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 effectively integrated into long-term pedestrian behavior forecasting. We present a novel intention-aware motion prediction framework, which consists of a Residual Bidirectional LSTM (ReBiL) and a mutable intention filter. Instead of learning step-wise displacement, we propose learning offset to warp a nominal intention-aware linear prediction, giving residual learning a physical intuition. Our intention filter is inspired by genetic algorithms and particle filtering, where particles mutate intention hypotheses throughout the pedestrian motion with ReBiL as the motion model. Through experiments on a publicly available dataset, we show that our method outperforms baseline approaches and the robust performance of our method is demonstrated under abnormal intention-changing scenarios.

中文翻译：

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用于长期行人轨迹预测的意图感知残差双向 LSTM

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。来自人类意图和行为模式的关键见解需要有效地整合到长期行人行为预测中。我们提出了一种新颖的意图感知运动预测框架，它由残差双向 LSTM (ReBiL) 和可变意图过滤器组成。我们建议学习偏移量来扭曲名义上的意图感知线性预测，而不是学习逐步位移，从而使残差学习具有物理直觉。我们的意图过滤器受到遗传算法和粒子过滤的启发，其中粒子以 ReBiL 作为运动模型在整个行人运动中改变意图假设。通过对公开数据集的实验，