The selectable granularity strategy, while effective, may have limitations in handling a wider range of trajectory patterns. One potential limitation is the fixed granularity options (e.g., GL=2, GL=4, GL=8) that may not be optimal for all types of trajectories. To improve this strategy, adaptive granularity selection techniques can be implemented. This approach involves dynamically adjusting the granularity based on the characteristics of each trajectory. Machine learning algorithms can be used to learn the optimal granularity for different trajectory patterns, considering factors such as velocity changes, curvature, and interaction with the environment. By incorporating adaptive granularity selection, the model can better capture the nuances of various trajectory patterns and improve prediction accuracy across a wider range of scenarios.