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’s 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.

中文翻译：

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使用可变意图过滤器和扭曲 LSTM 的长期行人轨迹预测

轨迹预测是机器人安全导航和与行人互动的关键能力之一。需要整合来自人类意图和行为模式的关键见解，以有效预测长期行人行为。因此，我们提出了一个包含可变意图过滤器和 Warp LSTM（MIF-WLSTM）的框架，以同时估计人类意图并执行轨迹预测。可变意图过滤器的灵感来自粒子过滤和遗传算法，其中粒子表示可以在整个行人运动过程中发生变异的意图假设。我们的 Warp LSTM 不是预测随时间的顺序位移，而是学习在由名义意图感知线性模型预测的完整轨迹上生成偏移量，该模型在过滤过程中考虑了意图假设。