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    A Systematic Literature Review on the Effects of Non-Driving Tasks on the Takeover Process in Highly Automated Driving

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    https://www.riss.kr/link?id=A108497491

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    다국어 초록 (Multilingual Abstract) kakao i 다국어 번역

    Objective: The aim of this research is to analyze the characteristics of non-driving related tasks (NDRT) and determine how they affect the transition of control in highly automated driving.
    Background: Highly automated driving systems are intelligent and assistive systems that are mostly used in commercial vehicles. These technologies aid drivers in the task of driving, allowing them freedom to attend to other tasks. However, these highly automated driving systems will continue to demand driver intervention in driving.
    Therefore, it is necessary to explore the characteristics of NDRT and their influence when regaining control of the vehicle.
    Method: A systematic literature review was conducted to investigate the effects of NDRT on the transition of control in highly automated driving, considering task characteristics, type of measurement, and experimental results from previous studies.
    A total of 27 articles were selected for the final analysis based on the selection criteria.
    Results: The literature review results showed that, depending on the task characteristics, NDRT can be classified into experimental tasks or natural tasks. In highly automated driving, NDRTs are generally used to simulate different drivers' states before a transition of control occurs. The transition of control is measured by evaluating and analyzing each of the stages of the takeover process. These measures are divided into takeover performance measures and post-takeover performance measures. The influence of NDRT in the transition of control differs between studies which can be explained by the specific NDRT selected in each of the research and the measure selected to analyze the transition of control. Although there is a difference in the effect of NDRT, it is agreeable that NDRT has an important influence on drivers' state in highly automated driving, and can affect the transition of control.
    Conclusion: In highly automated driving, NDRT are a key factor that influences the transition of control. The driver's cognitive, physical, and visual resources used during the NDRT can affect each of the processes and tasks that the drivers have to perform to regain control of the vehicle.
    Application: This research provides insights into the influence of NDRT in highly automated driving and its effect on each of the processes of transition of control.
    It allows an understanding of the impact of drivers' state before the takeover is performed.
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    Objective: The aim of this research is to analyze the characteristics of non-driving related tasks (NDRT) and determine how they affect the transition of control in highly automated driving. Background: Highly automated driving systems are intelligent...

    Objective: The aim of this research is to analyze the characteristics of non-driving related tasks (NDRT) and determine how they affect the transition of control in highly automated driving.
    Background: Highly automated driving systems are intelligent and assistive systems that are mostly used in commercial vehicles. These technologies aid drivers in the task of driving, allowing them freedom to attend to other tasks. However, these highly automated driving systems will continue to demand driver intervention in driving.
    Therefore, it is necessary to explore the characteristics of NDRT and their influence when regaining control of the vehicle.
    Method: A systematic literature review was conducted to investigate the effects of NDRT on the transition of control in highly automated driving, considering task characteristics, type of measurement, and experimental results from previous studies.
    A total of 27 articles were selected for the final analysis based on the selection criteria.
    Results: The literature review results showed that, depending on the task characteristics, NDRT can be classified into experimental tasks or natural tasks. In highly automated driving, NDRTs are generally used to simulate different drivers' states before a transition of control occurs. The transition of control is measured by evaluating and analyzing each of the stages of the takeover process. These measures are divided into takeover performance measures and post-takeover performance measures. The influence of NDRT in the transition of control differs between studies which can be explained by the specific NDRT selected in each of the research and the measure selected to analyze the transition of control. Although there is a difference in the effect of NDRT, it is agreeable that NDRT has an important influence on drivers' state in highly automated driving, and can affect the transition of control.
    Conclusion: In highly automated driving, NDRT are a key factor that influences the transition of control. The driver's cognitive, physical, and visual resources used during the NDRT can affect each of the processes and tasks that the drivers have to perform to regain control of the vehicle.
    Application: This research provides insights into the influence of NDRT in highly automated driving and its effect on each of the processes of transition of control.
    It allows an understanding of the impact of drivers' state before the takeover is performed.

