Autonomous Vehicle Decision-Making Algorithms, Interconnected Sensor Networks, and Big Geospatial Data Analytics in Smart Urban Mobility Systems
Autonomous Vehicle Decision-Making Algorithms, Interconnected Sensor Networks, and Big Geospatial Data Analytics in Smart Urban Mobility Systems

Summary/Abstract: We develop a conceptual framework based on a systematic and comprehensive literature review on autonomous vehicle decision-making algorithms, interconnected sensor networks, and big geospatial data analytics in smart urban mobility systems. Building our argument by drawing on data collected from AAA, AHAS, AUVSI, BCG, Brookings, Capgemini, Gallup, GHSA, Kennedys, ORC, Perkins Coie, SAE, Statista, and World Economic Forum, we performed analyses and made estimates regarding how computer vision, path and motion planning algorithms, and machine learning-based predictors are pivotal in lane detection and congestion monitoring, reducing traffic collisions and related fatalities, and optimizing obstacle avoidance, navigation flow prediction, and trajectory planning for connected and autonomous vehicles across smart and sustainable driverless urban mobility. The data for this research were gathered via an online survey questionnaire. Descriptive statistics of compiled data from the completed surveys were calculated when appropriate.

• Details
• Contents