Pedestrian Trajectory Prediction Using Dynamics-Based Deep Learning

Pedestrian trajectory prediction plays an impor- tant 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 ex- plicit constraints enforced on predicted trajectories. We present a dynamics-based deep learning framework with a novel asymp- totically 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.