Abstract
Cyber-physical systems (CPS) have broad applications in the automotive, avionics, robotics, healthcare, and power grid, where the cyber components involving information processing and networking closely interact with the physical processes. Conventionally, there is a separate design flow of CPS. For instance, control algorithms managing the physical dynamics are designed using model-based approaches, without considering details of the cyber implementation platforms. Modern CPS are getting increasingly intelligent and connected. A new design methodology taking all the layers of CPS and their interplays into account is being developed, aiming for assurance of safety and security, as well as high robustness and resource efficiency. This chapter presents the technical background of CPS, with an emphasis on the cyber and physical interactions, corresponding to the new design methodology. Case studies on connected autonomous vehicles (CAVs) are used to illustrate the most recent development in CPS.
The need for connection and community is primal, as fundamental as the need for air, water, and food.
Dean Ornish
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
References
W. Chang, S. Chakraborty, Resource-aware automotive control systems design: a cyber-physical systems approach. Found. Trends® Electron. Des. Autom. 10(4), 249–369 (2016)
W. Chang, D. Roy, L. Zhang, S. Chakraborty, Model-based design of resource-efficient automotive control software, ICCAD, 2016
W. Chang, D. Goswami, S. Chakraborty, L. Ju, C. Xue, S. Andalam, Memory-aware embedded control systems design. IEEE Trans. Comput. Aided Des. Integr. Circuits and Syst. 36(4), 586–599 (2017)
W. Chang, D. Roy, X. Hu, S. Chakraborty, Cache-aware task scheduling for maximizing control performance, DATE, 2018
W. Chang, D. Goswami, S. Chakraborty, A. Hamann, OS-aware automotive controller design using non-uniform sampling. ACM Trans. Cyber-Phys. Sys. 2(4), 26 (2018)
W. Chang, A. Proebstl, D. Goswami, M. Zamani, S. Chakraborty, Battery- and aging-aware embedded control systems for electric vehicles, RTSS, 2014
W. Chang, A. Proebstl, D. Goswami, M. Zamani, S. Chakraborty, Reliable CPS design for mitigating semiconductor and battery aging in electric vehicles, CPSNA, 2015
SAE, J3016, Taxonomy and definitions for terms related to on-road motor vehicle automated driving systems, 2013
ISO, ISO 26262, Road Vehicles – Functional Safety (ISO, Geneva, Switzerland, 2011)
ISO, ISO/PRF PAS 21448, Road Vehicles – Safety of the Intended Functionality (ISO, Geneva, Switzerland, 2018)
I. Goodfellow, Y. Bengio, A. Courville, Deep Learning, vol 1 (MIT Press, Cambridge, 2016)
IEEE, IEEE Standard Adoption of ISO/IEC 15026-1 – Systems and Software Engineering – Systems and Software Assurance (IEEE, New York, 2014)
K. Varshney, Engineering safety in machine learning, ITA, 2016
D. Amodei, C. Olah, J. Steinhardt, P. Christiano, J. Schulman, D. Mane, Concrete problems in AI safety, arXiv preprint, 2016
D. Sculley, G. Holt, D. Golovin, E. Davydov, T. Phillips, D. Ebner, V. Chaudhary, M. Young, J. Crespo, D. Dennison, Hidden technical debt in machine learning systems, Advances in Neural Information Processing Systems, 2503–2511, 2015
A. Nguyen, J. Yosinski, J. Clune, Deep neural networks are easily fooled: High confidence predictions for unrecognizable images, CVPR, 2015
A. Kurakin, I. Goodfellow, S. Bengio, Adversarial examples in the physical world, arXiv preprint, 2016
J. Metzen, T. Genewein, B. Bischoff, On Detecting Adversarial Perturbations (ICLR, 2017). [Online]. Available: https://openreview.net/forum?id=SJzCSf9xg
