Venkatesh Gowda P K

1. System Testing for Airbag Control Unit (ACU):

• Performed ECU flashing using a needle board setup, PCDiag, and Compa tools.
• Executing and validating system-level test cases for the ACU. Utilized CAN and UDS protocols for message-level diagnostics and fault detection.
• Used Vector CANoe for configuring simulations, monitoring signals, and executing CAPL scripts for automated test scenarios.

Customers: TATA, Hyundai, Kia.

Modules Handled: Pendulum Test, Crash Simulation (CrashSim), Scrapping, and Customer Diagnostics.

Project Description: System testing of ACUs ensures the proper deployment and response of airbag systems during crash and diagnostic events. Through hardware-in-the-loop testing and simulation, system behavior under various real-world scenarios is validated. Accurate flashing, parameter tuning, and fault diagnostics contribute to compliance with stringent automotive safety standards, and help detect any deviations in sensor data interpretation or system timing. The ultimate goal is to confirm that the airbag system performs reliably under all specified operating conditions.

2. Algorithm Testing and Management for ADAS Sensor Projects:

• Reviewed system requirements (IBM DOORS) for ADAS components, and authored related test cases.
• Interfaced with development and validation teams to ensure accurate requirement coverage, and timely issue resolution.
• Automated KPI evaluation and simulation result validation using Python scripts.
• Conducted performance and regression test validation for the ADAS MFC5xx series, generating test reports and statistical analyses.
• Supported V&V campaign traceability and closure using internal tools (IMS, Collaborator).

Customers: Knorr Bremse, Traton (MAN & SCANIA), Iveco

Components Handled: Object Detection (OD), Ego Motion (EMO).

Project Description: The algorithm plays a pivotal role in determining the surroundings of the driven vehicle. Hence, assuring the detection of objects, overhead structures, traffic signs, guardrails, etc. The output of testing delivers the list of safety-critical and function-relevant objects and their necessary attributes.

• Developed classification algorithms for land cover using CNN, SVM, and Random Forest.
• Collected and validated satellite imagery against ground truth using remote sensing tools.
• Automated preprocessing and analysis workflows in Python for multispectral image datasets.