While the attention mechanism and social-pooling approach offer significant benefits in improving trajectory prediction accuracy, they also have potential limitations that can be addressed to enhance the network's robustness:
Attention Mechanism Limitations: The attention mechanism may struggle with capturing long-range dependencies or subtle patterns in trajectories. To address this, incorporating multi-head attention or self-attention mechanisms can help the network focus on different aspects of the input data simultaneously, improving its ability to capture complex relationships.
Social-Pooling Challenges: Social-pooling may face difficulties in handling large-scale interactions between multiple fighters or in scenarios with dense fighter clusters. To mitigate this, hierarchical social-pooling structures can be implemented to capture interactions at different levels of granularity, enabling the network to model complex spatial relationships more effectively.
Combating Overfitting: Both attention mechanisms and social-pooling can be prone to overfitting, especially in scenarios with limited training data. Regularization techniques such as dropout or batch normalization can be applied to prevent overfitting and improve the network's generalization capabilities.