## 4 Results and Discussion
### Classification Results  
Using the minimum distance between the current positions of the ego vehicle and predefined cluster centroids of track positions, the nearest cluster indices were determined for both the ego vehicle and the tracks at each timestamp. A comprehensive overview of the dynamics of the interaction between the ego vehicle and the surrounding tracks is shown in Figure 9. At various time instants, it details when each track interacts with the ego vehicle. This figure depicts both scenarios in which multiple tracks interact with the ego vehicle simultaneously, as well as scenarios in which separate tracks engage at different intervals. For the purpose of distinguishing static clusters from dynamic clusters, the threshold is calculated by accumulating all relative velocities of interactions in a histogram, and calculating the mean plus standard deviation. An adaptive threshold method for detecting human motion is based on the method proposed in [52]. A histogram is shown in Figure 10: dynamic velocities appear to the right of the threshold, and static velocities appear to the left. Some dynamic points are misclassified as static points and vice versa, but our model correctly classifies most of the points. Our proposed method has been validated over time as shown in Figure 11, demonstrating both static and dynamic classification accuracy. We are able to distinguish between static clusters (blue points below the threshold line) and dynamic clusters (red points above the threshold line). Using this validation, we are able to confirm that the threshold we chose reliably classifies interactions, as we are already familiar with the expected behavior of track clusters over time. As a result, the majority of points are correctly classified, demonstrating the effectiveness of our model. Figure 9: Timeline of ego vehicle interactions with clustered tracks across 393 frames. Each colored line corresponds to a GNG-clustered track ID. This interaction window supports downstream classification and trajectory modeling. Our classification model is further validated by computing the confusion matrix [53], shown in Figure 12. We compare the true labels (dynamic or static) with the predicted labels. A true class is defined based on known interactions, such as Track 14 is being dynamic, while the other tracks remain static. The predicted classes are determined using our classification algorithm, which utilizes threshold values derived from the mean and standard deviations of the relative velocities. The confusion matrix shows the following metrics. * True positive (TP): The 317 dynamic interactions are correctly classified. * True negative (TN): The 95 static interactions are correctly classified. * False positive (FP): 43 static interactions are misclassified as dynamic. * False negative (FN): 17 dynamic interactions are misclassified as static. Figure 11: Classification result based on the \(\mu+\sigma\) velocity threshold. Blue tracks are labeled static; red tracks are dynamic. Figure 10: Histogram of relative velocities between the ego vehicle and track clusters. A threshold at \(\mu+\sigma\) is applied to separate dynamic tracks (in red) from static ones (in blue). The results of the performance metrics are shown in Table 4. The classification model we developed achieved an accuracy of 87% in classifying static and dynamic tracks, demonstrating its effectiveness. Based on the high recall of 94%, most dynamic interactions are correctly identified. A precision of 88% indicates a good balance between true positives and false positives. The F1 score of 91% indicates that our model exhibits a strong balance between precision and recall, which confirms the robustness of the model. Following the same methodological approach, we extended our classification algorithm to the KITTI dataset [54], which comprises 1101 LiDAR frames.