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arXiv · 2309.09021

Pedestrian Trajectory Prediction Using Dynamics-based Deep Learning

Honghui Wang·Weiming Zhi·Gustavo Batista·Rohitash Chandra
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Abstract

Pedestrian trajectory prediction plays an important role in autonomous driving systems and robotics. Recent work utilizing prominent deep learning models for pedestrian motion prediction makes limited a priori assumptions about human movements, resulting in a lack of explainability and explicit constraints enforced on predicted trajectories. We present a dynamics-based deep learning framework with a novel asymptotically stable dynamical system integrated into a Transformer-based model. We use an asymptotically stable dynamical system to model human goal-targeted motion by enforcing the human walking trajectory, which converges to a predicted goal position, and to provide the Transformer model with prior knowledge and explainability. Our framework features the Transformer model that works with a goal estimator and dynamical system to learn features from pedestrian motion history. The results show that our framework outperforms prominent models using five benchmark human motion datasets.

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Honghui Wang, Weiming Zhi, Gustavo Batista, Rohitash Chandra. 2023-09-16. Pedestrian Trajectory Prediction Using Dynamics-based Deep Learning. https://arxiv.org/abs/2309.09021

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