The deployment of a fault diagnosis strategy in the Smart Distance Keeping (SDK) system with a decentralized architecture is\r\npresented. The SDK system is an advanced Adaptive Cruise Control (ACC) system implemented in a Renault-Volvo Trucks vehicle\r\nto increase safety by overcoming some ACC limitations. One of the main differences between this new system and the classical ACC\r\nis the choice of the safe distance. This latter is the distance between the vehicle equipped with the ACC or the SDK system and the\r\nobstacle-in-front (which may be another vehicle). It is supposed fixed in the case of the ACC, while variable in the case of the SDK.\r\nThe variation of this distance depends essentially on the relative velocity between the vehicle and the obstacle-in-front. The main\r\ngoal of this work is to analyze measurements, issued from the SDK elements, in order to detect, to localize, and to identify some\r\nfaults that may occur. Our main contribution is the proposition of a decentralized approach permitting to carry out an on-line\r\ndiagnosis without computing the global model and to achieve most of the work locally avoiding huge extra diagnostic information\r\ntraffic between components. After a detailed description of the SDK system, this paper explains the model-based decentralized\r\nsolution and its application to the embedded diagnosis of the SDK system inside Renault-Volvo Truck with five control units\r\nconnected via a CAN-bus using ââ?¬Å?Hardware in the Loopââ?¬Â (HIL) technique.We also discuss the constraints that must be fulfilled.
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