摘要
【目标】针对L3级自动驾驶系统操作期间驾驶人易产生心智游移的问题,提出了一种基于驾驶人眼动、心率等生理指标构建心智游移状态预测模型的理论方法。【方法】通过模拟试验收集驾驶者的眼动指标和心电数据,依据MAAS量表分数将其分为高心智游移倾向组与低心智游移倾向组,并标定了心智游移发生的显著影响因素,从而构建驾驶者心智游移预测模型。【结果】高心智游移倾向组的驾驶者更易发生心智游移,且随着驾驶时间的延长,心智游移的频率显著增加。在心智游移状态下,驾驶者的注视时长更长,瞳孔直径更小,扫视速度更慢,扫视幅度更小,并且对与驾驶无关区域的注视时长百分比更高,但心率变异性指标无显著差异。以驾驶时长、心智游移倾向及5个显著眼动指标为输入特征变量,结合混淆矩阵和被试者工作特征曲线,确定基于粒子群优化算法的随机森林模型对驾驶者心智游移状态的预测效果最佳。【结论】该心智游移状态预测模型有效揭示了高心智游移倾向组驾驶者在操作期间的显著特征,研究结果为实现对驾驶者心智游移的动态监测与干预提供了科学依据,有助于提升自动驾驶系统的安全性。
[Objective]To address the driver mind wandering during the operation of L3-level autonomous driving systems,the theoretical method for constructing a mind wandering prediction model was proposed,based on physiological indicators derived from drivers’eye movement and heart rate data.[Method]Eye movement and ECG data were collected from drivers through simulation tests.Based on scores from the mindful attention awareness scale(MAAS),the drivers were categorized into two groups,i.e.,those with a high tendency for mind wandering and those with a low tendency.The significant influencing factors contributing to mind wandering were identified to facilitate the development of a predictive model.[Result]Drivers in the high tendency group exhibited greater likelihood of experiencing mind wandering,with the frequency of such occurrences significantly increasing with longer driving durations.During episodes of mind wandering,drivers demonstrated longer gaze durations,smaller pupil diameters,slower sweep speeds,reduced sweep amplitudes,and a higher percentage of gaze directed toward driving-irrelevant areas.No significant differences were observed in heart rate variability.Utilizing driving duration,mind wandering tendencies,and five key eye movement metrics as input features,it was determined that the random forest model optimized by particle swarm optimization exhibited the best predictive performance regarding drivers’mind wandering states.[Conclusion]The proposed model effectively identifies significant characteristics of drivers prone to mind wandering during operation.The findings provide a scientific basis for dynamic monitoring and intervention strategies aimed at mitigating driver wandering,ultimately contributing to enhanced safety in autonomous driving systems.
作者
王永岗
赵梓乔
魏文欣
彭志鹏
程延秋
WANG Yonggang;ZHAO Ziqiao;WEI Wenxin;PENG Zhipeng;CHENG Yanqiu(School of Transportation Engineering,Chang’an University,Xi’an,Shaanxi 710018,China;School of Economics Management,Xi’an Technological University,Xi’an,Shaanxi 710021,China)
出处
《公路交通科技》
北大核心
2025年第7期21-29,共9页
Journal of Highway and Transportation Research and Development
基金
国家自然科学基金项目(52302443)。
关键词
智能交通
心智游移预测
模拟驾驶
眼动特征
心电信号
PSO-RF模型
intelligent transport
mind wandering prediction
driving simulation
eye movement characteristics
ECG signal
PSO-RF model
