PEDESTRIAN TRAJECTORY PREDICTION WITH ATTENTION AND SOCIAL BEHAVIOR INTEGRATION

Accurate prediction of pedestrian trajectories is of great significance for intelligent monitoring and safe navigation of autonomous driving.The trajectory of pedestrians is not only affected by the pedestrians themselves, but also by the interaction with the surrounding pedestrians.In recent years, the introduction of attention mechanism has further enhanced the model's ability to focus on key information, making the prediction more accurate.Therefore, this study proposes a pedestrian trajectory prediction model combining self-attention mechanism and social behavior characteristics.This method uses the newly studied KAN network architecture to initially encode pedestrian movement information, adopts improved social pooling layer to model the spatial characteristics of pedestrians, adaptively pays attention to the most useful adjacent information, and provides suggestions for better spatial modeling.The self-attention module is used to further extract the motion features of the target pedestrians and the surrounding pedestrians at the current moment to obtain more connected spatial features, so that the model can process and understand the sequence data more effectively and efficiently, predict the future trajectory of the target pedestrians more accurately, and enhance the performance of the prediction process.Experiments on public data sets ETH and UCY show that the prediction accuracy of this model is significantly improved compared with other methods.