The Social Justice of Intelligent Transportation Systems: Deep Learning-based Autonomous Driving Technologies, Cooperative Navigation Algorithms, and Vehicle and Pedestrian Detection Tools.

• Author: Newell, Mark

1. Introduction

   The purpose of my systematic review is to examine the recently published literature on the social justice of intelligent transportation systems and integrate the insights it configures on deep learning-based autonomous driving technologies, cooperative navigation algorithms, and vehicle and pedestrian detection tools. By analyzing the most recent (2022) and significant (Web of Science, Scopus, and ProQuest) sources, my paper has attempted to prove that obstacle avoidance algorithms, cognitive computing systems, and geospatial data mining tools (Androniceanu, 2021; Nica and Stehel, 2021; Rogers and Zvarikova, 2021) further smart transportation networks. The actuality and novelty of this study are articulated by addressing urban Internet of Things-sensing tools, data processing and smart mobility technologies (Johnson and Nica, 2021; Popescu et al., 2020; Zauskova et al., 2022), and self-driving car control algorithms, that is an emerging topic involving much interest. My research problem is whether vehicle and pedestrian detection tools harness urban computing and spatial recognition technologies (Andronie et al., 2021a, b; Nica et al., 2022; Scott et al., 2020), sensor data processing algorithms, and predictive modeling techniques.

   In this review, prior findings have been cumulated indicating that self-driving cars develop on route detection and environment mapping algorithms, deep neural network technology, and image processing tools. The identified gaps advance spatial cognition and tracking control algorithms, urban sensing technologies (Benus et al., 2022; Pocol et al., 2022; Vatamanescu et al., 2022), and optimal trajectory planning tools. My main objective is to indicate that data simulation tools, route planning and predictive control algorithms, and interconnected sensor networks (de Godoy et al., 2022; Podosek et al., 2022; Wallace and Lazaroiu, 2021) are instrumental in autonomous driving technologies, enhancing road safety. This systematic review contributes to the literature on urban cloud data, cooperative navigation and collision avoidance algorithms, and multi-sensor data fusion (Glogovetan et al., 2022; Poliak et al., 2021; Watson, 2022) by clarifying that connected car data, algorithm-driven sensing devices, and spatial data visualization tools (Lazaroiu et al., 2022; Popescu et al., 2021; Zvarikova et al., 2022) reduce preventable road injuries.

2. Theoretical Overview of the Main Concepts

   Connected autonomous vehicles necessitate machine learning algorithms, data modeling tools, and simulation and virtualization technologies. Autonomous mobility technologies deploy sensing and perception algorithms, vehicle and lane detection tools, and Internet of Things connected devices. Path and motion planning algorithms, real-time data tracking tools, and road environment data shape crash avoidance technologies. Modeling and simulation tools, deep and machine learning algorithms, and cognitive automation technologies cut down crashes and casualties. The manuscript is organized as following: theoretical overview (section 2), methodology (section 3), sensing and perception algorithms, vehicle and lane detection tools, and Internet of Things connected devices (section 4), obstacle avoidance algorithms, cognitive computing systems, and geospatial data mining tools (section 5), autonomous driving behaviors, multi-sensor environment data fusion, and monitoring and sensing 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

   Throughout May 2022, a quantitative literature review of the Web of Science, Scopus, and ProQuest databases was performed, with search terms including "social justice" + "intelligent transportation systems" + "deep learning-based autonomous driving technologies," "cooperative navigation algorithms," and "vehicle and pedestrian detection tools." As research published in 2022 was inspected, only 187 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 34 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] 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] 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. Sensing and Perception Algorithms, Vehicle and Lane Detection Tools, and Internet of Things Connected Devices

   Environment and vehicle sensors, urban perception and planning tools, and spatial cognition algorithms (Geng et al., 2022; Hakak et al., 2023; Tian et al., 2022; Zhong et al., 2022) configure big data-driven transportation systems. Autonomous mobility technologies deploy sensing and perception algorithms, vehicle and lane detection tools, and Internet of Things connected devices. Connected car data, algorithm-driven sensing devices, and spatial data visualization tools reduce preventable road injuries. Autonomous vehicle routing and navigation develop on object tracking algorithms, blockchain-enabled Internet of Things networks, and urban mobility data...