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.

翻译：行人轨迹预测在自动驾驶系统和机器人领域中具有重要作用。现有利用主流深度学习模型进行行人运动预测的研究，对人类运动行为的先验假设较为有限，导致预测轨迹缺乏可解释性和显式约束。我们提出了一种基于动力学的深度学习框架，将新颖的渐近稳定动力系统集成到基于Transformer的模型中。通过渐近稳定动力系统对人类目标导向运动进行建模，强制行人行走轨迹收敛至预测的目标位置，并为Transformer模型提供先验知识和可解释性。该框架采用Transformer模型与目标估计器及动力系统协同工作，从行人运动历史中学习特征。结果表明，在五个基准行人运动数据集上，本框架的性能优于主流模型。