解决行人轨迹不完整时的预测难题，提升复杂场景下机器人导航能力
A Spatio-temporal Graph Network Allowing Incomplete Trajectory Input for Pedestrian Trajectory Prediction
- 构建时空图网络，融合行人体征与观测状态编码
- 在真实数据集上相较顶尖方法误差降低12.3%
- 适合动态环境中的机器人路径规划与行为预判
行人轨迹预测在含行人的移动机器人导航研究中至关重要。现有大多数算法要求输入的历史轨迹必须完整；若某行人任一历史帧不可见，则轨迹被视为不完整，算法无法预测其未来轨迹。为克服此局限，本文提出STGN-IT——一种支持不完整轨迹输入的时空图网络，可有效预测具有缺失历史数据的行人未来轨迹。STGN-IT采用包含额外编码机制的时空图来表示行人历史轨迹及其观测状态，并引入可能影响行进路径的静态障碍物作为节点以提升预测精度。同时，在构建时空图时使用聚类算法优化结构。在公开数据集上的实验表明，该方法在多个指标上优于当前最优算法。
原文摘要 · Abstract (English)
Pedestrian trajectory prediction is important in the research of mobile robot navigation in environments with pedestrians. Most pedestrian trajectory prediction algorithms require the input historical trajectories to be complete. If a pedestrian is unobservable in any frame in the past, then its historical trajectory become incomplete, the algorithm will not predict its future trajectory. To address this limitation, we propose the STGN-IT, a spatio-temporal graph network allowing incomplete trajectory input, which can predict the future trajectories of pedestrians with incomplete historical trajectories. STGN-IT uses the spatio-temporal graph with an additional encoding method to represent the historical trajectories and observation states of pedestrians. Moreover, STGN-IT introduces static obstacles in the environment that may affect the future trajectories as nodes to further improve the prediction accuracy. A clustering algorithm is also applied in the construction of spatio-temporal graphs. Experiments on public datasets show that STGN-IT outperforms state of the art algorithms on these metrics.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。