Shan Bao

Research Associate Professor

Dr. Bao is a human factors researcher who has led and conducted multiple, large, simulator and naturalistic-driving studies for industry and government sponsors. Her areas of expertise include the statistical analysis of crash datasets and naturalistic data, vulnerable road user safety, experimental design, algorithm development to identify driver states and movement, evaluation of driving-safety technologies, measurement of driver performance, driver decision making, and statistical and stochastic modeling techniques. She has been invited to give multiple keynote speeches and served on expert panels at different conferences or meetings. She has also made technical presentations on scientific project results at many international conferences with a wide range of audiences. Dr. Bao is the author of the recent IEEE e-learning course of ‘Human Factors in Automated Vehicles”.

Expertise:

Let’s Connect

Phone

(734) 963-1127

Location

UMTRI

2901 Baxter Rd. Rm 305

Ann Arbor, MI 48109

Primary Website

umtri.umich.edu

Additional Information

Additional Appointments

Associate Professor, IMSE Department, University of Michigan-Dearborn

Adjunct Associate Professor, CEE Department, University of Michigan, Ann Arbor

Education

PhD, Mechanical & Industrial Engineering, University of Iowa, 2009.

MS, Mechanical Engineering, Hefei University of Technology, 2003.

BS, Mechanical Engineering, Hefei University of Technology, 2000.

Service

  • Member, Faculty Promotions and Appointments Committee
  • Member, Budget Committee
  • Member, TRB Human Factors Committee
  • Member, Transportation Research Board, Human Factors in Automated Vehicle Technologies Sub-committee
  • Member, Transportation Research Board, Young Drivers Sub-committee
  • Member and former chair, Surface Transportation Technical Group, Human Factors and Ergonomics Society

Awards

  • Best researcher-led paper award, 2014 Transportation Research Board Data Contest, Washington DC, USA 
  • Deep Scenario: City Scale Scenario Generation for Automated Driving System Testing and Evaluation. US DOT University Transportation Center: Center for Connected and Automated Transportation, 2019~2022. 
  • How Do Pedestrians Interact with a Driverless Car? Mcity, 2020-2021.
  • Examination Connected Vehicle Technology based Eco-Routing Choices. US DOT University Transportation Center: Center for Connected and Automated Transportation, 2019~2021. 
  • Developing Pedestrian- Related Corner Case Scenarios for Self-Driving Cars Testing Using Naturalistic Driving Data and Crash Data. Toyota Research Institute, 2019~2020. 
  • A Naturalistic Bicycling Study in the Ann Arbor Area, funded by Toyota Research Institute, 2017~2018. 
  • Evaluation of the Efficacy of Multiple Training Strategies on Drivers’ Safe Operation and Trust Calibration of Level 2 Automated Vehicle Systems. Mcity.  05/2016 – 12/2017. 
  • Human Factors Research on Seatbelt Interlock Systems. National Highway Traffic Safety Administration (Prime). 09/2013 – 12/2017. 
  • An Investigation of Drivers’ Adaptation Behavior and Decision Making when Interacting with Automated and Connected Vehicle Technologies. Mcity.  05/2015 – 04/2017. 
  • The Research of Distracted Driving Detection. Honda R&D America Inc, 2016 – 2018. 
  • Identify Abnormal Driving State Using Naturalistic Driving. Toyota automotive company (Toyota Class Action Settlement Safety Research and Education Program) through a subcontract from Texas Transportation Institute. 12/2013 -01/2017.