Speaker: Soheil Sohrabi, Doctoral Candidate, Zachry Department of Civil and Environmental Engineering, Texas A&M Transportation Institute.
Faculty Host: : Dr. Bahar Dadashova, Texas A&M Transportation Institute
Abstract: Automated vehicles (AV) are designed to perform dynamic driving tasks safely, but there are certain situations that AVs were faced safety challenges. Despite the fact that successful development and deployment of AVs is contingent upon their superior safety performance comparing to conventional vehicles, AV safety has not been validated yet. In this seminar, first, we will learn about the existing knowledge about AV safety evaluation and identify gaps in the literature. Then, a new methodology will be introduced that addresses some of the gaps in AV safety evaluations. This seminar will be concluded with the results of implementing the proposed methodology on AVs road tests data. This seminar series is co-organized by the Department of Landscape Architecture and Urban Planning, Transportation Institute, and Institute of Data Science at Texas A&M University.
This seminar series is co-organized by Department of Landscape Architecture and Urban Planning, Transportation Institute, and Institute of Data Science at Texas A&M University
Biography: Soheil Sohrabi is a doctoral candidate at the Zachry Department of Civil and Environmental Engineering and a Graduate Research Assistant at the Texas A&M Transportation Institute. Soheil’s research concerns leveraging data analytics and statistical models to understand the safety and health implications of new transportation technologies. Soheil’s research has received multiple awards, namely, the Daniel B. Fambro award from the Institute of Transportation Engineers and the American Statistical Association’s Transportation Statistics Interest Groups’ student paper award.
The Transportation Data Science Seminar Series is sponsored by the Department of Landscape Architecture and Urban Planning, the Texas A&M Transportation Institute and the Texas A&M Institute of Data Science. Please contact Xinyue Ye (LUAP), Michael Martin (TTI), or Xiao Li (TTI) for further information,
You can also click this link to join the seminar
