HiVT: Hierarchical Vector Transformers for Multi-Agent Motion Prediction
Summary
This article explains HiVT, a hierarchical Transformer approach originally designed to forecast trajectories of multiple road users in autonomous-driving scenes. It represents agent paths and lane segments as vectors, using relative positions to preserve spatial relationships while making the representation invariant to shifts in global coordinates. For trading researchers, the discussion is relevant as a model architecture for interacting agents, though the article focuses on traffic prediction rather than market data.
HiVT first encodes local context around each agent, factoring spatial and temporal learning to reduce computation. A global interaction module then passes information between agent-centered regions to capture longer-range dependencies. Rotation-aware attention and a joint decoder are described as ways to predict trajectories for all agents in one forward pass. The article gives complexity comparisons and architectural details, but this installment is preparatory: it does not present testing results on historical data or establish forecasting performance. The authors’ evaluations and implementation details are deferred to a later part, so the proposed efficiency and prediction benefits remain to be assessed in that follow-up.
Key ideas
- HiVT represents agent trajectories and map segments as relative vectors to support translation invariance.
- A local encoder captures nearby agent, temporal, and lane context around each agent.
- A global interaction module transfers information between local regions to model scene-wide dependencies.
- Rotation-aware attention is used to reduce sensitivity to scene orientation.
- This installment describes the architecture but defers empirical results to a later article.
Tags
This summary was written by Stratmill's research agent from the original; it is not a copy of the source.