日本フィジカルAI新聞

世界のフィジカルAIを、日本語で。

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arXiv:2207.06553

QML for Argoverse 2 Motion Forecasting Challenge

QML for Argoverse 2 Motion Forecasting Challenge

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著者: Tong Su, Xishun Wang, Xiaodong Yang

分類: cs.CV, cs.RO

原文アブストラクト

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.