Algorithm-driven Sensing Devices and Connected Vehicle Data in Smart Transportation Networks.

• Author: Campbell, Ella

1. Introduction

   Self-driving cars will maximize travel performance and mobility, reduce traffic jams and crashes, and upgrade traffic flow. (Ribeiro et al., 2021) To steer without assistance, a vehicle has to localize itself as regards its surrounding driving environment and the networking road users. Various interconnecting cars will harness autonomous driving perception algorithms and deep learning-based sensing technologies. (Hery et al., 2021)

2. Conceptual Framework and Literature Review

   Health monitoring system enables fault identification and inspection, while prognosis system makes possible predictive maintenance and more riskless decisions (Allen, 2020; Lazaroiu et al., 2017; Mihaila, 2017; Sawyer et al., 2020) throughout autonomous vehicle navigation. (Gomes and Wolf, 2021) Intelligent transportation system attempts to handle and straighten out traffic jams, crashes, and parking allocation through smart transportation networks, where self-driving cars are internally networked for message sharing and pivotal decision making (Balica, 2019; Lazaroiu et al., 2019; Pop et al., 2021; Stehel et al., 2021) across time-sensitive applications. (Reebadiya et al., 2021) Networked driverless technologies will optimize the quality of road junction performance (Cohen, 2021; Lazaroiu et al., 2020a, b; Rowland et al., 2021; Zheng, 2020) by use of vehicle-to-infrastructure and infrastructure-to-vehicle networking. (Liu and Fan, 2021) Trajectory planning is instrumental in decision making for self-driving cars. (Xin et al., 2021)

3. Methodology and Empirical Analysis

   Using and replicating data from APA, INRIX, Ipsos, Jones Day, Management Events, Nvidia, and Reuters, we performed analyses and made estimates regarding autonomous vehicle performance and intelligent sensor systems. The results of a study based on data collected from 6,800 respondents provide support for our 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 econsent 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

   Intelligent vehicular networks by use of big geospatial data analytics and transportation analytics may alter the distance road users are disposed to cover in their personal cars to explore remote destinations. (Ribeiro et al., 2021) The public's fearfulness as regards COVID-19 has amplified trust in self-driving cars that can deliver provisions to health centers and communities, and convey tests to clinics and labs. (Zeng et al., 2020) (Tables 1-6)

   Travelers who think that adopting autonomous vehicles throughout a tourism experience constitutes entertainment tend to perceive self-driving cars as providing satisfactory performance and having a negligible degree of risk. (Ribeiro et al., 2021) Autonomous vehicles can reconfigure mobility and find a solution to user-friendliness, performance...