•Established Simulation Environment to perform following ADAS features testing:-Adaptive Cruise Control -Lane Keeping System-Pre Collision Assist-Traffic sign recognition-Blind Spot Monitoring-Lane change warning assist-Auto high beam -Glare free high beam-Evasive steering assist-Cross traffic alert-Clear Exit Alert•Incorporated simulation solutions for improved DV methods•Spearheaded simulation and automation projects to increase technical capabilities of the lab•Assessed test equipment and capability for long term viability and competitiveness•Bolster the integration of novel network architecture in breadboards •Implemented simulation solutions to increase test case coverage & capability•Global Simulation Support- Ford of Mexico & Ford of Australia•Speedometer & Odometer Test Automation using dSPACE Simulator•U71x FDJ & Sign-Off Program DAT Simulation Support•Collaborated with CMA (CAN Message Authentication) Team to troubleshoot CMA Bus Signal-Message •Smooth Dimming Feature Testing & Simulation on U625 Breadboard•Standardized Engine Simulation Cart Development•OAT Simulation•Wheel Speed Sensor Simulation using NI Simulator to increase test coverage•Camera Direct Injection on P702 BEV Program•Accessory Protocol Interface Module (APIM) Automation using Python Scripts & Virtual Development Platform•Trailer Light Check Feature Testing Automation for P708 Program•Ecocoach Feature Testing Automation for CX483 Program•Odometer Sign-off Testing for S650, P708, CX727, U611 & U625 Program•OTA Software Deployment Testing on CX727, P702 & U540 Vehicles•Provisioned ECG & TCU modules to troubleshoot issues associated with connected vehicle features•Expedited software management process for all the 45 ECUs of all 30 test vehicles by developing & executing MATLAB Automation scripts•Executed & analyzed Key-Offload (KOL) test after each Software update to ensure battery current does not exceed predetermined value for each vehicle program

Solid state on-chip Lidar for autonomous driving: High-efficiency end-fire 3D optical phased array based on a multi-layer Si3N4/SiO2 platform

Localization of Autonomous Vehicle using Sensor Fusion and Nonlinear Filtering • Synthesized artificial driving environment for feature testing using MATLAB-Simulink• Enhanced the reliability of data coming from IMU and GPS using sensor fusion algorithms • Estimated the location of ego vehicle with an error of less than 10 cm using EKF and PF algorithmModelling and Simulation of Behavioral Planner for Robust Autonomous Highway Driving• Orchestrated the selection of highway maneuvers using Prediction and Cost function-based algorithm • Concatenated and clustered RADAR data using sensor fusion techniques • Innovated high level task of decision making using Finite State Machines using MATLAB-SimulinkDevelopment and Execution of Testing Scenarios for ADAS L-2 Features (AEB, FCW, ACC, LKA) • Barrier in the Path of the Ego Vehicle at 85m• A Truck steers into the same lane as the Ego Vehicle• Ego Vehicle comes across the intersection where other cars are passing by• Left lane change with no vehicle coming beside for 4 sec

• Optimized PVC and sealant application process employing VA-VE (Value Analysis-Value Engineering) techniques resulted into annual savings of $85,000 and $53,000 respectively• Exposure to PLM software by studying over 190 VA-VE ideas through digital simulation

• Streamlined manpower by carrying out work-study analysis to increase the average utilization of each worker from 65.2% to 81.5% without affecting production volume