Multi-Sensor Fusion Technology, Spatial Simulation and Environment Mapping Algorithms, and Real-World Connected Vehicle Data in Smart Sustainable Urban Mobility Systems.

• Author: Potcovaru, Ana-Madalina

1. Introduction

   Object detection and tracking tools, spatial computing algorithms, and collision avoidance technologies configure smart urban mobility. The purpose of our systematic review is to examine the recently published literature on smart sustainable urban mobility systems and integrate the insights it configures on multi-sensor fusion technology, spatial simulation and environment mapping algorithms, and real-world connected vehicle data. By analyzing the most recent (2019-2022) and significant (Web of Science, Scopus, and ProQuest) sources, our paper has attempted to prove that spatial data visualization tools, cognitive computing systems, and self-driving car control algorithms (Andronie et al, 2021a, b; de Godoy et al, 2022; Nica, 2018) shape connected vehicle technologies. The actuality and novelty of this study are articulated by addressing virtual data modeling tools, autonomous vehicle control systems, cloud computing technologies, and Internet of Things connected sensors (Blake and Frajtova Michalikova, 2021; Lazaroiu et al, 2017; Popescu, 2018; Vatamanescu et al, 2020), that is an emerging topic involving much interest. Our research problem is whether road anomaly detection tools, blockchain and data fusion technologies, and trajectory estimation algorithms (Barbu et al, 2021; Kral et al, 2020; Pop et al, 2021) assist smart traffic planning and analytics.

   In this review, prior findings have been cumulated indicating that automated collision avoidance systems, data monitoring algorithms, and virtual navigation tools (Blake, 2022; Mircica, 2020; Valaskova et al, 2022) reduce crash severities. The identified gaps advance autonomous vehicle planning algorithms, object localization and mapping tools, and spatial computing technology. Our main objective is to indicate that urban network infrastructures develop on self-driving car perception systems, road anomaly detection tools, and machine learning algorithms. This systematic review contributes to the literature on deep learning-based sensing technologies, networked digital infrastructures, and automated collision avoidance systems (Balcerzak et al, 2022; Dusmanescu et al., 2016; Nica et al., 2022) by clarifying that geospatial mapping tools, sensing and computing technologies, and machine vision algorithms optimize traffic efficiency.

2. Theoretical Overview of the Main Concepts

   Autonomous vehicle control systems integrate mobile wireless sensor networks, visual recognition tools, and spatial data analytics. Driving support systems and vehicle detection technologies necessitate computer vision. Harnessing traffic data and images optimizes vehicle operations in terms of driving safety and comfort, collision avoidance, and travel efficiency. Connected vehicle technologies integrate autonomous driving algorithms and behaviors, real-time data analytics, and route perception and safety tools. The manuscript is organized as following: theoretical overview (section 2), methodology (section 3), autonomous driving algorithms and behaviors, real-time data analytics, and route perception and safety tools (section 4), automated collision avoidance systems, data monitoring algorithms, and virtual navigation tools (section 5), multi-object detection and tracking tools, mobile cloud and edge computing systems, and road environment data (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 March 2022, with search terms including "smart sustainable urban mobility systems" + "multi-sensor fusion technology," "spatial simulation and environment mapping algorithms," and "real-world connected vehicle data." The search terms were determined as being the most employed words or phrases across the analyzed literature. As we analyzed research published between 2019 and 2022, only 88 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 13, chiefly empirical, sources (Tables 1 and 2). Extracting and inspecting publicly accessible files (scholarly sources) as evidence, before the research began no institutional ethics approval was required. 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).

4. Autonomous Driving Algorithms and Behaviors, Real-Time Data Analytics, and Route Perception and Safety Tools

   Spatial data visualization tools, autonomous vehicle sensors and actuators, big data-enabled visual perception systems, and Internet of Things-based connected devices (Li et al., 2021; Zou et al., 2021) reduce fatalities. Harnessing traffic data and images optimizes vehicle operations in terms of driving safety and comfort, collision avoidance, and travel efficiency. The growing cyber connectivity will optimize vehicle control, networking, and diagnostic operations, resulting in enhanced vehicle- and system-level performance and coherent mobility management. Urban network infrastructures develop on self-driving car perception systems, road anomaly detection tools, and machine learning algorithms.

   Deep learning-based sensing technologies, networked digital infrastructures, and automated collision avoidance systems (Aoki and Rajkumar, 2022; Shannon et al., 2021) decrease roadway injuries. Connected vehicle technologies integrate autonomous driving algorithms and behaviors, realtime data analytics, and route perception and safety tools. A growing rate of advanced driver-assistance systems and higher-level autonomous vehicles throughout the road environment constitute a incentive for change as regards vehicle ownership and occupancy rates.

   Data visualization and virtual simulation modeling tools, sensor and data...