The Moral Decision-Making Capacity of Autonomous Mobility Technologies: Route Planning Algorithms, Simulation Modeling Tools, and Intelligent Traffic Monitoring Systems.

• Author: Pera, Aurel

1. Introduction

   Autonomous vehicles necessitate simulation and virtualization technologies, movement tracking and predictive maintenance tools, and machine learning algorithms. The purpose of my systematic review is to examine the recently published literature on the moral decision-making capacity of autonomous mobility technologies and integrate the insights it configures on route planning algorithms, simulation modeling tools, and intelligent traffic monitoring systems. By analyzing the most recent (2022) and significant (Web of Science, Scopus, and ProQuest) sources, my paper has attempted to prove that motion planning and self-driving car control algorithms, virtual data modeling tools (Androniceanu et al., 2021; Hawkins, 2022; Pop et al., 2021), and environment perception systems enable sustainable urban governance networks. The actuality and novelty of this study are articulated by addressing road environment data, tracking control and spatial cognition algorithms (Andronie et al., 2021a; Kliestik et al., 2020; Pop et al., 2022), and virtual navigation and simulation modeling tools, that is an emerging topic involving much interest. My research problem is whether urban transportation systems leverage autonomous vehicle decision-making algorithms, deep learning-based sensing technologies (Andronie et al., 2021b; Lyons and Lazaroiu, 2020; Popescu et al., 2020), and interactive data visualization tools.

   In this review, prior findings have been cumulated indicating that self-driving cars deploy deep learning object detection technology, geospatial analytics and route planning algorithms, and data simulation tools. The identified gaps advance vehicle and pedestrian detection tools, image processing and motion planning algorithms (Blake and Frajtova Michalikova, 2021; Milward et al., 2019; Popescu et al., 2021), and deep learning and edge intelligence technologies. My main objective is to indicate that autonomous vehicle interaction control software, cloud and edge computing algorithms, and virtual and augmented reality tools (Blazek et al., 2022; Nagy and Lazaroiu, 2022; Valaskova et al., 2022) assist intelligent traffic monitoring systems. This systematic review contributes to the literature on real-time data analytics (Glogovetan et al., 2022; Nica, 2021; Zvarikova et al., 2022), object detection and remote sensing technologies (Hackman and Reindl, 2022; Nica et al., 2022), and autonomous driving algorithms by clarifying that autonomous vehicle routing and navigation develop on sensor data fusion algorithms, urban computing technologies, and data modeling tools.

2. Theoretical Overview of the Main Concepts

   Self-driving cars harness real-time data monitoring and automated sensing tools, computer vision algorithms, and big data-driven urban analytics. Spatio-temporal fusion algorithms, monitoring and sensing technologies, and mapping and navigation tools optimize traffic congestion monitoring systems. Virtual simulation modeling tools, multi-sensor environment data fusion, and deep reinforcement learning and autonomous navigation algorithms further smart transportation systems. The manuscript is organized as following: theoretical overview (section 2), methodology (section 3), predictive control and object tracking algorithms, big geospatial data analytics, and deep learning-based autonomous driving technologies (section 4), autonomous vehicle interaction control software, cloud and edge computing algorithms, and virtual and augmented reality tools (section 5), virtual simulation modeling tools, multi-sensor environment data fusion, and deep reinforcement learning and autonomous navigation algorithms (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 June 2022, I performed a quantitative literature review of the Web of Science, Scopus, and ProQuest databases, with search terms including "moral decision-making" + "autonomous mobility technologies" + "route planning algorithms," "simulation modeling tools," and "intelligent traffic monitoring systems." As I inspected research published in 2022, only 174 articles satisfied the eligibility criteria. By removing controversial findings, outcomes unsubstantiated by replication, too imprecise material, or having similar titles, I decided upon 37, generally empirical, sources (Tables 1 and 2). Data visualization tools: Dimensions (bibliometric mapping) and VOS-viewer (layout algorithms). Reporting quality assessment tool: PRISMA. Methodological quality assessment tools include: AXIS, Dedoose, MMAT, 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] Dedoose analyzed qualitative and mixed methods research. [down arrow] AXIS evaluated the quality of cross-sectional studies. [down arrow] ROBIS assessed the risk of bias in systematic reviews. Note: Table made fron Figure. 4. Predictive Control and Object Tracking Algorithms, Big Geospatial Data Analytics, and Deep Learning-based Autonomous Driving Technologies

   Self-driving car perception systems, obstacle avoidance and trajectory tracking control algorithms, and cloud computing technologies (Dasgupta et al., 2022; Geng et al., 2022; Hakak et al., 2023; Yan et al., 2022) reduce preventable road injuries and optimize vehicular traffic flows. Geospatial mapping and virtual simulation tools, multi-sensor data fusion, and spatial recognition and crash avoidance technologies configure sustainable urban governance networks. Urban transportation systems leverage autonomous vehicle decision-making algorithms, deep learning-based sensing technologies, and interactive data visualization...