Driving and tiredness: Results of the behaviour observation of a simulator study with special focus on automated driving

Q3 Engineering Transactions on Transport Sciences Pub Date : 2020-09-11 DOI:10.5507/tots.2020.011
C. Kaufmann, M. Frühwirth, D. Messerschmidt, Maximilian Moser, A. Eichberger, S. Arefnezhad
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引用次数: 3

Abstract

The development of automated driving is an ongoing process; nonetheless, certain problems remain unresolved. One of them is the question when the automated vehicle control system should hand over the control to a human driver and whether this can be done in a safe way. What happens if a driver is not ready to take over? Can the system somehow estimate the status of the driver? The WACHsens simulator study was designed with the aim to gain more knowledge about when and how drivers are getting sleepy with special focus on automated driving.The overall goal of the project was to merge data from vegetative vigilance, camera observation and driving behaviour. This article describes the process of the driving behaviour observation and the evaluation of the data collected during the observation. An enhanced observation scheme made it possible to determine, at any point in time of the 30 minutes drives, in which posture the test person is and in what degree of drowsiness the test person is. It is based on the variables and scales which have been used in other studies such as ORD (Observer Rating of Drowsiness) and ORS (Observer Rated Sleepiness). They were linked to the observation method of the Vienna driving test to allow continuous observation. 197 simulator test drives from 50 test persons were analyzed by the observers. Four different scenarios were evaluated for all test subjects: tired/manual, tired/automatic, rested/manual, and rested/automatic. The aim of the observation analysis was to investigate differences in body movements and activities according to personal characteristics (age, gender, driving experience, experience with assistance systems) and regarding the different scenarios. The categorization of the drowsiness level of the test persons by the observers corresponds very well with the subjective assessment of the test subjects (measured by the Karolinska sleepiness scale KSS). A comparison of the different scenarios shows that most of the signs of sleepiness or situations in which the test subjects fell asleep were observed during the tired/automated trips. But even during the rested/automated drive over 40% of the test persons showed signs of tiredness, roughly the same number actually fell asleep as in the tired/manual drive. No significant differences between the personal characteristics (gender, age, and experience with assistance systems) regarding the number of body movements (change of position and activities) or sleepiness levels could be found. A significant difference was found between the different scenarios and the comparisons between the tired/rested trips and the manual/automated trips regarding the moment in which the test persons showed first signs of tiredness. During the automated trips and/or if the test subjects showed signs of progressing weariness, the first signs of tiredness were registered significantly earlier than during the trips in which the test subjects drove manually and/or were rested. The results show that the mode of operation - manual or automated driving - impacts the time course and level of sleepiness while driving. This sheds light on the importance to carefully evaluate driving automation systems that assume a driver as emergency fallback. Further research is recommended to investigate safe modes of control hand over in automated driving.
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驾驶和疲劳:一项模拟器研究的行为观察结果,特别关注自动驾驶
自动驾驶的发展是一个持续的过程;尽管如此,某些问题仍未得到解决。其中一个问题是,自动车辆控制系统何时应该将控制权移交给人类驾驶员,以及这一过程是否安全。如果司机没有准备好接管会发生什么?系统能否以某种方式估计驾驶员的状态?WACHsens模拟器研究的目的是获得更多关于驾驶员何时以及如何昏昏欲睡的知识,并特别关注自动驾驶。该项目的总体目标是将植物警戒、相机观察和驾驶行为的数据合并在一起。本文描述了驾驶行为观察的过程以及对观察过程中收集到的数据的评价。一个增强的观察方案可以确定,在30分钟车程的任何时间点,测试者处于何种姿势,以及测试者的困倦程度。它是基于其他研究中使用的变量和量表,如ORD(观察者困倦评级)和ORS(观察者困倦评级)。他们与维也纳驾驶考试的观察方法相联系,允许连续观察。由观察员对50名测试人员的197个模拟器试驾进行了分析。对所有测试对象评估了四种不同的场景:疲劳/手动、疲劳/自动、休息/手动和休息/自动。观察分析的目的是根据个人特征(年龄、性别、驾驶经验、辅助系统经验)和不同场景调查身体运动和活动的差异。观察者对被试者的困倦程度的分类与被试者的主观评价(由卡罗林斯卡困倦量表KSS测量)非常吻合。对不同场景的比较表明,大多数困倦的迹象或测试对象入睡的情况都是在疲劳/自动旅行中观察到的。但即使在休息/自动驾驶时,也有超过40%的测试者表现出疲劳的迹象,实际上睡着的人数与疲劳/手动驾驶时大致相同。个人特征(性别、年龄和使用辅助系统的经验)在身体运动次数(位置和活动的变化)或困倦程度方面没有显著差异。在不同的场景和疲劳/休息旅行与手动/自动旅行之间的比较中发现了显著的差异,这是关于测试人员首次显示疲劳迹象的时刻。在自动驾驶和/或如果测试对象显示出疲倦的迹象,疲劳的第一个迹象明显早于测试对象手动驾驶和/或休息的旅行。结果表明,操作模式——手动驾驶或自动驾驶——会影响驾驶时的睡眠时间和程度。这揭示了仔细评估自动驾驶系统的重要性,这些系统将驾驶员视为紧急后备人员。建议进一步研究自动驾驶中的安全控制模式。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Transactions on Transport Sciences
Transactions on Transport Sciences Environmental Science-Management, Monitoring, Policy and Law
CiteScore
1.40
自引率
0.00%
发文量
0
审稿时长
13 weeks
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