Diversity-Driven Multi-Head Attention for Enhanced Trajectory Prediction

Trajectory prediction is a core task in areas such as autonomous driving and robot navigation, where the goal is to predict future motion trajectories based on historical trajectories and environmental data. This task involves multiple complex challenges: first, trajectory prediction needs to deal with significant pattern diversity, such as multiple interacting behaviors and possible paths in dynamic environments; second, the task requires finding a balance between long-term historical information and local dynamic changes in order to accurately capture the target’s motion trends; furthermore, the generalization ability and robustness of the model are especially critical in the face of unseen data; and finally, trajectory prediction needs to satisfy efficient reasoning requirements in realtime, further increasing the complexity of the task. To address these challenges, we propose an innovative ensemble learning framework named Diversity-Driven Multi-Head Attention (DD-MHA), which leverages the diversity of independently trained attention modules to significantly enhance the robustness and accuracy of trajectory predictions. Integrated learning improves model diversity and robustness through the synergy of multiple sub-models, which is highly compatible with the requirements of the trajectory prediction task. In particular, the Multi-Head Attention (MHA) mechanism, as a core component in current state-of-the-art models, shows great potential in capturing complex relationships and diverse patterns in data. The approach creates multiple subsets of training data by random sampling and trains multiple independent MHA modules separately. By aggregating outputs from these diverse MHA modules during inference, our method effectively captures richer trajectory patterns, resulting in significantly improved prediction performance and generalization. This scheme effectively mitigates the problems of pattern diversity and long-term dependency in trajectory prediction, and improves generalization performance. While it introduces additional computational cost, it achieves better accuracy and robustness, making it suitable for scenarios where predictive performance is prioritized over inference speed.