Deep Learning-based Ethical Judgments in Connected Vehicle Technologies: Route Planning Algorithms, Spatial Data Visualization Tools, and Real-Time Predictive Analytics.

• Author: Duncan, George

1. Introduction

   Intelligent transportation systems require sensing and computing technologies, motion planning and machine vision algorithms, and real-time predictive analytics. The purpose of my systematic review is to examine the recently published literature on deep learning-based ethical judgments in connected vehicle technologies and integrate the insights it configures on route planning algorithms, spatial data visualization tools, and real-time predictive analytics. By analyzing the most recent (2022) and significant (Web of Science, Scopus, and ProQuest) sources, my paper has attempted to prove that smart mobility technologies develop on autonomous vehicle planning algorithms, spatial data modeling tools (Androniceanu et al., 2021; Johnson and Nica, 2021; Pelau et al., 2021), and cyber-physical cognitive systems. The actuality and novelty of this study are articulated by addressing virtual simulation and geospatial mapping tools (Andronie et al., 2021; Krizanova et al., 2019; Rogers and Zvarikova, 2021), computer vision and path planning algorithms, and autonomous vehicle interaction control software, that is an emerging topic involving much interest. My research problem is whether cooperative navigation and route planning algorithms, modeling and simulation tools, and data fusion technologies (Benus et al., 2022; Lazaroiu et al., 2020; Scott et al., 2020) articulate sustainable urban governance networks.

   In this review, prior findings have been cumulated indicating that spatial data visualization tools, trajectory tracking control and image processing algorithms, and simulation and virtualization technologies (Carter, 2022; Milward et al., 2019; Vatamanescu et al., 2022) enable dynamic route optimization. The identified gaps advance data simulation and optimal trajectory planning tools (Glogovetan et al., 2022; Nica et al., 2022b; Zauskova et al., 2022), predictive control algorithms, and connected car data. My main objective is to indicate that Internet of Things sensing infrastructures, visual recognition tools, and deep and machine learning algorithms are pivotal in blockchain-based smart transportation systems. This systematic review contributes to the literature on data mining and movement tracking tools, predictive modeling algorithms, and autonomous driving and remote sensing technologies (de Godoy et al., 2022; Nica et al., 2022a; Wallace and Lazaroiu, 2021) by clarifying that connected autonomous vehicles require deep convolutional neural networks, big data-driven urban analytics, and remote sensing data fusion techniques.

2. Theoretical Overview of the Main Concepts

   Self-driving cars integrate visual object detection-based systems, virtual and augmented reality tools, and data mining techniques. Sensing and navigation systems, spatial data visualization tools, and cloud and edge computing algorithms articulate smart urban governance. Sensor data processing and geospatial analytics algorithms, cognitive computing systems, and automated simulation modeling tools enhance road safety. The manuscript is organized as following: theoretical overview (section 2), methodology (section 3), autonomous vehicle planning algorithms, spatial data modeling tools, and cyber-physical cognitive systems (section 4), real-time data processing tools, sensor data fusion and object detection algorithms, and intelligent transport systems (section 5), spatial data visualization tools, trajectory tracking control and image processing algorithms, and simulation and virtualization technologies (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

   I carried out a quantitative literature review of ProQuest, Scopus, and the Web of Science throughout May 2022, with search terms including "deep learning-based ethical judgments" + "connected vehicle technologies" + "route planning algorithms," "spatial data visualization tools," and "real-time predictive analytics." As I analyzed research published in 2022, only 168 papers met the eligibility criteria. By removing controversial or unclear findings (scanty/unimportant data), results unsupported by replication, undetailed content, or papers having quite similar titles, I decided on 34, chiefly 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, Distiller SR ROBIS, 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] Dedoose analyzed qualitative and mixed methods research. [down arrow] ROBIS assessed the risk of bias in systematic reviews. [down arrow] AXIS evaluated the quality of cross-sectional studies. [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. Note: Table made from Figure. 4. Autonomous Vehicle Planning Algorithms, Spatial Data Modeling Tools, and Cyber-Physical Cognitive Systems

   Remote sensing systems, tracking control and signal processing algorithms, and simulation modeling and visual recognition tools (Boddupalli et al., 2022; Hakak et al., 2023; Lu et al., 2022; Tajaddini and Vu, 2022) decrease motor vehicle collisions. Spatial cognition and motion planning algorithms, virtual navigation and mobility data processing tools, and smart infrastructure sensors configure intelligent vehicular networks. Intelligent transportation systems deploy data fusion technologies, mobile...