To further enhance the clustering of trajectories and ensure the generated trajectories are even more representative of the original expert data, several techniques can be explored:
Hierarchical Clustering: Implementing hierarchical clustering algorithms can help in organizing trajectories into a hierarchy of clusters based on their similarities. This can provide a more granular and detailed grouping of trajectories, allowing for more precise augmentation.
Density-Based Clustering: Utilizing density-based clustering algorithms like DBSCAN or OPTICS can help in identifying clusters of varying shapes and sizes, ensuring that trajectories with similar characteristics are grouped together even in complex and dense regions of the data.
Feature Engineering: Incorporating additional features such as road conditions, weather, or traffic density into the clustering process can provide a more comprehensive representation of the driving scenarios. By considering a broader range of features, the clustering algorithm can better capture the nuances of different driving situations.
Semi-Supervised Learning: Leveraging semi-supervised learning techniques can enable the clustering algorithm to utilize both labeled expert trajectories and unlabeled data for clustering. This can improve the clustering accuracy and robustness by incorporating a larger and more diverse set of trajectories.