摘要
利用大众普遍使用的智能手环设计了驾驶人愤怒驾驶行为检测方法,为愤怒驾驶行为有效监测提供了新的途径和方法。本文招聘50名驾驶人开展模拟驾驶实验,设计了引发愤怒的模拟驾驶场景,利用手环采集数据获取心率指标HR和RR.mean、SDNN、RMSSD、PNN50、SDSD、HF、LF、LF/HF八个心率变异性(HRV)指标,对采集指标与愤怒驾驶行为进行关联研究,筛选显著性影响指标,利用支持向量机(SVM)、K-近邻(KNN)和线性判别分析(LDA)3种方法建立愤怒驾驶行为检测模型,并对其进行验证。结果表明:KNN算法的模型愤怒识别效果最好,对愤怒强度识别的准确率能达到75%,对愤怒状态识别的准确率为86%。结果表明:可穿戴式设备(智能手环)可以合理地检测驾驶人的愤怒情绪状态及愤怒情绪强度。
A method for detecting drivers'angry driving behavior has been designed using widely used popular smart bracelet,which provides a new way and method for effective monitoring of angry driving behavior.50 drivers were recruited to conduct a simulated driving experiment,and a simulated driving scene that caused anger was designed.Then,heart rate index HR and eight heart rate variability(HRV)indexes such as RR.mean,SDNN,RMSSD,PNN50,SDSD,HF,LF and LF/HF obtained from bracelet collection data were used to study the correlation between the acquisition indexes and the angry driving behavior,and screen the significant influence indexes Finally,using three methods,namely support vector machine(SVM),K-nearest neighbor(KNN)and linear discriminant analysis(LDA),established and verified the detection model of angry driving behavior.The results show that the model based on KNN algorithm has the best performance on anger recognition.The accuracy of anger intensity recognition can reach 75%,and the accuracy of anger state recognition is 86%.The results show that the wearable device(smart bracelet)can reasonably detect the driver's anger state and anger intensity.
作者
牛世峰
于士杰
刘彦君
马冲
NIU Shi-feng;YU Shi-jie;LIU Yan-jun;MA Chong(Key Laboratory of Automotive Transportation Safety Assurance Technology for Transportation Industry,Chang'an University,Xi'an 710064,China;School of Automobile,Chang'an University,Xi'an 710064,China)
出处
《吉林大学学报(工学版)》
CSCD
北大核心
2024年第12期3505-3512,共8页
Journal of Jilin University:Engineering and Technology Edition
基金
国家重点研发计划项目(2019YFB1600500)
商用重卡安全与节能智能辅助平台“科学家+工程师”队伍项目。
关键词
载运工具运用工程
愤怒驾驶行为
机器学习
智能手环
心率变异性
vehicle application engineering
anger driving behavior
machine learning
smart bracelet
heart rate variability
