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融合逻辑判断机制的CNN-GRU换道意图识别方法

任立海 康鈺泽 刘煜 蒋成约

湖南大学学报(自然科学版)2025,Vol.52Issue(6):69-77,9.
湖南大学学报(自然科学版)2025,Vol.52Issue(6):69-77,9.DOI:10.16339/j.cnki.hdxbzkb.2025177

融合逻辑判断机制的CNN-GRU换道意图识别方法

Recognition Method of Lane Change Intention Based on CNN-GRU Integrated with Logic Judgment Mechanism

任立海 1康鈺泽 1刘煜 2蒋成约1

作者信息

  • 1. 重庆理工大学 汽车零部件先进制造技术教育部重点实验室,重庆 400054
  • 2. 中国汽车工程研究院股份有限公司,重庆 401122
  • 折叠

摘要

Abstract

Accurately identifying the lane change intention of vehicles is a key strategy for improving the reliability of driving assistance systems and ensuring road safety.A novel method that combines convolutional neural network(CNN)and gated recurrent unit(GRU),integrated with a logic judgment mechanism,was proposed to effectively recognize the lane change intentions of vehicles.First,test data from twenty volunteers were recorded using a driving simulator including three categories:left lane change,right lane change,and straight driving.The data was used to construct a sample set of lane change intention.Secondly,a CNN-GRU model was built using vehicle driving characteristics and driver behavior data,with the CNN layer being employed to extract features as input to the GRU layer.Finally,a logic judgment layer was integrated into the intention recognition network to address the temporal dependencies of lane change intentions by setting probability thresholds.To validate the validity of the method in this study,the model was compared and analyzed with a CNN that was integrated with long short-term memory(LSTM)and GRU.The results show that the proposed model achieved recognition accuracies of 98.5%for left lane changes,96.7%for right lane changes,and 95.2%for straight driving,demonstrating higher accuracy compared with other models.

关键词

汽车工程/汽车安全/主动安全系统/深度学习/卷积神经网络

Key words

automotive engineering/vehicle safety/active safety systems/deep learning/convolutional neural networks

分类

交通运输

引用本文复制引用

任立海,康鈺泽,刘煜,蒋成约..融合逻辑判断机制的CNN-GRU换道意图识别方法[J].湖南大学学报(自然科学版),2025,52(6):69-77,9.

基金项目

国家自然科学基金资助项目(51405050),National Natural Science Foundation of China(51405050) (51405050)

汽车噪声振动和安全技术国家重点实验室开放基金资助项目(NVHSKL-202004),Open Foundation of State Key Laboratory of Vehicle NVH and Safety Technology(NVHSKL-202004) (NVHSKL-202004)

重庆市技术创新与应用发展专项面上项目(cstc2019jscx-msxmX0412),General Project of Chongqing Technology In-novation and Application Development(cstc2019jscx-msxmX0412) (cstc2019jscx-msxmX0412)

重庆理工大学研究生教育高质量发展行动计划资助成果(gzlcx20222119),Chongqing University of Technology Action Plan for Quality Development of Graduate Education(gzlcx20222119). (gzlcx20222119)

湖南大学学报(自然科学版)

OA北大核心

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