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The System Usability Scale (SUS) is used across industries, to evaluate a product’s ease of use. As the automotive industry increases its digital footprint, the SUS has found its application as a simple and reliable assessment of various in-vehicle human machine interfaces. These evaluations cover a broad scope and it is important to design studies with participant fatigue, study time, and study cost in mind. Reducing the number of items in the SUS questionnaire could save researchers time and resources. The SUS is a ten-item questionnaire that can measure usability and learnability, depending on the system. These ten questions are highly correlated to each other suggesting the SUS score can be determined with fewer items. Thus, the focus of this paper is two-fold: using principal component analysis (PCA) to determine the dimensionality of SUS and using this finding to reduce variables and build a regression equation for SUS scores for in-vehicle human machine interfaces.

There is a strong business case for automotive interfaces to undergo usability testing throughout their product development lifecycle. System Usability Scale (SUS) is a simple and standard measure of usability. To meet the timing needs for product development, usability testing needs to be performed in a quick, cost effective manner. Hence the required sample size of participants for a usability study is one of the critical factors. To determine an acceptable sample size, a Monte Carlo simulation using SUS scores from eleven different in-vehicle automotive interface usability studies was used to create 500,000 subsamples of different sample sizes. The percentage of subsamples with mean scores within the confidence interval of the population mean was calculated. At a subsample size of thirty-five, 95% of the subsamples have a mean SUS score within the 95% confidence interval of the population mean.

There is a strong business case for automotive companies to improve by understanding what consumers want, like and dislike. Various aspects of ergonomics such as reach, visibility, usability, feel are dependent on measuring consumer’s ability, opinions and satisfaction. Rating scales (such as adjective, continuous, logarithmic, etc.) are used to measure these complex attitudes. It is essential the correct rating scale and appropriate analysis methods are used to capture these attitudes. Previous psychology research has been conducted on the performance of different rating scales. This ratings scale research focused on scales and their reliability and validity for various applications. This paper will summarize past research, discuss the use of rating scales specific to vehicle ergonomics, and analyze the results of an automotive interface study that correlates the seven-point adjective rating scale to the system usability score (SUS).

Measurement of usability with the System Usability Scale (SUS) is successfully applied to products in many industries. The benefit of any measurement scale, however, is limited by the repeatability of the associated testing process. For SUS, these factors can include sample size, study protocol, previous experience, and pre study exposure to the system being tested. Differences in user exposure can influence the usability assessment of interfaces which could affect the validity of SUS scores.

Vehicle target setting is an evolving process based on continually changing internal (management, standards) and external (competitive and legal) requirements. In addition to evolving requirements, the process for establishing and documenting targets may not be clear. The objective of this paper is to detail the overall process of target setting, the critical factors to consider, and key definitions for each stage of the process. It will describe the complete process from early competitive benchmarking to final verification testing. Setting targets for a vehicle requires definition and thorough benchmarking of the competition, an understanding of the key attributes used to describe the vehicles' performance, and a clearly defined set of requirements. These requirements will be regulatory, corporate and competitively based and grouped by clearly defined, customer perceived attributes which can be cascaded to specific vehicle systems.