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    참고문헌 (Reference)

    1 Liang, N., "Using eye-tracking to investigate the effects of pre-takeover visual engagement on situation awareness during automated driving" 157 : 106143-, 2021

    2 NHTSA, "US department of transportation policy on automated vehicle development"

    3 Dogan, E., "Transition of control in a partially automated vehicle: Effects of anticipation and non-driving-related task involvement" 46 : 205-215, 2017

    4 Yang, L., "The implication of non-driving activities on situation awareness and take-over performance in level 3 automation" IEEE 5075-5080, 2020

    5 Yoon, S. H., "The effects of takeover request modalities on highly automated car control transitions" 123 : 150-158, 2019

    6 SAE International, "Taxonomy and definitions for terms related to driving automation systems for on-road motor vehicles" SAE 2018

    7 Eriksson, A., "Takeover time in highly automated vehicles: noncritical transitions to and from manual control" 59 (59): 689-705, 2017

    8 Wu, C., "Take-over performance and safety analysis under different scenarios and secondary tasks in conditionally automated driving" 7 : 136924-136933, 2019

    9 Booth, A., "Systematic approaches to a successful literature review" 205-206, 2016

    10 McKerral, A., "Supervising the self-driving car: situation awareness and fatigue during automated driving" 315-320, 2019

    1 Liang, N., "Using eye-tracking to investigate the effects of pre-takeover visual engagement on situation awareness during automated driving" 157 : 106143-, 2021

    2 NHTSA, "US department of transportation policy on automated vehicle development"

    3 Dogan, E., "Transition of control in a partially automated vehicle: Effects of anticipation and non-driving-related task involvement" 46 : 205-215, 2017

    4 Yang, L., "The implication of non-driving activities on situation awareness and take-over performance in level 3 automation" IEEE 5075-5080, 2020

    5 Yoon, S. H., "The effects of takeover request modalities on highly automated car control transitions" 123 : 150-158, 2019

    6 SAE International, "Taxonomy and definitions for terms related to driving automation systems for on-road motor vehicles" SAE 2018

    7 Eriksson, A., "Takeover time in highly automated vehicles: noncritical transitions to and from manual control" 59 (59): 689-705, 2017

    8 Wu, C., "Take-over performance and safety analysis under different scenarios and secondary tasks in conditionally automated driving" 7 : 136924-136933, 2019

    9 Booth, A., "Systematic approaches to a successful literature review" 205-206, 2016

    10 McKerral, A., "Supervising the self-driving car: situation awareness and fatigue during automated driving" 315-320, 2019

    11 Gerber, M. A., "Self-interruptions of non-driving related tasks in automated vehicles : Mobile vs head-up display" 1-9, 2020

    12 Lotz, A., "Response times and gaze behavior of truck drivers in time critical conditional automated driving take-overs" 64 : 532-551, 2019

    13 Moher, D., "Preferred reporting items for systematic reviews and metaanalyses: the PRISMA statement" 6 (6): e1000097-, 2009

    14 Yoon, S. H., "Non-driving-related tasks, workload, and takeover performance in highly automated driving contexts" 60 : 620-631, 2019

    15 Yoon, S. H., "Modeling takeover time based on non-driving-related task attributes in highly automated driving" 92 : 103343-, 2021

    16 Alrefaie, M. T., "In a heart beat: Using driver's physiological changes to determine the quality of a takeover in highly automated vehicles" 131 : 180-190, 2019

    17 Köhn, T., "Improving take-over quality in automated driving by interrupting non-driving tasks" 510-517, 2019

    18 Azevedo-Sa, H., "How internal and external risks affect the relationships between trust and driver behavior in automated driving systems" 123 : 102973-, 2021

    19 Baek, S. J., "How do humans respond when automated vehicles request an immediate vehicle control takeover?" 341-345, 2019

    20 Borojeni, S. S., "From reading to driving: priming mobile users for take-over situations in highly automated driving" 1-12, 2018

    21 Lin, R., "Exploring the self-regulation of secondary task engagement in the context of partially automated driving: A pilot study" 64 : 147-160, 2019

    22 Du, N., "Evaluating effects of cognitive load, takeover request lead time, and traffic density on drivers' takeover performance in conditionally automated driving" 66-73, 2020

    23 Ou, Y. K., "Effects of different takeover request interfaces on takeover behavior and performance during conditionally automated driving" 162 : 106425-, 2021

    24 Minhas, S., "Effects of Non-Driving Related Tasks During Self-Driving Mode" 2020

    25 Blommer, M., "Driver brake vs. steer response to sudden forward collision scenario in manual and automated driving modes" 45 : 93-101, 2017

    26 Tanshi, F., "Design of Conditional Driving Automation Variables to Improve Takeover Performance" 52 (52): 170-175, 2019

    27 Wang, J., "Bridging gaps among human, assisted, and automated driving with DVIs: a conceptional experimental study" 20 (20): 2096-2108, 2018

    28 Clark, H., "Age differences in the takeover of vehicle control and engagement in non-driving-related activities in simulated driving with conditional automation" 106 : 468-479, 2017

    29 Kim, H., "A study on the control authority transition characteristics by driver information" IEEE 1562-1563, 2019

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