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| Title | The Pedestrian Trajectory Prediction Model Based On Hierarchical Envelope Domain Adaptation; [基于分级包络域适应的行人轨迹预测模型] |
|---|---|
| ID_Doc | 56197 |
| Authors | Li Y.-M.; Li W.-Z.; Zhang X.-H.; Wang P.; Hu J. |
| Year | 2025 |
| Published | Tien Tzu Hsueh Pao/Acta Electronica Sinica, 53, 4 |
| DOI | http://dx.doi.org/10.12263/DZXB.20240562 |
| Abstract | In complex environments, short-term pedestrian trajectory prediction finds extensive applications in auton⁃ omous driving, social robotics, intelligent security, and smart city infrastructures. Interactions among pedestrians and be⁃ tween pedestrians and their environment exhibit multi-scale complexities and uncertainties, posing substantial challenges. Although current deep learning models are effective in uncovering complex pedestrian interactions, they typically assume uniform motion patterns across various scenes, thereby neglecting potential distributional discrepancies. While domain adap⁃ tation models partially address this issue, they often overlook the multi-level characteristics of pedestrian interactions and environmental influences. To address these challenges, this study proposes a pedestrian trajectory prediction model founded on hierarchical envelope domain adaptation. We design a local-level envelope sample construction module by establishing local-level pedestrian adjacency relationships. An individual-level envelope sample construction module is devised based on individual pedestrian relationships. These two modules are subsequently integrated to form a bi-level envelope sample con⁃ struction module. Leveraging the bi-level envelope sample construction module, we compute the spatio-temporal feature dis⁃ tribution of all pedestrian trajectories to construct global-level envelope samples. Employing the attention mechanism and cross-domain distribution alignment, we respectively design the local-level envelope domain adaptation and global-level en⁃ velope domain adaptation modules. These modules are then integrated into a unified framework using a weighted prediction loss function, which is jointly optimized. The experimental section utilizes two representative public datasets and compares them with five representative algorithm models. Comprehensive validation is conducted through ablation studies, parameter analysis, method comparison, and trajectory visualization. The experimental results in the ETH and UCY datasets show that compared with T-GNN, the average displacement error is reduced by 22.7% and the final displacement error is reduced by 19.8%. For the full version of the article, please refer to the link: https://github.com/LWZ9910/MESC-HEDA.git. © 2025 Chinese Institute of Electronics. All rights reserved. |
| Author Keywords | domain adaptation; envelope samples; hierarchical envelopes; multilevel; pedestrian trajectory prediction |
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