Trajectory generation and trajectory prediction are two critical tasks in autonomous driving, which generate various trajectories for testing during development and predict the trajectories of surrounding vehicles during operation, respectively. In recent years, emerging data-driven deep learning-based methods have shown great promise for these two tasks in learning various traffic scenarios and improving average performance without assuming physical models. However, it remains a challenging problem for these methods to ensure that the generated/predicted trajectories are physically realistic. This challenge arises because learning-based approaches often function as opaque black boxes and do not adhere to physical laws. Conversely, existing model-based methods provide physically feasible results but are constrained by predefined model structures, limiting their capabilities to address complex scenarios. To address the limitations of these two types of approaches, we propose a new method that integrates kinematic knowledge into neural stochastic differential equations (SDE) and designs a variational autoencoder based on this latent kinematics-aware SDE (LK-SDE) to generate vehicle motions. Experimental results demonstrate that our method significantly outperforms both model-based and learning-based baselines in producing physically realistic and precisely controllable vehicle trajectories. Additionally, it performs well in predicting unobservable physical variables in the latent space.

翻译：轨迹生成与轨迹预测是自动驾驶中的两个关键任务，分别用于开发过程中生成多样化测试轨迹以及运行时预测周围车辆的轨迹。近年来，基于数据驱动的深度学习方法在这两个任务中展现出巨大潜力，能够学习各种交通场景并提升平均性能，而无需假设物理模型。然而，如何确保生成/预测的轨迹具备物理真实性仍然是这些方法面临的挑战性问题。这一挑战源于基于学习的方法通常作为不透明的黑箱运作，且不遵循物理定律。相比之下，现有的基于模型的方法虽能提供符合物理规律的结果，但受限于预定义模型结构，难以应对复杂场景。为解决这两类方法的局限性，我们提出了一种新方法，将动力学知识融入神经随机微分方程（SDE）中，并基于此潜在动力学感知SDE（LK-SDE）设计了一个变分自编码器用于生成车辆运动。实验结果表明，我们的方法在生成物理真实且精确可控的车辆轨迹方面显著优于基于模型和基于学习的基线方法。此外，该方法在潜在空间中预测不可观测物理变量的任务上也表现出色。