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1	Uncontrolled intersection coordination of the autonomous vehicle based on multi-agent reinforcement learning. McSey, Isaac Arnold January 2023 (has links) This study explores the application of multi-agent reinforcement learning (MARL) to enhance the decision-making, safety, and passenger comfort of Autonomous Vehicles (AVs)at uncontrolled intersections. The research aims to assess the potential of MARL in modeling multiple agents interacting within a shared environment, reflecting real-world situations where AVs interact with multiple actors. The findings suggest that AVs trained using aMARL approach with global experiences can better navigate intersection scenarios than AVs trained on local (individual) experiences. This capability is a critical precursor to achieving Level 5 autonomy, where vehicles are expected to manage all aspects of the driving task under all conditions. The research contributes to the ongoing discourse on enhancing autonomous vehicle technology through multi-agent reinforcement learning and informs the development of sophisticated training methodologies for autonomous driving. Autonomous Vehicles (AVs) Road Safety Fuel Efficiency Business Dynamics Intersections Human-Driven Vehicles (HDVs) Pedestrians Algorithmic Interactions Uncontrolled Intersections Global Insights Safety Improvements Comfort Improvements Learning Process Global Experiences Complex Environments Passenger Comfort Navigation Computer Sciences Datavetenskap (datalogi) Information Systems

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Uncontrolled intersection coordination of the autonomous vehicle based on multi-agent reinforcement learning.