Xin Tao
ABSTRACT
Cyber-Physical Systems (CPS) are a result of highly cross-disciplinary processes and are evolving to perform increasingly challenging tasks in dynamically changing environments. This leads to an increasing CPS complexity and therefore the management of uncertainty to ensure the trustworthiness of these systems is needed. Our paper focuses on uncertainty management (UM) both in general and more specifically in the context of CPS situation awareness (SA). The motivation behind this is the important role of SA and its many inherent uncertainties. To this end, firstly, a literature review is conducted to acquire the state of the art of UM. Later, we present findings and observations from the literature review, with two main challenges identified - inconsistent understanding and terminology among a multitude of uncertainty perspectives, and a lack of collaboration among different communities. On this basis, lastly, two case studies are conducted to exemplify the challenges and provide brief ideas on how to deal with them. The whole investigation in the paper suggests an urgent strengthening of common understanding through enhanced collaboration and regulations.
- E. R. Griffor, C. Greer, D. A. Wollman, and M. J. Burns, "Framework for cyber-physical systems: volume 1, overview," Gaithersburg, MD, Jun. 2017.Google Scholar
Cross Ref
- A. Washington, R. A. Clothier, B. P. Williams, J. Silva, and others, "Managing uncertainty in the system safety assessment of unmanned aircraft systems," in 17th Australian International Aerospace Congress: AIAC 2017, 2017, p. 611.Google Scholar
- R. McAllister et al., "Concrete Problems for Autonomous Vehicle Safety: Advantages of Bayesian Deep Learning," in Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence, 2017, pp. 4745--4753. Google ScholarCross Ref
- R. L. Iman and J. C. Helton, "An Investigation of Uncertainty and Sensitivity Analysis Techniques for Computer Models," Risk Analysis, vol. 8, no. 1, pp. 71--90, Mar. 1988.Google ScholarCross Ref
- R. L. Winkler, "Uncertainty in probabilistic risk assessment," Reliability Engineering & System Safety, vol. 54, no. 2-3, pp. 127--132, Nov. 1996.Google ScholarCross Ref
- W. Oberkampf, J. Helton, and K. Sentz, "Mathematical representation of uncertainty," in 19th AIAA Applied Aerodynamics Conference, 2001, no. April.Google Scholar
- S. Shafaei, S. Kugele, M. H. Osman, and A. Knoll, "Uncertainty in Machine Learning: A Safety Perspective on Autonomous Driving," vol. 1698, M. Felici and K. Kanoun, Eds. Berlin, Heidelberg: Springer Berlin Heidelberg, 2018, pp. 458--464.Google Scholar
- K. Czarnecki and R. Salay, "Towards a Framework to Manage Perceptual Uncertainty for Safe Automated Driving," in International Conference on Computer Safety, Reliability, and Security, 2018, pp. 439--445.Google Scholar
- Y. Gal, "Uncertainty in Deep Learning," PhD thesis, University of Cambridge, 2016.Google Scholar
- "Web Of Science." [Online]. Available: http://www.webofknowledge.com. [Accessed: 04-Sep-2019].Google Scholar
- M. Strickland, G. Fainekos, and H. Ben Amor, "Deep Predictive Models for Collision Risk Assessment in Autonomous Driving," in 2018 IEEE International Conference on Robotics and Automation (ICRA), 2018, pp. 1--8.Google Scholar
- J. and O. JCGM, "Evaluation of measurement data-Guide to the expression of uncertainty in measurement," Int. Organ. Stand. Geneva ISBN, vol. 50, p. 134, 2008.Google Scholar
- B. N. Taylor and C. E. Kuyatt, "Guidelines for evaluating and expressing the uncertainty of NIST measurement results," 1994.Google Scholar
- M. R. Endsley, "Toward a Theory of Situation Awareness in Dynamic Systems," Human Factors: The Journal of the Human Factors and Ergonomics Society, vol. 37, no. 1, pp. 32--64, Mar. 1995.Google ScholarCross Ref
- D. Furno et al., "Towards an agent-based architecture for managing uncertainty in situation awareness," in 2011 IEEE Symposium on Intelligent Agent (IA), 2011, pp. 1--6.Google Scholar
- S. and others Keele, "Guidelines for performing systematic literature reviews in software engineering," 2007.Google Scholar
- T. Helldin, G. Falkman, M. Riveiro, and S. Davidsson, "Presenting system uncertainty in automotive UIs for supporting trust calibration in autonomous driving," in Proceedings of the 5th International Conference on Automotive User Interfaces and Interactive Vehicular Applications - AutomotiveUI '13, 2013, pp. 210--217. Google Scholar
- J. Ward, G. Agamennoni, S. Worrall, and E. Nebot, "Vehicle collision probability calculation for general traffic scenarios under uncertainty," in 2014 IEEE Intelligent Vehicles Symposium Proceedings, 2014, no. Iv, pp. 986--992.Google Scholar
- R. Schubert, "Evaluating the Utility of Driving: Toward Automated Decision Making Under Uncertainty," IEEE Transactions on Intelligent Transportation Systems, vol. 13, no. 1, pp. 354--364, Mar. 2012. Google ScholarDigital Library
- Z. Ghahramani, "Probabilistic machine learning and artificial intelligence," Nature, vol. 521, no. 7553, pp. 452--459, May 2015.Google ScholarCross Ref
