Asleep at the automated wheel-Sleepiness and fatigue during highly automated driving

被引:118
|
作者
Vogelpohl, Tobias [1 ]
Kuehn, Matthias [2 ]
Hummel, Thomas [2 ]
Vollrath, Mark [1 ]
机构
[1] Tech Univ Carolo Wilhelmina Braunschweig, Gaussstr 23, D-38106 Braunschweig, Germany
[2] Unfallforsch Versicherer, Wilhelmstr 43-43G, D-10117 Berlin, Germany
来源
关键词
Fatigue; Sleep; Automated driving; Transition to manual; Take-over request; DRIVER FATIGUE; PERFORMANCE; DEPRIVATION; ALERTNESS; SITUATIONS; DROWSINESS; ALCOHOL;
D O I
10.1016/j.aap.2018.03.013
中图分类号
TB18 [人体工程学];
学科分类号
1201 ;
摘要
Due to the lack of active involvement in the driving situation and due to monotonous driving environments drivers with automation may be prone to become fatigued faster than manual drivers (e.g. Schomig et al., 2015). However, little is known about the progression of fatigue during automated driving and its effects on the ability to take back manual control after a take-over request. In this driving simulator study with No = o60 drivers we used a three factorial 2o x o2o x o12 mixed design to analyze the progression (12o x o5omin; within subjects) of driver fatigue in drivers with automation compared to manual drivers (between subjects). Driver fatigue was induced as either mainly sleep related or mainly task related fatigue (between subjects). Additionally, we investigated the drivers' reactions to a take-over request in a critical driving scenario to gain insights into the ability of fatigued drivers to regain manual control and situation awareness after automated driving. Drivers in the automated driving condition exhibited facial indicators of fatigue after 15 to 35omin of driving. Manual drivers only showed similar indicators of fatigue if they suffered from a lack of sleep and then only after a longer period of driving (approx. 40omin). Several drivers in the automated condition closed their eyes for extended periods of time. In the driving with automation condition mean automation deactivation times after a take-over request were slower for a certain percentage (about 30%) of the drivers with a lack of sleep (Mo = o3.2; SDo = o2.1os) compared to the reaction times after a long drive (Mo = o2.4; SDo = o0.9os). Drivers with automation also took longer than manual drivers to first glance at the speed display after a take-over request and were more likely to stay behind a braking lead vehicle instead of overtaking it. Drivers are unable to stay alert during extended periods of automated driving without non-driving related tasks. Fatigued drivers could pose a serious hazard in complex take-over situations where situation awareness is required to prepare for threats. Driver fatigue monitoring or controllable distraction through non-driving tasks could be necessary to ensure alertness and availability during highly automated driving.
引用
收藏
页码:70 / 84
页数:15
相关论文
共 50 条
  • [31] Sleep in automated driving - Effects of time of day and chronotype on sleepiness and sleep inertia
    Tomzig, Markus
    Wo, Johanna
    Kremer, Christina
    Baumann, Martin
    TRANSPORTATION RESEARCH PART F-TRAFFIC PSYCHOLOGY AND BEHAVIOUR, 2024, 102 : 16 - 31
  • [32] Control Concepts as Fallback Solution for Highly Automated Driving
    Perner, Marcus
    Gebhardt, Martin
    Heine, Simon
    ATZ worldwide, 2020, 122 (05) : 26 - 29
  • [33] Using fNIRS to Verify Trust in Highly Automated Driving
    Perello-March, Jaume R.
    Burns, Christopher G.
    Woodman, Roger
    Elliott, Mark T.
    Birrell, Stewart A.
    IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, 2023, 24 (01) : 739 - 751
  • [34] Road Data as Prior Knowledge for Highly Automated Driving
    Kuehn, Wolfgang
    Mueller, Michael
    Hoeppner, Tom
    20TH EURO WORKING GROUP ON TRANSPORTATION MEETING, EWGT 2017, 2017, 27 : 222 - 229
  • [35] Physiological Measures of Risk Perception in Highly Automated Driving
    Perello-March, Jaume R.
    Burns, Christopher G.
    Birrell, Stewart A.
    Woodman, Roger
    Elliott, Mark T.
    IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, 2022, 23 (05) : 4811 - 4822
  • [36] SOCA: Domain Analysis for Highly Automated Driving Systems
    Butz, Martin
    Heinzemann, Christian
    Herrmann, Martin
    Oehlerking, Jens
    Rittel, Michael
    Schalm, Nadja
    Ziegenbein, Dirk
    2020 IEEE 23RD INTERNATIONAL CONFERENCE ON INTELLIGENT TRANSPORTATION SYSTEMS (ITSC), 2020,
  • [37] Highly Accurate Scenario and Reference Data for Automated Driving
    Bock, Julian
    Vater, Lennart
    Krajewski, Robert
    Moers, Tobias
    ATZ worldwide, 2021, 123 (5-6) : 50 - 55
  • [38] The interaction between highly automated driving and the development of drowsiness
    Schoemig, Nadja
    Hargutt, Volker
    Neukum, Alexandra
    Petermann-Stock, Ina
    Othersen, Ina
    6TH INTERNATIONAL CONFERENCE ON APPLIED HUMAN FACTORS AND ERGONOMICS (AHFE 2015) AND THE AFFILIATED CONFERENCES, AHFE 2015, 2015, 3 : 6652 - 6659
  • [39] Vibrotactile Displays: A Survey With a View on Highly Automated Driving
    Petermeijer, Sebastiaan M.
    de Winter, Joost C. F.
    Bengler, Klaus J.
    IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, 2016, 17 (04) : 897 - 907
  • [40] Highly Automated Driving-Disruptive Elements and Consequences
    Galbas, Roland
    ADVANCED MICROSYSTEMS FOR AUTOMOTIVE APPLICATIONS 2016: SMART SYSTEMS FOR THE AUTOMOBILE OF THE FUTURE, 2016, : 141 - 152