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논문 기본 정보

자료유형
학술저널
저자정보
윤용덕 (고려대학교) 명노해 (고려대학교)
저널정보
대한인간공학회 대한인간공학회지 대한인간공학회지 제38권 제2호
발행연도
2019.4
수록면
61 - 71 (11page)
DOI
10.5143/JESK.2019.38.2.61

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초록· 키워드

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Objective: The aim of this study is to investigate the effects of Takeover Request Lead Time (TORlt) to Driver Workload in conditionally autonomous driving (CAD).
Background: Takeover request (TOR) is important issue in conditionally autonomous driving. A human driver must be ready to take control of the vehicle when TOR is given. However, driver could be underload when autonomous driving lasts for a long time or be overload if NDRT (Non-driving related task) is highly demanding or TOR appears suddenly. Therefore, investigating driver workload in takeover situation is essential. This paper focuses on effects of TORlt to driver workload.
Method: To investigate effects of TORlt to driver workload in CAD, experiment were conducted on the takeover situation through a driving simulator. Experiment were conducted on 7 levels of TORlt (3, 7, 10, 15, 30, 45, 60s) and ghost session. Also, the participants performed NDRT to assume CAD. During the experiments, subjective and physiological workload was measured by DALI (Driving Activity Load Index) and respiration rate respectively.
Results: TORlt has a large effect on driver workload. When TORlt was 3 seconds, the subjective and objective workload was the highest. As TORlt increases, both of workload decreases. For subjective workload, there was a slight increase in workload when TORlt was long, but it was not statistically significant. For physiological workload, the workload continued to decline, but there was no statistically significant difference for longer TORlt In addition, there was a significant positive correlation between subjective workload and physiological workload. Therefore, subjective workload can be replaced by physiological workload in takeover situation.
Conclusion: As TORlt increases, the driver workload decreases, but the difference is not significant if TORlt was long. Also, subjective workload measurement could be replaced by measuring physiological signals.
Application: Through this paper, it has been found that physiological measure could be an alternative for measuring workload in autonomous vehicle. Therefore, in autonomous vehicles, sensors that measure physiological signals will be installed to determine the driver`s workload.

목차

1. Introduction
2. Method
3. Results
4. Discussion
5. Conclusion
References

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UCI(KEPA) : I410-ECN-0101-2019-530-000667038