- M. Zhang, "Uncertainty-wise Cyber-Physical Systems Testing," PhD thesis, University of Oslo, 2018.Google Scholar
- S. A. Seshia, D. Sadigh, and S. S. Sastry, "Towards Verified Artificial Intelligence," arXiv preprint arXiv:1606.08514, Jun. 2016.Google Scholar
- B. Kim, K. Park, and K. Yi, "Probabilistic Threat Assessment with Environment Description and Rule-based Multi-Traffic Prediction for Integrated Risk Management System," IEEE Intelligent Transportation Systems Magazine, vol. 9, no. 3, pp. 8--22, 2017.Google ScholarCross Ref
- E. Landre, R. D. Alexander, T. P. Kelly, and R. Eastwood, "Towards a safety case for runtime risk and uncertainty management in safety-critical systems," in 8th IET International System Safety Conference incorporating the Cyber Security Conference 2013, 2013, pp. 4.1--4.1.Google Scholar
- A. Carvalho, S. Lefévre, G. Schildbach, J. Kong, and F. Borrelli, "Automated driving: The role of forecasts and uncertainty --- A control perspective," European Journal of Control, vol. 24, pp. 14--32, 2015.Google ScholarCross Ref
- S. Jha and V. Raman, "Automated Synthesis of Safe Autonomous Vehicle Control Under Perception Uncertainty," in NASA Formal Methods Symposium, Cham: Springer, 2016, pp. 117--132. Google Scholar
- J. Schneider, A. Wilde, and K. Naab, "Probabilistic approach for modeling and identifying driving situations," in 2008 IEEE Intelligent Vehicles Symposium, 2008, pp. 343--348.Google Scholar
- D. Allaire and K. Willcox, "Uncertainty assessment of complex models with application to aviation environmental policy-making," Transport Policy, vol. 34, no. June, pp. 109--113, Jul. 2014.Google ScholarCross Ref
- D. Feng, L. Rosenbaum, and K. Dietmayer, "Towards Safe Autonomous Driving: Capture Uncertainty in the Deep Neural Network For Lidar 3D Vehicle Detection," in 2018 21st International Conference on Intelligent Transportation Systems (ITSC), 2018, pp. 3266--3273.Google Scholar
- A. Al-Taie, H. K. Hahn, and L. Linsen, "Uncertainty estimation and visualization in probabilistic segmentation," Computers & Graphics, vol. 39, no. 1, pp. 48--59, Apr. 2014. Google ScholarDigital Library
- Z. N. Sunberg, C. J. Ho, and M. J. Kochenderfer, "The value of inferring the internal state of traffic participants for autonomous freeway driving," in 2017 American Control Conference (ACC), 2017, pp. 3004--3010.Google Scholar
- P. Secchi, E. Zio, and F. Di Maio, "Quantifying uncertainties in the estimation of safety parameters by using bootstrapped artificial neural networks," Annals of Nuclear Energy, vol. 35, no. 12, pp. 2338--2350, Dec. 2008.Google ScholarCross Ref
- Y. Gal and Z. Ghahramani, "Dropout as a Bayesian approximation: Representing model uncertainty in deep learning," in international conference on machine learning, 2016, pp. 1050--1059. Google Scholar
- C. Blundell, J. Cornebise, K. Kavukcuoglu, and D. Wierstra, "Weight Uncertainty in Neural Networks," arXiv preprint arXiv:1505.05424, May 2015. Google Scholar
- P. Sermanet et al., "Mapping and planning under uncertainty in mobile robots with long-range perception," in 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2008, pp. 2525--2530.Google Scholar
- A. Kendall and Y. Gal, "What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?," Advances in Neural Information Processing Systems, pp. 5574--5584, 2017. Google Scholar
- T. Aven, P. Baraldi, R. Flage, and E. Zio, Uncertainty in risk assessment: the representation and treatment of uncertainties by probabilistic and non-probabilistic methods. John Wiley & Sons, 2013. Google Scholar
- R. L. Boring, "Reconciling Resilience with Reliability: The Complementary Nature of Resilience Engineering and Human Reliability Analysis," Human Factors and Ergonomics Society Annual Meeting Proceedings, vol. 53, no. 20, pp. 1589--1593, Oct. 2009.Google ScholarCross Ref
- D. Sadigh and A. Kapoor, "Safe Control under Uncertainty," arXiv preprint arXiv:1510.07313, Oct. 2015.Google Scholar
- G. J. Klir and R. M. Smith, "On measuring uncertainty and uncertainty-based information: Recent developments," Annals of Mathematics and Artificial Intelligence, vol. 32, no. 1--4, pp. 5--33, 2001. Google Scholar
- G. Grote, "Promoting safety by increasing uncertainty -- Implications for risk management," Safety Science, vol. 71, no. PB, pp. 71--79, Jan. 2015.Google ScholarCross Ref
- "The arcitectural representation of the waterfall model." [Online]. Available: https://en.wikipedia.org/wiki/Waterfall_model. [Accessed: 04-Sep-2019].Google Scholar
- S. Standard, "J3016," Taxonomy and Definitions for Terms Related to On-Road Motor Vehicle Automated Driving Systems, vol. 4, pp. 593--598, 2014.Google Scholar
- M. Törngren and U. Sellgren, "Complexity Challenges in Development of Cyber-Physical Systems," in Principles of modeling, Springer Festschrift" LNCS Post-proceedings, 2018, pp. 478--503.Google Scholar
Index Terms
- Uncertainty Management in Situation Awareness for Cyber-Physical Systems: State of the Art and Challenge
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