지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2020.6
- 수록면
- 179 - 189 (11page)
- DOI
- 10.5143/JESK.2020.39.3.179
이용수
초록· 키워드
오류제보하기
Objective: The aim of this study is to investigate the effects of information level on takeover performance and workload in conditionally autonomous driving (CAD).
Background: Takeover Request (TOR) is important in conditionally autonomous driving (CAD) while driver must drive in certain situations. A driver receives help from the information given by the car when TOR is given. It is known that specific information helps driver more effectively than simple signals. However, information level may affect the driver"s takeover performance and the workload. So, investigating takeover performance and workload in takeover situation is necessary. This paper focuses on the optimal level of information for takeover performance.
Method: To investigate the effects of information level on takeover performance and workload in CAD, an experiment on the takeover situation using a driving simulator was designed. The required information for takeover was extracted through a prior survey and the information level was distinguished. In the scenarios with six information levels, participants deal with takeovers. During the experiments, takeover performance was measured by hands-on-time and distance to obstacle, and workload was measured by DALI (Driving Activity Load Index) and PCPS (Percent Change of Pupil Size).
Results: Takeover performance was reduced when the level of information was too low or too high. The takeover time measured through hands-on-time showed no significant difference when the level is too low or too high. Takeover performance, measured by the distance to obstacles, decreased and showed a significant difference when the level is too low or too high. For subjective workload, there was no significant difference when the level is too low or too high. Subjective workload was measured by DALI. For objective workload, there was no significant difference. Objective workload was measured by PCPS.
Conclusion: Takeover performance measured by the distance to obstacles tend to decrease when the information level is too low or too high. Therefore, it is important to provide an appropriate level of information in the case of takeovers.
Application: Through this paper, providing an appropriate level of information is more effective for takeover in CAD. Therefore, it will be used to reduce risk of accidents in design of TOR displays in conditionally autonomous driving vehicles in the future.
Background: Takeover Request (TOR) is important in conditionally autonomous driving (CAD) while driver must drive in certain situations. A driver receives help from the information given by the car when TOR is given. It is known that specific information helps driver more effectively than simple signals. However, information level may affect the driver"s takeover performance and the workload. So, investigating takeover performance and workload in takeover situation is necessary. This paper focuses on the optimal level of information for takeover performance.
Method: To investigate the effects of information level on takeover performance and workload in CAD, an experiment on the takeover situation using a driving simulator was designed. The required information for takeover was extracted through a prior survey and the information level was distinguished. In the scenarios with six information levels, participants deal with takeovers. During the experiments, takeover performance was measured by hands-on-time and distance to obstacle, and workload was measured by DALI (Driving Activity Load Index) and PCPS (Percent Change of Pupil Size).
Results: Takeover performance was reduced when the level of information was too low or too high. The takeover time measured through hands-on-time showed no significant difference when the level is too low or too high. Takeover performance, measured by the distance to obstacles, decreased and showed a significant difference when the level is too low or too high. For subjective workload, there was no significant difference when the level is too low or too high. Subjective workload was measured by DALI. For objective workload, there was no significant difference. Objective workload was measured by PCPS.
Conclusion: Takeover performance measured by the distance to obstacles tend to decrease when the information level is too low or too high. Therefore, it is important to provide an appropriate level of information in the case of takeovers.
Application: Through this paper, providing an appropriate level of information is more effective for takeover in CAD. Therefore, it will be used to reduce risk of accidents in design of TOR displays in conditionally autonomous driving vehicles in the future.
목차
1. Introduction
2. Method
3. Results
4. Discussion
5. Conclusion
References
참고문헌 (22)
참고문헌 신청
Cho, H. S. / 2013 / 2010 Traffic Congestion Costs: Estimation and Trend Analysis :1~102
Carsten, O. / 2012 / Control task substitution in semiautomated driving: Does it matter what aspects are automated? / Human Factors 54(5):747~761
Epple, S. / 2018 / The sooner the better: Drivers' reactions to two-step take-over requests in highly automated driving / Proceedings of the Human Factors and Ergonomics Society Annual Meeting 62(1):1883~1887
Forster, Y. / 2017 / Driver compliance to take-over requests with different auditory outputs in conditional automation / Accident Analysis & Prevention 109:18~28
Gold, C. / 2013 / "Take over!" How long does it take to get the driver back into the loop? / Proceedings of the Human Factors and Ergonomics Society Annual Meeting 57(1):1938~1942
Carsten, O. / 2012 / Control task substitution in semiautomated driving: Does it matter what aspects are automated? / Human Factors 54(5):747~761
Epple, S. / 2018 / The sooner the better: Drivers' reactions to two-step take-over requests in highly automated driving / Proceedings of the Human Factors and Ergonomics Society Annual Meeting 62(1):1883~1887
Forster, Y. / 2017 / Driver compliance to take-over requests with different auditory outputs in conditional automation / Accident Analysis & Prevention 109:18~28
Gold, C. / 2013 / "Take over!" How long does it take to get the driver back into the loop? / Proceedings of the Human Factors and Ergonomics Society Annual Meeting 57(1):1938~1942
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