基于决策树的驾驶疲劳等级分析与判定

Driver Drowsiness Level Analysis and Predication based on Decision Tree

摘要:

为了提高疲劳检测的精度，通过驾驶模拟试验采集了15位中青年有经验驾驶员的车辆横向位置、方向盘操控、眼动等多源数据并计算疲劳特征指标，同时采集驾驶员主观疲劳程度并通过视频回放进行校核，在此基础上建立疲劳等级与特征指标的决策树模型，结果表明，对于区别疲劳等级最显著的变量有闭眼时间比例(percentage of eye closure,PERCLOS)、车道偏移标准差、越线时空面积、方向盘反转率，且上述变量与疲劳等级呈正相关；PERCLOS为最优的疲劳等级划分变量，并获取了2个重要阈值：当PERCLOS小于2.8%时，驾驶员处于严重疲劳状态的比例为零；当PERCLOS大于21.9%时，驾驶员处于未疲劳状态的比例为零；该模型预测的总正确率为64.31%.为了校验模型，从15位驾驶员中随机选取了4位进行模型校验试验.校核结果表明该模型的正确率达63.22%.模型在2次试验中都未发现将严重疲劳识别为未疲劳的情况.

关键词:驾驶模拟器; 疲劳驾驶; 决策树; 疲劳等级; 生理参数; 驾驶行为参数

Abstract:

In order to improve the accuracy of drowsiness detection, in this study multi source data for young and middle aged experienced drivers including vehicle lateral position, steering wheel controlling, and eye movement are collected in a driving simulator experiment. Meanwhile, the subjective drowsiness level of the drivers were also recorded and validated by replaying the videos. Based on those data, the decision tree model was established. The results indicate that the most significant variables to estimate drowsiness level are percentage of eye closure(PERCLOS), the standard deviation of lateral position, the time space area of lane crossing, the reverse rate of steering wheel and those variables are positively correlated to drowsiness level. Among these variables, PERCLOS is the best variable to divide drowsiness level. When PERCLOS is lower than 2.8%, there are no drivers in seriously drowsy state and when PERCLOS is higher than 21.9%, there are no drivers in non drowsy state; the total correct predicting rate is 64.31%. To verification the model, 4 drivers were selected from the 15 drivers randomly. The results of model validation showed the correct predicting rate of the decision tree model is 63.22%. In both experiments, the decision tree model doesn’t mistake seriously drowsy state for non drowsy state.

Key words:driving simulator; drowsy driving; decision tree; drowsiness level; physiological index; driving behavior index