Title: Machine Learning-Enabled and Ultra-Low Latency Connected Transportation
Speaker: Shih-Chun Lin , Ph.D.
Date: Friday, March 26, 2021
SYNOPSIS
Connected autonomous vehicles (CAVs) emerge as one major paradigm shift of 6G industry verticals in the autonomous driving and human society while introducing more technological challenges in wireless network infrastructures and deployment. As the technology for a single autonomous vehicle becomes mature, real challenges come from the reliable, safe, real-time operation of connected transportation to achieve ubiquitous and prompt information exchanges with massive CAVs. Vehicles may also exchange their perception data or maneuver plans with other cars or acquire information from remote cloud servers or road-site units through the advance of vehicle-to-everything (V2X) technologies. Since August 2018, 3GPP has launched the normative works of New Radio V2X and enhanced ultra-reliable and low latency communication in 3GPP Release 16. By December 2019, the 3GPP RAN Plenary meeting approved 24 new projects for 3GPP Release-17 with one primary focus of bringing sidelink capabilities from automotive to smartphones and public safety.
This seminar will present emerging key aspects of ultra-low latency vehicular network architecture and discuss the latest industrial practice of intelligent transportation systems. The talk will cover state-of-the-art research progresses in CAV and V2X areas, technological deployment efforts by the 3GPP Cellular V2X with SAE-levels, IEEE DSRC/WAVE, and ITU-T, and the current development of computing-enabled CAV infrastructures in the NC-CAV Center.
ABOUT THE SPEAKER
Dr. Shih-Chun Lin is an assistant professor with the Department of Electrical and Computer Engineering at North Carolina State University, where he leads the Intelligent Wireless Networking (iWN) Laboratory. He received a Ph.D. degree in electrical and computer engineering from Georgia Institute of Technology, Atlanta, USA, in 2017, and an M.S. degree in communication engineering and a B.S. degree in electrical engineering from National Taiwan University, Taiwan. He has published more than 47 peer-reviewed papers, holds 10 U.S. patents, and received the Best Student Paper Award Runner-up in IEEE SCC 2016. He served as a TPC member for numerous international conferences and received the Distinguished TPC Member Award in IEEE INFOCOM 2020.
As a pioneer of using software-defined networking (SDN) models for wireless system management, Dr. Lin has been invited to demonstrate AI-based RF analytic with SDR-SDN architecture in 2019 Beyond 5G SDR University Showcase by Air Force Research Laboratory (AFRL). He leads a project of distributed machine learning supported by Cisco Systems and is researching the low-latency edge computing infrastructure for CAV deployment for Thrust 2 in the NC-CAV center. His research interests include 5G and beyond architectures, mobile edge computing, AIoT, machine learning techniques, mathematical optimization, and performance evaluation.