Local causal dynamic integrated global mode guidance transformer network for pedestrian trajectory prediction.

Abstract

Pedestrian trajectory prediction is crucial for autonomous vehicles, which face challenges in integrating complex spatiotemporal dynamics, managing multi-modal future behaviors, and ensuring real-time performance. This paper introduces the Local-Global Collaborative Transformer Network (LGCMT) to address these issues. LGCMT features an innovative local-global collaborative encoder comprising two key modules: a Sparse Causal Temporal Attention (SCT-MSA) module, designed to extract fine-grained local causal dynamics, and a Global Context Encoder that utilizes Cosine Similarity Attention to capture macro-level spatiotemporal patterns. For multi-modal prediction, LGCMT employs a parallel Non-Autoregressive (NAR) decoder guided by a motion pattern library, which efficiently generates diverse trajectory candidates covering key future likelihoods. Extensive evaluations on the standard ETH/UCY benchmarks and the large-scale Stanford Drone Dataset (SDD) demonstrate LGCMT's robust performance. On ETH/UCY, the model improves ADE and FDE by approximately 4.8% and 5.6% compared to the competitive TUTR baseline. Moreover, the proposed framework achieves exceptional inference efficiency, establishing LGCMT as a potent solution that effectively balances accuracy, multi-modality, and operational speed for real-time applications.