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STMF-IE: A Spatial-Temporal Multi-Feature Fusion and Intention-Enlightened Decoding Model for Vehicle Trajectory Prediction

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成果类型:
期刊论文
作者:
Kai Gao;Xunhao Li;Lin Hu;Xinyu Liu;Jinlai Zhang;...
作者机构:
[Lin Hu; Xinyu Liu; Jinlai Zhang; Ronghua Du] College of Automotive and Mechanical Engineering, Changsha University of Science and Technology, Changsha, China
Jiangsu Key Laboratory of Urban ITS, Department of Intelligent Transportation and Spatial Informatics, School of Transportation, Southeast University, Nanjing, China
HNU College of Mechanical and Vehicle Engineering, Hunan University, Changsha, China
[Yongfu Li] Key Laboratory of Intelligent Air-Ground Cooperative Control for Universities in Chongqing, College of Automation, Chongqing University of Posts and Telecommunications, Chongqing, China
[Kai Gao] College of Automotive and Mechanical Engineering, Changsha University of Science and Technology, Changsha, China<&wdkj&>HNU College of Mechanical and Vehicle Engineering, Hunan University, Changsha, China
语种:
英文
期刊:
IEEE Transactions on Vehicular Technology
ISSN:
0018-9545
年:
2025
卷:
74
期:
3
页码:
4004-4018
基金类别:
10.13039/100014717-National Outstanding Youth Science Fund Project of National Natural Science Foundation of China (Grant Number: 52325211) 10.13039/501100001809-National Natural Science Foundation of China (Grant Number: 52172399) Natural Science Foundation of Hunan (Grant Number: 2024JJ5023) 10.13039/501100005230-Natural Science Foundation of Chongqing (Grant Number: CSTB2022NSCQ-LZX0025) 10.13039/501100012269-Science and Technology Innovative Research Team in Higher Educational Institutions of Hunan Province
机构署名:
本校为第一机构
院系归属:
汽车与机械工程学院
摘要:
Accurate trajectory prediction of surrounding vehicles is crucial for ensuring the safety of autonomous vehicles. However, current methods based on neural networks still have room for improvement by further reducing their long-term error. This challenge stems from extracting temporal feature dependencies and mining spatial interactions in lane change scenarios. Previous research has not adequately established the connection between the past and the future features. To this end, we propose a spatial-temporal multi-feature fusion and intention-en...

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