In this paper, we present a novel trajectory prediction model for autonomous driving, combining a Characterized Diffusion Module and a Spatial-Temporal Interaction Network to address the challenges posed by dynamic and heterogeneous traffic environments. Our model enhances the accuracy and reliability of trajectory predictions by incorporating uncertainty estimation and complex agent interactions. Through extensive experimentation on public datasets such as NGSIM, HighD, and MoCAD, our model significantly outperforms existing state-of-the-art methods. We demonstrate its ability to capture the underlying spatial-temporal dynamics of traffic scenarios and improve prediction precision, especially in complex environments. The proposed model showcases strong potential for application in real-world autonomous driving systems.

翻译：本文提出了一种新颖的自动驾驶轨迹预测模型，该模型结合了特征化扩散模块与时空交互网络，以应对动态异构交通环境带来的挑战。通过引入不确定性估计与复杂智能体交互机制，我们的模型显著提升了轨迹预测的准确性与可靠性。在NGSIM、HighD及MoCAD等公开数据集上的大量实验表明，本模型在性能上显著优于现有先进方法。我们验证了该模型能够有效捕捉交通场景中潜在的时空动态特性，并在复杂环境中尤其提升预测精度。所提出的模型展现出在实际自动驾驶系统中应用的强大潜力。