Motion Planning and Object Recognition Algorithms, Vehicle Navigation and Collision Avoidance Technologies, and Geospatial Data Visualization in Network Connectivity Systems.

• Author: Konecny, Vladimir

1. Introduction

   Predictive control and sensor fusion algorithms, visual recognition and mobility simulation tools, and blockchain-enabled Internet of Things networks shape urban transportation systems. The purpose of our systematic review is to examine the recently published literature on network connectivity systems and integrate the insights it configures on motion planning and object recognition algorithms, vehicle navigation and collision avoidance technologies, and geospatial data visualization. By analyzing the most recent (2019-2022) and significant (Web of Science, Scopus, and ProQuest) sources, our paper has attempted to prove that urban big data analytics develops on data tracking apps, geospatial mapping technologies, sensor data fusion, and intelligent routing systems. The actuality and novelty of this study are articulated by addressing mapping and navigation tools, autonomous driving algorithms and behaviors, and sensing and computing technologies (Glogovetan et al., 2022; Nica et al., 2022; Poliak et al., 2021a, b), that is an emerging topic involving much interest. Our research problem is whether Remote sensing data fusion techniques, predictive simulation tools, and trajectory planning algorithms (Lazaroiu, 2018; Peters, 2022a, b; Popescu Ljungholm, 2022) assist urban computing technologies.

   In this review, prior findings have been cumulated indicating that sensor data processing and accurate path tracking tools, autonomous vehicle decision-making algorithms, and predictive urban analytics shape networked transport systems. The identified gaps advance visual perception and recognition systems, route planning and environment mapping algorithms, and predictive maintenance tools. Our main objective is to indicate that cloud and edge computing algorithms, remote sensing systems, and visualization and analytics tools (Andronie et al, 2021a, b; Lazaroiu et al, 2022) configure smart transportation networks. This systematic review contributes to the literature on deep learning object detection and 3D virtual simulation technologies, real-world connected vehicle data, geospatial data mining tools, and predictive modeling algorithms (Kral et al, 2020; Obada and Dabija, 2022; Pop et al, 2022; Zvarikova et al, 2022) by clarifying that self-driving cars require visual perception algorithms, vehicle and pedestrian detection tools, and transportation analytics.

2. Theoretical Overview of the Main Concepts

   Autonomous vehicle driving algorithms, multi-sensor environmental data fusion, environment perception systems, and accurate visual object tracking tools cut down traffic accidents. Autonomous vehicle routing and navigation necessitate simulation modeling algorithms, geospatial data mining tools, and smart environmental sensors. The manuscript is organized as following: theoretical overview (section 2), methodology (section 3), sensor data processing and accurate path tracking tools, autonomous vehicle decision-making algorithms, and predictive urban analytics (section 4), visual perception and recognition systems, route planning and environment mapping algorithms, and predictive maintenance tools (section 5), data tracking apps, geospatial mapping technologies, sensor data fusion, and intelligent routing 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

   Throughout April 2022, a quantitative literature review of the Web of Science, Scopus, and ProQuest databases was performed, with search terms including "network connectivity systems" + "motion planning and object recognition algorithms," "vehicle navigation and collision avoidance technologies," and "geospatial data visualization." The search terms were determined as being the most employed words or phrases across the analyzed literature. As research published between 2019 and 2022 was inspected, only 89 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, we selected 13 mainly 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, MMAT, ROBIS, and SRDR (Figures 1-6).

4. Sensor Data Processing and Accurate Path Tracking Tools, Autonomous Vehicle Decision-Making Algorithms, and Predictive Urban Analytics

   Mapping and navigation tools, autonomous driving algorithms and behaviors, and sensing and computing technologies (Li et al., 2021a; Song and Huh, 2021) configure smart transportation systems. Self-driving cars require visual perception algorithms, vehicle and pedestrian detection tools, and transportation analytics. Decision-making algorithms can be instrumental in driving comfort, reducing traffic congestions and car crashes, and thus enhancing traffic safety. Autonomous vehicle driving algorithms, multi-sensor environmental data fusion, environment perception systems, and accurate visual object tracking tools cut down traffic accidents.

   Vehicular communication and deep learning-based sensing technologies, big geospatial data analytics, real-time data tracking and monitoring tools, and predictive control algorithms (Shannon et al., 2021; Zou et al., 2021) optimize smart transportation networks. Connected mobility will be of service to travelers and society by easing traffic, optimizing rider convenience, and decreasing accidents. Autonomous vehicle adoption to the road environment will considerably cut down the volume and severity of collisions.

   Connected vehicle data, object tracking and prediction tools, and lane detection algorithms (Acheampong and Cugurullo, 2019; Kanagaraj et al., 2021) are pivotal in...