Autonomous Driving Algorithms and Behaviors, Sensing and Computing Technologies, and Connected Vehicle Data in Smart Transportation Networks.

• Author: Clayton, Elizabeth

1. Introduction

   Self-driving cars may have to make moral decisions involving imminent and unavoidable harm to either the passengers, pedestrians, or other road users, resulting in the need for robustness optimization (Androniceanu, 2021; Kliestik et al., 2021; Kovacova and Lewis, 2021; Poliak et al., 2021) in autonomous vehicle design, possibly considering sensitive socio-demographic data. (Gill, 2021) Deep learning techniques configure the vehicle speed of self-driving cars in extreme weather conditions such as fog and rain situations. (Kim et al, 2021) Autonomous vehicle adoption intention comprises perceived usefulness and ease of use, in addition to social influence and facilitating conditions. (Park et al, 2021)

2. Conceptual Framework and Literature Review

   Self-driving cars will operate exemplarily in decreasing the volume of stops, queue extent, and waiting time. (Rezaei and Caulfield, 2021) Urban mobility services articulate the safety, effectiveness, and viability (Andronie et al., 2021; Kovacova et al, 2020; Lazaroiu et al, 2020; Rowland et al, 2021) of road infrastructures for autonomous vehicles. (Aoyama and Leon, 2021) Autonomous vehicle planning algorithms can optimize road safety by not being subject to human deficiencies (e.g., lapses of attention). (Bazilinskyy et al., 2021) Computer vision assists self-driving cars in object detection. (Abdar et al., 2021) Autonomous driving algorithms are instrumental in sensor and data processing methods as regards obstacle avoidance and route planning. (Kim and Choi, 2021) Self-driving cars and vehicular communication technologies can share sensor data with other road users or infrastructures across the surrounding environments. (Rodriguez-Corbo et al, 2021) Vehicle and pedestrian detection is decisive in autonomous driving technologies by use of deep learning-based sensing technologies. (Chen et al, 2021)

3. Methodology and Empirical Analysis

   Building our argument by drawing on data collected from AAA, AUVSI, CARiD, Deloitte, McKinsey, Perkins Coie, Schoettle & Sivak (2014), Statista, Thomas et al. (2015), and YouGov, we performed analyses and made estimates regarding how autonomous vehicle planning and driving algorithms can optimize road safety, being instrumental in sensor and data processing methods by cutting down crashes and casualties through a massive volume of information that can be shared between self-driving cars and roadside infrastructure. Data collected from 5,900 respondents are tested against the research model. 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: AAA, AUVSI, CARiD, Deloitte, McKinsey, Perkins Coie, Schoettle & Sivak (2014), Statista, Thomas et al. (2015), and YouGov. Study participants: 5,900 individuals provided an informed e-consent.

   Mean and standard deviation, t-test, exploratory factor analysis, and data normality were inspected using SPSS. 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. 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 break-off rate among individuals who logged onto the survey and completed at least one item is 0.2%.

   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

   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. 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.

   All data were interrogated by employing graphical and numeric exploratory data analysis methods. 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 computing systems and databases enable data gathering and...