Sustainable and Smart Urban Transport Systems: Sensing and Computing Technologies, Intelligent Vehicular Networks, and Data-driven Automated Decision-Making.

• Author: Peters, Elisabeth
• Position: Report

1. Introduction

   Configuration of cutting-edge autonomous vehicle testing protocols necessitates steady safety data gathering systems. (Ehsani et al., 2020) Sensor devices, processing networks, and fusion algorithms are pivotal in a data fusion system associated with autonomous driving. (Mora et al., 2020) Self-driving cars provide increased safety, energy efficiency, environmental enhancement, and data-driven mobility in smart urbanism. (Zhu et al., 2020) Autonomous vehicles articulate comfort and reasonable behavior throughout the driving process by use of intelligent vehicular networks. (Huang et al., 2020)

2. Conceptual Framework and Literature Review

   Sensing and computing technologies and big data-driven algorithmic decisionmaking should be advanced (Kliestik et al., 2018; Lazaroiu et al., 2017; Trettin et al., 2019) for autonomous vehicle instantaneous identification of road boundaries and extended curbs. (Mora et al., 2020) Mainstream media and social media influence the acceptance and adoption of self-driving cars by use of users' beliefs and perceptions of autonomous vehicles. (Zhu et al., 2020) The implementation of intelligent vehicular networks (Andrei et al., 2020; Kliestik et al., 2020; Lazaroiu et al., 2019; Peters et al., 2020) will shape urban development (Dusmanescu et al., 2016; Kovacova et al., 2019; Mircica, 2019; Popescu et al., 2019), resulting in more coherent road use and decreased demand for parking spaces. (Legene et al., 2020) The development of connected and automated vehicles have altered the urban transport systems. (Qu et al., 2020) Connected and networked driving is instrumental in transportation efficiency as road transport is a crucial process in which crashes may lead to deaths and injuries. (Hancock et al., 2020) The detection of pedestrians and other road users constitutes a challenge in the design of self-driving cars. (Song et al., 2020)

3. Methodology and Empirical Analysis

   Building my argument by drawing on data collected from ANSYS, Atomik Research, AUVSI, Axios, BCG, Brookings, Capgemini, Ipsos, McKinsey, and Perkins Coie, I performed analyses and made estimates regarding sensing and computing technologies, intelligent vehicular networks, and data-driven automated decision-making. Data collected from 4,800 respondents are tested against the research model by using structural equation modeling.

4. Results and Discussion

   Connected and autonomous transport systems may thoroughly alter the automotive sector and advance decision-making self-driving car control algorithms which are required to intensify sociotechnical shifts to data-driven sustainable transportation. (Mora et al., 2020) The belief in an individual's capability to harness an autonomous vehicle is a pivotal predictor of self-driving car perception and behavior intention in interconnected sensor networks. (Zhu et al., 2020) Urban mobility technologies should prioritize the safety of users at risk while amplifying traffic flow in network connectivity systems. (El Hamdani et al., 2020) Seniors residing in both urban and peripheral regions may take advantage of the convenience of autonomous vehicles, leading to expanded social sustainability and inclusion in elderly groups. (Faber and van Lierop, 2020) (Tables 1-13)

   Self-driving cars may redefine smart mobility, decrease the societal burden of vehicle collisions and enhance health imbalances. (Ehsani et al., 2020) Connected and autonomous transport systems will bring about the wide-ranging technological advancements needed to configure environmentally sustainable traffic systems. (Mora et al., 2020) Consolidating human-machine network and providing early support and concrete experiences of self-driving cars will assist in increasing acceptance and adoption by potential users. (Zhu et al., 2020) A large-scale remote monitoring system for autonomous vehicles may optimize safety, further self-driving car deployment, and furnish a source of employment. (Hampshire et al., 2020) Autonomous vehicles will decrease traffic jam as a result of enhanced efficiency (Ferdman, 2020) and networking with Internet of Things will supply road safety and security. (Lal et al., 2020) Deep learning-based sensing and computing technologies can monitor self-driving cars and other road users, detect possible overlaps, and harness big data-driven algorithmic decision-making expediently throughout networked transport systems. (Xing et al., 2020)

   The large-scale technological advancements...