
Digital Twin
A digital twin is a digital replica of a living or non-living physical entity, whose technology can be adopted to build a vehicle-to-cloud framework.
Learn more-Call for papers to the special issue on Autonomous and Cooperative Driving in Specific Traffic Environments in IEEE Open Journal on Intelligent Transportation Systems.
-Call for papers to the special issue on Digital Twins and Parallel Intelligence for Intelligent Vehicles and Intelligent Transportation System in IEEE Transactions on Intelligent Vehicles.
A digital twin is a digital replica of a living or non-living physical entity, whose technology can be adopted to build a vehicle-to-cloud framework.
Learn moreEdge computing is a distributed computing paradigm that brings computation and data storage closer to the sources of data.
Learn moreSoftware-defined vehicle can be described as the characterization and implementation of vehicle features as software functions and services running on shared or centralized compute.
Learn moreThe development of technologies embedded in connected and autonomous vehicles (CAVs) increases the potential of cyber-attacks.
Learn moreParallel driving/transportation is proposed under a cloud-based cyber-physical-social systems, which can include parallel testing, parallel learning, and many other modules.
Learn moreVCPS realizes mobile cloud computing services where vehicles themselves or mobile devices (e.g., smartphones and tablets of drivers or passengers in vehicles) play a role of both cloud server and cloud client.
Learn moreBefore IoT applications are implemented in real transportation systems, modeling and simulation can be conducted to study their effectiveness.
Learn moreField implementation of IoT applications can be conducted on real-world vehicles and make them internet of vehicles.
Learn moreDr. Wang is currently a Principal Researcher at Toyota Motor North America R&D - InfoTech Labs in Silicon Valley, leading the “Digital Twin” project with the aim to build an AI-based data-driven vehicle-edge-cloud framework. He received the Ph.D. in Mechanical Engineering from the University of California, Riverside in 2019, and the bachelor degree in Mechanical Engineering and Automation from Beijing University of Posts and Telecommunications in 2015. His research interests include motion planning and control, driver behavior modelling, and digital twin of connected and automated vehicles.
Dr. Wang is the recipient of the National Center for Sustainable Transportation Dissertation Award, issued by U.S. Department of Transportation. He also received the Vincent Bendix Automotive Electronics Engineering Award (i.e., best paper in 2019) from SAE International. Dr. Wang is serving various roles in multiple academic organizations, including Associate Editor of SAE International Journal of Connected and Automated Vehicles, Committee Member in the IEEE Intelligent Transportation Systems Society-Technical Committee on Cooperative and Connected Vehicles, Committee Member in the IEEE Control Systems Society-Technical Committee on Smart Cities, Committee Member in the IEEE Industrial Electronics Society-Technical Committee on Industrial Cyber-Physical Systems, and Member in the SAE On Road Automated Driving (ORAD) Simulation Task Force. He also organized five workshops in IEEE, including four in IEEE ITSS (ITSC’20, IV’20, ITSC’20 and IV’21) as the lead organizer.
Learn more about Dr. WangDr. Qi Zhu is a tenured Associate Professor at the Department of Electrical and Computer Engineering (ECE) in Northwestern University. He was an Assistant Professor and later Associate Professor at the ECE Department in University of California, Riverside from 2011 to 2017, and a Research Scientist at the Strategic CAD Labs in Intel from 2008 to 2011. Dr. Zhu received a Ph.D. in EECS from University of California, Berkeley in 2008, and a B.E. in CS from Tsinghua University in 2003. His research interests include design automation for cyber-physical systems (CPS) and Internet of Things, cyber-physical security, safe and secure machine learning for CPS, and system-on-chip design, with applications in domains such as automotive electronic systems, connected vehicles, and energy-efficient buildings. He received best paper awards at Design Automation Conference (DAC) 2006, DAC 2007, International Conference on Cyber-Physical Systems (ICCPS) 2013, and ACM Transactions on Design Automation of Electronic Systems (TODAES) 2016. He received the NSF CAREER award in 2016, the IEEE Technical Committee on Cyber-Physical Systems (TCCPS) Early-Career Award in 2017, and the Humboldt Research Fellowship for Experienced Researchers.
Dr. Zhu is an Associate Editor for IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems (TCAD), ACM Transactions on Cyber-Physical Systems (TCPS), and IET Cyber-Physical Systems: Theory & Applications. He has served as an Associate Editor for IEEE TCCPS, IEEE TC-VLSI, and ACM SIGDA Newsletters; and as a Guest Editor for Proceedings of the IEEE, ACM TCPS, IEEE Transactions on Automation Science and Engineering (T-ASE), Journal of Systems Architecture (JSA), and Integration, the VLSI Journal. Dr. Zhu is the Conference Chair of IEEE TCCPS, and VP of Young Professionals at IEEE Council for Electronic Design Automation (CEDA). He has served as the General Chair for the 15th IEEE International Conference on Embedded Software and Systems (ICESS), the Program Chair for the 1st and 2nd ACM/IEEE Workshops on Design Automation for CPS and IoT (DESTION), the General Chair for the 3rd DESTION, and on the technical program committee and as organizer for a number of conferences in design automation, cyber-physical systems, embedded systems, and real-time systems, including DAC, ICCAD, DATE, ASP-DAC, ICCPS, ICPS, CODES+ISSS, RTSS, RTAS, ICESS, GLVLSI, SAC, SIES, MEMOCODE, SAMOS, FDL, etc. He received the ACM SIGDA Service Award in 2015.
Learn more about Dr. Zhu