Abstract
With recent efforts to make vehicles intelligent, solutions based on machine learning have been accepted to the ecosystem. These systems in the automotive domain are growing fast, speeding up the promising future of highly and fully automated driving, and respectively, raising new challenges regarding safety assurance approaches. Uncertainty in data and the machine learning methods is a key point to investigate one of the main origins of safety-related concerns. In this work, we inspect this issue in the domain of autonomous driving with an emphasis on four safety-related cases, then introduce our proposals to address the challenges and mitigate them. The core of our approach is on introducing monitoring limiters during development time of such intelligent systems.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Amodei, D., Olah, C., Steinhardt, J., Christiano, P., Schulman, J., Mané, D.: Concrete problems in AI safety. CoRR abs/1606.06565 (2016)
Blei, D.M., Kucukelbir, A., McAuliffe, J.D.: Variational inference: a review for statisticians. J. Am. Stat. Assoc. 112(518), 859–877 (2017). https://doi.org/10.1080/01621459.2017.1285773
Burton, S., Gauerhof, L., Heinzemann, C.: Making the case for safety of machine learning in highly automated driving. In: Tonetta, S., Schoitsch, E., Bitsch, F. (eds.) SAFECOMP 2017. LNCS, vol. 10489, pp. 5–16. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-66284-8_1
Feld, M., Müller, C.: The automotive ontology: managing knowledge inside the vehicle and sharing it between cars. In: Proceedings of the 3rd International Conference on Automotive User Interfaces and Interactive Vehicular Applications, pp. 79–86. ACM (2011). https://doi.org/10.1145/2381416.2381429
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). https://doi.org/10.1007/978-3-319-63387-9_1
International Organization for Standardization: ISO 26262: Road vehicles-functional safety. International Standard ISO/FDIS 26262 (2011)
Kane, A., Chowdhury, O., Datta, A., Koopman, P.: A case study on runtime monitoring of an autonomous research vehicle (ARV) system. In: Bartocci, E., Majumdar, R. (eds.) RV 2015. LNCS, vol. 9333, pp. 102–117. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-23820-3_7
McAllister, R., et al.: Concrete problems for autonomous vehicle safety: advantages of Bayesian deep learning. In: International Joint Conferences on Artificial Intelligence, Inc. (2017). https://doi.org/10.24963/ijcai.2017/661
Pei, K., Cao, Y., Yang, J., Jana, S.: Towards practical verification of machine learning: The case of computer vision systems. CoRR abs/1712.01785 (2017)
Russell, S., Dewey, D., Tegmark, M.: Research priorities for robust and beneficial artificial intelligence. AI Mag. 36(4), 105–114 (2015)
Salay, R., Queiroz, R., Czarnecki, K.: An analysis of ISO 26262: Using machine learning safely in automotive software. CoRR abs/1709.02435 (2017)
Sölch, M., Bayer, J., Ludersdorfer, M., van der Smagt, P.: Variational inference for on-line anomaly detection in high-dimensional time series. CoRR abs/1602.07109 (2016)
Sutton, R.S., Barto, A.G.: Reinforcement Learning: An Introduction. Adaptive Computation and Machine Learning. MIT Press, Cambridge (1998)
Taylor, J., Yudkowsky, E., LaVictoire, P., Critch, A.: Alignment for advanced machine learning systems. Machine Intelligence Research Institute (2016)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Switzerland AG
About this paper
Cite this paper
Shafaei, S., Kugele, S., Osman, M.H., Knoll, A. (2018). Uncertainty in Machine Learning: A Safety Perspective on Autonomous Driving. In: Gallina, B., Skavhaug, A., Schoitsch, E., Bitsch, F. (eds) Computer Safety, Reliability, and Security. SAFECOMP 2018. Lecture Notes in Computer Science(), vol 11094. Springer, Cham. https://doi.org/10.1007/978-3-319-99229-7_39
Download citation
DOI: https://doi.org/10.1007/978-3-319-99229-7_39
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-99228-0
Online ISBN: 978-3-319-99229-7
eBook Packages: Computer ScienceComputer Science (R0)