WAKE: Towards Robust and Physically Feasible Trajectory Prediction for Autonomous Vehicles With WAvelet and KinEmatics Synergy

Abstract

Addressing the pervasive challenge of imperfect data in autonomous vehicle (AV) systems, this study pioneers an integrated trajectory prediction model, WAKE, that fuses physics-informed methodologies with sophisticated machine learning techniques. Our model operates in two principal stages: the initial stage utilizes a Wavelet Reconstruction Network to accurately reconstruct missing observations, thereby preparing a robust dataset for further processing. This is followed by the Kinematic Bicycle Model which ensures that reconstructed trajectory predictions adhere strictly to physical laws governing vehicular motion. The integration of these physics-based insights with a subsequent machine learning stage, featuring a Quantum Mechanics-Inspired Interaction-aware Module, allows for sophisticated modeling of complex vehicle interactions. This fusion approach not only enhances the prediction accuracy but also enriches the model's ability to handle real-world variability and unpredictability. Extensive tests using specific versions of MoCAD, NGSIM, HighD, INTERACTION, and nuScenes datasets featuring missing observational data, have demonstrated the superior performance of our model in terms of both accuracy and physical feasibility, particularly in scenarios with significant data loss—up to 75% missing observations. Our findings underscore the potency of combining physics-informed models with advanced machine learning frameworks to advance autonomous driving technologies, aligning with the interdisciplinary nature of information fusion.