TSCA-Net：用于可解释多模态行人轨迹预测的时空团块注意力
TSCA-Net: Temporal-Spatial Clique Attention for Interpretable Multimodal Pedestrian Trajectory Prediction
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中文总结 AI 辅助
针对拥挤环境中行人轨迹预测难题，提出TSCA-Net框架，含时空团块注意力、跨行人团块势、自适应KAN网格细化三个模块，经实验验证其性能最优，消融研究证实模块互补作用。
中文摘要 AI 辅助
在拥挤环境中进行准确的行人轨迹预测具有挑战性，因为人类运动的多模态不确定性和不同场景下运动动力学的复杂性。现有目标条件模型存在局限性。为此提出TSCA-Net，它基于三个互补模块构建。时空团块注意力模块引入可学习的时间门控；跨行人团块势模块通过动态团块势框架建模不对称成对代理关系；自适应KAN网格细化机制基于代理目标分布熵动态调整解码器的B样条网格分辨率。实验表明TSCA-Net性能达最优，综合消融研究证实了三个模块的互补作用。
英文摘要
Accurate pedestrian trajectory prediction in crowded environments remains challenging due to the multimodal uncertainty of human motion and the variable complexity of motion dynamics across different scene contexts. Existing goal-conditioned models rely on static displacement structures that assign equal weight to all historical time steps, standard graph attention mechanisms, and fixed-capacity motion decoders that cannot adapt to local prediction complexity. To address these limitations, we propose TSCA-Net, a trajectory prediction framework built upon three complementary modules. The Temporal-Spatial Clique Attention (TSCA) module introduces learnable temporal gating into clique-based goal-history interaction, enabling time-aware modulation of historical observations relative to each candidate goal. The Cross-Pedestrian Clique Potential (CPCP) module models asymmetric pairwise agent relationships through a dynamic clique potential framework with a time-varying social graph. The Adaptive KAN Grid Refinement (AKGR) mechanism dynamically adjusts the B-spline grid resolution of a Kolmogorov-Arnold Network-augmented LSTM decoder based on per-agent goal distribution entropy, balancing model expressiveness against overfitting across varying motion complexities. Extensive experiments on the ETH/UCY and Stanford Drone Dataset benchmarks demonstrate that TSCA-Net achieves state-of-the-art performance, with average ADE/FDE of 0.13/0.20 m on ETH/UCY and 6.95/10.43 pixels on SDD. Comprehensive ablation studies confirm the complementary contributions of all three proposed modules.
