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.
Uncontrolled intersection coordination of the autonomous vehicle based on multi-agent reinforcement learning.
2023-01-01
Hochschulschrift
Elektronische Ressource
Englisch
Autonomous Vehicles (AVs) , Road Safety , Fuel Efficiency , Business Dynamics , Intersections , Human-Driven Vehicles (HDVs) , Pedestrians , Multi-Agent Reinforcement Learning (MARL) , Multi-Agent Deep Deterministic Policy Gradient (MADDPG) , Algorithmic Interactions , Uncontrolled Intersections , Global Insights , Safety Improvements , Comfort Improvements , Learning Process , Global Experiences , Complex Environments , Passenger Comfort , Navigation , Computer Sciences , Datavetenskap (datalogi) , Computer Vision and Robotics (Autonomous Systems) , Datorseende och robotik (autonoma system) , Information Systems , Systemvetenskap , informationssystem och informatik
DDC: | 629 |
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