A Motion Certification Concept to Evaluate Operational Safety and Optimizing Operating Parameters at Runtime

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9338)


For technical systems, which perform highly automated or so-called autonomous actions, there exist a large demand to evaluate their operational safety in a uniform way at runtime based on the combination of environmental threats and the conditions of subordinated system modules. To guarantee a safe motion based on autonomous decisions we have introduced a universal and transparent certification process which not only takes functional aspects like environment detection and collision avoidance techniques into account but especially identifies the associated system condition itself as a key aspect for the determination of operational safety and for an automated optimization of operating parameters. Similar to a feedback loop possible constraints for environment perception of sensor components or the ability of actuator components to interact with their environment have to be taken into account to introduce a generalized safetyevaluation for the entire system. Therefore, a model is derived to evaluate the operational safety for the autonomous driving robot RAVON from TU Kaiserslautern based on an integrated behavior-based control (IB2C).


Condition monitoring Safety Autonomous vehicles Conditional safety certificates Modularity Adaptive systems Mobile robots 


  1. 1.
    Adamy, J., Bechtel, P.: Sicherheit mobiler Roborter (Safety of mobile robots). at-Automatisierungstechnik/Methoden und Anwendungen der Steuerungs-, Regelungs-und Informationstechnik 51(10), 435–444 (2003)Google Scholar
  2. 2.
    Liggesmeyer, P., Trapp, M.: Safety: Herausforderungen und lösungsansätze. In: Industrie 4.0 in Produktion, Automatisierung und Logistik. Springer Fachmedien Wiesbaden (2014)Google Scholar
  3. 3.
    Kaiser, B., Liggesmeyer, P., Maeckel, O.: A new component concept for fault trees. In: Australian Computer Society, I. (ed.): Proceedings of the 8th Australian Workshop on Safety Critical Systems and Software, vol. 33, pp. 37–46. Australian Computer Society, Canberra, Australia (2003)Google Scholar
  4. 4.
    Domis, D., Trapp, M.: Integrating safety analyses and component-based design. In: Harrison, M.D., Sujan, M.-A. (eds.) SAFECOMP 2008. LNCS, vol. 5219, pp. 58–71. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  5. 5.
    Stamatis, D.H.: Failure mode and effect analysis: FMEA from theory to execution. ASQ Quality Press, Milwaukee (2003)Google Scholar
  6. 6.
    Shalev, D.M., Tiran, J.: Condition-based fault tree analysis (CBFTA): A new method for improved fault tree analysis (FTA). Reliab. Eng. Syst. Saf. 92, 1231–1241 (2007)CrossRefGoogle Scholar
  7. 7.
    Kleinlützum, K., Brockmann, W., Rosemann, N.: Modellierung von anomalien in einer modularen roboter-steuerung. In: Berns, K., Luksch, T. (eds.) Autonome Mobile Systeme 2007, pp. 89–95. Springer, Berlin (2007)CrossRefGoogle Scholar
  8. 8.
    Schneider, D., Trapp, M.: Conditional safety certification of open adaptive systems. ACM Trans. Auton. Adapt. Syst. 8(2), 1–20 (2013)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Lehrstuhl für Software Engineering: DependabilityTechnische Universität KaiserslauternKaiserslauternGermany

Personalised recommendations