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QML for Argoverse 2 Motion Forecasting Challenge
- Publication Year :
- 2022
-
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.
Details
- Database :
- arXiv
- Publication Type :
- Report
- Accession number :
- edsarx.2207.06553
- Document Type :
- Working Paper