Objective: The present research compared and contrasted the workload associated with using in-vehicle information systems commonly available in five different automotive original equipment manufacturers (OEMs) with that of CarPlay and Android Auto when used in the same vehicles. Background: A growing trend is to provide access to portable smartphone-based systems (e.g., CarPlay and Android Auto) that support an expansion of various in-vehicle infotainment system features and functions. Method/Results: The study involved on-road testing of 24 participants in each configuration of five vehicles crossed with the three different infotainment systems: the embedded portion of the native OEM systems, CarPlay, and Android Auto. Our analysis found that workload was significantly greater for the embedded portion of the native OEM systems than for CarPlay and Android Auto. The strengths and weaknesses of each CarPlay and Android Auto traded off in such a way that the overall demand associated with using the two systems did not differ. Conclusion: CarPlay and Android Auto provided more functionality and resulted in lower levels of workload than the embedded portion of the native OEM infotainment systems. Application: Potential applications of this research include refinements to CarPlay and Android Auto to address variations in workload as a function of task type, the modality of interaction, and OEM implementation of the system.

Background New automobiles provide a variety of features that allow motorists to perform a plethora of secondary tasks unrelated to the primary task of driving. Despite their ubiquity, surprisingly little is known about how these complex multimodal in-vehicle information systems (IVIS) interactions impact a driver’s workload. Results The current research sought to address three interrelated questions concerning this knowledge gap: (1) Are some task types more impairing than others? (2) Are some modes of interaction more distracting than others? (3) Are IVIS interactions easier to perform in some vehicles than others? Depending on the availability of the IVIS features in each vehicle, our testing involved an assessment of up to four task types (audio entertainment, calling and dialing, text messaging, and navigation) and up to three modes of interaction (e.g., center stack, auditory vocal, and the center console). The data collected from each participant provided a measure of cognitive demand, a measure of visual/manual demand, a subjective workload measure, and a measure of the time it took to complete the different tasks. The research provides empirical evidence that the workload experienced by drivers systematically varied as a function of the different tasks, modes of interaction, and vehicles that we evaluated. Conclusions This objective assessment suggests that many of these IVIS features are too distracting to be enabled while the vehicle is in motion. Greater consideration should be given to what interactions should be available to the driver when the vehicle is in motion rather than to what IVIS features and functions could be available to motorists. Electronic supplementary material The online version of this article (10.1186/s41235-019-0166-3) contains supplementary material, which is available to authorized users.

Despite driver assistance systems being engineered to enhance safety, recent studies show the potential for some of these systems and deficient human–machine interfaces to cause unintended consequences on safety. The NHTSA, the Alliance of Automotive Manufacturers, and the European Commission have all released best practices and human factors guidelines for the development and assessment of function-aspecific interfaces. However, given their broad scope, none of these documents provides a rating and benchmarking tool for assessing design aspects pertinent to a wide spectrum of assistance systems, ranging from rearview cameras to lane keeping assist systems. In this article we detail the development of a scale for assessing the human–machine interfaces of 10 different assistance systems. The scale contains 59 items, developed through multiple iterations, in which a total of 94 distinct assistance systems available on vehicles of different makes and models underwent evaluation. For each system included, we provide a description of its characteristics, a list of items for assessment, and relevant references. Widely accepted industry (ISO, SAE) standards, design guidelines, and assessment methodologies were considered for the development of this scale. The adoption of this scale required at least two evaluators to rate each system against specific assessment items using the following 4-point scale: No Concern (3 points), Minor Concerns (2 points), Serious Concerns (1 point), Not Applicable (0 points). Final ratings resulting from this evaluation will aid evaluators in the benchmarking process, and in determining what specific design aspects of the systems assessed merit further attention.

Operating a motor vehicle is an everyday activity for millions of adults, and for many, it provides a mode of transportation that is critical to daily living. Driving is also one of the riskiest activities that most adults perform on a regular basis. Motor vehicle crashes are the leading cause of accidental injury deaths in the United States (Liu et al., 2015). Driving provides a unique opportunity to examine the attention economy in an everyday context. Some aspects of driving, such as maintaining lane position on predictable sections of the highway, can become relatively automatic, requiring little attention to be performed well (e.g., Medeiros-Ward et al., 2014). By contrast, reacting to unexpected or unpredictable events requires attention for successful driving performance. The objective of this chapter is to explore the relationship between attention economy and driving. We focus primarily on situations in which the performance of a concurrent non driving activity adversely affects driving.Inattentive and distracted driving occurs when a motorist fails to allocate sufficient attention to activities critical to safe driving (Regan et al., 2011;Regan & Strayer, 2014). In many circumstances, this involves diverting attention from driving to a concurrent activity that is unrelated to the safe Copyright American Psychological Association. Not for further distribution. • Strayer et al.operation of the vehicle (e.g., talking or texting on a smartphone). The degree to which driving is altered by a secondary task provides a metric for understanding the relationship between attention and driving (Strayer et al., 2015;. When a driver attempts to perform an activity unrelated to the primary task of driving, the attention allocated to the driving task decreases. Given the limited resources in the attention economy, there is a reciprocal relationship between the attention allocated to the two tasks: As the processing priority of an unrelated activity increases, the allocation of attention to the driving task decreases (e.g., Kahneman, 1973;Navon & Gopher, 1979). SPIDER: A FRAMEWORK FOR UNDERSTANDING DISTRACTED DRIVINGSafe driving requires a detailed awareness of the driving environment, which is often information dense and dynamic. Drivers must create and continuously update a mental model of the driving environment that reflects the current driving situation and contains detailed information about the speed and relative position of other vehicles and pedestrians, their own position within a lane, and many other hazards that may present themselves unexpectedly (Cooper et al., 2013;Lu et al., 2017).Situation awareness in driving depends on several mental processes, including scanning specific areas for indications of threats, predicting potential threats if they are not visible, identifying threats and objects in the driving scenario when they occur, deciding if an action is necessary, and executing appropriate responses-SPIDER for short (Fisher & Strayer, 2014;Strayer & Fisher, 2016). SPIDER comprises an active set of ps...

In-vehicle voice control systems are standard in most new vehicles. However, despite auditory-vocal interaction allowing drivers to keep their hands on the steering wheel and eyes on the forward roadway, recent findings indicate the potential for these systems to increase levels of workload and lead to lengthy interaction times. Although many studies have examined the distraction potential of interacting with in-vehicle voice control systems, more research is needed to understand the relationship between different system design components and workload. In this study, we investigate the role of system delay, system accuracy, and menu depth in determining the overall level of demand and interaction times on eight different 2017 model-year vehicles. Voice system accuracy was measured via playback of a pre-recorded sample of voice commands through a studio monitor mounted near the headrest. Menu depth and system delay were calculated by measuring, respectively, the number of interaction steps and total system processing time required to access common infotainment functions. These measures were validated through linear and multiple regression analyses with workload and task time collected in an on-road study. We found system delay and system accuracy to be significant predictors of task time and subjective measures of workload from the NASA Task Load Index and the Driving Activity Load Index. A In addition to providing valuable information on the role of separate voice control system design components on resulting levels of workload, these results extend past research by generalizing findings to multiple current auditory-vocal systems.