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.
Gong et al. (Mon,) studied this question.