论文标题
社交阶段：时空多模式未来轨迹预测
Social-STAGE: Spatio-Temporal Multi-Modal Future Trajectory Forecast
论文作者
论文摘要
本文考虑了与排名的多模式未来轨迹预测的问题。在这里，多模式和排名分别是指多个合理的路径预测和对这些预测的信心。我们提出了社会阶段，社会互动感知时空多发图表卷积网络，并具有新的多模式评估。我们的主要贡献包括使用相互作用和多发注意力对多模式的分析和制定，以及引入新指标来评估多模式预测的多样性和相关信心。我们在现有的公共数据集ETH和UCY上评估了我们的方法，并表明所提出的算法优于这些数据集上的艺术状态。
This paper considers the problem of multi-modal future trajectory forecast with ranking. Here, multi-modality and ranking refer to the multiple plausible path predictions and the confidence in those predictions, respectively. We propose Social-STAGE, Social interaction-aware Spatio-Temporal multi-Attention Graph convolution network with novel Evaluation for multi-modality. Our main contributions include analysis and formulation of multi-modality with ranking using interaction and multi-attention, and introduction of new metrics to evaluate the diversity and associated confidence of multi-modal predictions. We evaluate our approach on existing public datasets ETH and UCY and show that the proposed algorithm outperforms the state of the arts on these datasets.
