The ability to adapt to varying observation lengths is crucial for human trajectory prediction tasks, particularly in scenarios with limited observation lengths or missing data. Existing approaches mainly focus on introducing novel architectures or additional structural components, which substantially increase model complexity and present challenges for integration into existing models. We argue that current network architectures are sufficiently sophisticated to handle the Observation Length Shift problem, with the key challenge lying in improving feature representation for trajectories with limited lengths. To tackle this issue, we introduce a general and effective contrastive learning approach, called Contrastive Learning for Length Shift (CLLS). By incorporating contrastive learning during the training phase, our method encourages the model to extract length-invariant features, thus mitigating the impact of observation length variations. Furthermore, to better accommodate length adaptation tasks, we introduce a lightweight RNN network that, combined with CLLS, achieves state-of-the-art performance in both general prediction and observation length shift tasks. Experimental results demonstrate that our approach outperforms existing methods across multiple widely-used trajectory prediction datasets.

Trajectory prediction is an important task in autonomous driving and monitoring systems. Most of the existing methods pay little attention to the rapidly changing trajectory information, but how to effectively solve this problem is crucial to ensure pedestrian safety. The Gabor transform has inherent advantages for capturing instantaneously changing information. Therefore, for the first time, we introduce the Gabor transformation idea into pedestrian trajectory prediction and propose the Multi-scale Learnable Gabor Transform Network (MlgtNet), which establishes global and local contextual relationships from multi-dimensional and multi-scale perspectives. The network first uses the Multi-scale Feature Dimension Enhancement Module (MFDEM) ascending dimension trajectory sequence, and uses the Multi-scale Gabor Convolution Module (MGCM) to guide the model to establish the dependence of different distances from different dimensions to model the interrelationship between global/local features at different scales and different step sizes. Finally, the Feature Fusion Module (FFM) processes the multimodal information and fuses it with the multi-scale trajectory features to obtain the trajectory prediction representation in different visual fields. The representation results are then used for secondary fusion to obtain the global prediction results. Experimental results show that MlgtNet achieves state-of-the-art performance with its lightweight model size on the vast majority of widely used trajectory prediction datasets from different perspectives.