HiVT: Hierarchical Vector Transformer for Multi-Agent Motion Prediction

By Zikang Zhou, Luyao Ye, Jianping Wang, Kui Wu, Kejie Lu. Computer Vision and Pattern Recognition, 2022

486 citations recorded by the source corpus.

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 interaction modeling, our method can effectively and efficiently model a large number of agents in the scene. Meanwhile, we propose a translation-invariant scene representation and rotation-invariant spa-tial learning modules, which extract features robust to the geometric transformations of the scene and enable the model to make accurate predictions for multiple agents in a single forward pass. Experiments show that HiVT achieves the state-of-the-art performance on the Argoverse motion forecasting benchmark with a small model size and can make fast multi-agent motion prediction.

Subfields: Automotive Engineering, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/ef7bafebde8f609861a04347a18e17eb705cfd7f

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Automotive Engineering

1. Yu-Kai Huang
2. Jianping Wang
3. Zikang Zhou
4. Yung-Hui Li

In Computer Vision and Pattern Recognition

1. Yu-Kai Huang
2. Jianping Wang
3. Zikang Zhou
4. Yung-Hui Li

Citations in the corpus

• Query-Centric Trajectory Prediction (2023, methodology): “(i) While the flourishing forecasting models have achieved impressive performance on trajectory prediction benchmarks [7,13,49], today’s most advanced architectures specialized for this task [37, 38, 46, 56] fail to process the heterogeneous traffic scenes efficiently.”