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.

翻译：轨迹生成与轨迹预测是自动驾驶的两个关键任务，前者在开发阶段生成多样化轨迹，后者在运行阶段预测周围车辆的轨迹。尽管在提升性能方面取得了显著进展，但如何确保生成/预测的轨迹具有真实性、可解释性和物理可行性仍是一个具有挑战性的问题。现有基于模型的方法能提供可解释的结果，但受限于预定义的模型结构，难以应对复杂场景。相反，现有基于深度学习的方法在学习各种交通场景和提升整体性能方面展现出巨大潜力，但往往作为不透明的黑箱模型运作，缺乏可解释性。本研究将运动学知识与神经随机微分方程（SDE）相结合，基于一种新颖的潜在运动学感知随机微分方程（LK-SDE）开发了变分自编码器以生成车辆运动。我们的方法融合了基于模型和基于深度学习两种技术的优势。实验结果表明，该方法在生成真实、物理可行且可精确控制的车辆轨迹方面显著优于基线方法，同时惠及轨迹生成与预测两项任务。