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1	Cooperative Automated Vehicle Movement Optimization at Uncontrolled Intersections using Distributed Multi-Agent System Modeling Mahmoud, Abdallah Abdelrahman Hassan 28 February 2017 (has links) Optimizing connected automated vehicle movements through roadway intersections is a challenging problem. Traditional traffic control strategies, such as traffic signals are not optimal, especially for heavy traffic. Alternatively, centralized automated vehicle control strategies are costly and not scalable given that the ability of a central controller to track and schedule the movement of hundreds of vehicles in real-time is highly questionable. In this research, a series of fully distributed heuristic algorithms are proposed where vehicles in the vicinity of an intersection continuously cooperate with each other to develop a schedule that allows them to safely proceed through the intersection while incurring minimum delays. An algorithm is proposed for the case of an isolated intersection then a number of algorithms are proposed for a network of intersections where neighboring intersections communicate directly or indirectly to help the distributed control at each intersection makes a better estimation of traffic in the whole network. An algorithm based on the Godunov scheme outperformed optimized signalized control. The simulated experiments show significant reductions in the average delay. The base algorithm is successfully added to the INTEGRATION micro-simulation model and the results demonstrate improvements in delay, fuel consumption, and emissions when compared to roundabout, signalized, and stop sign controlled intersections. The study also shows the capability of the proposed technique to favor emergency vehicles, producing significant increases in mobility with minimum delays to the other vehicles in the network. / Ph. D. ITS Automated Vehicles Uncontrolled Intersections Networked Intersections Multi-agent Systems Distributed Systems Intelligent Agent Cooperation
2	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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Cooperative Automated Vehicle Movement Optimization at Uncontrolled Intersections using Distributed Multi-Agent System Modeling

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