只关注关键邻居，快速预测人行轨迹。
Selective Social-Interaction via Individual Importance for Fast Human Trajectory Prediction
- 基于个体重要性筛选关键邻居，提升效率
- 在JRDB数据集上速度更快，精度仍领先
- 适合实时轨迹预测场景，如自动驾驶
本文提出一种通过选择重要邻近行人来预测目标行人轨迹的架构。为实现有效的邻近行人筛选，设计了名为重要性估计器（Importance Estimator）的模块，输出每个邻近行人对目标行人未来轨迹预测的重要性。为避免基于重要性采样时非可微操作导致梯度阻断，采用Gumbel Softmax进行训练。在JRDB数据集上的实验表明，该方法在保持竞争性预测精度的同时显著提升了处理速度。
原文摘要 · Abstract (English)
This paper presents an architecture for selecting important neighboring people to predict the primary person's trajectory. To achieve effective neighboring people selection, we propose a people selection module called the Importance Estimator which outputs the importance of each neighboring person for predicting the primary person's future trajectory. To prevent gradients from being blocked by non-differentiable operations when sampling surrounding people based on their importance, we employ the Gumbel Softmax for training. Experiments conducted on the JRDB dataset show that our method speeds up the process with competitive prediction accuracy.
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