The Algorithmic Governance of Autonomous Driving Behaviors: Multi-Sensor Data Fusion, Spatial Computing Technologies, and Movement Tracking Tools.

• Author: Kovacova, Maria

1. Introduction

   Smart traffic analytics necessitates data mining tools, deep learning-based sensing technologies, and sustainable urban monitoring systems. The purpose of our systematic review is to examine the recently published literature on the algorithmic governance of autonomous driving behaviors and integrate the insights it configures on multi-sensor data fusion, spatial computing technologies, and movement tracking tools. By analyzing the most recent (2022) and significant (Web of Science, Scopus, and ProQuest) sources, our paper has attempted to prove that remote sensing data fusion techniques, computational object recognition and urban traffic modeling tools (Andronie et al., 2021; Milward et al., 2019; Pop et al., 2022), and multimodal transportation analytics decrease traffic jams. The actuality and novelty of this study are articulated by addressing autonomous driving technologies, lane detection algorithms, and adaptive and dynamic route planning tools (Crowell et al., 2022; Nica, 2021; Popescu et al., 2020b), that is an emerging topic involving much interest. Our research problem is whether virtual simulation and predictive control algorithms, data tracking apps, and urban computing and traffic sensing technologies (Blake and Frajtova Michalikova, 2021; Nagy and Lazaroiu, 2022; Popescu et al., 2020a) articulate intelligent vehicular networks.

   In this review, prior findings have been cumulated indicating that sustainable urban governance networks (Gasparin and Schinckus, 2022; Nica and Stehel, 2021; Popescu et al., 2022) integrate data visualization and multi-object tracking tools, predictive control and sensor fusion algorithms, and automated collision avoidance systems. The identified gaps advance self-driving car perception systems, multi-object detection tools, and data fusion technologies. Our main objective is to indicate that predictive simulation tools, cloud computing algorithms, and spatial data analytics (Kliestik et al., 2020; Nica et al., 2021; Valaskova et al., 2022) improve road traffic safety. This systematic review contributes to the literature on visualization and analytics tools (Lazaroiu et al., 2017; Pelau et al., 2021; Vatamanescu et al., 2020), remote sensing technologies, and self-driving car control algorithms by clarifying that autonomous driving perception algorithms require multi-object detection and tracking tools, big geospatial data analytics (Lyons and Lazaroiu, 2020; Poliak et al., 2021; Zvarikova et al., 2022), and cloud and edge computing technologies.

2. Theoretical Overview of the Main Concepts

   Vehicle routing and virtual simulation tools, sensor data processing algorithms, and technology-enabled smart logistics enhance traffic safety. Deep reinforcement learning techniques, ambient sound recognition software, and virtual simulation algorithms configure vehicular communication technologies. Urban network infrastructures develop on mobility simulation and behavior tracking tools, route planning and object localization algorithms, and sensor processing and cloud computing technologies. The manuscript is organized as following: theoretical overview (section 2), methodology (section 3), urban sensing and 3D virtual simulation technologies, augmented analytics and real-time image processing tools, and computer vision and object detection algorithms (section 4), vehicle routing and virtual simulation tools, sensor data processing algorithms, and technology-enabled smart logistics (section 5), vehicle navigation and geospatial mapping tools, Internet of Things sensing infrastructures, and autonomous driving 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, we performed a quantitative literature review of the Web of Science, Scopus, and ProQuest databases, with search terms including "algorithmic governance" + "autonomous driving behaviors" + "multi-sensor data fusion," "spatial computing technologies," and "movement tracking tools." As we inspected research published in 2022, only 186 articles satisfied the eligibility criteria. By eliminating controversial findings, outcomes unsubstantiated by replication, too imprecise material, or having similar titles, we decided upon 43, 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: AMSTAR, Distiller SR, MMAT, and ROBIS (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] Distiller SR screened and extracted the collected data. [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] AMSTAR evaluated the methodological quality of 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. [down arrow] ROBIS assessed the risk of bias in systematic reviews. [down arrow] Dedoose analyzed qualitative and mixed methods research. Note: Table made from Figure. 4. Urban Sensing and 3D Virtual Simulation Technologies, Augmented Analytics and Real-Time Image Processing Tools, and Computer Vision and Object Detection Algorithms

   Autonomous driving technologies, lane detection algorithms, and adaptive and dynamic route planning tools (Alsghan et al., 2022; Chen et al., 2022a; Gao et al., 2022a; Pang et al., 2022; Tang et al., 2022) enable sensing and navigation systems. Self-driving cars deploy reinforcement learning and multisensor fusion algorithms, virtual navigation and visual recognition tools, and object detection technologies. Virtual simulation and predictive control algorithms, data tracking apps, and urban computing and traffic sensing technologies articulate intelligent vehicular networks.

   Spatio-temporal fusion algorithms, virtual simulation modeling tools, and big data-driven urban analytics (Aguiar et al., 2022; Bhattacharya et al., 2022; Chen et al., 2022b; Qiu et al., 2022; Wang et al., 2022a) shape knowledge and social acceptance of and attitudes toward self-driving technologies. Deep reinforcement learning techniques, ambient sound recognition software, and virtual simulation algorithms configure vehicular communication technologies. Autonomous vehicles leverage urban cloud data, augmented and virtual technologies, and automated simulation modeling and digital mapping tools.

   Image processing and urban Internet of Things-sensing...