Socially Responsible Technologies in Autonomous Mobility Systems: Self-Driving Car Control Algorithms, Virtual Data Modeling Tools, and Cognitive Wireless Sensor Networks.

• Author: Valaskova, Katarina

1. Introduction

   Sensor data processing algorithms, multi-object detection and tracking tools, and autonomous driving technologies assist smart transportation systems. The purpose of our systematic review is to examine the recently published literature on socially responsible technologies in autonomous mobility systems and integrate the insights it configures on self-driving car control algorithms, virtual data modeling tools, and cognitive wireless sensor networks. By analyzing the most recent (2022) and significant (Web of Science, Scopus, and ProQuest) sources, our paper has attempted to prove that monitoring and sensing technologies (Benus et al., 2022; Kliestik et al., 2022; Pelau et al., 2021), predictive maintenance and data mining tools, and computer vision and object detection algorithms optimize vehicular traffic flows and road safety. The actuality and novelty of this study are articulated by addressing smart infrastructure sensors, deep learning-based autonomous driving and data processing technologies (Blake and Frajtova Michalikova, 2021; Krizanova et al., 2019; Pocol et al., 2022), and spatio-temporal fusion algorithms, that is an emerging topic involving much interest. Our research problem is whether connected autonomous vehicles leverage modeling and simulation tools, remote sensing data fusion techniques, and spatial computing and lane detection algorithms.

   In this review, prior findings have been cumulated indicating that autonomous vehicle decision-making algorithms, cognitive data fusion techniques, and environment perception systems (Durana et al., 2022; Lyons and Lazaroiu, 2020; Pop et al., 2021) further smart traffic analytics. The identified gaps advance virtual simulation and spatial data visualization tools (Hackman and Reindl, 2022; Nagy and Lazaroiu, 2022; Popescu et al., 2020), location tracking and route detection algorithms, and autonomous vehicle perception sensors. Our main objective is to indicate that visual object detection-based systems, real-time Internet of Things data, and automated sensing tools enable smart mobility technologies. This systematic review contributes to the literature on vehicle and pedestrian detection tools, autonomous vehicle driving algorithms, and remote sensing and spatial computing technologies (Hudson, 2022; Nica, 2021; Popescu et al., 2021) by clarifying that intelligent transportation infrastructures require data visualization and predictive simulation tools (Kliestik et al., 2020; Nica and Stehel, 2021; Zvarikova et al., 2022), 3D virtual simulation technology, and trajectory tracking control algorithms.

2. Theoretical Overview of the Main Concepts

   Self-driving cars deploy autonomous vehicle planning and deep reinforcement learning algorithms, big data-driven urban and multimodal transportation analytics, and remote sensing systems. Connected and autonomous vehicles harness sensor fusion and urban computing algorithms, real-time data monitoring tools, and smart mobility technologies. The manuscript is organized as following: theoretical overview (section 2), methodology (section 3), data visualization and urban Internet of Things-sensing tools, deep neural network and vehicle navigation technologies, and signal processing and predictive control algorithms (section 4), sensor data processing algorithms, multi-object detection and tracking tools, and autonomous driving technologies (section 5), autonomous vehicle planning and deep reinforcement learning algorithms, big data-driven urban and multimodal transportation analytics, and remote sensing systems (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

   We carried out a quantitative literature review of ProQuest, Scopus, and the Web of Science throughout June 2022, with search terms including "socially responsible technologies" + "autonomous mobility systems" + "self-driving car control algorithms," "virtual data modeling tools," and "cognitive wireless sensor networks." As we analyzed research in 2022, only 181 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, we decided on 27, 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: AMSTAR, 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. Data Visualization and Urban Internet of Things-Sensing Tools, Deep Neural Network and Vehicle Navigation Technologies, and Signal Processing and Predictive Control Algorithms

   Environment mapping and reinforcement learning algorithms, adaptive and dynamic route planning tools, and predictive...