Real-World Connected Vehicle Data, Deep Learning-based Sensing Technologies, and Decision-Making Self-Driving Car Control Algorithms in Autonomous Mobility Systems.

• Author: Welch, Carol

1. Introduction

   A sustainable mobility and urban public transport system articulates the implementation and social acceptance of self-driving cars. (Brovarone et al., 2021) The reliability of autonomous vehicle performance can be reshaped so as to optimize the operation of self-driving cars and of conventional human-driven cars in mixed traffic. (Mahdinia et al., 2021) As connected autonomous vehicle deployment rates strengthen, the steadiness of heterogeneous traffic is enhanced by use of big geospatial data analytics. (Cui et al., 2021)

2. Conceptual Framework and Literature Review

   The governance of movement and parking of self-driving cars across the urban road network configures their assimilation into the metropolitan mobility system. (Brovarone et al., 2021) Autonomous vehicles can catalyze behavioral alterations in tagging along behind human drivers. (Mahdinia et al., 2021) The circulation of autonomous vehicles should be synchronized so as to judiciously manage both access to integrated resources (e.g., crossroads and parking spots) and the carrying out of mobility operations (e.g., platooning and ramp merging). (Mariani et al., 2021) Designing traffic patterns that will accommodate self-driving cars and handle the intrinsic unpredictability necessitates the capacity to inspect the consequences of subsequent systems, while configuring robust transportation infrastructures. (Bucchiarone et al., 2021) An autonomous driving environment demands an incessant flow of information derived from intricate traffic data sets and conjecturing assessments (Lazaroiu, 2013; Majerova et al., 2020) to make essential and instantaneous decisions in undetermined circumstances. (Mushtaq et al., 2021) Autonomous vehicles will entail uncomplicated driving and practically no traffic accidents through big geospatial data analytics. (Shetty et al., 2021)

3. Methodology and Empirical Analysis

   Building my argument by drawing on data collected from AAA, Abraham et al. (2017), ANSYS, Atomik Research, AUVSI, Brookings, CivicScience, Deloitte, EY, HNTB, Ipsos, Kennedys, McKinsey, Perkins Coie, SAE, and Schoettle & Sivak (2014), I performed analyses and made estimates regarding the level of connected and autonomous vehicle adoption. Descriptive statistics of compiled data from the completed surveys were calculated when appropriate.

4. Survey Methods and Materials

   The interviews were conducted online and data were weighted by five variables (age, race/ethnicity, gender, education, and geographic region) using the Census Bureau's American Community Survey to reflect reliably and accurately the demographic composition of the United States. Any survey which did not reach greater than 50% completion was removed from subsequent analysis to ensure quality. The data was weighted in a multistep process that accounts for multiple stages of sampling and nonresponse that occur at different points in the survey process. Test data was populated and analyzed in SPSS to ensure the logic and randomizations were working as intended before launching the survey. To ensure high-quality data, data quality checks were performed to identify any respondents showing clear patterns of satisficing. The cumulative response rate accounting for non-response to the recruitment surveys and attrition is 2.5%. The break-off rate among individuals who logged onto the survey and completed at least one item is 0.2%. Sampling errors and test of statistical significance take into account the effect of weighting. Question wording and practical difficulties in conducting surveys can also introduce error or bias into the findings of opinion polls. The sample weighting was accomplished using an iterative proportional fitting process that simultaneously balanced the distributions of all variables. Stratified sampling methods were used and weights were trimmed not to exceed 3. Average margins of error, at the 95% confidence level, are +/-2%. The design effect for the survey was 1.3. For tabulation purposes, percentage points are rounded to the nearest whole number. The precision of the online polls was measured using a Bayesian credibility interval. Confirmatory factor analysis was employed to test for the reliability and validity of measurement instruments. Descriptive statistical analysis and multivariate inferential tests were undertaken for the survey responses and for the purpose of variable reduction in regression modeling. Multivariate analyses, and not univariate associations with outcomes, are more likely to factor out confounding covariates and more precisely determine the relative significance of individual variables. Independent t-tests for continuous variables or chi-square tests for categorical variables were employed. An Internet-based survey software program was utilized for the delivery and collection of responses. All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. An informed econsent was obtained from individual participants. Study participants were informed clearly about their...