Dynamic Evolution of Simulated Autonomous Cars in the Open World Through Tactics

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 880)


There is an increasing level of interest in self-driving cars. In fact, it is predicted that fully autonomous cars will roam the streets by 2020. For an autonomous car to drive by itself, it needs to learn. A safe and economic way to teach a self-driving car to drive by itself is through simulation. However, current car simulators are based on closed world assumptions, where all possible events are already known as design time. Nevertheless, during the training of a self-driving car, it is impossible to account for all the possible events in the open world, where several unknown events may arise (i.e., events that were not considered at design time). Instead of carrying out particular adaptations for known context events in the closed world, the system architecture should evolve to safely reach a new state in the open world. In this research work, our contribution is to extend a car simulator trained by means of machine learning to evolve at runtime with tactics when the simulation faces unknown context events.


Autonomous car Tactics Dynamic evolution Open world Machine learning 


  1. 1.
    Frederic, L.: All new Teslas are equipped with NVIDIA’s new drive PX 2 AI platform for self-driving.
  2. 2.
    Alférez, G.H., Pelechano, V.: Achieving autonomic web service compositions with models at runtime. Comput. Electr. Eng. 63, 332–352 (2017)CrossRefGoogle Scholar
  3. 3.
    Pereira, J.L., Rossetti, R.J.: An integrated architecture for autonomous vehicles simulation. In: Proceedings of the 27th Annual ACM Symposium on Applied Computing, pp. 286–292. ACM (2012)Google Scholar
  4. 4.
    Cheng, B.H., De Lemos, R., Giese, H., Inverardi, P., Magee, J., Andersson, J., Becker, B., Bencomo, N.,  Brun, Y., Cukic, B., et al.: Software engineering for self-adaptive systems: a research roadmap. Software engineering for self-adaptive systems, pp. 1–26. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  5. 5.
    Mohri, M., Rostamizadeh, A., Talwalkar, A.: Foundations of machine learning. MIT press (2012)Google Scholar
  6. 6.
    Alférez, G.H., Pelechano, V.: Facing uncertainty in web service compositions. In: 2013 IEEE 20th International Conference on Web Services (ICWS), pp. 219–226. IEEE (2013)Google Scholar
  7. 7.
    Baresi, L., Di Nitto, E., Ghezzi, C.: Toward open-world software: issues and challenges. Computer 39(10), 36–43 (2006)CrossRefGoogle Scholar
  8. 8.
    Coles, C.: Automated vehicles: a guide for planners and policymakers (2016)Google Scholar
  9. 9.
    Maurer, M., Gerdes, J.C., Lenz, B., Winner, H.: Autonomous driving: technical, legal and social aspects. Springer, Heidelberg (2016)Google Scholar
  10. 10.
    Wang, S., Heinrich, S., Wang, M., Rojas, R.: Shader-based sensor simulation for autonomous car testing. In: 2012 15th International IEEE Conference on Intelligent Transportation Systems, pp. 224–229. IEEE (2012)Google Scholar
  11. 11.
    Simon, C., Ludwig, T., Kruse, M.: Extracting sensor models from a scene based simulation. In: 2016 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI), pp. 259–264. IEEE (2016)Google Scholar
  12. 12.
    Boesch, P.M., Ciari, F.: Agent-based simulation of autonomous cars. IEEE Am. Control Conf. (ACC) 2015, 2588–2592 (2015)Google Scholar
  13. 13.
    Piovan, A.G.: A neural network for automatic vehicles guidance. ACE 10, 2 (2012)Google Scholar
  14. 14.
    Gechter, F., Contet, J.-M., Galland, S., Lamotte, O., Koukam, A.: Virtual intelligent vehicle urban simulator: application to vehicle platoon evaluation. Simul. Modell. Pract. Theory 24, 103–114 (2012)CrossRefGoogle Scholar
  15. 15.
    That, T.N., Casas, J.: An integrated framework combining a traffic simulator and a driving simulator. Procedia-Soc. Behav. Sci. 20, 648–655 (2011)CrossRefGoogle Scholar
  16. 16.
    Harrington, P.: Machine Learning in Action. Manning Publications (2012)Google Scholar
  17. 17.
    Scikit-Learn: sklearn.metrics.precision\_recall\_fscore\_support.

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.School of Engineering and TechnologyUniversidad de MontemorelosMontemorelosMexico

Personalised recommendations