Received the B.S. and M.S. degrees in aerospace engineering in 2012 and the Ph.D. degree in reliability engineering and system analysis in 2018, both from the Bauman Moscow State Technical University, Russia. From 2012 to 2016, used to be a Reliability Engineer in several major Russian radar and electronics companies. Since 2016, has been a System/Functional Safety Engineer with RoboCV, Russian-based warehouse automation start-up based in Skolkovo Innovation Centre, Moscow. Later in 2017 joined Arrival Ltd., a UK-based electric vehicles start-up where he was in the position of the Head of Functional Safety. Dr. Babaev is the author of more than 25 publications in reliability and safety engineering, as well as 2 patents in automotive and machinery safety areas. His research interests include reliability and safety engineering, system analysis, machine learning and statistics methods in safety. Dr. Babaev is a co-guest Editor of the MDPI 'Artificial Intelligence for Connected and Automated Vehicles' journal. He is also a part of UK ISO 21448 workgroup and TUV Rheinland certified functional safety engineer.
Case Study
Monday, June 30
12:00 pm - 12:30 pm
Live in San Francisco
Less Details
As the automotive industry continues to evolve, so do the methods for ensuring the safety of automotive products. In recent years, data-driven methods have emerged as a powerful tool for estimating crash severity and aiding risk assessment. This presentation will provide an overview of application of ML and Bayesian methods for accident severity estimation based on crash data collected in the US (CRSS) and UK (stats19 dataset). The presentation will also introduce potential applications of these methods for the wider scope of problems in the automotive industry.