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Source publication
This paper reports the development of a computer-aided engineering (CAE) software for human machine interface (HMI) designers to predict and benchmark the usability of in-vehicle infotainment systems. At the front end of the software a graphic user interface (GUI) was developed that allows HMI designers to create digital mockups of designs and setu...
Context in source publication
Context 1
... 1: The setup starts with creating digital mockups of the infotainment system designs to be tested. A virtual device could be created by first selecting from pre-defined device types that are common for in-vehicle infotainment systems (e.g., physical panel, touch screen), setting its properties (Figure 2), and adding standard and customizable widgets (e.g., buttons, knobs, texts) onto the device by filling up the widget table (Figure 3). To help create the mockups, the software provides a visualization of the current design. ...
Citations
... There are some unique characteristics of QN-MHP observed from the studies. First, the model was used to quantify cognitive workload (i.e., NASA-TLX dimensions) and situation awareness (based on Situation Awareness Global Assessment Technique (SAGAT)) (Bi et al., 2015;Feng et al., 2017;Feng et al., 2014;Jeong & Liu, 2017b, 2018Rehman et al., 2019;Wu & Liu, 2006;Wu et al., 2012). Second, QN-MHP was used in many studies to measure visual attention allocation (Feng, 2015;Fuller, 2010;Lim et al., 2010;Sanghavi, 2020;Tsimhoni, 2004), which indicates the capability of QN-MHP to model human perception as compared to GOMS and ACT-R. ...
Objective
This study investigated the use of human performance modeling (HPM) approach for prediction of driver behavior and interactions with in-vehicle technology.
Background
HPM has been applied in numerous human factors domains such as surface transportation as it can quantify and predict human performance; however, there has been no integrated literature review for predicting driver behavior and interactions with in-vehicle technology in terms of the characteristics of methods used and variables explored.
Method
A systematic literature review was conducted using Compendex, Web of Science, and Google Scholar. As a result, 100 studies met the inclusion criteria and were reviewed by the authors. Model characteristics and variables were summarized to identify the research gaps and to provide a lookup table to select an appropriate method.
Results
The findings provided information on how to select an appropriate HPM based on a combination of independent and dependent variables. The review also summarized the characteristics, limitations, applications, modeling tools, and theoretical bases of the major HPMs.
Conclusion
The study provided a summary of state-of-the-art on the use of HPM to model driver behavior and use of in-vehicle technology. We provided a table that can assist researchers to find an appropriate modeling approach based on the study independent and dependent variables.
Application
The findings of this study can facilitate the use of HPM in surface transportation and reduce the learning time for researchers especially those with limited modeling background.
... In order to understand the impact of an improved driver experience, it is important to study and investigate the specific details required for each task. It should be possible to assess the impact on the cognitive load of a driver when performing a task, due to an improved infotainment interface design [4]. ...
... But, as seen in the results of the experiment conducted by Feng et al (2014), digital models and well-designed prototypes can be used to test design concepts and properly evaluate the usability of an interface (i.e. an IVIS menu interface) that supports the performance of tertiary tasks. Being labor, time and cost effective, the use of such high-fidelity prototypes ultimately allows the designer/researcher to explore a larger design space and address usability issues at the early stages of the system design process [13]. ...
Automobile manufacturers are constantly trying to keep up with current technology trends and respond to today's societal challenges. Cars have become more than just a means for transportation, they are now a fully embedded technological 'multifunctional living space'. The increased complexity of automotive user interfaces, driven by the need for using consumer electronic devices in cars as well as improving comfort, and the overall user/driver experience, has sparked a range of new research within this field of study. The number of infotainment functions (controlled as tertiary tasks) with which a user can interact while driving has greatly increased, making the primary task of driving even more challenging. In creating a better automotive user experience, automobile manufacturers have unintentionally increased the cognitive load of the driver when they are performing these tertiary tasks. This research study aims to reduce the cognitive load of the driver/user by suggesting novel, simple menu design interfaces, which add design improvements in two function aspects (entertainment and comfort) by implementing visual, hands-free and gestural based interfaces. This research study will add to the body of literature aimed at improving driver safety and driving experience while performing a tertiary task.
... For the finger swipe gesture model, a main operator "Swipe-with-finger" was created using the 3-dimentional Fitts' law regression equation from Cha and Myung (2013). Operators other than "Swipe-with-finger", such as "Lookat", "Reach-with-hand", and "Click-with-finger", were adopted from Feng et al. (2014). In addition, as tasks to be examined, two swipe-related tasks were developed: (1) the "single swiping task" (including only one-time swipe gesture) and (2) the "comprehensive swiping task" (including repeated multiple times of swipe gestures until a specific goal is completed). ...
Although swiping (also called flicking) is one of the commonly used touchscreen gestures, few modeling studies have been conducted. In this paper, a computational model that focuses on touchscreen swipe gestures was developed by extending the QN-MHP (Queuing Network-Model Human Processor) architecture. The model assumed that the swiped-route follows a three-dimensional path. To model the finger swipe gesture, an operator (i.e., “Swipe-with-finger”) for the Queuing Network Cognitive Architecture was developed using an existing regression equation for predicting the finger movement time in 3D space (Cha and Myung, 2013). The model was validated with two corresponding experimental results in the literature. As a result, the swiping times generated by the model were well fit with the human subject data.
This paper describes the development of a computer-aided engineering (CAE) software toolkit for designers of in-vehicle infotainment systems to predict and benchmark the system usability, such as task completion time, eye glance behaviors, and mental workload. A digital driver model was developed based on the task-independent cognitive architecture of QN-MHP (Queuing Network-Model Human Processor). At the front end of the software a graphical user interface (GUI) was developed that allows designers to create digital mockups of the designs and simulate drivers performing secondary tasks while steering a vehicle. To validate the software outputs, an experiment using human drivers was conducted on a fix-based driving simulator with a radio-tuning task as a test case. Three typical in-vehicle infotainment systems that have the function of radio tuning were investigated (a touch screen, physical buttons, and a knob). The results show that the software was able to generate task completion time, total eyes-off-road time, and mental workload estimates that were similar to the empirical data. The software toolkit has the potential to be a supplemental tool for designers to explore a larger design space and address usability issues at the early design stages with lower cost in time and manpower.
