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QML for Argoverse 2 Motion Forecasting Challenge

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arxiv 2207.06553 v1 pith:ASDD4FGG submitted 2022-07-13 cs.CV cs.RO

Tong Su , Xishun Wang , Xiaodong Yang This is my paper · ORCID
classification cs.CVcs.RO
keywords forecastingmotionargoversechallengemoduleaccuracyapplicationsautonomous
verification ladder T0 review T1 audit T2 compute T3 formal
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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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Cited by 1 Pith paper

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  1. HAMF: A Hybrid Attention-Mamba Framework for Joint Scene Context Understanding and Future Motion Representation Learning

    cs.CV 2025-05 conditional novelty 5.0 of 10

    HAMF feeds learnable future motion tokens into the scene encoder alongside road and agent tokens, then uses a Mamba decoder to output six diverse trajectories, achieving competitive Argoverse 2 results with 3.0M parameters.

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