Urban Mobility Technologies, Algorithm-driven Sensing Devices, and Machine Learning-based Ethical Judgments in a Connected Vehicle Environment.

• Author: Walker, Amanda
• Position: Report

1. Introduction

   User acceptance or adoption is pivotal in forecasting implementation rates, furthering autonomous vehicle embracing, and assisting policymakers and manufacturers. (Yuen et al., 2020) Self-driving cars may alter the manner users are transported. (Cokyasar and Larson, 2020) Groundbreaking driver assistance systems and intelligent vehicular networks harness machine learning technologies (Andrei et al., 2016a, b; Durst, 2019; Nica et al., 2018) to articulate smart connected mobility services. (Fernandez et al., 2020)

2. Conceptual Framework and Literature Review

   Autonomous vehicles are being advanced for mass public deployment. (Karnouskos, 2020) The increasing self-determination of groundbreaking technologies may lead to a perceived loss of control (Cera et al., 2019; Nica et al., 2014; Nica et al., 2019; Popescu et al., 2017a, b), resulting in user resistance to acceptance and adoption of self-driving cars. (Baccarella et al., 2020) Networked driverless technologies should precisely identify the objects on the road by inspecting the captured images. (Wang et al., 2020) Psychological mechanisms shape the decision-making in relation to public evaluation of the admissible risk of autonomous vehicles. (Liu et al., 2020)

3. Methodology and Empirical Analysis

   Building my argument by drawing on data collected from AUDI AG, AUVSI, Brookings, Capgemini, CivicScience, Ipsos, Kennedys, Perkins Coie, and Pew Research Center, we performed analyses and made estimates regarding algorithm-driven sensing devices and machine learning-based ethical judgments in a connected vehicle environment. The data for this research were gathered via an online survey questionnaire and were analyzed through structural equation modeling on a sample of 5,200 respondents.

4. Results and Discussion

   Autonomous vehicles may observe road hazards as effectively as a human driver. (Sultan et al., 2020) System-level stakeholders see networked driverless technologies (Popescu, 2014) as a substitute to private car ownership, but automotive companies perceive it as a complement to the current car-based mobility patterns. (Baumgartinger-Seiringer et al., 2020) (Tables 1-15)

   As accidents may be inevitable, autonomous vehicles will make high-priority decisions shaping human lives. (Karnouskos, 2020) Perceived approachability and perceived convenience are instrumental in clarifying the contingent consequences of novelty seeking and technology anxiety on acceptance and adoption intention of autonomous driving systems. (Baccarella et al., 2020) Collisions involving self-driving cars have cast doubt on safety and decreased public trust in connected and autonomous transport systems. (Ehsani et al., 2020) The paradigms of attitudes and subjective norms (Sion, 2019a, b) may indicate intended use of self-driving cars. (Kaye et al., 2020) Autonomous vehicles, by being adopted on a large scale, may bring about more pollution in opposition to conventional means of mass transit. (Kim et al., 2020) Unsatisfactory lighting enhancement and the governance of commute-period traffic and road users at risk should be optimized by use of deep learning-based sensing and computing technologies for self-driving car-related road safety. (Ye et al., 2020) The wide-ranging technological change generated by intelligent vehicular networks associated with self-driving car-based transport systems may carry out societal functions while satisfying the critical requirement for sustainable transport solutions. (Mora et al., 2020)

   Risk should be decreased and public confidence should be built as self-driving cars are being put into practical use on public roads. (Ehsani et al., 2020) Perceiving and grasping road conditions are pivotal for autonomous vehicle detection and navigation that are determined by the networking between sensors and machine learning-empowered systems. (Mora et al., 2020) Transportation users' perceptions and adoption practices of self-driving cars are related to the hope for significantly diminished road fatalities. (Mason et al., 2020) Mainstream media improve potential users' confidence of fully autonomous vehicles, while social media consolidate subjective norms. (Zhu et al., 2020) Households having high income and constant car buyers tend to accept and adopt connected and autonomous vehicles. (Sharma and Mishra, 2020) The accelerating connected and networked driving of on-road vehicles in the direction of a thoroughly...