Model-based decentralized embedded diagnosis inside vehicles: Application to Smart Distance Keeping function

In this paper, 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 version of the Adaptive Cruise Control (ACC) system, implemented in a Renault-Volvo Trucks vehicle. 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 be produced. 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 deploy it on several control units. This paper explains the model-based decentralized solution and its application to the embedded diagnosis of the SDK system inside 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.

[1]  M. Omizo,et al.  Modeling , 1983, Encyclopedic Dictionary of Archaeology.

[2]  Vehicle Diagnostics – The whole Story , 2007 .

[3]  Hervé Ressencourt.  Diagnostic hors-ligne à base de modèles : approche multi-modèle pour la génération automatique de séquences de tests : application au domaine de l'automobile , 2008 .

[4]  Wenbin Qiu.  Decentralized/distributed failure diagnosis and supervisory control of discrete event systems , 2005 .

[5]  Davis Lc.  Effect of adaptive cruise control systems on traffic flow. , 2004 .

[6]  Olivier Heron,et al.  Smart Distance Keeping: Modeling and Perspectives for Embedded Diagnosis , 2010, 2010 International Conference on Intelligent Systems, Modelling and Simulation.

[7]  Yin Wang.  New Results on Decentralized Diagnosis of Discrete Event Systems , 2004 .

[8]  Raja Sengupta,et al.  Diagnosability of discrete-event systems , 1995, IEEE Trans. Autom. Control..

[9]  Marcel Staroswiecki,et al.  A game-theoretic approach to decision in FDI , 2003, IEEE Trans. Autom. Control..

[10]  Neville A. Stanton,et al.  From fly-by-wire to drive-by-wire: Safety implications of automation in vehicles , 1996 .

[11]  L. Davis.  Effect of adaptive cruise control systems on traffic flow. , 2004, Physical review. E, Statistical, nonlinear, and soft matter physics.

[12]  Michael A. Goodrich,et al.  Model-based human-centered task automation: a case study in ACC system design , 2003, IEEE Trans. Syst. Man Cybern. Part A.

[13]  Lena Nilsson.  Safety effects of adaptive cruise controls in critical traffic situations , 1995 .

[14]  A. Willsky,et al.  Analytical redundancy and the design of robust failure detection systems , 1984 .

[15]  P. Greening.  On-board diagnostics for control of vehicle emissions , 1994 .

[16]  Paul M. Frank,et al.  Issues of Fault Diagnosis for Dynamic Systems , 2010, Springer London.

[17]  John D. Lee,et al.  Trust in Automation: Designing for Appropriate Reliance , 2004, Hum. Factors.

[18]  Raja Parasuraman,et al.  Humans and Automation: Use, Misuse, Disuse, Abuse , 1997, Hum. Factors.

[19]  C. Nohra.  Diagnostic de défauts sur un moteur diesel , 2009 .

[20]  P. O'Reilly.  An overview of the potential contribution of diagnostics to improving vehicle safety and reducing vehicle emissions , 1994 .

[21]  Aaas News,et al.  Book Reviews , 1893, Buffalo Medical and Surgical Journal.