Autonomous Vehicle Ethics in Networked Transport Systems: Spatial Cognition Algorithms, Mobility Data Processing Tools, and Deep Learning-based Sensing Technologies.

• Author: Morley, Nancy

1. Introduction

   Adaptive and dynamic route planning tools, predictive modeling techniques, and signal processing algorithms decrease motor vehicle collisions and preventable road injuries. The purpose of my systematic review is to examine the recently published literature on autonomous vehicle ethics in networked transport systems and integrate the insights it configures on spatial cognition algorithms, mobility data processing tools, and deep learning-based sensing technologies. By analyzing the most recent (2022) and significant (Web of Science, Scopus, and ProQuest) sources, my paper has attempted to prove that monitoring and sensing technologies (Andronie et al., 2021; Nica et al., 2021; Popescu Ljungholm, 2022), automated simulation modeling tools, and sensor-based navigation systems assist sustainable urban governance networks. The actuality and novelty of this study are articulated by addressing geospatial mapping and simulation modeling tools (Blake, 2022; Pocol et al., 2022; Rogers and Zvarikova, 2021), environment and vehicle sensors, and crash avoidance technologies, that is an emerging topic involving much interest. My research problem is whether interconnected sensor networks integrate visual recognition and image processing tools (Lazaroiu et al., 2017; Popescu et al., 2020), smart mobility technologies, and trajectory tracking control algorithms.

   In this review, prior findings have been cumulated indicating that big geospatial data analytics (Gasparin and Schinckus, 2022; Pelau et al., 2021; Scott et al., 2020), cloud computing and urban sensing technologies, and virtual reality modeling tools diminish motor vehicle collision frequency. The identified gaps advance autonomous vehicle routing and navigation technologies, interactive data visualization tools (Johnson and Nica, 2021; Poliak et al., 2021; Valaskova et al., 2022), and sensor data processing algorithms. My main objective is to indicate that geospatial data mining tools (Kliestik et al., 2022; Pop et al., 2021; Wallace and Lazaroiu, 2021), motion planning algorithms, and cognitive wireless networks shape autonomous vehicle routing and smart urban mobility behaviors. This systematic review contributes to the literature on simulation modeling and machine learning algorithms (Milward et al., 2019; Zauskova et al., 2022), data fusion technologies, and urban Internet of Things-sensing tools by clarifying that connected autonomous vehicles necessitate urban traffic modeling and data visualization tools, deep neural network technology, and predictive control algorithms.

2. Theoretical Overview of the Main Concepts

   Remote sensing technologies, autonomous vehicle steering algorithms, and geospatial mapping tools enable data-driven smart sustainable urbanism. Autonomous vehicles harness virtual simulation tools, cooperative navigation and remote sensing algorithms, and spatial recognition technologies. Deep learning-based autonomous driving technologies, traffic congestion monitoring systems, and object localization algorithms enable sustainable urban governance networks. The manuscript is organized as following: theoretical overview (section 2), methodology (section 3), vehicle navigation technologies, multi-sensor data fusion, and path planning and spatial simulation algorithms (section 4), virtual simulation tools, cooperative navigation and remote sensing algorithms, and spatial recognition technologies (section 5), autonomous vehicle driving algorithms, visual recognition tools, and deep learning object detection technology (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 "autonomous vehicle ethics" + "networked transport systems" + "spatial cognition algorithms," "mobility data processing tools," and "deep learning-based sensing technologies." As research published in 2022 was inspected, only 185 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 36 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] 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 from Figure. 4. Vehicle Navigation Technologies, Multi-Sensor Data Fusion, and Path Planning and Spatial Simulation Algorithms

   Smart infrastructure sensors, deep and machine learning algorithms, and predictive maintenance and optimal trajectory planning tools (Chen et al., 2022; Dasgupta et al., 2022; Hakak et al., 2023; Tajaddini and Vu, 2022) configure intelligent vehicular networks. Deep learning-based autonomous driving technologies, traffic congestion monitoring systems, and object localization algorithms enable sustainable urban governance networks. Autonomous driving algorithms, smart logistics, and cloud and edge computing technologies articulate intelligent transportation infrastructures.

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