CiPN-TP: a channel-independent pretrained network via tokenized patching for trajectory prediction

Abstract

Trajectory prediction is highly essential for accurate navigation. Existing deep learning-based approaches always encounter serious performance degradation when facing shifted data or unseen scenarios. For learning transferable representations across different scenarios, the promising pretraining technique is applied to trajectory prediction tasks. However, relevant studies employ point-level masking mechanisms, which cannot capture local motion information across multiple time steps. Additionally, for trajectory data that couples multiple motion states, extracting the temporal dependencies within each state sequence remains highly challenging. To tackle this issue, we propose a channel-independent pretrained network via tokenized patching for efficient vehicle trajectory prediction, and it is composed of tokenized patch masking, channel-independent extractor (CiE), and state decoupling-mixing (SDM). Specifically, first of all, based on the designed tokenized patching scheme, TPM is established to represent local information and long-term relations in masked sequences. Then, through a series of weight-shared dense layers, CiE is designed to capture the individual dependencies among state sequences in an unsupervised pretraining manner. Moreover, by decoupling the complicated trajectory into pseudo-state representations, SDM is proposed to independently reconstruct the state sequences and further carry out representation mixing operations, to realize available trajectory predictions. Finally, extensive experiments show that our framework is effective and achieves the state-of-the-art performance on the INTERACTION and Argoverse2 datasets.