The Algorithmic Governance of Autonomous Driving Behaviors: Multi-Sensor Data Fusion, Spatial Computing Technologies, and Movement Tracking Tools
The Algorithmic Governance of Autonomous Driving Behaviors: Multi-Sensor Data Fusion, Spatial Computing Technologies, and Movement Tracking Tools

Summary/Abstract: This paper provides a systematic literature review of studies investigating autonomous driving technologies, lane detection algorithms, and adaptive and dynamic route planning tools. The analysis highlights that autonomous driving perception algorithms require multi-object detection and tracking tools, big geospatial data analytics, and cloud and edge computing technologies. Throughout June 2022, we performed a quantitative literature review of the Web of Science, Scopus, and ProQuest databases, with search terms including “algorithmic governance” + “autonomous driving behaviors” + “multi-sensor data fusion,” “spatial computing technologies,” and “movement tracking tools.” As we inspected research published in 2022, only 186 articles satisfied the eligibility criteria. By eliminating controversial findings, outcomes unsubstantiated by replication, too imprecise material, or having similar titles, we decided upon 43, generally 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, MMAT, and ROBIS.

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