MSH-MCCT is a multi-source human-in-the-loop mixed cloud control testbed that uses mixed digital twins to enable real-time interaction between physical and virtual CAVs and HDVs.
Dense reinforcement learning for safety validation of autonomous vehicles,
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The paper surveys CRL literature, proposes a taxonomy of methods into four categories based on knowledge storage and transfer, reviews metrics and benchmarks, and outlines challenges and future research directions.
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Multi-Source Human-in-the-Loop Digital Twin Testbed for Connected and Autonomous Vehicles in Mixed Traffic Flow
MSH-MCCT is a multi-source human-in-the-loop mixed cloud control testbed that uses mixed digital twins to enable real-time interaction between physical and virtual CAVs and HDVs.
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A Survey of Continual Reinforcement Learning
The paper surveys CRL literature, proposes a taxonomy of methods into four categories based on knowledge storage and transfer, reviews metrics and benchmarks, and outlines challenges and future research directions.