Applying Big Data and Machine Learning to Enable Self-Driving Vehicles and Intelligent Transportation
Sarah Johnson
Department of Computer Science and Engineering University of Tuvalu - School of Engineering and Technology
Keywords: Perception, Prediction, Planning, Control, Fleet Management, Infrastructure Integration, Validation
Abstract
Self-driving vehicles and intelligent transportation systems have the potential to revolutionize mobility and transform how we travel. Realizing this vision will require leveraging big data and machine learning techniques to equip vehicles with the capabilities for automated driving and enable intelligent infrastructure. This paper provides an overview of the state-of-the-art in applying big data and machine learning to self-driving vehicles and intelligent transportation. Key topics covered include: sensor data collection and management, perception systems for localization and mapping, prediction and behavior modeling, motion planning and control, interaction with human drivers and pedestrians, fleet management and coordination, infrastructure integration, and real-world deployment. Challenges such as safety validation, systemic impacts, and data privacy are also discussed. With continued innovation in artificial intelligence and growth in availability of multimodal transportation data, the synergistic application of big data and machine learning can overcome the remaining hurdles toward fully automated driving and realize smarter, safer, and more efficient mobility.
Author Biography
Sarah Johnson, Department of Computer Science and Engineering University of Tuvalu - School of Engineering and Technology
