图模体矩阵增强下的扩散模型轨迹生成方法OA

In practical intelligent transportation systems,failures of IoT may lead to data interruption,underscoring the importance of trajectory data generation algorithms.Existing trajectory generation methods can be categorized into two types,both of which exhibit limitations.Physics-based algorithms are unable to adapt to dynamically changing traffic scenarios,whereas data-driven methods tend to overlook interactions among vehicles.To address complex scenarios such as intersections,this paper proposes a trajectory generation method based on a conditional diffusion model.The proposed method introduces a motif matrix to quantify the influence of surrounding vehicles on the target vehicle,and employs a graph attention mechanism to accurately identify key neighboring vehicle information,and the conditional information is incorporated into the reverse denoising process of the diffusion model to generate vehicle trajectories.Experiments conducted on the nuScenes dataset and a self-collected trajectory dataset demonstrate that the proposed method outperforms existing mainstream models across multiple accuracy evaluation metrics and is more consistent with kinematic principles.