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.

通过引入一种名为FlexiLength Network (FLN)的通用有效框架，以对付观测时间变长而导致表现显著下降的问题，本研究在多个数据集上进行全面实验证明了所提出的FLN框架的有效性和灵活性，该框架能够增强现有的轨迹预测技术对于不同观测时期的鲁棒性，并实现更准确的未来轨迹预测。

适应长度变化：用于轨迹预测的FlexiLength网络