Efficient Vehicle Trajectory Prediction With Goal Lane Segments and Dual-Stream Cross Attention

Real-time and efficient prediction of plausible trajectories of surrounding traffic agents is essential for autonomous driving. In reality, the motion of agents depends not only on their goal intents, but is also constrained by road topology. Especially for vehicles, lane geometry can significantly influence their future trajectories. In this paper, the constraining and guiding roles of lane networks are investigated, and a novel vehicle trajectory prediction model based on the goal lane segment is proposed. Specifically, a Dual-Stream Cross Attention Module (DSCAM) is developed that incorporates goal lane segment prediction into the process of collecting various interaction information. This achieves scene-consistent predictions while reducing resource consumption and inference latency. Then, learnable refinement tokens are used to adaptively refine coarse-grained trajectories during the feature decoding process, with the goal of improving prediction accuracy and ensuring temporal consistency. Extensive experiments with the Argoverse-1 and nuScenes datasets demonstrate that our model performs competitively and well-balanced. Notably, on the nuScenes, our model with only 0.44 million (M) parameters has an inference latency of less than six milliseconds (ms), making it ideal for large-scale production autonomous driving systems that require high computational resources, operational efficiency, and prediction performance.