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The advance of technology and the demand for new features on vehicles has been challenging the automotive industry to find ways to speed up its development process, while increases the robustness and quality of its products. On this context, the embedded software development for vehicles has been directly impacted. In other words, the number of Electronic Control Units (ECUs) considerably increased on the past few years and the number of lines of code, as well as their complexity, have been exponentially increased. In order to deal with this new reality, besides the automotive test prototypes used during the development, the automotive industry has been using different virtual environment to develop, verify and validate its products.

This paper approaches the use of Hardware-in-the-Loop (HiL) test framework to perform automated verification test of Seatbelt Reminder Function in automotive Instrument Panel Cluster (IPC). The goal of this test was to verify if the Belt-Minder™ Feature performs its seatbelt warning function sounding a chime and illuminating a warning light when the user has his seatbelt unbuckled in compliance with safety standards and regulations. The manual execution of this test requires many resources like prototype vehicle, fuel, many engineers' work hours for each test, etc. However, testing dynamically with HiL system some of those mentioned resources are not required like the prototype vehicle and other are reduced, for example, the time taken to have report on hands. The most important among the advantages, was the possibility of testing with HiL earlier in the V-Diagram.

This paper approaches the use of machine vision as an automation tool for verification tests in automotive Instrument Panel Cluster (IPC). A computer integrated with PXI modular instruments, machine vision software and Integrated Development Environment (IDE) composes the test system. The IPC is verified in closed-loop using the Hardware-in-the-Loop (HiL) technique in which the HiL system simulates all Electronic Control Units (ECUs) that interact with the IPC. Every simulated ECUs signals are sent to the IPC over CAN (Controller Area Network) bus or hardwired I/O using PXI modules integrated with IDE and its responses are captured by cameras. Using machine vision such images are subjected to Digital Image Processing (DIP) techniques as pattern matching, edge detection and Optical Character Recognition (OCR), which can be applied to interpret speedometer, tachometer, fuel gauges, display and warning lights.