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
| Task | Dataset | Result | Rank | |
|---|---|---|---|---|
| Motion forecasting | Argoverse 2 Motion Forecasting Dataset (test) | Miss Rate (K=6)19 | 90 | |
| Motion Prediction | Argoverse 2.0 (val) | minFDE (6s)1.39 | 8 |
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