ABSTRACT. This article reviews and advances existing literature concerning motion planning and object recognition algorithms, vehicle navigation and collision avoidance technologies, and geospatial data visualization in network connectivity systems. In this research, previous findings were cumulated showing that sensor data processing and accurate path tracking tools, autonomous vehicle decision-making algorithms, and predictive urban analytics shape networked transport systems. 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.” 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. 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.

Keywords: geospatial data visualization; vehicle navigation; collision avoidance

How to cite: Konecny, V., Jaśkiewicz, M., and Downs, S. (2022). “Motion Planning and Object Recognition Algorithms, Vehicle Navigation and Collision Avoidance Technologies, and Geospatial Data Visualization in Network Connectivity Systems,” Contemporary Readings in Law and Social Justice 14(1): 89–104. doi: 10.22381/CRLSJ14120226.

Received 15 April 2022 • Received in revised form 21 July 2022
Accepted 27 July 2022 • Available online 30 July 2022