ABSTRACT. Based on an in-depth survey of the literature, the purpose of the paper is to explore autonomous vehicle routing and navigation, mobility simulation and traffic flow prediction tools, and deep learning object detection technology in smart sustainable urban transport systems. We contribute to the literature by indicating that multi-sensor environmental data fusion, environment perception systems, and deep convolutional neural networks are pivotal in connected autonomous vehicles. Throughout April 2022, a quantitative literature review of the Web of Science, Scopus, and ProQuest databases was performed, with search terms including “smart sustainable urban transport systems” + “autonomous vehicle routing and navigation,” “mobility simulation and traffic flow prediction tools,” and “deep learning object detection technology.” As research published between 2021 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 15 mainly empirical sources. Data visualization tools: Dimensions (bibliometric mapping) and VOSviewer (layout algorithms). Reporting quality assessment tool: PRISMA. Methodological quality assessment tools include: AMSTAR, Dedoose, Distiller SR, and SRDR.

Keywords: autonomous vehicle; routing; navigation; deep learning object detection

How to cite: Poliak, M., Jurecki, R., and Buckner, K. (2022). “Autonomous Vehicle Routing and Navigation, Mobility Simulation and Traffic Flow Prediction Tools, and Deep Learning Object Detection Technology in Smart Sustainable Urban Transport Systems,” Contemporary Readings in Law and Social Justice 14(1): 25–40. doi: 10.22381/CRLSJ14120222.

Received 18 April 2022 • Received in revised form 17 July 2022
Accepted 24 July 2022 • Available online 30 July 2022