Resource Allocation for Platoon Digital Twin Networks
Vehicle platooning involves a group of vehicles driving in close coordination, maintaining short inter-vehicle distances to improve road capacity, reduce fuel consumption for following vehicles, and enhance overall driving safety. Achieving this coordination requires continuous exchange and processing of environmental sensor data. To further enhance control and service performance without overloading individual vehicles, maintaining digital twins (DTs) of platooning vehicles has emerged as a promising approach. A platoon digital twin (PDT), which integrates the DTs of all platoon members, can serve as a unified interface for coordinated traffic management. However, the effectiveness of PDT-based applications relies heavily on the quality of the PDT, which in turn depends on timely and accurate synchronization with the physical platoon that requires robust communication and computation resources. In this talk, we present our recent work on joint communication and computation resource allocation to support high-quality PDTs under highly dynamic vehicle mobility. We model the problem as an M-th order Markov Decision Process (MDP) to better capture the temporal dynamics of the system. Our solution leverages a multi-agent Deep Deterministic Policy Gradient (DDPG) framework, enhanced with temporal feature extraction, to adapt to rapidly changing network conditions and improve resource allocation decisions.
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- Date: 22 Apr 2025
- Time: 06:00 PM UTC to 07:00 PM UTC
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Dongmei Zhao of McMaster University
Resource Allocation for Platoon Digital Twin Networks
Vehicle platooning involves a group of vehicles driving in close coordination, maintaining short inter-vehicle distances to improve road capacity, reduce fuel consumption for following vehicles, and enhance overall driving safety. Achieving this coordination requires continuous exchange and processing of environmental sensor data. To further enhance control and service performance without overloading individual vehicles, maintaining digital twins (DTs) of platooning vehicles has emerged as a promising approach. A platoon digital twin (PDT), which integrates the DTs of all platoon members, can serve as a unified interface for coordinated traffic management. However, the effectiveness of PDT-based applications relies heavily on the quality of the PDT, which in turn depends on timely and accurate synchronization with the physical platoon that requires robust communication and computation resources. In this talk, we present our recent work on joint communication and computation resource allocation to support high-quality PDTs under highly dynamic vehicle mobility. We model the problem as an M-th order Markov Decision Process (MDP) to better capture the temporal dynamics of the system. Our solution leverages a multi-agent Deep Deterministic Policy Gradient (DDPG) framework, enhanced with temporal feature extraction, to adapt to rapidly changing network conditions and improve resource allocation decisions.
Biography:
Prof. Dongmei Zhao received the Ph.D degree in Electrical and Computer Engineering from University of Waterloo, Waterloo, Ontario, Canada in June 2002. In July 2002 she joined the Department of Electrical and Computer Engineering at McMaster University, where she is a professor. She is currently an Associate Editor for the IEEE Internet of Things Journal. She also serves as a Co-Chair for the Emerging Technologies, 6G and Beyond Track of the IEEE Vehicular Technology Conference (VTC) Fall 2025. She is a Distinguished Lecturer of IEEE Vehicular Technology Society. Her recent research interests focus on network resource management and digital twins.
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Address:Ontario, Canada