Could We Issue Driving Licenses to Autonomous Vehicles?

  • Jingyue LiEmail author
  • Jin Zhang
  • Nektaria Kaloudi
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11094)


Many companies are studying autonomous vehicles. One trend in the development of control algorithms for autonomous vehicles is the use of deep-learning approaches. The general idea is to simulate a human driver’s decision-making and behavior in various scenarios without necessarily knowing why the decision is made. In this position paper, we first argue that traditional safety analysis methods need to be extended to verify deep-learning-based autonomous vehicles. Then, we propose borrowing ideas from the process of issuing driving licenses to human drivers to verify autonomous vehicles. Verification of autonomous vehicles could focus on sufficient training as well as mental and physical health checks. Based on this position, we list several challenges that need to be addressed.


Autonomous vehicle Artificial Intelligence Deep learning Verification Safety Security 



This work is supported by the SAREPTA (Safety, autonomy, remote control and operations of industrial transport systems) project, which is financed by Norwegian Research Council with Grant No. 267860.


  1. 1.
    Google: The Google self-driving car. Accessed May 2018
  2. 2.
    Hawkins, A.J.: Uber self-driving car saw pedestrian but didn’t brake before fatal crash, feds say. Accessed 24 May 2018
  3. 3.
    Greenberg, A.: Hackers remotely kill a Jeep on the highway. Accessed 21 July 2015
  4. 4.
    SAE International: Automated vehicles: levels of automation. Accessed May 2018
  5. 5.
    Sallab, A.E.L., et al.: Deep reinforcement learning framework for autonomous driving. Electron. Imaging 2017(19), 70–76 (2017)CrossRefGoogle Scholar
  6. 6.
    Bojarski, M., et al.: End to end learning for self-driving cars. arXiv preprint arXiv:1604.07316 (2016)
  7. 7.
    Huval, B., et al.: An empirical evaluation of deep learning on highway driving. arXiv preprint arXiv:1504.01716 (2015)
  8. 8.
    Navarro, A., et al.: Development of an autonomous vehicle control strategy using a single camera and deep neural networks. SAE Technical Paper 01-0035 (2018)Google Scholar
  9. 9.
    NVIDIA Deep Learning Institute: Deep learning for autonomous vehicles-perception. Accessed May 2018
  10. 10.
    Griessnig, G., Schnellbach, A.: Development of the 2nd edition of the ISO 26262. In: Stolfa, J., Stolfa, S., O’Connor, R.V., Messnarz, R. (eds.) EuroSPI 2017. CCIS, vol. 748, pp. 535–546. Springer, Cham (2017). Scholar
  11. 11.
    The Hansen Report on Automotive Electronics: Standardization efforts on autonomous driving safety. Accessed Feb 2017
  12. 12.
    WAYMO: Waymo Safety Report: On the road to fully self-driving. Accessed May 2018
  13. 13.
    Tian, Y., et al.: DeepTest: automated testing of deep-neural-network-driven autonomous cars. arXiv preprint arXiv:1708.08559 (2017)
  14. 14.
    Huang, X., Kwiatkowska, M., Wang, S., Wu, M.: Safety verification of deep neural networks. In: Majumdar, R., Kunčak, V. (eds.) CAV 2017. LNCS, vol. 10426, pp. 3–29. Springer, Cham (2017). Scholar
  15. 15.
    Katz, G., Barrett, C., Dill, D.L., Julian, K., Kochenderfer, M.J.: Reluplex: an efficient SMT solver for verifying deep neural networks. In: Majumdar, R., Kunčak, V. (eds.) CAV 2017. LNCS, vol. 10426, pp. 97–117. Springer, Cham (2017). Scholar
  16. 16.
    Koch, R., et al.: A revised attack taxonomy for a new generation of smart attacks. Comput. Inf. Sci. 7(3), 18 (2014)Google Scholar
  17. 17.
    Brundage, M., et al.: The malicious use of artificial intelligence: forecasting, prevention, and mitigation. arXiv preprint arXiv:1802.07228 (2018)
  18. 18.
    Giaretta, A., Dragoni, N.: Community targeted spam: a middle ground between general spam and spear phishing. arXiv preprint arXiv:1708.07342 (2017)
  19. 19.
    Seymour, J., Tully, P.: Weaponizing data science for social engineering: automated E2E spear phishing on Twitter. Black Hat USA (2016)Google Scholar
  20. 20.
    Kim, Y.M.: The stealth media? Groups and targets behind divisive issue campaigns on Facebook (2018)Google Scholar
  21. 21.
    Ribeiro, M.T., et al.: Model-agnostic interpretability of machine learning. arXiv preprint arXiv:1606.05386 (2016)
  22. 22.
    Schenkelberg, F.: Comparing human and machine capability. Accessed 2018

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Norwegian University of Science and TechnologyTrondheimNorway

Personalised recommendations