[1]程 鑫,周经美,刘霈源,等.融合注意力机制与时序特征的异常驾驶行为识别算法[J].长安大学学报(自然科学版),2024,44(6):103-113.[doi:10.19721/j.cnki.1671-8879.2024.06.010]
　CHENG Xin,ZHOU Jing-mei,LIU Pei-yuan,et al.Abnormal driving behavior recognition algorithm combining attention mechanism and timing features[J].Journal of Chang’an University (Natural Science Edition),2024,44(6):103-113.[doi:10.19721/j.cnki.1671-8879.2024.06.010]
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融合注意力机制与时序特征的异常驾驶行为识别算法()
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长安大学学报（自然科学版）[ISSN:1006-6977/CN:61-1281/TN]

卷:
第44卷
期数:
2024年6期
页码:
103-113
栏目:
交通工程
出版日期:
2024-12-30

文章信息/Info

Title:
Abnormal driving behavior recognition algorithm combining attention mechanism and timing features
文章编号:
1671-8879(2024)06-0103-11
作者:
程 鑫1; 2; 周经美3; 刘霈源3; 牛亚妮1; 张晓静1; 王孜健1; 4
(1. 长安大学 信息工程学院,陕西 西安 710018; 2. 公安部交通管理科学研究所,江苏 无锡 214151; 3. 长安大学 电子与控制工程学院,陕西 西安 710018; 4. 山东高速集团有限公司创新研究院,山东 济南 250002)
Author(s):
CHENG Xin1; 2;  ZHOU Jing-mei3;  LIU Pei-yuan3;  NIU Ya-ni1; ZHANG Xiao-jing1;  WANG Zi-jian1; 4
(1. School of Information Engineering, Chang'an University, Xi'an 710018, Shaanxi, China; 2. Traffic Management Research Institute of the Ministry of Public Security, Wuxi 214151, Jiangsu, China; 3. School of Electronics and Control Engineering, Chang'an University, Xi'an 710018, Shaanxi, China; 4. Innovation Research Institute of the Shandong Hi-speed Group CO.,LTD, Jinan 250002, Shandong, China)
关键词:
交通工程;  异常驾驶行为识别;  ATFN模型;  时序特征;  注意力机制
Keywords:
traffic engineering;  abnormal driving behavior recognition;  ATFN model;  timing feature;  attention mechanism
分类号:
U495
DOI:
10.19721/j.cnki.1671-8879.2024.06.010
文献标志码:
A
摘要:
针对目前驾驶行为识别数据维度高、检测难度大,存在精度不高及稳定性较弱等问题,提出一种融合注意力机制与时序特征的异常驾驶行为识别算法。通过传感器采集驾驶人执行特定驾驶行为片段的过程数据并进行数据清洗,清洗后的数据进行模板化处理依次被规整为N×N×C和M×C以适应网络模型输入; 构建融合注意力机制与时序特征的网络(ATFN)模型,完成对急加/减速、急左/右转弯、急左/右变道、正常驾驶等7种驾驶行为的分类识别。在公开数据集上与长短时记忆网络算法(LSTM)、融合注意力机制的长短记忆网络算法(ALSTM)、融合卷积的长短时记忆网络算法(CLSTM)进行了对比分析。试验结果表明:LSTM、ALSTM、CLSTM与本文ATFN算法平均准确率分别为92.94%、94.28%、90.98%、95.47%,ATFN模型精度最高,相比其他3种模型分别提升了2.53%、1.19%、4.49%; 结合损失值、精确率和F1值等指标,ATFN模型整体性能最优。该算法效果良好且稳定性较高,满足实际检测精度需求,可为异常行为预警和驾驶风险评估提供技术支持。
Abstract:
Due to the high data dimension, difficulty in detection, low accuracy and weak stability of driving behavior recognition, an abnormal driving behavior recognition algorithm that integrates attention mechanism and timing features was proposed. The process data of drivers performing specific segments of driving behavior through sensors was collected and cleaned. The cleaned data was processed by template and then normalized into N×N×C and M×C to adapt to the input of network model. The ATFN model was constructed by integrating attention mechanism and timing features to complete the classification and recognition of seven driving behaviors, including sharp acceleration/deceleration, sharp left/right turn, sharp left/right lane change and normal driving. Compared with LSTM, ALSTM and CLSTM algorithms on the open data set. The results show that the average accuracy of LSTM, ALSTM, CLSTM and ATFN algorithm in this paper is 92.94%、94.28%、90.98%、95.47%, respectively, and the accuracy of ATFN model is the highest. Compared with the other three models, the increase is 2.53%、1.19%、4.49% respectively. Combined with loss value, recall rate and F1, ATFN model has the best overall performance. The algorithm in this paper has good effect and high stability, meets the requirement of actual detection accuracy, and can provide technical support for abnormal behavior warning and driving risk assessment.4 tabs, 7 figs, 29 refs.

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备注/Memo

备注/Memo:
收稿日期:2024-07-13
基金项目:国家自然科学基金项目(52102452,52302491); 陕西省重点研发计划项目(2023-YBGY-119,2023-YBGY-120); 陕西省自然科学基础研究计划项目(2023-JC-YB-523); 交通运输部重点科技项目(2022-ZD6-079)
作者简介:程 鑫(1990-),男,陕西西安人,副教授,工学博士,E-mail:xincheng@hd.edu.cn。
通讯作者:周经美(1991-),女,陕西西安人,副教授,工学博士,E-mail:jmzhou@chd.edu.cn。
更新日期/Last Update: 2024-12-30