摘要
设计了一种车辆深度交互编码并结合基于注意力机制的解码器模型,该模型同时输出车辆多模态行为预测结果和未来轨迹预测分布。使用公开的NGSIM US-101和I-80数据集评估所提出的模型,并且对模型多模态行为机动预测进行了定性分析。结果表明:该模型具有较好的均方根误差值(RMSE),在提升了计算效率的基础上获得了更高的轨迹预测精度。
A vehicle deep-interaction coding model combined with a decoder based on the attentionmechanism to solve this problem was presented in this work.The model′s multi-modal behavior predictions and trajectory predictions were output.The proposed model is evaluated by the public NGSIM US-101 and I-80 data sets.The results show that the model has a better root mean square error and achieves higher trajectory prediction accuracy while improving computational efficiency.This paper also shows a qualitative analysis of the prediction of multi-modal behavior maneuvering.
作者
田彦涛
黄兴
卢辉遒
王凯歌
许富强
TIAN Yan-tao;HUANG Xing;LU Hui-qiu;WANG Kai-ge;XU Fu-qiang(College of Communication Engineering,Jilin University,Changchun 130022,China;Key Laboratory of Bionic Engineering,Ministry of Education,Jilin University,Changchun 130022,China)
出处
《吉林大学学报(工学版)》
EI
CAS
CSCD
北大核心
2023年第5期1474-1480,共7页
Journal of Jilin University:Engineering and Technology Edition
基金
国家自然科学基金区域创新发展联合基金项目(U19A2069)。
关键词
车辆工程
轨迹预测
多模态预测
注意力机制
门控循环单元
vehicle engineering
trajectory prediction
multimodal prediction
attention mechanism
gated recurrent unit