Should I take over? Does system knowledge help drivers in making take-over decisions while driving a partially automated car?

Boelhouwer, Anika; Beukel, Arie Paul van den; Voort, Mascha C. van der; Martens, Marieke

doi:10.1016/j.trf.2018.11.016

Cited by 48 publications

(22 citation statements)

References 35 publications

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“…It was decided not to include a knowledge test to determine the participants' explicit knowledge about the automated systems. In our previous studies [22], we found that a good score on the initial knowledge test did not predict actual use of the automation in the driving simulator study.…”

Section: Variablesmentioning

confidence: 68%

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Supporting Drivers of Partially Automated Cars through an Adaptive Digital In-Car Tutor

et al. 2020

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Drivers struggle to understand how, and when, to safely use their cars’ complex automated functions. Training is necessary but costly and time consuming. A Digital In-Car Tutor (DIT) is proposed to support drivers in learning about, and trying out, their car automation during regular drives. During this driving simulator study, we investigated the effects of a DIT prototype on appropriate automation use and take-over quality. The study had three sessions, each containing multiple driving scenarios. Participants needed to use the automation when they thought that it was safe, and turn it off if it was not. The control group read an information brochure before driving, while the experiment group received the DIT during the first driving session. DIT users showed more correct automation use and a better take-over quality during the first driving session. The DIT especially reduced inappropriate reliance behaviour throughout all sessions. Users of the DIT did show some under-trust during the last driving session. Overall, the concept of a DIT shows potential as a low-cost and time-saving solution for safe guided learning in partially automated cars.

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Section: Variablesmentioning

confidence: 68%

“…The first one is to stimulate the use of owners' manuals. However, not only are these usually long and complicated, studies suggest that practise is required to fully support safe automation use [22][23][24]. Driving simulators in particular allow drivers to practise with rare but critical driving situations [25][26][27].…”

Section: Introductionmentioning

confidence: 99%

Supporting Drivers of Partially Automated Cars through an Adaptive Digital In-Car Tutor

et al. 2020

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“…While simulators have uses, they may not necessarily prepare drivers for an automated driving task. 'Learning by doing' is essential (Boelhouwer et al, 2019) and drivers require training strategies to support them during handover situations. Drivers who are trained in 'real-life' AV may benefit from improved skills, knowledge and safer automated-to manual-recovery, as their driving experience provides a more transparent view of how highly automated cars operate (Ebnali et al, 2019), and specialised training improves response time (Payre et al, 2016).…”

Section: How Responsibility Leads To Liabilitymentioning

confidence: 99%

Legal issues in automated vehicles: critically considering the potential role of consent and interactive digital interfaces

Pattinson

Chen

Basu

2020

Humanit Soc Sci Commun

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Some of the first ‘automated’ vehicles to be deployed on our roads will require a system of shared driving with a human driver. While this creates technical and operational challenges, the law must also facilitate such a transfer. One method may be to obtain the driver’s consent to share operational responsibility and to delineate legal responsibility between vehicle and driver in the event of an accident. Consent is a voluntary agreement where an individual is aware of the potential consequences of their consent, including the risks. The driver of a partially automated vehicle must be informed of potential risks before giving consent to share operational responsibility. This paper will refer to the inherent dangers associated with shared operational responsibility, in particular where there has been a request for the driver to take back control from the automated vehicle during the journey. Drivers are likely to experience delay in regaining situational awareness, making such operational transfers hazardous. It is argued that where an interactive digital interface is used to convey information, such as driver responsibility, risk and legal terms, drivers may fail to sufficiently process such communications due to fundamental weaknesses in human–machine interaction. The use of an interactive digital interface alone may be inadequate to effectively communicate information to drivers. If the problems identified are not addressed, it is argued that driver consent may be inconsequential, and fail to facilitate a predicable demarcation of legal responsibility between automated vehicles and drivers. Ongoing research into automated vehicle driver training is considered as part of the preparation required to design driver education to a level whereby drivers may be able to sufficiently understand the responsibilities involved in operating a partially automated vehicle, which has implications for future driver training, licensing and certification.

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“…Gold demonstrated that traffic density increased the complexity of take-over tasks and affected drivers’ performance [ 20 ]. Boelhouwer examined how influential the information manual of an automated driving system was on driver’s take-over decisions [ 21 ]. The study suggested that the information manual provided no support on drivers’ performance and drivers needed to be guided based on individual preferences and specific situations to make correct take-over decisions.…”

Section: Introductionmentioning

confidence: 99%

Take-Over Intention during Conditionally Automated Driving in China: Current Situation and Influencing Factors

Feng

et al. 2021

IJERPH

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Drivers’ take-over intention is important for the design of the automated driving systems and affects the safety of automated driving. This study explored the influence factors on drivers’ take-over intention during conditionally automated driving, examined the correlations among factors through path analysis, and established a take-over intention model. A questionnaire survey was conducted in Hefei, China, and a sample of 277 drivers was obtained. Our study shows that the average take-over intention of those aged under 20 is lower than that of the older age groups. In the positive emotions (PE) scenarios, the take-over intention of aged 31–40 is significantly higher than that of the other age groups. Education and occupation have a significant influence on the take-over intention. The perceived ease of use (PEofU) and perceived usefulness (PU) of automated driving are significantly negatively correlated with drivers’ take-over intention in the road conditions (RC) and climate conditions (CC) scenarios. In addition, through path model analysis, our study shows that trust in the safety of autonomous vehicles (AVs) plays an important role in drivers’ take-over intention. Technology acceptance, risk perception and self-efficacy has indirectly correlated with take-over intention through trust in the safety of AVs. In general, drivers with lower technology acceptance, lower self-efficacy and higher risk perception are less likely to trust automated driving technology and have shown stronger intention to take-over the control of the vehicles.

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Should I take over? Does system knowledge help drivers in making take-over decisions while driving a partially automated car?

Cited by 48 publications

References 35 publications

Supporting Drivers of Partially Automated Cars through an Adaptive Digital In-Car Tutor

Supporting Drivers of Partially Automated Cars through an Adaptive Digital In-Car Tutor

Legal issues in automated vehicles: critically considering the potential role of consent and interactive digital interfaces

Take-Over Intention during Conditionally Automated Driving in China: Current Situation and Influencing Factors

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