首页> 外文会议>International Conference on HCI in Mobility, Transport, and Automotive Systems;International Conference on Human-Computer Interaction >Evaluation of Driver Drowsiness While Using Automated Driving Systems on Driving Simulator, Test Course and Public Roads
【24h】

Evaluation of Driver Drowsiness While Using Automated Driving Systems on Driving Simulator, Test Course and Public Roads

机译:在驾驶模拟器,测试路线和公共道路上使用自动驾驶系统时驾驶员的困倦状况评估

获取原文

摘要

This paper describes an investigation of evaluation indices for assessing driver conditions when using an automated driving system. We focused on a driver drowsiness in the automated mode. A driving simulator experiment was conducted to identify evaluation indices which were sensitive to the subjective evaluation of the driver's drowsiness. The following indices were calculated based on the driver's eye movement data recorded for 60 s before the Rtl (Request to Intervene): number of blinks, duration of blinking, PERCLOS (Percent of Eyelid Closure), pupil diameter, number of saccade, amplitude of saccade, and velocity of saccade. We also measured the driver's driving performance after a transition from the automated driving to the manual driving mode. The results of the driving simulator experiment suggested that PERCLOS was sensitive to the subjective assessment of the reduction of the driver's alert level. And this index was highly related to the time to initiate driver's steering operation after the Rtl presentation. We have developed a prototype of the driver monitoring system that detects drivers' eyelid movements. The findings obtained from a test course experiment and a public road experiment indicated the effectivity of the driver monitoring system for evaluating quantitatively the driver's drowsiness in the automated driving condition. The results of the public road experiment imply that the duration of blinking as well as PERCLOS might be necessary to estimate the delay of the steering response time after the transition to manual driving.
机译:本文介绍了使用自动驾驶系统时评估驾驶员状况的评估指标的研究。我们专注于自动模式下的驾驶员睡意。进行了驾驶模拟器实验,以识别对驾驶员睡意的主观评估敏感的评估指标。根据在Rtl(请求介入)之前60秒钟记录的驾驶员眼动数据计算以下指标:眨眼次数,眨眼持续时间,PERCLOS(眼睑闭合百分比),瞳孔直径,扫视次数,振幅扫视和扫视速度。从自动驾驶模式转换为手动驾驶模式后,我们还测量了驾驶员的驾驶性能。驾驶模拟器实验的结果表明,PERCLOS对降低驾驶员警觉水平的主观评估很敏感。而且该指数与Rtl演示后启动驾驶员转向操作的时间高度相关。我们已经开发了驾驶员监视系统的原型,该系统可以检测驾驶员的眼睑运动。从测试过程实验和公共道路实验中获得的结果表明,驾驶员监控系统在定量评估自动驾驶条件下驾驶员的睡意方面的有效性。公用道路实验的结果表明,可能需要眨眼的持续时间以及PERCLOS来估计过渡到手动驾驶后转向响应时间的延迟。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号