Intention-convolution and hybrid-attention network for vehicle trajectory prediction

Trajectory prediction aims to estimate the future location of the vehicle based on its historical motion state, which is essential for driving decision-making and local motion planning of smart vehicles. However, affected by the multiple complex interaction in the traffic scene, predicting future trajectory accurately is a challenging task. The majority of existing methods only focus on modeling the inter-vehicle interaction, while ignoring the influence of road alignment and driver's lane-change intention, making the poor performance of models, especially for long-term prediction or when the vehicle maneuvers laterally. To overcome the deficiencies, this paper proposes Intention-convolution and Hybrid-Attention Network (IH-Net) for reliable trajectory prediction. Specifically, we analyze the correlation of lane-change behavior and the motion state of the vehicle, and then the Intention-convolutional Social Pooling module (I-CS) is introduced to extract complete interaction including the driver's lane-change intention and inter-vehicle interaction. In addition, we construct a novel Hybrid Attention Mechanism (H-AM) to explore the trajectory periodicity formed under the restriction of road alignment, as well as the impacts of different features on trajectory prediction, which is used to improve the model's long-term prediction capacity. The model's prediction accuracy with RMSE loss function is tested on two public datasets NGSIM and highD, and the results demonstrate that IH-Net remarkably outperforms the state-of-art methods in long-term prediction.