CAE‐GAN: A hybrid model for vehicle trajectory prediction. Issue 12 (18th February 2022)

• Record Type: Journal Article
• Authors: Chen, Long; Zhou, Qiyang; Cai, Yingfeng; Wang, Hai; Li, Yicheng
• Is Part Of: IET intelligent transport systems. Volume 16:Issue 12(2022)
• Page Start: 1682
• Page End: 1696
• Publication Date: 2022-02-18
• Subjects: Intelligent transportation systems -- Periodicals; Electronics in transportation -- Periodicals; 388.31205
• DOI: 10.1049/itr2.12174
• Languages: English
• ISSNs: 1751-956X

Abstract

Abstract: Trajectory prediction of surrounding vehicles is a crucial capability of intelligent driving vehicles. In a scene, a vehicle and its surrounding vehicles constitute an integral system, and the vehicle's future motion trajectory is affected by the actions of surrounding vehicles. The influencing mode and degree are hidden in the relevant historical information of the vehicle and its neighbour vehicle. The existing trajectory prediction methods either do not consider the confidence of the predicted trajectory, or the accuracy requirement is ignored when considering the confidence of the predicted trajectory. In order to address this problem, a mixed Conditional AutoEncoder Generative Adversarial Network (CAE‐GAN) model based on the multi‐loss function is proposed. The proposed model uses the encoder–decoder structure with a convolutional social pool to extract general features. The generative adversarial networks (GANs) are used to extract the confidence features of the generated trajectories, which enables the proposed model to generate trajectories that are close to the real trajectories. In addition, a classifier structure based on an LSTM network is added to output the probability that the predicted trajectory belongs to a particular lateral maneuver so that the generated trajectory lateral maneuver of the model is consistent with the real trajectory lateral maneuver. The proposed model is evaluated using the publicly available NGSIM US‐101 and I‐80 datasets, and results show that the accuracy of the proposed model is higher than that of the existing methods. The proposed model achieves an average accuracy improvement of 16.34% in comparison to the most advanced existing models.