用可解释的运动库提升轨迹预测准确率
Recall to Predict: Grounding Motion Forecasting in Interpretable Motion Bank
- 构建物理合理轨迹的结构化运动库，通过对比学习生成
- 在Argoverse 2和Waymo数据集上实现高精度多模态预测
- 适合关注可解释性与真实场景泛化能力的研究者
运动预测常需在可解释性与预测准确性间权衡。传统基于锚点的架构依赖难以理解的隐向量查询，易出现隐空间坍缩；或采用简单轨迹采样，限制多模态多样性。本文提出一种端到端可微框架，将预测扎根于一个通过对比学习构建的综合性‘运动库’——即一组物理上可实现的轨迹嵌入空间。不从零开始回归路径，而是通过创新的锚点检索层动态调用明确的运动先验。该模块使用双层门控交叉注意力机制，对正交初始化的查询进行自适应调整，并利用直通式Gumbel-Softmax估计器实现离散轨迹选择，保持连续梯度流动。检索到的语义可解释锚点随后由DETR风格解码器进行几何精修，联合优化包含赢家通吃（WTA）运动学高斯混合模型（GMM）、隐空间多样性惩罚项及软最小加权终点损失。通过严格依赖多样且可解释的运动原型进行解码，本方法有效消除标准隐向量查询的‘黑箱’问题，在Argoverse 2和Waymo Open Motion数据集上达到具有竞争力的多模态预测性能。代码已开源：https://github.com/abviv/recall2predict
原文摘要 · Abstract (English)
Motion forecasting often requires trading interpretability for predictive accuracy. Standard anchor-based architectures rely on opaque latent queries that are highly prone to latent collapse, or naive trajectory sampling that limits multi-modal diversity. We propose an end-to-end differentiable framework that grounds predictions in a comprehensive "motion bank", a structured embedding space of physically realizable trajectories constructed via contrastive learning. Rather than regressing paths from a blank slate, our architecture dynamically retrieves explicit motion priors using a novel Anchor Retrieval Layer. This module adapts orthogonally initialized queries via a Dual-Level Gated Cross-Attention mechanism and executes discrete trajectory selection using a Straight-Through Gumbel-Softmax estimator to preserve continuous gradient flow. The retrieved semantically grounded anchors are then geometrically refined by a DETR-style decoder, optimized jointly with a Winner-Takes-All (WTA) kinematic Gaussian Mixture Model (GMM), a latent diversity penalty, and a soft-min weighted endpoint loss. By strictly conditioning the decoding phase on diverse, interpretable motion primitives, our approach eliminates the "black box" of standard latent queries while achieving competitive multi-modal accuracy on the Argoverse 2 and Waymo Open Motion datasets. Code is available at: https://github.com/abviv/recall2predict
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