论文标题
使用可变意图过滤和翘曲LSTM的长期行人轨迹预测
Long-term Pedestrian Trajectory Prediction using Mutable Intention Filter and Warp LSTM
论文作者
论文摘要
轨迹预测是机器人安全导航和与行人互动的关键功能之一。需要整合来自人类意图和行为模式的批判性见解,以有效预测长期行人行为。因此,我们提出了一个框架,该框架结合了可变意图滤波器和翘曲LSTM(Mif-WLSTM),以同时估计人类的意图并执行轨迹预测。可变意图过滤的灵感来自粒子过滤和遗传算法,其中粒子代表在整个行人运动中可以突变的意图假设。我们的翘曲LSTM并没有预测顺序位移,而是学会在由名义意图感知线性模型预测的完整轨迹上产生偏移,该模型考虑了在过滤过程中考虑意图假设。通过在公开可用数据集上的实验,我们表明我们的方法的表现优于基线方法,并在异常改变意图改变方案下证明了我们方法的稳健性能。代码可在https://github.com/tedhuang96/mifwlstm上找到。
Trajectory prediction is one of the key capabilities for robots to safely navigate and interact with pedestrians. Critical insights from human intention and behavioral patterns need to be integrated to effectively forecast long-term pedestrian behavior. Thus, we propose a framework incorporating a Mutable Intention Filter and a Warp LSTM (MIF-WLSTM) to simultaneously estimate human intention and perform trajectory prediction. The Mutable Intention Filter is inspired by particle filtering and genetic algorithms, where particles represent intention hypotheses that can be mutated throughout the pedestrian motion. Instead of predicting sequential displacement over time, our Warp LSTM learns to generate offsets on a full trajectory predicted by a nominal intention-aware linear model, which considers the intention hypotheses during filtering process. Through experiments on a publicly available dataset, we show that our method outperforms baseline approaches and demonstrate the robust performance of our method under abnormal intention-changing scenarios. Code is available at https://github.com/tedhuang96/mifwlstm.