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BANet: Motion Forecasting with Boundary Aware Network

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arxiv 2206.07934 v3 pith:JGROBAWQ submitted 2022-06-16 cs.CV cs.RO

Chen Zhang , Honglin Sun , Chen Chen , Yandong Guo This is my paper · ORCID
classification cs.CVcs.RO
keywords forecastingmotionlanevectorbanetboundarycenterlineelements
verification ladder T0 review T1 audit T2 compute T3 formal
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We propose a motion forecasting model called BANet, which means Boundary-Aware Network, and it is a variant of LaneGCN. We believe that it is not enough to use only the lane centerline as input to obtain the embedding features of the vector map nodes. The lane centerline can only provide the topology of the lanes, and other elements of the vector map also contain rich information. For example, the lane boundary can provide traffic rule constraint information such as whether it is possible to change lanes which is very important. Therefore, we achieved better performance by encoding more vector map elements in the motion forecasting model.We report our results on the 2022 Argoverse2 Motion Forecasting challenge and rank 1st on the test leaderboard.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  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.

  2. LANet: A Lane Boundaries-Aware Approach For Robust Trajectory Prediction

    cs.RO 2025-07 conditional novelty 4.0 of 10

    LANet adds lane boundaries and road edges to a transformer-based trajectory predictor and reports small benchmark gains, but lacks a controlled ablation and code.

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