Design of a Knowledge-Base Strategy for Capability-Aware Treatment of Uncertainties of Automated Driving Systems

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11094)


Automated Driving Systems (ADS) represent a key technological advancement in the area of Cyber-physical systems (CPS) and Embedded Control Systems (ECS) with the aim of promoting traffic safety and environmental sustainability. The operation of ADS however exhibits several uncertainties that if improperly treated in development and operation would lead to safety and performance related problems. This paper presents the design of a knowledge-base (KB) strategy for a systematic treatment of such uncertainties and their system-wide implications on design-space and state-space. In the context of this approach, we use the term Knowledge-Base (KB) to refer to the model that stipulates the fundamental facts of a CPS in regard to the overall system operational states, action sequences, as well as the related costs or constraint factors. The model constitutes a formal basis for describing, communicating and inferring particular operational truths as well as the belief and knowledge representing the awareness or comprehension of such truths. For the reasoning of ADS behaviors and safety risks, each system operational state is explicitly formulated as a conjunction of environmental state and some collective states showing the ADS capabilities for perception, control and actuations. Uncertainty Models (UM) are associated as attributes to such state definitions for describing and quantifying the corresponding belief or knowledge status due to the presences of evidences about system performance and deficiencies, etc. On a broader perspective, the approach is part of our research on bridging the gaps among intelligent functions, system capability and dependability for mission-&safety-critical CPS, through a combination of development- and run-time measures.


Automated Driving System (ADS) Cyber-Physical System (CPS) Embedded Control System (ECS) Knowledge-Base (KB) Uncertainty Models (UM) Safety 



The research has been supported by the Swedish government agency for innovation systems (VINNOVA) in the ESPLANADE project (ref 2016-04268).


  1. 1.
  2. 2.
    SAE International: SAE J3016: Taxonomy and Definitions for Terms Related to Driving Automation Systems for on-Road Motor Vehicle (2016)Google Scholar
  3. 3.
    Frederick, H.-R., Waterman, D., Lenat, D.: Building Expert Systems. Addison-Wesley (1983). ISBN 0-201-10686-8Google Scholar
  4. 4.
    Chen, D., Lu, Z.: A methodological framework for model-based self-management of services and components in dependable cyber-physical systems. In: Zamojski, W., Mazurkiewicz, J., Sugier, J., Walkowiak, T., Kacprzyk, J. (eds.) DepCoS-RELCOMEX 2017. AISC, vol. 582, pp. 97–105. Springer, Cham (2018). Scholar
  5. 5.
    Kolagari, R., et al.: Model-based analysis and engineering of automotive architectures with EAST-ADL: revisited. Int. J. Concept. Struct. Smart Appl. (IJCSSA) 3(2), 25–70 (2015)Google Scholar
  6. 6.
    Johansson, R., et al.: A strategy for assessing safe use of sensors in autonomous road vehicles. In: Tonetta, S., Schoitsch, E., Bitsch, F. (eds.) SAFECOMP 2017. LNCS, vol. 10488, pp. 149–161. Springer, Cham (2017). Scholar
  7. 7.
    Miyajima, C., et al.: Analyzing driver gaze behavior and consistency of decision making during automated driving. In: IEEE Intelligent Vehicles Symposium, August 2015 (Iv) (2015)Google Scholar
  8. 8.
    Michon, J.A.: A critical view of driver behavior models: what do we know, what should we do? In: Human Behavior and Traffic Safety. Plenum (1985)Google Scholar
  9. 9.
    Albus, J.S., Proctor, F.G.: A reference model architecture for intelligent hybrid control systems. In: Proceedings of the IFAC, San Francisco, CA (1996)Google Scholar
  10. 10.
    Cimatti, A., et al.: NUSMV a new symbolic model checker. Int. J. Softw. Tools Technol. Transf. 2(4), 410–425 (2000)CrossRefGoogle Scholar
  11. 11.
    der Kiureghian, A., Ditlevsen, O.: Aleatory or epistemic? Does it matter? Struct. Saf. 31(2), 105–112 (2009)CrossRefGoogle Scholar
  12. 12.
    Hermann, G.M.: Quantifying uncertainty: modern computational representation of probability and applications, extreme man-made and natural hazards in dynamics of structures. In: NATO Security through Science Series, 2007, pp. 105–135 (2007)Google Scholar
  13. 13.
    Zhang, M., et al.: Understanding uncertainty in cyber-physical systems: a conceptual model. In: Wąsowski, A., Lönn, H. (eds.) ECMFA 2016. LNCS, vol. 9764, pp. 247–264. Springer, Cham (2016). Scholar
  14. 14.
    SysML. OMG Systems Modeling Language (OMG SysML™), OMGGoogle Scholar
  15. 15.
    Feiler, P.H., Gluch, D.P.: Model-Based Engineering with AADL: An Introduction to the SAE Architecture Analysis & Design Language. Addison-Wesley (2012)Google Scholar
  16. 16.
    Mackay, D.J.C.: Information Theory, Inference, and Learning Algorithms. Cambridge University Press, Cambridge (2003). ISBN 0-521-64298-1zbMATHGoogle Scholar
  17. 17.
    Aven, T., et al.: Uncertainty in Risk Assessment: The Representation and Treatment of Uncertainties by Probabilistic and Non-Probabilistic Methods. Wiley (2013)Google Scholar
  18. 18.
    Meedeniya, I., et al.: Evaluating probabilistic models with uncertain model parameters. Softw. Syst. Model. 13(4), 1395–1415 (2014)CrossRefGoogle Scholar
  19. 19.
    Ying, J., et al.: A hidden Markov model-based algorithm for fault diagnosis with partial and imperfect tests. IEEE Trans. Syst. Man. Cybern. Part C 30(4), 463–473 (2000)CrossRefGoogle Scholar
  20. 20.
    Zhang, X., Gu, C., Lin, J.: Support vector machines for anomaly detection. In: Intelligent Control and Automation, 2006, IEEE 6th World Congress on WCICA (2006)Google Scholar

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© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.KTH Royal Institute of TechnologyStockholmSweden
  2. 2.RISE - Research Institutes of SwedenBoråsSweden
  3. 3.Zenuity ABGöteborgSweden

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