## 4 Experiments
### Performance Evaluation
#### 4.2.2 Comparative Experiment
Therefore, compared to both the three-dimensional detection method and the visual prior information-based detection method, the proposed algorithm demonstrates robustness and reliable performance. We also conducted a supplementary test to verify the significant level of our proposed model across different datasets. We performed a t-test on the local and public datasets nuScenes. The null hypothesis (H0) posits that there is no significant difference in recognition accuracy between the two datasets, implying any observed difference is due to random errors. The alternative hypothesis (H1) suggests that a significant difference exists, indicating the difference is not random. We set the significance level \(\alpha\) at 0.05. The results from the local dataset served as the experimental group, while those from the public dataset were the control group. On the local dataset, the average accuracy of parking space recognition was 98.89%, compared to 97.08% on the public dataset. The standard deviations for both groups were calculated as 0.5086 and 0.1470, respectively. Using a one-tailed two-sample t-test with unequal variances, we obtained a t-value significant level of 0.0007, which is much lower than 0.05. Therefore, we accept the null hypothesis and believe that there is no significant difference in the recognition accuracy of the model on the two datasets. This shows that the performance of the model proposed in this paper on different datasets is random, and the performance of the model on the test set is the same as that on the training set or validation set, indicating that the model performs well on unseen data and the performance of the model on these two datasets is stable. In one words, this suggests that the model's performance is stable across different datasets, demonstrating its robustness on unseen data. Additionally, we conducted a comparison of storage usage and runtime among different algorithms. We utilized Lidar keyframe point cloud data from scene-0655 in the nuScenes, specifically the parking lot scenario, along with corresponding six-way surround-view image keyframe data. Each point cloud file records the (x, y, z) coordinates, intensity information, and ring index of individual points. This scene comprises 41 keyframes \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Scenario** & **Method** & **GT** & **TP** & **FP** & **Precision (\%)** & **Recall (\%)** & **F1 Score (\%)** \\ \hline \multirow{3}{*}{\begin{tabular}{c} Parking Lot \\ (Perpendicular) \\ \end{tabular} } & 3D point cloud-based [19] & & 128 & 6 & 95.52 & 94.81 & 95.17 \\  & AVM/Lidar fusion-based [3] & 135 & 57 & 0 & 100.00 & 42.22 & 59.38 \\  & The proposed & & 135 & 4 & 97.12 & 100.00 & 98.54 \\ \hline \multirow{3}{*}{\
\begin{tabular}{c} On-street Parking \\ (Parallel) \\ \end{tabular} } & 3D point cloud-based [19] & & 31 & 1 & 96.88 & 96.88 & 96.88 \\  & AVM/Lidar fusion-based [3] & 32 & 14 & 0 & 100.00 & 43.75 & 60.87 \\  & The proposed & & 31 & 1 & 96.88 & 96.88 & 96.88 \\ \hline \multirow{3}{*}{
\begin{tabular}{c} Total Number and \\ Average Score \\ \end{tabular} } & 3D point cloud-based [19] & & 159 & 7 & 95.78 & 95.21 & 95.50 \\  & AVM/Lidar fusion-based [3] & 167 & 71 & 0 & 100.00 & 42.51 & 59.66 \\ \cline{1-1}  & The proposed & & 166 & 5 & 97.08 & 99.40 & 98.22 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparative parking space detection performance based on the nuScenes datasets. of point cloud and 246 six-way surround-view image keyframes. We evaluated the output storage and runtime storage requirements and runtime of the three algorithms, as summarized in Table 7. While our algorithm shows comparable memory usage during program-execution evaluations, the storage requirements for the polarized matrix and BEV after dimensionality reduction in our method's output data are significantly lower than those of the original point cloud data.