Autonomous Vehicle Driving Algorithms, Deep Learning-based Sensing Technologies, and Big Geospatial Data Analytics in Smart Sustainable Intelligent Transportation Systems.

• Author: Rowland, Zuzana

1. Introduction

   Road infrastructure, object recognition, and crash avoidance optimization across sensing and navigation systems would decrease perceived risks about riding in autonomous vehicles, in addition to reduced traffic congestion and car accidents, and saved lives. (Gill, 2021) Sensor data collecting, processing, and monitoring systems (Barbu et al, 2021; Kral et al, 2019; Lu et al, 2020; Poliak et al., 2021a, b) assist in improving road traffic safety and reducing fatalities. (Mulder and Vellinga, 2021) Autonomous vehicle driving systems monitor and control car operations both in normal situations and unpredictable conditions. (Erdogan et al, 2021)

2. Conceptual Framework and Literature Review

   Self-driving cars can considerably enhance the quality of traffic by cutting down queue length, the amount of stops, and delay time through real-world connected vehicle data and transportation analytics. (Rezaei and Caulfield, 2021) Connected and autonomous vehicles optimize road user safety and mobility by decreasing roadway injuries and fatalities. (Yang and Fisher, 2021) Adverse weather conditions compromise sensor returns and make the surrounding environment unclear, affecting perception tasks (Andronie et al, 2021a, b; Scott, 2020; Valle, 2021) and thus the safe operation of autonomous vehicles. (Pitropov et al, 2021) Motion control and object recognition reduce road fatalities through machine learning algorithms. (Mulder and Vellinga, 2021) Remote sensors are deployed in environmental monitoring through deep learning algorithms (Kovacova et al., 2018; Lazaroiu et al., 2021; Olssen and Mace, 2021; Taylor, 2021) in real-time object detection and recognition. (Ma, 2021) Massively connected devices leverage mobile data traffic pivotal in collision avoidance, remote surveillance, and collaborative task handling. (Ali et al, 2021) Reliable and accurate visual object tracking in terms of contextual data and spatial variation (Konhausner et al, 2021; Lazaroiu et al, 2017; Mitchell and Krulicky, 2021; Pop et al, 2021) is decisive in traffic monitoring. (Elayaperumal and Joo, 2021)

3. Methodology and Empirical Analysis

   We inspected, used, and replicated survey data from AUVSI, BikePGH, Capgemini, CarGurus, CivicScience, GenPop, Ipsos, KPMG, Management Events, McKinsey, Perkins Coie, Pew Research Center, and Statista, performing analyses and making estimates regarding how motion control and object recognition improve road traffic safety and reduce fatalities by use of sensing and navigation systems and mobile data traffic. Descriptive statistics of compiled data from the completed surveys were calculated when appropriate.

4. Study Design, 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.

   Data sources: AUVSI, BikePGH, Capgemini, CarGurus, CivicScience, GenPop, Ipsos, KPMG, Management Events, McKinsey, Perkins Coie, Pew Research Center, and Statista.

   Study participants: 5,700 individuals provided an informed e-consent.

   This survey employs statistical weighting procedures to clarify deviations in the survey sample from known population features, which is instrumental in correcting for differential survey participation and random variation in samples. All data were interrogated by employing graphical and numeric exploratory data analysis methods. Results are estimates and commonly are dissimilar within a narrow range around the actual value. 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 (e.g., checking for high rates of leaving questions blank). Sampling errors and test of statistical significance take into account the effect of weighting. Question wording and practical difficulties in conducting surveys can 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 cumulative response rate accounting for non-response to the recruitment surveys and attrition is 2.5%.

   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. Addressing a significant knowledge gap in the literature, the research has complied with stringent methodology, reporting, and data analysis requirements.

   Flow diagram of study procedures

5. Statistical Analysis

   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. Descriptive analyses (mean and standard deviations for continuous variables and counts and percentages for categorical variables) were used. Descriptive statistical analysis and multivariate inferential tests were undertaken for the survey responses and for the purpose of variable reduction in regression modeling. The break-off rate among individuals who logged onto the survey and completed at least one item is 0.2%.

   Mean and standard deviation, t-test, exploratory factor analysis, and data normality were inspected using SPSS. To ensure reliability and accuracy of data, participants undergo a rigorous verification process and incoming data goes through a sequence of steps and multiple quality checks. Descriptive and inferential statistics provide a summary of the responses and comparisons among subgroups. AMOS-SEM analyzed the full measurement model and structural model.

   An Internet-based survey software program was utilized for the delivery and collection of responses. Panel research represents a swift method for gathering data recurrently, drawing a sample from a pre-recruited set of respondents. Behavioral datasets have been collected, entered into a spreadsheet, and cutting-edge computational techniques and empirical strategies have been harnessed for analysis. Groundbreaking...