Dr. Ching-Yao Chan is Co-Director, along with Prof. Trevor Darrell(link is external) and Prof. Kurt Keutzer(link is external), of Berkeley DeepDrive (BDD(link is external)). BDD is a research consortium dedicated to the research and development of technologies for intelligent autonomy, with applications for autonomous driving and robotics. Dr. Chan leads research projects on machine learning applications in autonomous driving and manages the BDD infrastructure projects in data and experimental vehicles.
Dr. Ching-Yao Chan is also a Researcher and a Program Manager at California PATH (Partners for Advanced Transportation Technology). PATH has been a pioneering organization spearheading the field of research on intelligent transportation systems since 1986. At PATH, Dr. Chan leads research projects in vehicle automation, advanced vehicular technologies, human factors, and traffic systems.
Dr. Chan has three decades of research experience in a broad range of automotive and transportation systems. His research spans from the development of driver-assistance and automated driving systems, sensing and wireless communication technologies, and highway network safety assessment.
- B.S., Mechanical Engineering, National Taiwan University, 1981
- M.S., Mechanical Engineering, University of California at Berkeley, 1985
- Ph.D., Mechanical Engineering, University of California at Berkeley, 1988
Click here for a recent list of publications(PDF file)
TECHNICAL BACKGROUND AND PROFESSIONAL CAREER
After receiving his Ph.D. degree in Mechanical Engineering from UC Berkeley in 1988, he worked in the private sector before returning to Berkeley in 1994. Prior to joining PATH, Dr. Chan worked in the field of vehicular passive safety systems. While being involved in the research, and development of crash sensing technologies, he also gained first-hand knowledge on general passive restraint systems, as he worked with automotive tier-one supplies and automotive OEMs. During 1990-1994, he worked in litigation support on accident reconstruction and participated in numerous cases of vehicle crashes, through which he gained insights on the interaction of drivers, vehicle characteristics, roadway environment, and their impacts on driving risks.
Due to his nationally recognized expertise in crash sensing and vehicular safety, Dr. Chan was invited by Society of Automotive Engineers (SAE) to provide tutorials to more than 500 automotive professionals in an SAE seminar series. He has given lectures to various organizations. He collaborated with SAE to publish a book and a video tutorial, and he was the recipient of the 1998 SAE Forest R. MacFarland Award for his outstanding contributions to engineering education.
Dr. Chan was also significantly involved in the research and development of vehicle automation technologies. During the years of the National Automated Highway Systems Consortium in 1990s, he represented PATH in the national working group of technology development and evaluation. Subsequently, he also worked in projects that involved the use of various technologies for vehicular automation systems. In 2003, he led a team of researchers and engineers in the Demonstration of Bus Automation Technology in San Diego. The project subsequently won the prestigious award of the Best of ITS Research Award from the ITS America in 2004.
Dr. Chan also collaborated with industrial and academic partners in developing and implementing communication-enabled cooperative systems in multiple projects. Applications include vehicle-to-vehicle and vehicle-to-infrastructure, vehicle-to-pedestrian, and road equipment-to-network operation scenarios. These projects were supported by and jointly conducted with federal and state governments, automaker consortium, and private-sector partners.
Dr. Chan served as a visiting Professor at the University of Tokyo, Institute of Industrial Science(link is external) from May 2006 to January 2007 and a visiting scholar at Institute of French National Transport Research (INRETS, which is now IFSTTAR(link is external)) in the summer of 2004.
- Vehicle Automation and Advanced Driver Assistance Systems (ADAS)
- Berkeley Deep Drive: Machine Learning for Autonomous Driving(link is external)
- Human Factors Studies and Human-Machine Interaction