Abstract:

In complex traffic scenarios， reliably and effectively predicting the trajectories of surrounding vehicles is crucial for the safe operation of autonomous vehicles. However， existing prediction methods often face challenges related to high computational overhead， making it difficult to achieve real-time and efficient trajectory prediction without sacrificing accuracy. Therefore， an innovative method called Dynamic Attention and Goal Guidance （DAGG） combining dynamic attention and goal guidance is proposed， which accurately captures the dynamics of changing scenes and identifies endpoint goals. To reduce redundant encoding and reasoning delay in continuous prediction， a local spatiotemporal reference framework is constructed that decouples intrinsic features from relative information between scene instances. Furthermore， an efficient and compact triple-factor attention fusion module is designed to aggregate local context features， capturing rich spatiotemporal background information. To achieve multimodal prediction and better utilize scene encoding， scene fusion features are injected into map information and a multimodal motion prediction decoding module is adopted to guide goal selection， capturing high-quality predicted goals while reducing the computational cost of goal-based trajectory generation. The validation results on the publicly available Argoverse dataset demonstrate that the proposed method achieves a minimum average displacement error （minADE） of 0.84 m and a minimum final displacement error （minFDE） of 1.26 m， significantly outperforming mainstream baseline models， which highlights its superior predictive capability in complex and dynamic scenarios.

Key words: autonomous driving, trajectory prediction, deep learning, attention mechanism