## 4 Experimental Results and Analysis
### Experimental Results  
This study selected the network model with 120 training cycles to verify the multitarget detection capability of the improved network model in different traffic scenarios. The testing results are shown in Figures 11-14. Figure 11 demonstrates the results of vehicle detection in a natural traffic scenario. As is seen, the 3D vehicle-detection algorithm is effective to achieve vehicle detection. The detection results are consistent with the actual determination results and the target calibration frame range is more accurate. Figure 12 shows the experimental results in the illuminated traffic condition. Specifically, Figure 12a is the result of the shadow occlusion case and Figure 12b is that of the light occlusion case. As is seen, even though the color and texture of the image both greatly change because of the disturbance of strong illumination toward the camera of the vehicle, the method proposed in this paper guarantees successful vehicle detection since  
Figure 11: Natural scene experiment results: (**a**) one-way oncoming traffic; (**b**) two-way oncoming traffic; (**c**) wide road; and (**d**) narrow roads. Figure 12: Illumination scene experiment results: (**a**) shade shading; and (**b**) light blocking. the inclusion of multimodal feature fusion makes it possible for the point cloud to provide the needed image information. Figure 13 shows the experimental results in the complex scenario, specifically of a distant vehicle in Figure 13a and an obscured vehicle in Figure 13b. This indicates that the method proposed in this paper shows its effectiveness in detecting multiple targets and great stability in dealing with the complex situation of multiple targets. Figure 14 provides the results in the complex roadway scenario--Figure 14a in a one-way roadway and Figure 14b in an intersecting roadway. The vehicle point cloud features are not distinctive because of the complex road conditions, such as vehicle occlusions and the oncoming, outgoing and lateral vehicles in different directions. According to the experimental results, the vehicle-detection accuracy in the complex conditions can be ensured through the acquisition of 2D information from the camera and the matching of the vehicle's position in the 3D environment by the millimeter-wave radar. Based on all the above results, the algorithm adopted in this paper is effective in detecting either obscured, illuminated or multiscale, multitarget vehicles by making up for the lack of cameras and millimeter-wave radar through multimodal fusion, no matter if it is in natural or complex road conditions. Figure 15 shows the network training loss and detection accuracy, where models in iteration periods of 60, 80, 100 and 120 were evaluated. As the training period lengthens and the learning rate changes, the network loss will effectively decrease and converge, indicating that the detection accuracy of the improved network model increases (see Table 1). Figure 14: Complex road section scene detection results: (**a**) one-way section; and (**b**) crossroads. Figure 13: Multitarget scene experiment results: (**a**) long-range vehicles; and (**b**) blocking of vehicles. In this study, the algorithm was compared with the current mainstream algorithm in the same dataset and condition, the results of which are shown in Table 2. In the comparison experiments, the 3D detection methods were divided into the original point cloud method, multiview method and image point cloud fusion method. The main algorithms in the different methods were differentiated but tested on the same KITTI dataset. The average detection accuracy of the algorithms was 84.71%. However, the small amount of redundant data and the low dimensionality of the classification features produce a higher accuracy in the method adopting this algorithm than in the others.