QML for Argoverse 2 Motion Forecasting Challenge

• URL: http://arxiv.org/abs/2207.06553v1
• Date: Wed, 13 Jul 2022 23:25:30 GMT
• Title: QML for Argoverse 2 Motion Forecasting Challenge
• Authors: Tong Su, Xishun Wang, Xiaodong Yang
• Abstract summary: In this report, we present an effective and efficient solution, which ranks the 3rd place in the Argoverse 2 Motion Forecasting Challenge 2022. For the real-world onboard applications, both accuracy and latency of a motion forecasting model are essential.
• Score: 7.785370190832619
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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