Biograph: Baigen Cai received the B.Sc., M.Sc., and Ph.D. degrees in traffic information engineering and control from Beijing Jiaotong University in 1987, 1990, and 2010, respectively. Since 1990, he has been a faculty member at School of Electronic and Information Engineering, Beijing Jiaotong University. He was a Visiting Scholar with the Ohio State University from 1998 to 1999. He is currently a professor and the Dean of the School of Computer and Information Technology, Beijing Jiaotong University. His research interests include intelligent transportation system, GNSS navigation, multi-sensor data fusion, intelligent traffic control, and system modeling & simulation. Prof. Cai holds several national and international cooperation R&D projects.
He is IEEE senior member, IEEE ITSS (Intelligent Transportation System Society) member, IRSE (Institute of Railway Signal Engineers) member. He currently holds positions as technical committee member of Chinese Association of Automation on Process Control and Industrial Control System Information, he is also the technical committee member on Intelligent Command and Dispatching at Chinese Institute of Command and Control.
Title: Beidou-based Next Generation Train Control
Abstract: Global Navigation Satellite System (GNSS) enables many applications in railway systems. GNSS only meets the requirements of non-safety relevant application requirements at meter level accuracy with ease. With the rapid development of Beidou Navigation Satellite System (BDS), the railway society is considering BDS adoption. Applying GNSS in signalling system to provide accurate and safe location solely from train-borne sensors is currently the hot issue in the development of next generation of train control system (NGTC).
In this presentation, the state-of-the-art R&D activities around the globe of applying GNSS into train control systems, the latest information of BDS and its feasible features for train control system are presented as the background for the Beidou-based train control. As an analysis of the train control requirements of low-density-lines, the architecture and the system characteristics of Beidou-based train control system are proposed, the key techniques including digital track map generation, multi-sensor fusion, virtual Balise concepts are introduced. Finally, the field test results using BDS and IMU sensor fusion results are analyzed. The illustration of BDS capability for safety-relevant applications is the promising future of next generation intelligent train control system.