Kinematics-aware Trajectory Generation and Prediction with Latent Stochastic Differential Modeling

TL;DR

This paper introduces a novel kinematics-aware latent stochastic differential equation model for vehicle trajectory generation and prediction, combining physical realism with data-driven flexibility to improve accuracy and controllability.

Contribution

It proposes a new LK-SDE model that integrates kinematic knowledge into neural stochastic differential equations for more realistic and controllable vehicle trajectories.

Findings

Outperforms baseline methods in trajectory realism and accuracy

Successfully predicts unobservable physical variables in latent space

Enhances physical plausibility in trajectory generation and prediction

Abstract

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.