HeLoFusion addresses significant challenges in multi-agent trajectory prediction by constructing local, multi-scale graphs for each agent. This innovative method is designed to capture both direct pairwise dependencies and complex interactions within groups of agents, such as vehicle platoons and pedestrian crowds. By implementing an aggregation-decomposition message-passing scheme, HeLoFusion effectively handles agent heterogeneity, learning type-dependent interactions that enhance prediction accuracy.
On the Waymo Open Motion Dataset, HeLoFusion sets new benchmarks in key performance metrics, showcasing its effectiveness in motion forecasting. The locality-focused architecture not only improves predictive performance but also provides a principled way of representing multi-level social contexts. The results highlight the importance of local interactions in complex environments and suggest that future advancements in multi-agent systems might similarly benefit from localized modeling strategies.
👉 Pročitaj original: arXiv AI Papers
