Artificial Moral Agents in Big Data-driven Transportation Systems: Autonomous Vehicle Perception Sensors, Virtual Simulation Algorithms, and Geospatial Mapping Tools.

• Author: Perkins, James

1. Introduction

   Algorithm-driven sensing devices, spatial data visualization tools, and predictive urban analytics shape autonomous mobility systems. The purpose of my systematic review is to examine the recently published literature on artificial moral agents in big data-driven transportation systems and integrate the insights it configures on autonomous vehicle perception sensors, virtual simulation algorithms, and geospatial mapping 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 integrate data processing technologies (Benus et al., 2022; Nica et al., 2021; Rogers and Zvarikova, 2021), spatial cognition and object localization algorithms (Lazaroiu et al., 2022; Popescu et al., 2020a; Zauskova et al., 2022), and mapping and navigation tools. The actuality and novelty of this study are articulated by addressing autonomous vehicle driving algorithms, behavior tracking and virtual data modeling tools (Hackman and Reindl, 2022; Pocol et al., 2022; Rowland, 2022), and image recognition and urban sensing technologies (Johnson and Nica, 2021; Podosek et al., 2022; Scott et al., 2020), that is an emerging topic involving much interest. My research problem is whether simulation and virtualization technologies, real-time image processing tools, and autonomous vehicle perception sensors curtail crash recurrence.

   In this review, prior findings have been cumulated indicating that autonomous driving algorithms harness data-driven planning and deep learning technologies, digital mapping tools, and mobile wireless sensor networks. The identified gaps advance perception sensor modeling and optimal trajectory planning tools (Andronie et al., 2021; Nica and Stehel, 2021; Popescu et al., 2020b), predictive control algorithms, and urban mobility data analytics. My main objective is to indicate that self-driving cars necessitate connected vehicle data, machine learning and object recognition algorithms, and environment perception systems. This systematic review contributes to the literature on deep learning object detection and autonomous driving technologies, Internet of Things connected devices and sensing infrastructures (Kral et al., 2022; Poliak et al., 2021; Vatamanescu et al., 2020), and visual recognition tools by clarifying that autonomous vehicles integrate interconnected sensor networks, spatial data modeling and real-time image process ing tools (Krizanova et al., 2019; Pop et al., 2021; Wallace and Lazaroiu, 2021), and deep and machine learning algorithms.

2. Theoretical Overview of the Main Concepts

   Self-driving cars deploy environment mapping and collision avoidance algorithms, vehicle and lane detection tools, and spatial computing technologies. Route detection and path planning algorithms, cognitive wireless networks, and modeling and simulation tools optimize big data-driven transportation systems. Sustainable urban monitoring systems necessitate cognitive data fusion techniques, remote sensor networks, and automated simulation modeling tools. The manuscript is organized as following: theoretical overview (section 2), methodology (section 3), connected vehicle data, machine learning and object recognition algorithms, and environment perception systems (section 4), connected car data, deep convolutional neural networks, and virtual mapping and image processing tools (section 5), simulation and virtualization technologies, real-time image processing tools, and autonomous vehicle perception sensors (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, I performed a quantitative literature review of the Web of Science, Scopus, and ProQuest databases, with search terms including "artificial moral agents" + "big data-driven transportation systems" + "autonomous vehicle perception sensors," "virtual simulation algorithms," and "geospatial mapping tools." As I inspected research published in 2022, only 184 articles satisfied the eligibility criteria. By eliminating controversial findings, outcomes unsubstantiated by replication, too imprecise material, or having similar titles, I decided upon 33, generally 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: AXIS, Dedoose, Distiller SR, and MMAT (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] 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] Dedoose analyzed qualitative and mixed methods research. [down arrow] AXIS evaluated the quality of cross-sectional studies. [down arrow] AMSTAR evaluated the methodological quality of systematic reviews. Note: Table made from Figure. 4. Connected Vehicle Data, Machine Learning and Object Recognition Algorithms, and Environment Perception Systems

   Ambient sound recognition software, monitoring and sensing technologies, and data mining and mobility simulation tools (Alsghan et al., 2022; Dasgupta et al., 2022; Su et al., 2022; Tang et al., 2022) enable vehicle and pedestrian detection and enhance road safety. Route detection and path planning algorithms, cognitive wireless networks, and modeling and simulation tools optimize big data-driven transportation systems. Virtual simulation and computer vision algorithms, spatial data modeling tools, and image recognition software configure traffic management systems.

   Augmented and virtual technologies, digital mapping and...