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.
A coupled driving simulator to investigate the interaction between bicycles and automated vehicles
2 Pith papers cite this work. Polarity classification is still indexing.
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A simulation pipeline reconstructs real vehicle-e-scooter interactions and extends them to higher-risk scenarios using a Social Force Model to validate autonomous vehicle collision avoidance.
citing papers explorer
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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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Dynamic Risk Generation for Autonomous Driving: Naturalistic Reconstruction of Vehicle-E-Scooter Interactions
A simulation pipeline reconstructs real vehicle-e-scooter interactions and extends them to higher-risk scenarios using a Social Force Model to validate autonomous vehicle collision avoidance.