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- Ahlström, Christer, 1977-, et al.
(författare)
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Road Vehicle Automation and Its Effects on Fatigue, Sleep, Rest, and Recuperation
- 2023. - 1
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Ingår i: The Handbook of Fatigue Management in Transportation. - Boca Raton : CRC Press. - 9781003213154
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Bokkapitel (övrigt vetenskapligt/konstnärligt)abstract
- Assisted and automated driving brings new challenges and opportunities when it comes to driver fatigue. With lower levels of vehicle automation, driver monotony and boredom in combination with demanding attentive monitoring leads to higher levels of fatigue, especially during the night when the sleep pressure is high. With higher levels of vehicle automation, when the driver is not required to continuously monitor the roadway and the automation system, task-related fatigue can be counteracted by engaging in non-driving-related activities. Finally, with the highest levels of vehicle automation, it may even become possible for drivers to sleep while on the move. Aside from making it possible for private car drivers to take strategic naps during a drive, this also opens up the possibility for more flexible, risk management-based, hour of service regulations for professional drivers. This chapter summarises the current state of the art on how assisted and piloted driving affects driver fatigue and how automation may facilitate recovery and recuperation from fatigue while on the move. It also covers how automated functions will impact driver monitoring systems and how new ways of counteracting driver fatigue may arise when automation is available.
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| 2. |
- Klein, Olivier, et al.
(författare)
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A Practical Guide for Transparency in Psychological Science
- 2018
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Ingår i: Collabra: Psychology. - : University of California Press. - 2474-7394. ; 4:1
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Forskningsöversikt (refereegranskat)abstract
- The credibility of scientific claims depends upon the transparency of the research products upon which they are based (e.g., study protocols, data, materials, and analysis scripts). As psychology navigates a period of unprecedented introspection, user-friendly tools and services that support open science have flourished. However, the plethora of decisions and choices involved can be bewildering. Here we provide a practical guide to help researchers navigate the process of preparing and sharing the products of their research (e.g., choosing a repository, preparing their research products for sharing, structuring folders, etc.). Being an open scientist means adopting a few straightforward research management practices, which lead to less error prone, reproducible research workflows. Further, this adoption can be piecemeal – each incremental step towards complete transparency adds positive value. Transparent research practices not only improve the efficiency of individual researchers, they enhance the credibility of the knowledge generated by the scientific community.
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| 3. |
- Silberzahn, Raphael, et al.
(författare)
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Many analysts, one dataset : Making transparent how variations in analytical choices affect results
- 2018
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Ingår i: Advances in Methods and Practices in Psychological Science. - : Sage Publications. - 2515-2459 .- 2515-2467. ; 1:3, s. 337-356
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Tidskriftsartikel (refereegranskat)abstract
- Twenty-nine teams involving 61 analysts used the same dataset to address the same research question: whether soccer referees are more likely to give red cards to dark skin toned players than light skin toned players. Analytic approaches varied widely across teams, and estimated effect sizes ranged from 0.89 to 2.93 in odds ratio units, with a median of 1.31. Twenty teams (69%) found a statistically significant positive effect and nine teams (31%) observed a non-significant relationship. Overall 29 differentanalyses used 21 unique combinations of covariates. We found that neither analysts' prior beliefs about the effect, nor their level of expertise, nor peer-reviewed quality of analysis readily explained variation in analysis outcomes. This suggests that significant variation in the results of analyses of complex data may be difficult to avoid, even by experts with honest intentions. Crowdsourcing data analysis, a strategy by which numerous research teams are recruited to simultaneously investigate the same research question, makes transparent how defensible, yet subjective analytic choices influence research results.
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