Autonomous Driving Perception Algorithms and Urban Mobility Technologies in Smart Transportation Systems.

• Author: Green, Linda

1. Introduction

   Human-driven vehicles consume less fuel and generate decreased emissions for the most part when tagging along behind an autonomous car. (Mahdinia et al., 2021) There is relevant unpredictability as regards autonomous vehicle performance, accommodations by users, and impact on transport and energy systems in smart sustainable urbanism. (Stasinopoulos et al., 2021)

2. Conceptual Framework and Literature Review

   Less speed changes and optimized speed improvements of human drivers when tagging along behind autonomous vehicles may enhance safety. (Mahdinia et al., 2021) How to monitor the interactions of autonomous vehicles with other road users across mixed traffic environments is challenging. (Camara et al., 2021) Cities that aim to adopt self-driving cars throughout their transportation networks should guide their deployment in manners that integrate commuter acceptance patterns, in addition to being environmentally beneficial. (Kontar et al., 2021) Autonomous and connected vehicle technologies may generate significant modifications in travel behavior and transportation network operations. (Hasnat et al., 2021) The performance and soundness of the perception module in an autonomous vehicle constitute the necessary conditions for the effectiveness of the integrated driverless system by use of big geospatial data analytics. (Cui et al., 2020)

3. Methodology and Empirical Analysis

   The data used for this study was obtained and replicated from previous research conducted by Adobe Analytics, ANSYS, Atomik Research, AUVSI, Brookings, Capgemini, Charles Koch Institute, Deloitte, eMarketer, GenPop, Ipsos, Kennedys, McKinsey, Perkins Coie, Pew Research Center, SAE, and Statista. We performed analyses and made estimates regarding sustainable mobility and urban public transport systems. Data collected from 6,400 respondents are tested against the research model. 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 e-consent was obtained from individual participants. Study participants were informed clearly about their freedom to opt out of the study at any point of time without providing justification for doing so.

5. Results and Discussion

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