The content discusses the importance of pedestrian trajectory prediction in autonomous driving systems and robotics. It introduces a dynamics-based deep learning framework that outperforms existing models by integrating an asymptotically stable dynamical system into a Transformer model. The study focuses on human motion analysis, deep learning models evolution, goal-targeted motion representation, collision avoidance behaviors, and performance evaluation using benchmark datasets.
The research highlights the significance of explainability and explicit constraints in predicting desired trajectories for autonomous entities. By introducing prior knowledge into deep learning models, the proposed framework enhances trajectory prediction accuracy. The ablation study confirms the substantial impact of the novel asymptotically stable dynamical system on improving prediction performance.
Furthermore, insights gained from trajectory visualization demonstrate DDL's ability to imitate human behaviors like convergence to destinations and collision avoidance. The results showcase DDL's superiority over existing methods in accurately forecasting human movements across various scenarios.
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by Honghui Wang... at arxiv.org 03-12-2024
https://arxiv.org/pdf/2309.09021.pdfDeeper Inquiries