QML for Argoverse 2 Motion Forecasting Challenge

Su, Tong, Wang, Xishun, Yang, Xiaodong

arXiv.org Artificial Intelligence 

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 [1] in the Argoverse 2 Motion Forecasting Challenge 2022.

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