Intention-Aware Vehicle Trajectory Prediction Based on Spatial-Temporal Dynamic Attention Network for Internet of Vehicles

Vehicle trajectory prediction is a keystone for the application of the internet of vehicles (IoV). With the help of deep learning and big data, it is possible to understand the between-vehicle interaction pattern hidden in the complex traffic environment. In this paper, we propose a novel spatial-temporal dynamic attention network for vehicle trajectory prediction, which can comprehensively capture temporal and social patterns in a hierarchical manner. The social relation between vehicles is captured at each timestamp and thus retains the dynamic variation of interaction. The temporal correlation in terms of individual motion state as well as social interaction is captured by different sequential models. Furthermore, a driving intention-specific feature fusion mechanism is proposed such that the extracted temporal and social features can be integrated adaptively for the maneuver-based multi-modal trajectory prediction. Experimental results on two real-world datasets show that compared with the state-of-the-art algorithms, our proposal achieves comparable prediction performance for short-term prediction, however, works much better for long-term prediction. Additionally, various ablation analysis is provided to evaluate the effectiveness of our proposed network components. The code will be available at https://xbchen82.github.io/resource/ .