Sikai Chen

Purdue University

chen1670@purdue.edu

LinkedIn | Google Scholar

Biography

Post-Doctoral Researcher – Center for Connected and Automated Transportation
Post-Doctoral Researcher – NEXTRANS Center at Purdue University
Visiting Research Fellow – Robotics Institute at the School of Computer Science at Carnegie Mellon University

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.

Their Research

Development of AI-Based and Control-Based Systems for Safe and Efficient Operations of Connected and Autonomous Vehicles
Principal Investigator:
Samuel Labi & Sikai Chen
Research Thrusts: Control & OperationsEnabling TechnologyModeling & Implementation
Development of Situational Awareness Enhancing Systems for AV-Manual Handover and Other Tasks
Principal Investigator:
Samuel Labi & Sikai Chen
Research Thrusts: Enabling Technology, Human Factors, Modeling & Implementation
Large Network Multi-Level Control for CAV and Smart Infrastructure: AI-based Fog-Cloud Collaboration
Principal Investigator:
Sikai Chen, Samuel Labi, & Kumares Sinha
Research Thrusts: Control & OperationsEnabling Technology, Infrastructure Design & Management, Modeling & Implementation
Using Virtual Reality Techniques to Investigating Interactions Between Fully Autonomous Vehicles and Vulnerable Road Users
Principal Investigator:
Samuel Labi & Sikai Chen
Research Thrusts: Enabling Technology, Human Factors