The Social Ethics of Autonomous Vehicle Routing and Navigation: Spatial Recognition Technologies, Environment Mapping Algorithms, and Mobility Simulation Tools.

• Author: Barnes, Robert

1. Introduction

   Data and mobility simulation tools, tracking control and predictive modeling algorithms, and ambient sound recognition software decrease motor vehicle collisions and enhance traffic safety. The purpose of my systematic review is to examine the recently published literature on the social ethics of autonomous vehicle routing and navigation and integrate the insights it configures on spatial recognition technologies, environment mapping algorithms, and mobility simulation tools. By analyzing the most recent (2022) and significant (Web of Science, Scopus, and ProQuest) sources, my paper has attempted to prove that autonomous vehicles necessitate computer vision and sensor fusion algorithms, interactive data visualization and behavior tracking tools (Andronie et al., 2021; Nica et al., 2021; Scott et al., 2020), and connected car data. The actuality and novelty of this study are articulated by addressing connected vehicle data, spatial recognition technologies (Gasparin and Schinckus, 2022; Nica et al., 2022; Vatamanescu et al., 2020), and machine learning and computer vision algorithms (Lyons, 2022; Rogers and Zvarikova, 2021), that is an emerging topic involving much interest. My research problem is whether autonomous mobility technologies leverage vehicle and pedestrian detection tools, road environment data, and environment mapping and predictive control algorithms.

   In this review, prior findings have been cumulated indicating that self-driving car control algorithms, remote sensor networks, and environment perception systems further smart urban governance. The identified gaps advance autonomous vehicle planning algorithms, smart mobility technologies (Johnson and Nica, 2021; Poliak et al., 2021; Vatamanescu et al., 2022), and visual object detection-based systems. My main objective is to indicate that visual analytics algorithms, data mining techniques (Kliestik et al., 2022; Pop et al., 2021; Wallace and Lazaroiu, 2021), and Internet of Things connected devices reduce crashes and casualties. This systematic review contributes to the literature on autonomous vehicle interaction control software, cooperative navigation and route planning algorithms, and simulation modeling and image processing tools (Lazaroiu et al., 2017; Popescu et al., 2020; Zauskova et al., 2022) by clarifying that autonomous driving technologies integrate predictive urban analytics, virtual simulation and sensor data processing algorithms (Lazaroiu et al., 2020; Popescu et al., 2022), and optimal trajectory planning tools.

2. Theoretical Overview of the Main Concepts

   Vehicular communication technologies deploy urban cloud data, motion planning and remote sensing algorithms, and cognitive computing systems. Intelligent transport systems develop on object tracking and image processing algorithms, urban perception and planning tools, and cloud computing technologies. Connected autonomous vehicles harness real-time Internet of Things data, spatio-temporal fusion algorithms, and deep learning-based sensing technologies. The manuscript is organized as following: theoretical overview (section 2), methodology (section 3), visual perception and route planning algorithms, multi-sensor data fusion, and crash avoidance and spatial computing technologies (section 4), real-time data analytics, autonomous navigation algorithms, and remote sensing data (section 5), urban sensing and data fusion technologies, spatial cognition and collision avoidance algorithms, and motion planning and virtual navigation tools (section 6), discussion (section 7), synopsis of the main research outcomes (section 8), conclusions (section 9), limitations, implications, and further directions of research (section 10).

3. Methodology

   Throughout May 2022, a quantitative literature review of the Web of Science, Scopus, and ProQuest databases was performed, with search terms including "social ethics" + "autonomous vehicle routing and navigation" + "spatial recognition technologies," "environment mapping algorithms," and "mobility simulation tools." As research published in 2022 was inspected, only 188 articles satisfied the eligibility criteria. By taking out controversial or ambiguous findings (insufficient/irrelevant data), outcomes unsubstantiated by replication, too general material, or studies with nearly identical titles, I selected 35 mainly empirical sources (Tables 1 and 2). Data visualization tools: Dimensions (bibliometric mapping) and VOSviewer (layout algorithms). Reporting quality assessment tool: PRISMA. Methodological quality assessment tools include: AMSTAR, Dedoose, Distiller SR, and SRDR (Figures 1-6).

   Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) guidelines were used that ensure the literature review is comprehensive, transparent, and replicable. The flow diagram, produced by employing a Shiny app, presents the stream of evidence-based collected and processed data through the various steps of a systematic review, designing the amount of identified, included, and removed records, and the justifications for exclusions.

   To ensure compliance with PRISMA guidelines, a citation software was used, and at each stage the inclusion or exclusion of articles was tracked by use of custom spreadsheet. Justification for the removal of ineligible articles was specified during the full-text screening and final selection.

   Figure 6 Screening and quality assessment tools To ensure first-rate standard of evidence, a systematic search of relevant databases including peer-reviewed published journal articles was conducted using predefined search terms, covering a range of research methods and data sources. Reference lists of all relevant sources were manually reviewed for additional relevant citations. [down arrow] Titles of papers and abstracts were screened for suitability and selected full texts were retrieved to establish whether they satisfied the inclusion criteria. All records from each database were evaluated by using data extraction forms. Data covering research aims, participants, study design, and method of each paper were extracted. [down arrow] The inclusion criteria were: (i) articles included in the Web of Science, Scopus, and ProQuest databases, (ii) publication date (2022), (iii) written in English, (iv) being an original empirical research or review article, and (v) particular search terms covered; (i) conference proceedings, (ii) books, and (iii) editorial materials were eliminated from the analysis. [down arrow] SRDR gathered, handled, and analyzed the data for the systematic review, being configured as an archive and tool harnessed in data extraction through transparent, efficient, and reliable quantitative techniques. Elaborate extraction forms can be set up, meeting the needs of research questions and study designs. [down arrow] Distiller SR screened and extracted the collected data. [down arrow] AMSTAR evaluated the methodological quality of systematic reviews. [down arrow] The quality of academic articles was determined and risk of bias was measured by MMAT, that tested content validity and usability of selected studies in terms of screening questions, type of design, corresponding quality criteria, and overall quality score. [down arrow] ROBIS assessed the risk of bias in systematic reviews. [down arrow] AXIS evaluated the quality of cross-sectional studies. [down arrow] Dedoose analyzed qualitative and mixed methods research. Note: Table made from Figure. 4. Visual Perception and Route Planning Algorithms, Multi-Sensor Data Fusion, and Crash Avoidance and Spatial Computing Technologies

   Connected vehicle data, spatial recognition technologies, and machine learning and computer vision algorithms (Geng et al., 2022; Masello et al., 2022; Su et al., 2022; Tang et al., 2022; Zhong et al., 2022) configure smart transportation networks. Autonomous mobility technologies leverage vehicle and pedestrian detection tools, road environment data, and environment mapping and predictive control algorithms...