Informative Causality-Based Vehicle Trajectory Prediction Architecture for Domain Generalization

Vehicle trajectory prediction is a promising technology for improving the performance of Cellular Vehicle-to-Everything (C-V2X) applications by providing future road states. Various vehicle trajectory prediction methods have been proposed to increase the accuracy of the predicted trajectory. Although the existing vehicle trajectory prediction methods can accurately predict the future trajectory under the assumption that data comply with the Independent and Identically Distributed (IID), their performance is seriously degraded in practical implementation due to the ubiquitous distribution shifts in vehicle trajectory data. To improve the universality of the vehicle trajectory prediction method, generalizing the method to an environment that never appeared in the training data, namely, the Domain Generalization (DG) task, should be considered. Thus, we propose a plug-and-play inFORmaTive caUsality-based vehicle trajectory predictioN architecturE (FORTUNE) to improve the DG capability of vehicle trajectory prediction methods. First, a novel structural causal model (SCM) of vehicle trajectory prediction is established to simulate the causality of the data-generating process. Second, we utilize the principle of mutual information to learn the invariant representation of the SCM. Third, an invariant knowledge-transferring module is proposed to increase learning ability without destroying the structure of the original model. The results from simulation experiments demonstrate that the proposed scheme can significantly improve the DG capability of vehicle trajectory prediction methods.