HiVT: Hierarchical Vector Transformer for Multi-Agent Motion Prediction
City University of Hong Kong · City University of Hong Kong, Shenzhen Research Institute · +2 more institutions
Abstract
Accurately predicting the future motions of surrounding traffic agents is critical for the safety of autonomous ve-hicles. Recently, vectorized approaches have dominated the motion prediction community due to their capability of capturing complex interactions in traffic scenes. How-ever, existing methods neglect the symmetries of the prob-lem and suffer from the expensive computational cost, facing the challenge of making real-time multi-agent motion prediction without sacrificing the prediction performance. To tackle this challenge, we propose Hierarchical Vector Transformer (HiVT) for fast and accurate multi-agent motion prediction. By decomposing the problem into local con-text extraction and global…
Citation impact
- FWCI
- 74.30
- Percentile
- 100%
- References
- 74
Authors
5Topics & keywords
- Computer science
- Artificial intelligence
- Invariant (physics)
- Transformer
- Benchmark (surveying)
- Machine learning
- Mathematics
- Engineering