|Table of Contents|

Abnormal driving behavior recognition algorithm combining attention mechanism and timing features(PDF)

长安大学学报（自然科学版）[ISSN:1006-6977/CN:61-1281/TN]

Issue:
2024年6期
Page:
103-113
Research Field:
交通工程
Publishing date:

Info

Title:
Abnormal driving behavior recognition algorithm combining attention mechanism and timing features
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)
Keywords:
traffic engineering;  abnormal driving behavior recognition;  ATFN model;  timing feature;  attention mechanism
PACS:
U495
DOI:
10.19721/j.cnki.1671-8879.2024.06.010
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.

References:

[1] 徐 洪.基于分形多步预测的高速公路异常驾驶行为预警研究[D].哈尔滨:哈尔滨工业大学,2017.
XU Hong.Study on freeway abnormal driving behavior forewarning based on fractal multi-step forecast[D].Harbin:Harbin Institute of Technology,2017.
[2]WANG W S,XI J Q,LI X H.Statistical pattern recognition for driving styles based on Bayesian probability and kernel density estimation[J].IET Intelligent Transport Systems,2019,13(1):22-30.
[3]WANG H,ZHANG C,SHI T W,et al.Real-time EEG-based detection of fatigue driving danger for accident prediction[J].International Journal of Neural Systems,2015,25(2):369-498.
[4]MU Z D,HU J F,YIN J H.Driving fatigue detecting based on EEG signals of forehead area[J].International Journal of Pattern Recognition and Artificial Intelligence,2017,31(5):1-12.
[5]WAN P,WU C Z,LIN Y Z,et al.Optimal threshold determination for discriminating driving anger intensity based on EEG wavelet features and ROC curve analysis[J].Information,2016,7(3):52.
[6]FU R R,WANG H,ZHAO W B.Dynamic driver fatigue detection using hidden Markov model in real driving condition[J].Expert Systems With Applications,2016,63:397-411.
[7]HU J F,MIN J L.Automated detection of driver fatigue based on EEG signals using gradient boosting decision tree model[J].Cognitive Neurodynamics,2018,12(4):431-440.
[8]吴福华,汪同庆.利用深度信息检测驾驶员头部姿态[J].烟台大学学报(自然科学与工程版),2017,30(2):155-159,166.
WU Fu-hua,WANG Tong-qing.Estimation of driver's head pose by using depth information[J].Journal of Yantai University(Natural Science and Engineering Edition),2017,30(2):155-159,166.
[9]MANDAL B,LI L Y,WANG G S,et al.Towards detection of bus driver fatigue based on robust visual analysis of eye state[J].IEEE Transactions on Intelligent Transportation Systems,2017,18(3):545-557.
[10]张 波.基于机器视觉的复杂工况下驾驶人疲劳状态检测方法研究[D].北京:清华大学,2015.
ZHANG Bo.Driver drowsiness detection in complicated conditions based on computer vision[D].Beijing:Tsinghua University,2015.
[11]张 迪.基于浮动车GPS数据分析的车辆相对异常驾驶行为研究[D].杭州:杭州电子科技大学,2018.
ZHANG Di.Research on relative abnormal driving behavior of vehicles based on floating car GPS data analysis[D].Hangzhou:Hangzhou Dianzi University,2018.
[12]CAI Z Q,PEI W,ZHU Y Y,et al.Inferring and analysis drivers violation behavior through trajectory[M].Cham:Springer International Publishing,2017.
[13]杨龙海,徐 洪,张 春.基于GPS数据的高速公路车辆异常行为检测[J].重庆交通大学学报(自然科学版),2018,37(5):97-103.
YANG Long-hai,XU Hong,ZHANG Chun.Vehicle abnormal behavior detection on freeway based on global positioning system data[J].Journal of Chongqing Jiaotong University(Natural Science),2018,37(5):97-103.
[14]阳 静.汽车危险驾驶行为检测系统的设计与实现[D].武汉:华中科技大学,2018.
