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.
翻译: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。 人类意图和行为模式的关键洞察力需要整合,以有效预测长期行人行为。 因此,我们提出一个框架,包括一个静电感过滤器和Warp LSTM(MIF-WLSTM),以同时估计人类意图和进行轨迹预测。 静电感过滤器受粒子过滤和遗传算法的启发, 粒子代表着在整个行人运动中可以突变的意向假设。 我们的Warp LSTM 学会了一种完全轨迹上的抵消, 由一种名义意向觉悟线性线性模型预测, 用于考虑过滤过程中的意图假说。 通过在公开的数据集上进行实验, 我们展示了我们的方法超越了基线方法, 并展示了我们方法在异常的意向改变情景下所表现的稳健性。