Multi-Vehicle Collaborative Learning for Trajectory Prediction With Spatio-Temporal Tensor Fusion

• Published in: IEEE transactions on intelligent transportation systems 2022-01, Vol.23 (1), p.236-248
• Main Authors: Wang, Yu, Zhao, Shengjie, Zhang, Rongqing, Cheng, Xiang, Yang, Liuqing
• Format: Article
• Language: English
• Subjects: Coders; Collaborative learning; Collaborative work; Gallium nitride; Generative adversarial networks; Intelligent transportation systems; Intelligent vehicles; Learning; Mathematical analysis; Predictive models; Qualitative analysis; spatio-temporal tensor fusion; Tensile stress; Tensors; Trajectories; Trajectory; vehicle trajectory prediction; Vehicles
• ISSN: 1524-9050; 1558-0016
• DOI: 10.1109/TITS.2020.3009762

Accurate behavior prediction of other vehicles in the surroundings is critical for intelligent transportation systems. Common practices to reason about the future trajectory are through their historical paths. However, the impact of traffic context is ignored, which means the beneficial environment information is deserted. Although a few methods are proposed to exploit the surrounding vehicle information, they simply model the influence according to spatial relations without considering the temporal information among them. In this paper, a novel multi-vehicle collaborative learning with spatio-temporal tensor fusion model for vehicle trajectory prediction is proposed, which introduces a novel auto-encoder social convolution mechanism and a fancy recurrent social mechanism to model spatial and temporal information among multiple vehicles, respectively. Furthermore, the generative adversarial network is incorporated into our framework to handle the inherent multi-modal characteristics of the agent motion behavior. Finally, we evaluate the proposed multi-vehicle collaborative learning model on NGSIM US-101 and I-80 benchmark datasets. Experimental results demonstrate that the proposed approach outperforms the state-of-the-art for vehicle trajectory prediction. Additionally, we also present qualitative analyses of the multi-modal vehicle trajectory generation and the impacts of surrounding vehicles on trajectory prediction under various circumstances.