Modeling and Deployment of Model-Based Decentralized Embedded Diagnosis inside Vehicles: Application to Smart Distance Keeping Function

Abstract

The deployment of a fault diagnosis strategy in the Smart Distance Keeping (SDK) system with a decentralized architecture is presented. The SDK system is an advanced Adaptive Cruise Control (ACC) system implemented in a Renault-Volvo Trucks vehicle to increase safety by overcoming some ACC limitations. One of the main differences between this new system and the classical ACC is the choice of the safe distance. This latter is the distance between the vehicle equipped with the ACC or the SDK system and the obstacle-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. The variation of this distance depends essentially on the relative velocity between the vehicle and the obstacle-in-front. The main goal of this work is to analyze measurements, issued from the SDK elements, in order to detect, to localize, and to identify some faults that may occur. Our main contribution is the proposition of a decentralized approach permitting to carry out an on-line diagnosis without computing the global model and to achieve most of the work locally avoiding huge extra diagnostic information traffic between components. After a detailed description of the SDK system, this paper explains the model-based decentralized solution and its application to the embedded diagnosis of the SDK system inside Renault-Volvo Truck with five control units connected via a CAN-bus using “Hardware in the Loop” (HIL) technique. We also discuss the constraints that must be fulfilled.