Towards a Framework to Manage Perceptual Uncertainty for Safe Automated Driving

  • Krzysztof CzarneckiEmail author
  • Rick Salay
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11094)


Perception is a safety-critical function of autonomous vehicles and machine learning (ML) plays a key role in its implementation. This position paper identifies (1) perceptual uncertainty as a performance measure used to define safety requirements and (2) its influence factors when using supervised ML. This work is a first step towards a framework for measuring and controling the effects of these factors and supplying evidence to support claims about perceptual uncertainty.


Perception triangle Machine learning Safety assurance 


  1. 1.
    Bussemaker, K.: Sensing requirements for an automated vehicle for highway and rural environments. Master’s thesis, TU Delft (2014)Google Scholar
  2. 2.
    Gal, Y., Ghahramani, Z.: Dropout as a Bayesian approximation: Representing model uncertainty in deep learning. In: 33rd International Conference on Machine Learning, ICML 2016, vol. 3, pp. 1651–1660 (2016)Google Scholar
  3. 3.
    Quoc Viet Hung, N., Tam, N.T., Tran, L.N., Aberer, K.: An evaluation of aggregation techniques in crowdsourcing. In: Lin, X., Manolopoulos, Y., Srivastava, D., Huang, G. (eds.) WISE 2013. LNCS, vol. 8181, pp. 1–15. Springer, Heidelberg (2013). Scholar
  4. 4.
    International Organization for Standardization: Guide to the expression of uncertainty in measurement (1995)Google Scholar
  5. 5.
    International Organization for Standardization: ISO 26262: Road Vehicles – Functional Safety (2011)Google Scholar
  6. 6.
    Koopman, P., Wagner, M.: Toward a framework for highly automated vehicle safety validation. SAE Technical Paper 2018-01-1071 (2018).
  7. 7.
    Sabokrou, M., Khalooei, M., Fathy, M., Adeli, E.: Adversarially learned one-class classifier for novelty detection. arXiv preprint arXiv:1802.09088 (2018)
  8. 8.
    SAE On-Road Automated Driving (Orad) Committee: SAE J3016-Taxonomy and Definitions for Terms Related to On-Road Motor Vehicle Automated Driving Systems. SAE-Society of Automotive Engineers (2014)Google Scholar
  9. 9.
    SAE Vehicle Electrical System Security Committee and others: SAE J3061-Cybersecurity Guidebook for Cyber-Physical Automotive Systems. SAE-Society of Automotive Engineers (2016)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.University of WaterlooWaterlooCanada

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