为了准确检测驾驶疲劳状态，利用高仿真度驾驶模拟器进行模拟实验，采集了驾驶行为和眼动数据，并进行了主观疲劳程度调查．在此基础上，设计了23种无侵入检测指标，从与疲劳的相关性、二元检测性能、对道路线形的敏感性和个体一致性4方面，评估了指标的性能并按综合性能排序．研究结果表明：眼闭合时间比例与主观疲劳程度（Karolinska sleepiness scale，KSS）的相关性最高，为0．443；二元检测性能最好的是眼闭合时间比例和车道偏移标准差；眼动指标在曲-直路段的变化幅度均低于20％；当KSS为7时，除车中心越线时间比例外，其余指标均存在显著的个体差异；综合性能最高的指标依次为车道偏移标准差、闭眼时间比例和越线期间横向平均速度．%To detect the drowsiness state of the driver more accurately,a high-fidelity driving simulator was adopted to collect the driving behavior and eye movement data of drivers, and questionnaires were used to collect their subjective sleepiness scales. Based on these data,23 non-intrusive indicators were developed. The indicators were evaluated from the aspects of their correlation with drowsiness,binary classification performance,sensitivity to road alignment,and individual homogeneity;and then ranked in terms of comprehensive performance. The results show that the percentage of eyelid closure time (PERCLOS)has the highest correlation (0. 443)with the Karolinska sleepiness scale (KSS ). The best binary classification performance of eye movement and driving indicators lies on PERCLOS and the lateral position standard deviation (LPSD ). For eye movement indicators,the difference between straight line and curve is lower than 20%. When KSS is 7,all the indicators show significant individual difference except the percentage of lane crossing time of vehicle center. The top three non-intrusive indicators with the best comprehensive performance are PERCLOS, LPSD,and the average lateral speed during lane departure.
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