Invited talk by Dr. Peisong Li
Title: Edge Computing-enabled Construction of Intelligent Transportation Systems via Reinforcement Learning Methods
Abstract: The integration of edge computing with intelligent transportation systems (ITS) offers a promising approach for enhancing traffic management and constructing intelligent vehicles. However, this integration presents several challenges. For instance, achieving fast data processing and decision making is critical for effective traffic control, making low-latency computation and response essential. In this presentation, the architectural design of the edge computing-enabled ITS will be discussed, leveraging reinforcement learning methods to optimize task scheduling and resource allocation.
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- 2001 Longxiang Blvd
- Shenzhen, Guangdong
- China
- Building: Chengdao Building
- Room Number: RM 208
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- Co-sponsored by The Chinese University of Hong Kong, Shenzhen
Speakers
Edge Computing-enabled Construction of Intelligent Transportation Systems via Reinforcement Learning Methods
Biography:
Peisong Li received his PhD degree in Electrical and Electronic Engineering from the University of Liverpool in 2024, Master degree from Shanghai Maritime University in 2020, and Bachelor degree from Guilin University of Electronic Technology in 2017. He has totally 13 publications (e.g. IEEE T-ITS, IEEE T-IV, IF) and 4 granted patents. The highest citation number of a single technical paper on Google Scholar is over 340. His research interests include edge computing, edge AI, reinforcement learning, vehicular network, and intelligent transportation.