Visual and Cognitive Demands of CarPlay, Android Auto, and Five Native Infotainment Systems

Strayer, David L.; Cooper, Joel M.; McCarty, Madeleine M.; Getty, Douglas; Wheatley, Camille L.; Motzkus, Conner J.; Goethe, Rachel M.; Biondi, Francesco; Horrey, William J.

doi:10.1177/0018720819836575

Cited by 42 publications

(33 citation statements)

References 28 publications

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“…Indeed, if a single stimulus detection task paradigm includes a frequent stimulus, then this could keep drivers engaged in the drive thus reducing the likelihood of observing vigilance decrements. Even at lower frequencies, if a detection task impacts driving performance, this would suggest that it is creating a cognitive state that is more similar to secondary task distracted driving, although others have argued that detection tasks place no additional demand on drivers and do not impact driving performance or vigilance (Strayer et al, 2019).…”

Section: Detection Tasksmentioning

confidence: 99%

Underload on the Road: Measuring Vigilance Decrements During Partially Automated Driving

McWilliams

Ward

2021

Front. Psychol.

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Partially automated vehicle technology is increasingly common on-road. While this technology can provide safety benefits to drivers, it also introduces new concerns about driver attention. In particular, during partially automated driving (PAD), drivers are expected to stay vigilant so they can readily respond to important events in their environment. However, using partially automated vehicles on the highway places drivers in monotonous situations and requires them to do very little. This can place the driver in a state of cognitive underload in which they experience a very small amount of cognitive demand. In this situation, drivers can exhibit vigilance decrements which impact their ability to respond to on-road threats. This is of particular concern in situations when the partially automated vehicle fails to respond to a potentially critical situation and leaves all responsibility to safely navigate to the driver. This paper reviews situations that lead to vigilance decrements and characterizes the different methodologies of measuring driver vigilance during PAD, highlighting their advantages and limitations. Based on our reading of the literature, we summarize several factors future research on vigilance decrements in PAD should consider.

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Section: Detection Tasksmentioning

confidence: 99%

Underload on the Road: Measuring Vigilance Decrements During Partially Automated Driving

McWilliams

Ward

2021

Front. Psychol.

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“…The survey was a 4-page self-administered questionnaire developed in English and translated into Spanish by bilingual DPH staff; cultural and linguistic relevance were pretested prior to finalizing the instrument. The survey questionnaire was divided into three parts: (1) questions about socio-demographic characteristics; (2) questions about participants' driving experiences, including recent accident history in the past 12 months; and (3) questions about the distractions that participants engaged in while driving during the past 30 days.…”

Section: Methodsmentioning

confidence: 99%

“…The estimated number of fatalities from distraction-affected crashes has fluctuated very little during the past 5 years−2,923 in 2013 to 2,935 in 2017. This public safety problem is exacerbated by the increasing sources of distraction behind the wheel, in part attributed to the ubiquity of in-vehicle information systems from emergent technologies (2).…”

Section: Introductionmentioning

confidence: 99%

Driving Distractions Among Public Health Center Clients: A Look at Local Patterns During the Infancy of Distracted Driving Laws in California

Lyu

Jewell

Cloud

et al. 2019

Front. Public Health

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Objective: To provide a baseline of various driving behaviors and to identify opportunities for prevention of distracted driving during the infancy of state laws that prohibited cellphone use while operating a motor vehicle, the 2010–2011 Distracted Driving Survey collected information on multiple distracted driving behaviors from lower-income clients of three designated, multi-purpose public health centers in Los Angeles County. Methods: Descriptive and multivariable negative binomial regression analyses were performed to examine patterns of driving distractions using the Distracted Driving Survey dataset ( n = 1,051). Results: The most common distractions included talking to other passengers ( n = 912, 86.8%); adjusting the radio, MP3, or cassette player ( n = 873, 83.1%); and adjusting other car controls ( n = 838, 79.7%). The median number of distinct distractions per survey participant was 11 (range: 0–32). Factors predicting the number of distinct distractions included being male [incidence rate ratio (IRR): 1.14; 95% confidence interval (CI): 1.06, 1.23], having a lower education (IRR: 0.73; 95% CI: 0.62, 0.84), and having more years of driving experience (IRR: 1.67; 95% CI: 1.33, 2.11). A variety of distractions, including cellphone use and texting, were predictive of increased motor vehicle crashes in the prior 12 months ( p < 0.05). Conclusions: Distracted driving beyond cellphone use and texting were common in the survey sample, suggesting a need for additional public education and more inclusive distracted driving laws that cover these other activity types.

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“…Here, the concept of trust comes from vehicular social networks (VSN) where the relationship is established over a short period for collaboration [6]. Few real-world examples of such social networking for information sharing are CarPlay [7], FourSquare, 1 Life360, 2 KarPooler and UberPool. 3 Similar ideas can help determine a trust factor to identify the credibility of vehicles.…”

Section: Introductionmentioning

confidence: 99%

Blockchain-Based Reputation Management for Task Offloading in Micro-Level Vehicular Fog Network

et al. 2020

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With the widespread adoption of the internet of things (IoT) technologies towards building a smart city, connected devices often offload computation tasks to nearby edge locations (base stations) to reduce overall computation and network delay. However, serving an ever-increasing number of end devices at these traditional edge locations is becoming impossible, subsequently making them fail to deliver the agreed quality of service to all requesting devices. However, the backend cloud data center is available to serve these requests but incurred additional communication delay, thus, unsuitable for delay-sensitive applications. Furthermore, the fact that the underlying network is inherently ad hoc which makes it prone to malicious nodes affecting its overall performance. In this work, we propose a secure fog computing paradigm where roadside units (RSUs) are used to offload tasks to nearby fog vehicles based on repute scores maintained at a distributed blockchain ledger. The experimental results demonstrate a significant performance gain in terms of queuing time, end-to-end delay, and task completion rate when compared to the baseline queuing-based task offloading scheme.

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Visual and Cognitive Demands of CarPlay, Android Auto, and Five Native Infotainment Systems

Cited by 42 publications

References 28 publications

Underload on the Road: Measuring Vigilance Decrements During Partially Automated Driving

Underload on the Road: Measuring Vigilance Decrements During Partially Automated Driving

Driving Distractions Among Public Health Center Clients: A Look at Local Patterns During the Infancy of Distracted Driving Laws in California

Blockchain-Based Reputation Management for Task Offloading in Micro-Level Vehicular Fog Network

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