  Trajectory prediction plays an important role in various applications,
including autonomous driving, robotics, and scene understanding. Existing
approaches mainly focus on developing compact neural networks to increase
prediction precision on public datasets, typically employing a standardized
input duration. However, a notable issue arises when these models are evaluated
with varying observation lengths, leading to a significant performance drop, a
phenomenon we term the Observation Length Shift. To address this issue, we
introduce a general and effective framework, the FlexiLength Network (FLN), to
enhance the robustness of existing trajectory prediction techniques against
varying observation periods. Specifically, FLN integrates trajectory data with
diverse observation lengths, incorporates FlexiLength Calibration (FLC) to
acquire temporal invariant representations, and employs FlexiLength Adaptation
(FLA) to further refine these representations for more accurate future
trajectory predictions. Comprehensive experiments on multiple datasets, ie,
ETH/UCY, nuScenes, and Argoverse 1, demonstrate the effectiveness and
flexibility of our proposed FLN framework.
