Automated test development for vehicle instrument panel cluster using Hardware-in-the-loop (HIL) and Computer Vision

Automation of validation tests can significantly save time and improve accuracy. This thesis presents a method to automate the tests done on the Instrument Panel Cluster (IPC) of a car by using Hardware-in-the-Loop (HIL) and Optical Character Recognition (OCR). HIL technique helps to test the system with real signals and the system with the camera captures pictures to do OCR analysis for the extraction of messages displayed in the IPC. The developed OCR feature is added to the existing automation tool of the FCA group and the tests conducted in the internal test benches of Maserati. OCR technique is widely used in the automotive sector for validation testing of the IPC. In this thesis, the development is done by first performing a sequence of image processing on the captured image of the IPC and then feeding it to the OCR engine with the required language. The result showed the system to work efficiently as it extracted the messages from the captured images with confidence values close to 90 percent.The testing was done in different languages and low confidence values were found only for some languages with complex letters. After the developed OCR feature was integrated to the internal automation tool, tests were carried out both in the functional test bench and the integration test bench. A test case was defined based on a specific vehicle function and the final pass or fail report generated automatically.