8a,b, the number of ground points is actually similar. The time consumption of the segmentation performed using the plane and sphere models is shown in Figure 10. We can see that the segmentation using the plane model has the lowest cost and provides a stable performance. Over-segmentation causes the point clouds of vehicle tires to be incorrectly segmented as ground point clouds. However, these over-segmented ground point clouds have a much wider distribution on this normal. Therefore, we calculated the over-segmentation threshold coefficients based on the distribution of ground point clouds on the normal. We performed statistical analysis of the distribution of the ground point cloud on its normal to determine whether the coefficients lead to over-segmentation. This was confirmed using the method of subjective evaluation, as shown in Figure 8c,d. The point clouds of the vehicle tires appear higher on this normal. Therefore, we automatically determined the over-segmentation threshold coefficients based on the distribution of ground point clouds  
Figure 10: The time consumption of segmentation by plane and sphere models. Figure 9: Segmentation efficiency of plane and sphere models. on the normal. Based on the results shown in Figures 8-10, we selected the plane model with a coefficient of 0.03 for segmentation. Additionally, Figure 11 shows the segmentation performance of an actual acquisition, where the ground and vehicle points are clearly segmented, effectively reducing the proportion of ground points. We acquired the data underneath the vehicle using our system and compared the existing mature methods of point cloud building with our method.