## 4 Experiments
### Performance Evaluation
#### 4.2.2 Comparative Experiment  
The comparative experiment is based on the open dataset nuScenes. This paper uses the methods proposed in references [3; 19] as comparative algorithms. Reference [19] is a Lidar-only method that extracts free space by detecting the 3D information of the vehicle. First, 3D vehicle detection is performed based on the CenterPoint [43]. Then, the parking slots are calculated using the vehicle size and the relative position between the vehicles, and the parking slots are tracked [44]. Finally, a filter method is used to further detect obstacles within the parking space box in the ROI to achieve the final free space detection. Reference [3] is a method that uses visual images as prior map information and combines Lidar recognition to detect empty parking spaces. First, the surround image data of the parking lot is binarized to extract parking line information, which is used as the prior information of the parking space. Secondly, the vehicle point cloud is extracted using Lidar detection data, and then the parking line prior information is superimposed and fused with the vehicle point cloud. Finally, the empty parking space detection result based on visual prior information is obtained. The algorithm in this paper and the comparative algorithms were tested with open datasets. The schematic diagram of the experimental results is shown in Figure 18, and the experimental results are shown in Table 6.  
Figure 18: Visualization diagram of free space detection. (**a**) Polarized matrix visualization of the proposed algorithm in the scene from Figure 15a. Different colors represent different depth values. (**b**) Free space detection visualization of the proposed algorithm in the scene from Figure 15a: The red rectangle denotes the ROI, the yellow indicates free space meeting the size constraints of the ego vehicle, and the green represents the non-compliant workshop area. (**c**) Free space detection visualization of the method in reference [19] in the scene from Figure 15a: The green rectangle denotes obstacle vehicles, and the yellow represents free space. (**d**) Lane line binarization schematic of the method in reference [3] under weak texture conditions, corresponding to Figure 15a: The red circle denotes lane line texture information. The red circle highlights subtle lane line texture under weak lighting conditions, which may be difficult to discern due to minimal pixel intensity differences. (**e**) Lane line binarization schematic of the method in reference [3] under clear texture but subtle pixel differences due to overexposure, corresponding to Figure 15b. As depicted in Figure 16, combined with the results presented in Table 6, our method demonstrates superior performance. From a principle analysis perspective, the approach of reference [19] relies on a pre-trained 3D detection and tracking network. The accuracy of this method is influenced by the parameters of the pre-trained network, resulting in a relative number of missed detections of obstacle vehicles. In contrast, our method begins with ground filtering, isolates the ROI and grid map markers, and reduces the impact of noise points on obstacle vehicle recognition. Figure 18d demonstrates the binarization effect of parking lines without corner points in the scene of Figure 15a. The binarization effect shown in Figure 18e corresponds to Figure 15b, where parking lines are marked, but the texture of the ground area and parking lines in the image is similar due to excessive light. While rich visual texture can provide accurate and clear prior map information for parking space recognition, it is heavily influenced by lighting conditions. When texture information is weak or absent, AVM/Lidar fusion [3] fails to deliver satisfactory parking space detection performance, as indicated in Table 6. Whether in parking lots or roadside parking lots, the number of missed parking spaces by this method exceeds 50%.