Context adaptable driver information – Or, what do whom need and want when?

Davidsson, Staffan; Alm, Håkan

doi:10.1016/j.apergo.2013.12.004

Cited by 17 publications

(12 citation statements)

References 3 publications

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“…This study detected that changes in workload make a difference in the likelihood of engagement. Certain mobile phone tasks could be blocked or made more compatible with driving using context-aware technology (see Davidsson & Alm, 2014 for a definition of context adaptable driver information). Future research in human-system integration is therefore necessary.…”

Section: Implications For Road Safetymentioning

confidence: 99%

Should I Text or Call Here? A Situation‐Based Analysis of Drivers’ Perceived Likelihood of Engaging in Mobile Phone Multitasking

et al. 2018

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This study investigated how situational characteristics typically encountered in the transport system influence drivers' perceived likelihood of engaging in mobile phone multitasking. The impacts of mobile phone tasks, perceived environmental complexity/risk, and drivers' individual differences were evaluated as relevant individual predictors within the behavioral adaptation framework. An innovative questionnaire, which includes randomized textual and visual scenarios, was administered to collect data from a sample of 447 drivers in South East Queensland-Australia (66% females; n = 296). The likelihood of engaging in a mobile phone task across various scenarios was modeled by a random parameters ordered probit model. Results indicated that drivers who are female, are frequent users of phones for texting/answering calls, have less favorable attitudes towards safety, and are highly disinhibited were more likely to report stronger intentions of engaging in mobile phone multitasking. However, more years with a valid driving license, self-efficacy toward self-regulation in demanding traffic conditions and police enforcement, texting tasks, and demanding traffic conditions were negatively related to self-reported likelihood of mobile phone multitasking. The unobserved heterogeneity warned of riskier groups among female drivers and participants who need a lot of convincing to believe that multitasking while driving is dangerous. This research concludes that behavioral adaptation theory is a robust framework explaining self-regulation of distracted drivers.

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Section: Implications For Road Safetymentioning

confidence: 99%

Should I Text or Call Here? A Situation‐Based Analysis of Drivers’ Perceived Likelihood of Engaging in Mobile Phone Multitasking

et al. 2018

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“…With the greater use of LCD displays within vehicles, future development could be influenced by knowledge of current context, supported by vehicle information data, including speed and on-board GPS and by the potential of V2X communication. The push for contextdriven information has been supported within previous work exploring drivers' information desires (Davidsson and Alm 2014). Across a series of interviews with 33 drivers, Davidsson and Alm (2014) found that information which drivers ranked as highly important in one context was not required in a different context.…”

Section: Control Task Analysismentioning

confidence: 88%

Driving towards a greener future: an application of cognitive work analysis to promote fuel-efficient driving

Stanton

Allison

2019

Cogn Tech Work

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Driving is a daily encountered task for many. Unlike the majority of life's daily hassles, however, the act of driving has significant environmental repercussions. Supporting the development of environmentally conscious driving techniques and developing tools and interfaces to reduce the environmental impact of driving is warranted to minimise the negative impact of these actions. The current paper documents the development of a complete cognitive work analysis (CWA) to support environmentally conscious driving. The paper proposes that the use of the CWA approach-enabled examination of the fuelefficient driving task and consideration of the role numerous objects, agents and skills can play in facilitating fuel-efficient driving. In addition to the traditional CWA process, this paper shows how the revealed finding can be used as the basis for developing specifications that can be taken forward to allow for the development of novel in-vehicle interfaces to support fuel-efficient driving.

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“…Banks and Stanton (2016) used interviews to investigate how well participants understood the automation and the information provided. Moreover, Davidsson and Alm (2014) have proposed a new method for investigating what type of information drivers need and when.…”

Section: Mental Model Information and Feedbackmentioning

confidence: 99%

Assessment selection in human-automation interaction studies: The Failure-GAM 2 E and review of assessment methods for highly automated driving

Grane

2018

Applied Ergonomics

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Highly automated driving will change driver's behavioural patterns. Traditional methods used for assessing manual driving will only be applicable for the parts of human-automation interaction where the driver intervenes such as in hand-over and take-over situations. Therefore, driver behaviour assessment will need to adapt to the new driving scenarios. This paper aims at simplifying the process of selecting appropriate assessment methods. Thirty-five papers were reviewed to examine potential and relevant methods. The review showed that many studies still relies on traditional driving assessment methods. A new method, the Failure-GAME model, with purpose to aid assessment selection when planning a study, is proposed and exemplified in the paper. Failure-GAME includes a systematic step-by-step procedure defining the situation, failures (Failure), goals (G), actions (A), subjective methods (M), objective methods (M) and equipment (E). The use of Failure-GAME in a study example resulted in a well-reasoned assessment plan, a new way of measuring trust through feet movements and a proposed Optimal Risk Management Model. Failure-GAME and the Optimal Risk Management Model are believed to support the planning process for research studies in the field of human-automation interaction.

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Context adaptable driver information – Or, what do whom need and want when?

Cited by 17 publications

References 3 publications

Should I Text or Call Here? A Situation‐Based Analysis of Drivers’ Perceived Likelihood of Engaging in Mobile Phone Multitasking

Should I Text or Call Here? A Situation‐Based Analysis of Drivers’ Perceived Likelihood of Engaging in Mobile Phone Multitasking

Driving towards a greener future: an application of cognitive work analysis to promote fuel-efficient driving

Assessment selection in human-automation interaction studies: The Failure-GAM 2 E and review of assessment methods for highly automated driving

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