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QML for Argoverse 2 Motion Forecasting Challenge

Authors :
Su, Tong
Wang, Xishun
Yang, Xiaodong
Publication Year :
2022

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.

Details

Database :
arXiv
Publication Type :
Report
Accession number :
edsarx.2207.06553
Document Type :
Working Paper