CAJ | 학술논문

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．
위료준학검측가사피로상태，이용고방진도가사모의기진행모의실험，채집료가사행위화안동수거，병진행료주관피로정도조사．재차기출상，설계료23충무침입검측지표，종여피로적상관성、이원검측성능、대도로선형적민감성화개체일치성4방면，평고료지표적성능병안종합성능배서．연구결과표명：안폐합시간비례여주관피로정도（Karolinska sleepiness scale，KSS）적상관성최고，위0．443；이원검측성능최호적시안폐합시간비례화차도편이표준차；안동지표재곡-직로단적변화폭도균저우20％；당KSS위7시，제차중심월선시간비예외，기여지표균존재현저적개체차이；종합성능최고적지표의차위차도편이표준차、폐안시간비례화월선기간횡향평균속도．
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.