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.
QML for Argoverse 2 Motion Forecasting Challenge
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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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HAMF: A Hybrid Attention-Mamba Framework for Joint Scene Context Understanding and Future Motion Representation Learning
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.