Autonomous Vehicle Interaction Control Software and Smart Sustainable Urban Mobility Behaviors in Network Connectivity Systems.

• Author: Gordon, Alison

1. Introduction

   Autonomous vehicles will articulate smart transportation systems (Adams et al., 2021; Kral et al., 2020; Lazaroiu et al., 2020; Suler et al., 2021; Walker et al., 2020) and impact traffic congestion and urban sprawl. (Pettigrew, 2021) Public perception evaluation assists in better comprehending the acceptance of self-driving cars and clarifying likely manners of straightening out public concerns. (Rahman et al., 2021) Users are chiefly interested in the ability of autonomous vehicles to proceed through hazardous situations (risk barriers) and in the decreased satisfaction of driving and reduced feelings of freedom (usage barriers) related to self-driving cars. (Casidy et al., 2021)

2. Conceptual Framework and Literature Review

   Precise trajectory prediction of the other road users facilitates diminished risk path planning in readiness for self-driving cars. (Wang et al., 2021) Deep learning excels in object detection and categorization for autonomous vehicles. (Xiong et al., 2021) The behavior planning methods predict the actions of road users and judiciously determine the safest operation for self-driving cars. (Sharma et al., 2021) Autonomous vehicles will optimize travel safety and mobility. (Jing et al., 2021) A connected autonomous vehicle network operates on a certain spatial scope configuring an environment where traffic data is transmitted and directions are disseminated (Dawson, 2021; Krizanova et al., 2019; Lazaroiu et al., 2020; Taylor et al., 2020) for monitoring the movements of self-driving cars. High-level interaction among autonomous vehicles assisted by concerted planning and supervision of their behaviors (Lazaroiu et al., 2019; Popescu Ljungholm and Olah, 2020; Tucker, 2021; Wade et al., 2021) can relevantly improve the safety and mobility effectiveness of self-driving car operations. (Chen et al., 2021) The suitable design of decision-making processes for self-driving cars should encompass dynamical option of driving velocity and route alternatives on a transportation network. (Huang et al., 2021) Trust in self-driving car performance to a limited extent mediates the link between perceived autonomous vehicle performance risk and behavioral intention. (Waung et al., 2021)

3. Methodology and Empirical Analysis

   Using and replicating data from ANSYS, Atomik Research, AUVSI, Cap-gemini, CBS Interactive, Deloitte, Ipsos, Kennedys, McKinsey, Perkins Coie, Pew Research Center, Schoettle & Sivak (2014), TechRepublic, and ZDNet, I performed analyses and made estimates regarding subjective trust and reliance behavior in autonomous driving. 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...