인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
DBpia AI
AI 에이전트
AI 아이디어
AI 리더
AI 뷰어
Citeasy
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지원사업
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커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
피인용 0
초록·키워드
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.
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.
본문·목차
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UCI(KEPA) : I410-ECN-0101-2023-530-000455539
