文献类型： 外文期刊

作者： Xu, Rui ¹ ; Li, Jun ¹ ; Zhang, Shiyi ² ; Li, Lei ³ ; Li, Hulin ⁴ ; Ren, Guiying ³ ; Tang, Xinglong ³ ;

作者机构： 1.Chongqing Jiaotong Univ, Sch Mechanotron & Vehicle Engn, Chongqing 400074, Peoples R China

2.Chongqing Jiaotong Univ, Sch Shipping & Naval Architecture, Chongqing 400074, Peoples R China

3.Chongqing Acad Agr Sci, Inst Agr Machinery, Chongqing 400074, Peoples R China

4.Chongqing Jiaotong Univ, Sch Traff & Transportat, Chongqing 400074, Peoples R China

期刊名称：MECHANICAL SCIENCES （ 影响因子：1.5； 五年影响因子：1.4 ）

ISSN： 2191-9151

年卷期： 2025 年 16 卷 1 期

页码：

收录情况： SCI

摘要： Trajectory planning has undergone remarkable strides in recent times, especially in the behavior prediction of traffic participants. Given that strong coupling conditions such as pedestrians, vehicles, and roads restrict the interactive behavior of autonomous vehicles and other traffic participants, it has become critical to design a trajectory prediction algorithm based on traffic scenarios for autonomous-driving technology. In this paper, we propose a novel trajectory prediction algorithm based on Transformer networks, a data-driven method that ingeniously harnesses dual-input channels. The rationale underlying this approach lies in its seamless fusion of scene context modeling and multi-modal prediction within a neural network architecture. At the heart of this innovative framework resides the multi-headed attention mechanism, ingeniously deployed in both the agent attention layer and the scene attention layer. This finessing not only captures the profound interdependence between agents and their surroundings but also imbues the algorithm with a better real-time predictive prowess, enhancing computational efficiency. Eventually, substantial experiments with the Argoverse dataset will demonstrate improved trajectory accuracy, with the minimum average displacement error (MADE) and minimum final displacement error (MFDE) being reduced by 12 % and 31 %, respectively.