Trajectory generation and trajectory prediction are two critical tasks for
autonomous vehicles, which generate various trajectories during development and
predict the trajectories of surrounding vehicles during operation,
respectively. However, despite significant advances in improving their
performance, it remains a challenging problem to ensure that the
generated/predicted trajectories are realistic, explainable, and physically
feasible. Existing model-based methods provide explainable results, but are
constrained by predefined model structures, limiting their capabilities to
address complex scenarios. Conversely, existing deep learning-based methods
have shown great promise in learning various traffic scenarios and improving
overall performance, but they often act as opaque black boxes and lack
explainability. In this work, we integrate kinematic knowledge with neural
stochastic differential equations (SDE) and develop a variational autoencoder
based on a novel latent kinematics-aware SDE (LK-SDE) to generate vehicle
motions. Our approach combines the advantages of both model-based and deep
learning-based techniques. Experimental results demonstrate that our method
significantly outperforms baseline approaches in producing realistic,
physically-feasible, and precisely-controllable vehicle trajectories,
benefiting both generation and prediction tasks.

整合动力学知识与神经随机微分方程，基于一种新颖的潜在动力学感知 (SDE) 变分自编码器（LK-SDE），用于生成车辆运动的方法，在生成和预测任务中表现出明显优于基准方法的性能，产生逼真、物理可行且精确可控的车辆轨迹。