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QML for Argoverse 2 Motion Forecasting Challenge

About

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.

Tong Su, Xishun Wang, Xiaodong Yang• 2022

Related benchmarks

TaskDatasetResultRank
Motion forecastingArgoverse 2 Motion Forecasting Dataset (test)
Miss Rate (K=6)19
90
Motion PredictionArgoverse 2.0 (val)
minFDE (6s)1.39
8
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