Technologies of Transit and Mobile Values Implemented in Artificial Intelligence Algorithms that Control Fully Autonomous Driving Vehicles Caitlin McGinnis.

• Author: McGinnis, Caitlin

1. Introduction

   Autonomous cars may bring about diminished stress by apparently being able to operate more effectively than human drivers and consequently eradicate collisions, removing supervision from the driver and transferring it to the individual who determine the stopping place, but not the itinerary. (Bassett and Jones, 2019) Autonomous vehicles may statistically operate more cautiously than human drivers, but individuals may not be ready to inhabit a society in which nonperformance and deprivation seem, from a community-based view, to be arbitrary and strict relative to the corresponding requirements of human failures. (Hancock et al., 2019)

2. Conceptual Framework and Literature Review

   There is a restlessness between the public's concern in unrestrictedly assessing the unpredictability of self-driving cars and the value automobile manufacturers obtain from regulating the communication of data. (Mattioli, 2018) Autonomous vehicle technologies relocate, at a certain level, human supervision of cars to software entailing digital algorithms, shaping how human transportabilities are caused to undergo surveillance and how the latter can be organized. Networked autonomous technologies can monitor and handle who and what is being transferred and where, when and with what recurrence such actions are occurring. Because of the manner autonomous technologies depend on algorithms, such surveillance and decision-making may display a more obscured and dispersed form. (Bissell et al., 2018) As cars eventually are more linked to their external setting, the volume of attacks rises and the risk of weaknesses being capitalized on intensifies. (Sheehan et al., 2019)

3. Methodology and Empirical Analysis

   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.

4. Results and...