English

QML for Argoverse 2 Motion Forecasting Challenge

Computer Vision and Pattern Recognition 2022-07-15 v1 Robotics

Abstract

To safely navigate in various complex traffic scenarios, autonomous driving systems are generally equipped with a motion forecasting module to provide vital information for the downstream planning module. For the real-world onboard applications, both accuracy and latency of a motion forecasting model are essential. In this report, we present an effective and efficient solution, which ranks the 3rd place in the Argoverse 2 Motion Forecasting Challenge 2022.

Keywords

Cite

@article{arxiv.2207.06553,
  title  = {QML for Argoverse 2 Motion Forecasting Challenge},
  author = {Tong Su and Xishun Wang and Xiaodong Yang},
  journal= {arXiv preprint arXiv:2207.06553},
  year   = {2022}
}
R2 v1 2026-06-25T00:53:53.311Z