GAMDTP: Dynamic Trajectory Prediction with Graph Attention Mamba Network

Abstract

Accurate motion prediction of traffic agents is crucial for the safety and stability of intelligent decision-making autonomous driving systems. In this paper, we introduce GAMDTP, a novel graph attention-based network tailored for dynamic trajectory prediction. Specifically, we fuse the result of self attention and mamba-ssm through a gate mechanism, leveraging the strengths of both to extract features more efficiently and accurately, in each graph convolution layer. GAMDTP encodes the high-definition map(HD map) data and the agents’ historical trajectory coordinates and decodes the network’s output to generate the final prediction results. Additionally, recent approaches predominantly focus on dynamically fusing historical forecast results and rely on two-stage frameworks including proposal and refinement. To further enhance the performance of the two-stage frameworks we also design a scoring mechanism to evaluate the prediction quality during the proposal and refinement processes. Experiments on the Argoverse and INTERACTION datasets demonstrate that GAMDTP achieves state-of-the-art performance and has more advantages in capturing interaction features and ensuring security in dynamic trajectory prediction.