Advertisement

Safe Automotive Software

  • Karl Heckemann
  • Manuel Gesell
  • Thomas Pfister
  • Karsten Berns
  • Klaus Schneider
  • Mario Trapp
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6884)

Abstract

For automotive manufacturers and tier-1 suppliers, the upcoming safety standard ISO 26262 results in new requirements for the development of embedded electronics and software. In particular, the variety of driver assistance systems that autonomously influence the driving dynamics of a vehicle may have a high risk potential and require development in accordance with the normative guidelines. But especially for those systems whose function is typically not based solely on hardware but on complex software algorithms, safety certification can be very complex or even impossible. In this paper the problems of development of vehicle systems according to ISO 26262 are described. Finally an approach for a safety-oriented reference architecture is presented that introduces adaptive software safety cages. This architecture enables application of formal verification methods. Supported by multisensor data fusion this allows to reduce safety requirements for vehicle control systems.

Keywords

Model Check Vehicle System Electronic Control Unit Driver Assistance System Road Vehicle 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Alur, R., Henzinger, T., Ho, P.H.: Automatic symbolic verification of embedded systems. IEEE Transactions on Software Engineering (T-SE) 22(3), 181–201 (1996)CrossRefGoogle Scholar
  2. 2.
    Alur, R., Henzinger, T., Lafferriere, G., Pappas, G.: Discrete abstractions of hybrid systems. Proceedings of the IEEE 88(7), 971–984 (2000)CrossRefGoogle Scholar
  3. 3.
    AUTOSAR: Technical Safety Concept Status Report (2009)Google Scholar
  4. 4.
    Avizienis, A., Laprie, J.C., Randell, B.: Fundamental concepts of dependability. In: Proceedings of ISW 2000, 34th Information Survivability Workshop, pp. 7–12. IEEE, Los Alamitos (2000)Google Scholar
  5. 5.
    Bauer, K., Gentilini, R., Schneider, K.: Property driven three-valued model checking on hybrid automata. In: Ono, H., Kanazawa, M., de Queiroz, R. (eds.) WoLLIC 2009. LNCS, vol. 5514, pp. 218–229. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  6. 6.
    Chetouani, Y.: Fault detection by using the innovation signal: application to an exothermic reaction. Chemical Engineering and Processing 43(12), 1579–1585 (2004)CrossRefGoogle Scholar
  7. 7.
    Clarke, E., Grumberg, O., Long, D.: Model checking and abstraction. ACM Transactions on Programming Languages and Systems (TOPLAS) 16(5), 1512–1542 (1994)CrossRefGoogle Scholar
  8. 8.
    Durrant-Whyte, H., Henderson, T.C.: Multisensor data fusion. In: Siciliano, B., Khatib, O. (eds.) Springer Handbook of Robotics, pp. 585–610. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  9. 9.
    GM: GM unveils EN-V concept: A vision for future urban mobility. Website (2010), http://www.gmexpo2010.com/en-v/en/introduction/press (visited on April 29, 2011)
  10. 10.
    Hajiyev, C.: Testing the covariance matrix of the innovation sequence with sensor/actuator fault detection applications. International Journal of Adaptive Control and Signal Processing 24(9), 717–730 (2010)MathSciNetCrossRefzbMATHGoogle Scholar
  11. 11.
    Henzinger, T.: Verification of Digital and Hybrid Systems. In: Verification of Digital and Hybrid Systems. NATO Advanced Study Institute Series F: Computer and Systems Sciences, vol. 170, pp. 265–292. Springer, Heidelberg (2000)CrossRefGoogle Scholar
  12. 12.
    ISO/DIS 26262: Road Vehicles, Functional Safety Part 1 to 10 (2008)Google Scholar
  13. 13.
    McMillan, K.: The SMV system, symbolic model checking - an approach. Tech. Rep. CMU-CS-92-131, Carnegie Mellon University (1992)Google Scholar
  14. 14.
    Sha, L.: Using simplicity to control complexity. IEEE Software 18, 20–28 (2001)Google Scholar
  15. 15.
    Venkatasubramanian, V., Rengaswamy, R., Kavuri, S.N.: A review of process fault detection and diagnosis: Part ii: Qualitative models and search strategies. Computers & Chemical Engineering 27(3), 313–326 (2003)CrossRefGoogle Scholar
  16. 16.
    Venkatasubramanian, V., Rengaswamy, R., Yin, K., Kavuri, S.N.: A review of process fault detection and diagnosis: Part i: Quantitative model-based methods. Computers & Chemical Engineering 27(3), 293–311 (2003)CrossRefGoogle Scholar
  17. 17.
    Wenzel, M., Paulson, L.C., Nipkow, T.: The isabelle framework. In: Mohamed, O.A., Muoz, C., Tahar, S. (eds.) TPHOLs 2008. LNCS, vol. 5170, pp. 33–38. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  18. 18.
    Zhang, Y., Jiang, J.: Bibliographical review on reconfigurable fault-tolerant control systems. Annual Reviews in Control 32(2), 229–252 (2008)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Karl Heckemann
    • 1
  • Manuel Gesell
    • 2
  • Thomas Pfister
    • 3
  • Karsten Berns
    • 3
  • Klaus Schneider
    • 2
  • Mario Trapp
    • 1
  1. 1.Fraunhofer Institute for Experimental Software EngineeringKaiserslauternGermany
  2. 2.Embedded Systems Group, Department of Computer ScienceUniversity of KaiserslauternGermany
  3. 3.Robotics Research Lab, Department of Computer ScienceUniversity of KaiserslauternGermany

Personalised recommendations