Riken Mangukiya

"Effectiveness Assessment of the Advanced Driver Assistance Systems based on Real World Accident Scenarios " • Simulations based on real world accident scenarios (retrospective ) with integration of new ADAS (prospective) in IPG CarMaker • Effectiveness assessment of ADAS • Development of Injury Risk Function based on novel approach based on k-NN and polynomial regression • Representing a prospective catalog of relevant and representative accident scenarios Tools: IPG CarMaker, MATLAB/Simulink, Python

Internship : Machine Learning methods for the extraction of accident scenarios. - Worked with German Accident Database GIDAS Datenbank - Unsupervised Machine Learning, Clustering Methods -> To extract the familier as well as new accident scenarios from the past accidents in oder to further safety function development. - Injury Risk function development using KNN - Cluster visualisation of collisions between two vehicles using vehicle as a Box plot. Tools: Python, MATLAB & Power BI

Vehicle Integral Safety "Prediction of crash severity and restraint strategy using database approaches". • Preparation of two-stage FEM simulations for frontal impact, vehicle, and occupant level. • Writing a code of MATLAB scripts for extraction of data from the simulation. • Develop machine learning algorithms to predict the crash severity, activation of restraint systems, and their time. Tools: LS-Dyna, MATLAB, Microsoft Excel, Python, LaTex

Core Area : Vehicle Safety Vehicle Crash Mechanics and Biomechanics, Integrated Safety and Assistance Systems, SensorTechnologyandSignalProcessing, Testing and Simulation Methods for Vehicle Safety Systems, Automotive Radar System, MATLAB and Simulink for Automotive Design.