## 4 Experimental Results
### Nearest Map Node Search Results  
The evaluation method for the nearest map node retrieval experiments adopted commonly used metrics in loop-closure detection [55]. Specifically, when the vehicle \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Scene**} & \multirow{2}{*}{**Loop Number**} & \multicolumn{4}{c}{**Average Error (m)**} & \multicolumn{4}{c}{**Max Error (m)**} \\ \cline{3-10}  & & **Group 1** & **Group 2** & **Group 3** & **Group 4** & **Group 1** & **Group 2** & **Group 3** & **Group 4** \\ \hline \multirow{2}{*}{Industrial park} & 2 nd & 0.31 & 0.34 & 0.44 & 0.37 & 1.67 & 2.14 & 2.85 & 2.38 \\  & 3 rd & 0.38 & 0.40 & 0.51 & 0.43 & 1.21 & 1.89 & 1.85 & 1.91 \\ \hline \multirow{2}{*}{Mall} & 2 nd & 0.39 & 0.41 & 0.41 & 0.40 & 1.98 & 2.08 & 1.99 & 1.80 \\  & 3 rd & 0.42 & 0.42 & 0.47 & 0.45 & 1.64 & 1.62 & 2.23 & 1.60 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of the trajectory prediction results for each experimental group. was positioned between two adjacent map nodes, it was typically considered that both the nearest map node and the second nearest map node are the correct matching results. In other words, when the frame error was equal to 0 or equal to 1, both were considered correct matches. Here, the \"frame error\" refers to the node difference between the map nodes obtained in the experiment and the ground truth. The value of \(K\) in the WH-KNN algorithm is a crucial factor directly related to the accuracy of the nearest map node retrieval. Therefore, before conducting the experiments on retrieving the nearest map nodes, it was essential to determine a reasonable value for \(K\) through experimentation. In this paper, Group 1 from each dataset was selected for experimentation, and an optimal value for \(K\) was chosen within the threshold range [2.8]. The experimental results are presented in Table 4. In the industrial park's underground parking scene, the second loop of data achieved the best performance at \(K=6\), with a nearest map retrieval accuracy of 96.37% and an average error of 0.039 frames. For the third loop of data, the optimal result was achieved at \(K=5\), with a nearest map retrieval accuracy of 94.77% and an average error of 0.059 frames. In the mall's underground parking scene, both loops of data performed optimally at \(K=5\), with nearest map retrieval accuracies of 95.36% and 93.67%, and average errors of 0.053 frames and 0.071 frames, respectively. From the above, it can be observed that the optimal performance was achieved at \(K=5\) for three of the four datasets. Only in the case of the second dataset in the industrial park's underground parking scene did \(K=5\) yield a suboptimal result. However, the difference in performance was minimal, with an accuracy of only 0.56% lower and an error of 0.006 frames higher than the optimal result. In summary, choosing \(K=5\) was a reasonable choice. After determining the value of \(K\), the experiments for the nearest map node retrieval based on 16 sets of data were conducted, and the experimental results are shown in Table 5. In the industrial park's underground parking scene, for our method, the accuracy for each group of experiments fell within the range of [93.03%, 96.09%], with average errors ranging from 0.045 to 0.080 frames. In contrast, for [PERSON]'s method, the accuracy for each group of experiments was within the range of [41.81%, 77.37%], with the average errors ranging from 0.246 to 1.268 frames. In the mall's underground parking scene, for our method, the accuracy for each group of experiments fell within the range of [92.52%, 95.63%], with the average errors ranging from 0.052 to 0.084 frames. In contrast, for [PERSON]'s method, the accuracy for each group of experiments was within the range of [42.34%, 49.62%], with average errors ranging from 1.030 to 1.238 frames.