驾驶行为多重分形特征在驾驶疲劳检测中的应用

Application of multi-fractal features of driving performance in driver fatigue detection

本文旨在分析驾驶行为多重分形特征对驾驶疲劳检测模型的提升作用。利用UCwin/Road驾驶模拟软件采集行驶速度、加速度、方向盘转角和方向盘角速度等数据,并计算数据的均值、标准差和多重分形特征,比较不同特征的使用是否会对支持向量机(SVM)驾驶疲劳检测模型的精度造成影响。研究表明:在多重分形特征指标中,加速度的奇异强度与驾驶员疲劳状态相关性显著,且受时间窗宽度影响较小;加速度的奇异强度能帮助提高驾驶疲劳检测模型的精度,具有一定的应用价值。

Driver fatigue caused by long-term driving is usually accompanied by a decline in driving performance.Therefore,driver fatigue threatens the safety of the drivers and passengers seriously.The development of driver fatigue detection technology can help remind the drivers and take relevant measures against driver fatigue,thereby improving traffic safety to a certain degree.The purpose of this paper is to improve the classification accuracy of the driver fatigue detection model.To achieve this goal,this paper introduces multi-fractal features of the driver behavior data.Six male subjects participated in the driving simulation experiment and UC-win/Road driving simulation software was used to collect driver behavior data such as driving speed,acceleration,steering wheel angle and steering wheel angular velocity.Indicators of the mean,the standard deviation and several different multi-fractal features of each kind of data were calculated.The changes of the drivers’subjective fatigue were also measured with a time interval of 600 s.And the relationship between the indicators and driver fatigue were measured.Additionally,the accuracies of driver fatigue detection models based on support vector machine(SVM)considering different features and different time window were compared.The research shows:The correlation between the singularity strength of acceleration(A0)and driver fatigue is significant;Compared with the correlation between other indicators and driver fatigue,that between A0 and driver fatigue is less affected by the time window width;The singularity strength of acceleration can help improve the accuracy of SVM;Compared with the time window of 15 s,a wider time window of 30 s can make the improvement more obvious.Therefore,it has certain application value in driver fatigue detection technology.