Smart Transportation Systems: Sustainable Mobilities, Autonomous Vehicle Decision-Making Algorithms, and Networked Driverless Technologies.

• Author: Nelson, Anthony
• Position: Report

1. Introduction

   Confidence and subjective norms shape individuals' perception of trust in autonomous vehicles. (Du et al., 2021) Governments and manufacturers of self-driving cars should decide how to program autonomous vehicles to make a moral judgment in circumstances where ethical interpretation is needed. (Yokoi and Nakayachi, 2020) Big geospatial data are instrumental for autonomous vehicles and smart transportation applications. (Wang et al., 2020a) Self-driving car systems may articulate a safer, more coherent, sustainable, and beneficial traffic ecosystem. (Utesch et al., 2020)

2. Conceptual Framework and Literature Review

   Confidence, subjective norms, and trust (Davis, 2020; Kliestik et al., 2018; Lazaroiu et al., 2019; Popescu et al., 2017a, b; Sheller, 2019) considerably influence acceptance and adoption of autonomous vehicles. (Du et al., 2021) If shared moral belief significantly shapes trust in self-driving cars, governments and manufacturers should consider whether autonomous vehicles act similarly in relation to users. (Yokoi and Nakayachi, 2020) Self-driving cars can shield users from the accountability for bringing injury to a pedestrian. (Gill, 2020) Technology-identity issues result in users' deliberate avoidance to autonomous vehicle penetration. (Wang et al., 2020b) Collecting reactions from users and grasping acceptability thresholds (Dusmanescu et al., 2016; Kliestik et al., 2020; Lazaroiu et al., 2020a, b; Popescu et al., 2018; Svabova et al., 2020) are pivotal to the large-scale harnessing of self-driving cars, while understanding public perception of intelligent vehicular networks is decisive for policymaking. (Das et al., 2020) Deep neural networks succeed in detecting objects in autonomous driving sensor data, articulating their decision logic by training in favor of coherent programming. (Utesch et al., 2020) The upsides and fashionableness of self-driving cars result in a breakthrough in the personal mobility and transportation sectors. (Yuen et al., 2020)

3. Methodology and Empirical Analysis

   Building my argument by drawing on data collected from AAA, Abraham et al. (2017), AUDI AG, Brookings, Capgemini, CivicScience, Ipsos, J.D. Power, McKinsey, Pew Research Center, and Schoettle & Sivak (2014), I performed analyses and made estimates regarding sustainable mobilities, autonomous vehicle decision-making algorithms, and networked driverless technologies. Structural equation modeling was used to analyze the collected data.

4. Results and Discussion

   Autonomous vehicle technology has become gradually developed with the advancement of machine learning and big data-driven algorithmic decision-making. (Du et al., 2021) The displays of attitudes are not bound up with the subsequent safe acceptance and adoption of self-driving cars. (Das et al., 2020) Driving is safer by preventing road accidents generated by human error (e.g., driver impairment by inattentiveness or tiredness). (Utesch et al., 2020) While smart mobility technologies may indicate that fewer cars are needed to transport users to and from their routine activities, network connectivity systems may lead to escalated traffic jams or cumulated miles traveled. (Cokyasar and Larson, 2020) (Tables 1-13)

   Autonomous driving systems monitor all features of the fluctuating operations throughout roadway and environmental circumstances. (Du et al., 2021) Shared moral belief constitutes an important component for trust in self-driving cars. (Yokoi and Nakayachi, 2020) Autonomous vehicle systems are designed to assimilate knowledge (Jakimowicz and Rzeczkowski, 2019; Lazaroiu et al., 2017; Popescu Ljungholm, 2019) and necessitate little preplanned algorithms to function (Groener, 2019; Kovacova et al., 2019; Peters et al., 2020; Popescu et al., 2019; Swadzba, 2019), being grasped as purposeful and having a significant perceived self-determination, consequently amplifying the accountability assigned to them and to their manufacturers. (Gill, 2020) The introduction of data-driven mobilities may affect users' self-identity as configured by driving. (Wang et al., 2020b) Autonomous vehicle technologies should be regulated and implemented in concrete roadway conditions. (Das et al., 2020) Chauffeuring is more expedient by relieving the driver of accountability and transitioning the operational function to decision-making self-driving car control...