A method based on Vision Transformer and multiple image information for vehicle lane-changing recognition in mixed traffic and connected environment

被引:1
作者
Ji, Peng [1 ]
Zhang, Chuang [1 ]
Zhang, Zichen [1 ]
机构
[1] Hebei Univ Engn, Sch Mech & Equipment Engn, Handan 056038, Peoples R China
来源
TRANSPORTATION LETTERS-THE INTERNATIONAL JOURNAL OF TRANSPORTATION RESEARCH | 2024年
关键词
Lane-changing intention; Vision Transformer; transfer learning; short-time Fourier transform; Gramian angular field; information fusion image; PREDICTION; INTERNET;
D O I
10.1080/19427867.2024.2377900
中图分类号
U [交通运输];
学科分类号
08 ; 0823 ;
摘要
To enhance the safety of autonomous vehicles in mixed traffic and connected environment, it is crucial to recognize the lane-changing intentions (LCIs) of human-driven vehicles for autonomous vehicles. This paper presents a novel method for LCI recognition, which extracts features from the driving state and relative motion of the target vehicle and its neighbors. The method applies short-time Fourier transform, Gramian angular summation field, and Gramian angular difference field to the time-series data, and generates three grayscale images, which are merged into one information fusion image (IFI) by image processing techniques. The IFIs are then classified into three categories: lane keeping, lane-changing left, and lane-changing right, using the Vision Transformer model with transfer learning to speed up convergence and reduce training cost. The experimental results demonstrate that the proposed method outperforms the traditional methods, achieving an accuracy of 95.65% for recognizing LCI 3s before the lane change point.
引用
收藏
页数:13
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