A bidirectional trajectory contrastive learning model for driving intention prediction

Abstract

Driving intention prediction with trajectory data of surrounding vehicles is critical to advanced driver assistance system for improving the accuracy of decision-making. Previous works mostly focused on trajectory representation based on supervised manners. However, learning generalized and high-quality representations from unlabeled data remains a very challenging task. In this paper, we propose a self-supervised bidirectional trajectory contrastive learning (BTCL) model that learns generalized trajectory representation to improve the performance of the driving intention prediction task. Different trajectory data augmentation strategies and a cross-view trajectory prediction task are constructed jointly as pretext task of contrastive learning. The pretext task can maximize the similarity among different augmentations of the same sample while minimizing similarity among augmentations of different samples. It can not only learn the high-quality representation of trajectory without labeled information but also improve the adversarial attacks on BTCL. Moreover, considering the vehicle trajectory forward and backward follows the same social norms and driving behavior constraints. A bidirectional trajectory contrastive learning module is built to gain more positive samples that further increasing the prediction accuracy in downstream tasks and transfer ability of the model. Experimental results demonstrate that BTCL is competitive with the state-of-the-art, especially for adversarial attack and transfer learning tasks, on real-world HighD and NGSIM datasets.