Research Review with Samuel Labi, Ph.D. and Sikai Chen, Ph.D.
Speaker(s): Samuel Labi, Ph.D., Professor of Civil Engineering, Purdue University
Associate Director, Center for Connected and Automated Transportation
Sikai Chen, Ph.D., Postdoctoral Researcher, Center for Connected and Automated Transportation (CCAT)
Presentation Title: Collision Avoidance Framework for Autonomous Vehicles under Imminent Situations
Date/Time: Tuesday, February 25th, 2020 | 2:00 PM ET
Continuing Education Units (CEU): .1*
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Abstract: There are three major safety assessment methods for transportation researchers: simulation, test track, and public roads. Test tracks provide a safe, cost-effective testing environment, but they typically cannot provide a level of background traffic that may be necessary for testing. Dr. Yiheng and Shuo Feng of U-M will present on a new safety assessment framework to address these limitations. This includes an augmented reality testing platform and a testing scenario library generation method that is implemented at the Mcity Test Facility, featuring SAE Level 4 vehicles.
Dr. Samuel Labi is a Professor at Purdue University’s Lyles School of Civil Engineering and the Associate Director of CCAT. He has authored two textbooks used in a number of universities worldwide: Transportation Decision Making and Introduction to Civil Engineering Systems. His research awards include TRB’s Woods and Mickel Award and ASCE’s Masters Award for outstanding and innovative work that advances transport systems. Dr. Labi is an associate editor for Computer-Aided Civil & Infrastructure Engineering, the Journal of Risk and Uncertainty in Engineering Systems, and the Journal of Infrastructure Systems. He is the chair of ASCE’s Economics & Finance committee and serves as a PI for five CCAT projects that address CAV human factors, driving simulation, infrastructure preparation, economics and finance, and operations modelling.
Dr. Chen is a member of two ASCE committees: Connected and Autonomous Vehicle Impacts, and Economics and Finance. His recently published award-winning thesis addressed the safety implications of roadway design and management, specifically examining new evidence and insights in the traditional and emerging (autonomous vehicle) operating environments. His research interests include transportation infrastructure systems evaluation, roadway safety, and policy analysis, and applications of machine learning and data mining in transportation and infrastructure systems. His current research at CCAT and the Robotics Institute focuses on deep learning, intelligent transportation systems, vehicle automation, simulation, and control.