H. Lin, M. Tegmark, D. Rolnick, Why does deep and cheap learning work so well? J. Stat. Phys. 168(6), 1223–1247 (2017)
J. Attenberg, P. Ipeirotis, F. Provost, Beat the machine: challenging humans to find a predictive model’s unknown unknowns. J. Data Inf. Qual. 6, 1–17 (2015)
K. Simonyan, A. Vedaldi, A. Zisserman, Deep inside convolutional networks: visualising image classification models and saliency maps, arXiv preprint, 2013
L. Hendriks, Z. Akata, M. Rohrbach, J. Donahue, B. Schiele, T. Darrel, Generating visual explanations, ECCV, 2016
S. Richter, V. Vineet, S. Roth, V. Koltun, Playing for data: Ground truth from computer games, ECCV, 2016
K. Pei, Y. Cao, J. Yang, S. Jana, Deepxplore: Automated whitebox testing of deep learning systems, SOSP, 2017
Y. Sun, X. Huang, D. Kroening, Testing deep neural networks, arXiv preprint, 2018
S. Checkoway, D. McCoy, B. Kantor, D. Anderson, H. Shacham, S. Savage, K. Koscher, A. Czeskis, F. Roesner, T. Kohno, Comprehensive experimental analyses of automotive attack surfaces, USENIX, 2011
P. Kleberger, T. Olovsson, E. Jonsson, Security aspects of the in-vehicle network in the connected car, IV, 2011
K. Koscher, A. Czeskis, F. Roesner, S. Patel, T. Kohno, S. Checkoway, D. McCoy, B. Kantor, D. Anderson, H. Shacham, S. Savage, Experimental security analysis of a modern automobile, SP, 2010
United States Department of Transportation, Security credential management system (SCMS)
H. Liang, M. Jagielski, B. Zheng, C.-W. Lin, E. Kang, S. Shiraishi, C. Nita-Rotaru, Q. Zhu, Network and system level security in connected vehicle applications, ICCAD, 2018
J. Kenney, Dedicated short-range communications (DSRC) standards in the United States. Proc. IEEE 99(7), 1162–1182 (2011)
M. Jagielski, N. Jones, C.-W. Lin, C. Nita-Rotaru, S. Shiraishi, Threat detection for collaborative adaptive cruise control in connected cars, WiSec, 2018
R. Carli, F. Fagnani, P. Frasca, S. Zampieri, Gossip consensus algorithms via quantized communication. Automatica 46(1), 70–80 (2010)
L. Lamport, Paxos made simple. ACM SIGACT News (Distributed Computing Column) 32(4), 51–58 (2001)
R. Olfati-Saber, R. Murray, Consensus problems in networks of agents with switching topology and time-delays. IEEE Trans. Autom. Control 49(9), 1520–1533 (2004)
C.-W. Lin, B. Zheng, Q. Zhu, A. Sangiovanni-Vincentelli, Security-aware design methodology and optimization for automotive systems. ACM Trans. Des. Autom. Electron. Sys. (TODAES) 21(1)., 18:), 1–26 (2015)
A. Benveniste, B. Caillaud, D. Nickovic, R. Passerone, J. Raclet, P. Reinkemeier, A. Sangiovanni-Vincentelli, W. Damm, T. Henzinger, K. Larsen, Contracts for system design, research report RR-8147, INRIA, 2012
B. Meyer, Applying “design by contract”. IEEE Comput. 25(10), 40–51 (1992)
L. Carloni, F. Bernardinis, C. Pinello, A. Sangiovanni-Vincentelli, M. Sgroi, Platform-based design for embedded systems, Embedded Systems Handbook, pp. 1281–1304, 2005
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this chapter
Cite this chapter
Chang, W., Burton, S., Lin, CW., Zhu, Q., Gauerhof, L., McDermid, J. (2020). Intelligent and Connected Cyber-Physical Systems: A Perspective from Connected Autonomous Vehicles. In: Firouzi, F., Chakrabarty, K., Nassif, S. (eds) Intelligent Internet of Things. Springer, Cham. https://doi.org/10.1007/978-3-030-30367-9_7
Download citation
DOI: https://doi.org/10.1007/978-3-030-30367-9_7
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-30366-2
Online ISBN: 978-3-030-30367-9
eBook Packages: EngineeringEngineering (R0)