YANG Jing.Design and implementation of dangerous driving behavior detection system[D].Wuhan:Huazhong University of Science and Technology,2018.
[15]DU W W,XING K,GONG H H.Smart phone based phubbing walking detection and safety warning[C]//ACM.Proceedings of the Workshop on Smart Internet of Things.New York:ACM,2017:1-6.
[16]孙 剑,张一豪,王俊骅.基于自然驾驶数据的分心驾驶行为识别方法[J].中国公路学报,2020,33(9):225-235.
SUN Jian,ZHANG Yi-hao,WANG Jun-hua.Detecting distraction behavior of drivers using naturalistic driving data[J].China Journal of Highway and Transport,2020,33(9):225-235.
[17]XU X Y,YU J D,CHEN Y Y,et al.Leveraging audio signals for early recognition of inattentive driving with smartphones[J].IEEE Transactions on Mobile Computing,2018,17(7):1553-1567.
[18]ZHANG J,WU Z C,LI F,et al.Attention-based convolutional and recurrent neural networks for driving behavior recognition using smartphone sensor data[J].IEEE Access,2019,7:148031-148046.
[19]ZHANG J,WU Z C,LI F,et al.Deep attentional factorization machines learning approach for driving safety risk prediction[J].Journal of Physics:Conference Series,2021,1732(1):1-8.
[20]WANG P Y,FU Y J,ZHANG J W,et al.You are how you drive:Peer and temporal-aware representation learning for driving behavior analysis[C]//ACM.Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining.New York:ACM,2018:2457-2466.
[21]ZHANG J,WU Z C,LI F,et al.A deep learning framework for driving behavior identification on in-vehicle CAN-BUS sensor data[J].Sensors,2019,19(6):1-17.
[22]ZHANG Y,LI J J,GUO Y H,et al.Vehicle driving behavior recognition based on multi-view convolutional neural network with joint data augmentation[J].IEEE Transactions on Vehicular Technology,2019,68(5):4223-4234.
[23]LI B,CHEN J E,HUANG Z X,et al.A new unsupervised deep learning algorithm for fine-grained detection of driver distraction[J].IEEE Transactions on Intelligent Transportation Systems,2022,23(10):19272-19284.
[24]WANG J,WU Z C,LI F,et al.A data augmentation approach to distracted driving detection[J].Future Internet,2020,13(1):1-11.
[25]LI W J,WANG J,REN T T,et al.Learning accurate,speedy,lightweight CNNs via instance-specific multi-teacher knowledge distillation for distracted driver posture identification[J].IEEE Transactions on Intelligent Transportation Systems,2022,23(10):17922-17935.
[26]惠 飞,郭 静,贾 硕,等.基于双向长短记忆网络的异常驾驶行为检测[J].计算机工程与应用,2020,56(24):116-122.
HUI Fei,GUO Jing,JIA Shuo,et al.Detection of abnormal driving behavior based on BiLSTM[J].Computer Engineering and Applications,2020,56(24):116-122.
[27]WANG J H,LONG Q L,PIRAHC,et al.Human action recognition on cellphone using compositional bidir-LSTM-CNN networks[C]//ZHOU P.Proceedings of the 2019 International Conference on Computer,Network,Communication and Information Systems(CNCI 2019).Paris:Atlantis Press,2019:687-692.
[28]季学武,费 聪,何祥坤,等.基于LSTM网络的驾驶意图识别及车辆轨迹预测[J].中国公路学报,2019,32(6):34-42.
JI Xue-wu,FEI Cong,HE Xiang-kun,et al.Intention recognition and trajectory prediction for vehicles using LSTM network[J].China Journal of Highway and Transport,2019,32(6):34-42.
[29]ROMERA E,BERGASA L M,ARROYO R.Need data for driver behaviour analysis?Presenting the public UAH-DriveSet[C]//IEEE.2016 IEEE 19th International Conference on Intelligent Transportation Systems(ITSC).New York:IEEE,2016:387-392.

Memo

Memo:
-
Last Update: 2024-12-30