ABSTRACT. Empirical research provides mixed results regarding technologies of transit and mobile values implemented in artificial intelligence algorithms that control fully autonomous driving vehicles. Using and replicating data from AUVSI, Capgemini Research Institute, Deloitte, Gallup, Ipsos/GenPop, McKinsey & Co., Perkins Coie, and Statista, I performed analyses and made estimates regarding reasons for choosing an autonomous car at no additional cost over a conventional car (improved fuel efficiency, improved safety, convenience/time saved, and prestige), the ways in which the industry has been affected by recent high-profile problems involving autonomous vehicles, and % of consumers who would be comfortable sharing their personal data with traditional car companies, state authorities responsible for road planning and urban development, technology companies, insurance companies, technology companies that are providing software to traditional car companies, tech startups providing driverless solutions, roadway organizations like privately owned tolling booths, surrounding vehicles, tax authorities, and nearby businesses/businesses on their route. Data were analyzed using structural equation modeling.

Keywords: artificial intelligence algorithm; autonomous driving vehicle; mobile value

How to cite: McGinnis, Caitlin (2019). “Technologies of Transit and Mobile Values Implemented in Artificial Intelligence Algorithms that Control Fully Autonomous Driving Vehicles,” Contemporary Readings in Law and Social Justice 11(1): 27–32. doi:10.22381/CRLSJ11120194

Received 7 March 2019 • Received in revised form 6 July 2019
Accepted 8 July 2019 • Available online 15 July 2019

Caitlin McGinnis
This email address is being protected from spambots. You need JavaScript enabled to view it.
The Big Data Analytics Research Unit
at CLI, Stuttgart, Germany

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