ABSTRACT. We draw on a substantial body of theoretical and empirical research on smart infrastructure sensors, deep learning-based autonomous driving and data processing technologies, and spatio-temporal fusion algorithms. In this research, prior findings were cumulated indicating that monitoring and sensing technologies, predictive maintenance and data mining tools, and computer vision and object detection algorithms optimize vehicular traffic flows and road safety. We carried out a quantitative literature review of ProQuest, Scopus, and the Web of Science throughout June 2022, with search terms including “socially responsible technologies” + “autonomous mobility systems” + “self-driving car control algorithms,” “virtual data modeling tools,” and “cognitive wireless sensor networks.” As we analyzed research in 2022, only 181 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 27, chiefly empirical, sources. Data visualization tools: Dimensions (bibliometric mapping) and VOSviewer (layout algorithms). Reporting quality assessment tool: PRISMA. Methodological quality assessment tools include: AMSTAR, Distiller SR, ROBIS, and SRDR.

Keywords: autonomous mobility system; self-driving car control algorithm; virtual data modeling tool; cognitive wireless sensor network

How to cite: Valaskova, K., Horak, J., and Lăzăroiu, G. (2022). “Socially Responsible Technologies in Autonomous Mobility Systems: Self-Driving Car Control Algorithms, Virtual Data Modeling Tools, and Cognitive Wireless Sensor Networks,” Contemporary Readings in Law and Social Justice 14(2): 172–188. doi: 10.22381/CRLSJ142202210.

Received 29 July 2022 • Received in revised form 27 November 2022
Accepted 29 November 2022 • Available online 30 November 2022