TSCA-Net: Temporal-Spatial Clique Attention for Interpretable Multimodal Pedestrian Trajectory Prediction
TSCA-Net proposes three complementary modules (Temporal-Spatial Clique Attention, Cross-Pedestrian Clique Potential, Adaptive KAN Grid Refinement) to improve pedestrian trajectory prediction in crowded environments, achieving state-of-the-art results on ETH/UCY and SDD benchmarks.
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[Submitted on 11 Jul 2026]
Title:TSCA-Net: Temporal-Spatial Clique Attention for Interpretable Multimodal Pedestrian Trajectory Prediction
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Abstract: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.
Comments: 10 pages, 4 figures, 4 tables. Submitted to the IEEE International Conference on Data Mining (ICDM) 2026, Applied Track
Subjects:
Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2607.11939 [cs.CV]
(or arXiv:2607.11939v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2607.11939
arXiv-issued DOI via DataCite
Submission history
From: Md Imam Ahasan [view email] [v1] Sat, 11 Jul 2026 08:32:02 UTC (10,993 KB)
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