Adaptive Error and Sensor Management for Autonomous Vehicles: Model-Based Approach and Run-Time System

  • Jelena Frtunikj
  • Vladimir Rupanov
  • Michael Armbruster
  • Alois Knoll
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8822)


Over the past few years semi-autonomous driving functionality was introduced in the automotive market, and this trend continues towards fully autonomous cars. While in autonomous vehicles data from various types of sensors realize the new highly safety critical autonomous functionality, the already complex system architecture faces the challenge of designing highly reliable and safe autonomous driving system. Since sensors are prone to intermittent faults, using different sensors is better and more cost effective than duplicating the same sensor type because of diversity of reaction of different sensor typesto the same environmental condition. Specifying and validating sensors and providing technical means that enable usage of data from different sensors in case of failures is a challenging, time-consuming and error-prone task for engineers. Therefore, in this paper we present our model-based approach and a run-time system that improves the safety of autonomous driving systems by providing reusable framework managing different sensor setups in a vehicle in a case of a error. Moreover, the solution that we provide enables adaptive graceful degradation and reconfiguration by effective use of the system resources. At the end we explain in an example when and how the approach can be applied.


safety sensor models autonomous driving systems adaptive graceful degradation 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Laprie, J.C.C., Avizienis, A., Kopetz, H.: Dependability: Basic Concepts and Terminology. Springer-Verlag New York, Inc. (1992)Google Scholar
  2. 2.
    Lehmann, G., Blumendorf, M., Trollmann, F., Albayrak, S.: Meta-modeling Runtime Models. In: MoDELS Workshops (2010)Google Scholar
  3. 3.
    Roth, E., Dirndorfer, T., von Neumann-Cosel, K., Fischer, M.-O., Ganslmeier, T., Kern, A., Knoll, A.: Analysis and Validation of Perception Sensor Models in an Integrated Vehicle and Environment Simulation. In: Proceedings of the 22nd Enhanced Safety of Vehicles Conference (2011)Google Scholar
  4. 4.
    Mamun, M., Berger, C., Hansson, J.: MDE-based Sensor Management and Verification for a Self-Driving Miniature Vehicle. In: Proceedings of the 13th Workshop on Domain-Specific Modeling (2013)Google Scholar
  5. 5.
    Frtunikj, J., Rupanov, V., Camek, A., Buckl, C., Knoll, A.: A Safety Aware Run-Time Environment for Adaptive Automotive Control Systems. In: Embedded Real-Time Software and Systems, ERTS2 (2014)Google Scholar
  6. 6.
    Shelton, C.P., Koopman, P., Nace, W.: A framework for scalable analysis and design of system-wide graceful degradation in distributed embedded systems. In: Proceedings of the Eighth International Workshop on Object-Oriented Real-Time Dependable Systems (2003)Google Scholar
  7. 7.
    Tichy, M., Giese, H.: Extending Fault Tolerance Patterns by Visual Degradation Rules. In: Proceedings of the Workshop on Visual Modeling for Software Intensive Systems, VMSIS (2005)Google Scholar
  8. 8.
    Urmson, C., et al.: Autonomous driving in urban environments: Boss and the Urban Challenge. Journal of Field Robotics Special Issue on the 2007 DARPA Urban Challenge, Part I (2008)Google Scholar
  9. 9.
    Bernhard, M., et al.: The Software Car: Information and Communication Technology (ICT) as an Engine for the Electromobility of the Future, Summary of results of the “eCar ICT System Architecture for Electromobility” research project sponsored by the Federal Ministry of Economics and Technology (2011)Google Scholar
  10. 10.
    Dmitri, D., et al.: Path Planning for Autonomous Vehicles in Unknown Semi-structured Environments. Sage Publications, Inc. (2010)Google Scholar
  11. 11.
    International Organization for Standardization: ISO/DIS 26262 - Road vehicles. Functional safety. Technical Committee 22, ISO/TC 22 (2011)Google Scholar
  12. 12.
    Broy, M., Kruger, I.H., Pretschner, A., Salzmann, C.: Engineering Automotive Software. Proceedings of the IEEE (2007)Google Scholar
  13. 13.
    Sommer, S., et al.: RACE: A Centralized Platform Computer Based Architecture for Automotive Applications. In: Vehicular Electronics Conference (VEC) and the International Electric Vehicle Conference, IEVC (2013)Google Scholar
  14. 14.
    Adler, R., Schaefer, I., Schuele, T.: Model-Based Development of an Adaptive Vehicle Stability Control System, Modellbasierte Entwicklung von eingebetteten Fahrzeugfunktionen, MBEFF (2008)Google Scholar
  15. 15.
    AUTOSAR Group: AUTomotive Open System ARchitecture (AUTOSAR) Release 4.1 (2013)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Jelena Frtunikj
    • 1
  • Vladimir Rupanov
    • 1
  • Michael Armbruster
    • 2
  • Alois Knoll
    • 3
  1. 1.Fortiss GmbHAn-Institut Technische Universität MünchenMünchenGermany
  2. 2.Corporate Research and TechnologiesSiemens AGMünchenGermany
  3. 3.Fakultät für InformatikTechnische Universität MünchenGarching bei MünchenGermany

Personalised recommendations