Organizations: Guangxi Power Grid Co., Ltd., Nanning, Guangxi 530000, China · Chongqing Liangjiang Satellite Mobile Communication Co., Ltd., Chongqing 400004, China · School of Microelectronics and Communication Engineering, Chongqing University, Chongqing 400044, China
Pedestrian trajectory prediction holds significant applications across autonomous driving, intelligent surveillance, and smart cities. However, the complexity and unpredictability of pedestrian interactions make this task a persistent challenge. Current models face two common limitations: 1) Consider only a single type of social coupling. This introduces redundant interactions. More critically, it fails to account for the varying nature of trajectory coupled across different scenarios and between different pedestrians. Consequently, models cannot deeply adapt to diverse scenes. 2) Inadequate domain shift handling. Although very few methods address domain shift, they rely on statistical criterion-based domain distribution alignment. Such approaches exhibit strong dependency on predefined statistical metrics. This leads to significant limitations in complex and dynamic environments. To address these problems, a Hierarchical Envelope Adversarial Domain Adaptation with Loosely-Tightly coupled model (HEADA-LTCTPM) is proposed. First, an envelope sample construction mechanism was proposed. It constructs envelope samples and extends them into graph structures. Secondly, an adversarial domain adaptation module was proposed. This integrates both local and global domain adaptation strategies. It dynamically adapts to diverse coupled relationships across scenarios. These innovations collectively enhance prediction accuracy and robustness. In the experimental section, two representative public datasets were employed for validation and comprehensive comparisons with six relevant baseline algorithms were conducted. Results demonstrate that our model achieves significantly higher accuracy compared to existing methods, with the ADE and FDE metrics reduced by 17.6% and 19.1% respectively. The time overhead meets practical requirements, which verifies the effectiveness of our key innovations.
Pedestrian trajectory prediction requires modeling temporal dynamics, multimodal cues, and social interactions in crowded environments. Existing methods often address these factors separately or entangle them in costly attention blocks, limiting scalability, flexibility, and interpretability. We propose a three-step hierarchical Transformer that explicitly separates temporal encoding, multimodal fusion, and scene-level interaction reasoning. Lightweight GRU summaries enable efficient cross-modal attention, while social attention over time--agent tokens captures inter-pedestrian influences at manageable cost. Experiments on JTA, JRDB, and the Pedestrians and Cyclists in Road Traffic dataset show state-of-the-art performance on real-world datasets (JRDB, Urban) and competitive results on JTA. Ablation and qualitative analyses confirm the contribution of each stage and the model's ability to anticipate complex behaviors such as early turning.
IMT Nord Europe, University of Lille, CNRS UMR 9189 - CRIStAL, F-59000 Lille, France · University of Lille, CNRS UMR 9189 - CRIStAL, F-59000 Lille, France · Explain, F-59000 Lille, France
Accurate prediction of real-world pedestrian trajectories is crucial for a wide range of robot-related applications. Recent approaches typically adopt graph-based or transformer-based frameworks to model interactions. Despite their effectiveness, these methods either introduce unnecessary computational overhead or struggle to represent the diverse and time-varying characteristics of human interactions. In this work, we present an Adaptive Relational Transformer (ART), which introduces a Temporal-Aware Relation Graph (TARG) to explicitly capture the evolution of pairwise interactions and an Adaptive Interaction Pruning (AIP) mechanism to reduce redundant computations efficiently. Extensive evaluations on ETH/UCY and NBA benchmarks show that ART delivers state-of-the-art accuracy with high computational efficiency.
Ruochen Li, Ziyi Chang, Junyan Hu +3
Department of Computer Science, Durham University, UK · the School of Computing and Information Systems, Singapore Management University, Singapore
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.
Md Mustafizur Rahman, Guangchao Yang, A F M Abdun Noor +3
College of Computer Science, Chongqing University, Chongqing, China · Department of Software Engineering, Daffodil International University, Dhaka, Bangladesh