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With self-driving vehicles no longer a pipe-dream of science fiction comes the growing pains of the new technology. Tesla Motors is the industry leader in implementing new self-driving technologies. Tesla has used new technology in many ways to improve the human factors of their cars, but there are also design flaws that represent threats to efficiency and safety. This paper details the good, the bad, and the ugly of Tesla’s designs, not as a way to negatively impact Tesla’s reputation, but to point out the potential human factors issues that relate to the rise of self-driving cars in general. While the future for autonomous cars has never seemed so promising, it is abundantly clear that we are nowhere near a reality in which the human does not need to be considered.

This paper presents an original method to create optimized color differences between each step of a CIELAB perceptually uniform color gradient. The relevant equations for the necessary color space transformations are discussed, along with the procedure to determine and optimize the color differences between each gradient step. A specific implementation for perceptually uniform color gradients is discussed for safe terrain clearance in manned aviation during a degraded visual environment.

Several methodologies in user centered research lead to the collection of large amounts of comments about a product or system. The growth of social media research has led to the development of sentiment analysis algorithms that computationally analyze the meaning of text. This paper utilized the Valence Aware Dictionary for sEntiment Reasoning (VADER) sentiment analysis technique to assess comments from a user centered design study for a rotorcraft degraded visual environment mitigation system. The sentiment analysis findings mirror results from the other measures of the user centered design study. This paper supports the use of sentiment analysis for large volumes of comment data from user centered design studies.