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This paper presents the development of an automated test validation tool for AppLink in an in-vehicle infotainment system making use of Hardware-in-the-Loop (HiL). AppLink is a feature that allows the driver with a connected smartphone to interact with the phone apps through the car’s infotainment system. Since the software of the compatible apps on both infotainment and AppLink can be updated, it’s mandatory that an expert engineer tests every software version released to ensure that is working properly and the user doesn’t have to deal with bugs in the vehicle, reducing the possibility of driver’s distraction. As a Minimum Viable Product (MVP), the validation tool development focuses on automating the smoke test set, since it covers the main functionalities of the system. To do so, the test scenarios are first programmed based on pre-conditions and test procedures specifications.

As the development of in-vehicle infotainment systems increases, center stack display, digital instrument panels and heads-up displays become much more common in modern vehicles. Several of these screens are touch displays and in order to execute automated test in those displays against new iterations of software two solutions are possible: embedded touch simulators or physically touch the screens with external actuators. Although simulators can be more practical and easier to setup, its availability depends on the parts suppliers, not being always the same software and setup for the same test cases. External actuators have advantage to test the software and physical components and have a constant setup, but usually commercial options are expensive and need specialized professionals to configure it as needed.

This paper presents a method for assessing hands-free phone calls quality in an actual car infotainment system using Hardware-in-the-Loop (HiL) simulations. Hands-free is a Bluetooth profile that allows the communication between the owner’s phone and its car Bluetooth system which is a common feature in modern cars. This work aims to give a quantitative evaluation of the hands-free feature performance when receiving a phone call according to accepted quality standards in the industry. To do this, several HiL tests were made simulating an incoming call situation where the driver has a smartphone paired via Bluetooth with the vehicle infotainment system. After call acceptance, a 1 kHz tone is sent from the calling phone to the driver’s phone and the output of interest is the electrical signal sent to the vehicle’s speakers representing the audio information.

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.