Significant progress has been made inthepast decade thanks to the availability of pedestrian trajectory datasets, which enable trajectory prediction methods to learn from pedestrians' past movements and predict future trajectories. However, these datasets and methods typically assume that theobservedtrajectory sequence iscomplete, ignoring real-world issues such as sensor failure, occlusion, and limited fields of view that can result in missing valuesinobservedtrajectories.

Li Zhang (lizhangfd@fudan.edu.cn) is the corresponding author. Previous methods, as depicted in (a), use only one mode query for each trajectory.

In this paper, we introduce a set of spatial-temporal logic rules as knowledge to explain human actions.

By pairing historical and future trajectories and applying contrastive learning on the encoded feature space, we enforce same-space consistency constraints.