Vehicle trajectory prediction model for unseen domain based on the invariance principle

Yifan Lu, Feng Yang, Xuanpeng Li

Abstract: Traditional vehicle trajectory prediction models widely exist the generalization problem towards unknown scenarios. In this paper, we address the generalization via the following ways. A conditional variational autoencoder based on invariance penalty is adopted to predict trajectory endpoints. In addition, we propose a domain division method to enhance the performance of the invariance principle and design the maneuver-related subtasks to reconstruct the consistent features of trajectories. The experiment is carried out on the INTERACTION dataset, which is well employed in the study of trajectory domains. Compared to the SOTA models, the mADE at 3s decreases from 1.16 to 0.53. The ablation study demonstrates the effectiveness of each module in our model. The results show that our method achieves excellence performance when generalized to unknown domains.