Multi-agent trajectory prediction with Hierarchical Coordinate-Based Representation

Trajectory prediction plays a crucial role in autonomous driving systems. Existing methods typically adopt agent-centric or scene-centric approaches to model driving scenarios. However, these approaches often lead to excessive redundant computations or pose estimation errors, which degrade both prediction efficiency and accuracy. To address these issues, we propose a novel multi-agent trajectory prediction model called Hierarchical Coordinate-Based Representation. This model decomposes the driving environment into two distinct components: global and local. In the global component, the interaction information is established and shared between all predicted agents, helping to reduce redundant computations. In the local component, each vehicle is assigned an individual reference frame, which mitigates the impact of pose variations and facilitates the extraction of temporal features. Furthermore, we introduce an adaptive anchor point generation method to tackle the challenge of capturing future trajectories across different driving scenarios. This method dynamically generates anchor points that are tailored to each specific scenario, guiding the prediction of trajectories for various modalities. The performance of the proposed model is evaluated on the Argoverse 1 and Argoverse 2 datasets. Experimental results demonstrate that Hierarchical Coordinate-Based Representation achieves competitive performance in terms of both efficiency and accuracy, outperforming state-of-the-art methods.