7 Intelligent and Connected Cyber-Physical Systems: A Perspective. . .
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driving systems, 2013
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11. I. Goodfellow, Y. Bengio, A. Courville, Deep Learning, vol 1 (MIT Press, Cambridge, 2016)
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Systems and Software Assurance (IEEE, New York, 2014)
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14. D. Amodei, C. Olah, J. Steinhardt, P. Christiano, J. Schulman, D. Mane, Concrete problems in
AI safety, arXiv preprint, 2016
15. 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
16. A. Nguyen, J. Yosinski, J. Clune, Deep neural networks are easily fooled: High confidence
predictions for unrecognizable images, CVPR, 2015
17. A. Kurakin, I. Goodfellow, S. Bengio, Adversarial examples in the physical world, arXiv
preprint, 2016
18. J. Metzen, T. Genewein, B. Bischoff, On Detecting Adversarial Perturbations (ICLR, 2017).
[Online]. Available: https://openreview.net/forum?id=SJzCSf9xg
19. H. Lin, M. Tegmark, D. Rolnick, Why does deep and cheap learning work so well? J. Stat.
Phys. 168(6), 1223–1247 (2017)
20. 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)
21. K. Simonyan, A. Vedaldi, A. Zisserman, Deep inside convolutional networks: visualising
image classification models and saliency maps, arXiv preprint, 2013
22. L. Hendriks, Z. Akata, M. Rohrbach, J. Donahue, B. Schiele, T. Darrel, Generating visual
explanations, ECCV, 2016
23. S. Richter, V. Vineet, S. Roth, V. Koltun, Playing for data: Ground truth from computer games,
ECCV, 2016
24. K. Pei, Y. Cao, J. Yang, S. Jana, Deepxplore: Automated whitebox testing of deep learning
systems, SOSP, 2017
25. Y. Sun, X. Huang, D. Kroening, Testing deep neural networks, arXiv preprint, 2018
26. 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
391
References
1. W. Chang, S. Chakraborty, Resource-aware automotive control systems design: a cyberphysical systems approach. Found. Trends ® Electron. Des. Autom. 10(4), 249–369 (2016)
2. W. Chang, D. Roy, L. Zhang, S. Chakraborty, Model-based design of resource-efficient
automotive control software, ICCAD, 2016
3. 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)
4. W. Chang, D. Roy, X. Hu, S. Chakraborty, Cache-aware task scheduling for maximizing control
performance, DATE, 2018
5. 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)
6. W. Chang, A. Proebstl, D. Goswami, M. Zamani, S. Chakraborty, Battery- and aging-aware
embedded control systems for electric vehicles, RTSS, 2014
7. W. Chang, A. Proebstl, D. Goswami, M. Zamani, S. Chakraborty, Reliable CPS design for
mitigating semiconductor and battery aging in electric vehicles, CPSNA, 2015
8. SAE, J3016, Taxonomy and definitions for terms related to on-road motor vehicle automated
driving systems, 2013
9. ISO, ISO 26262, Road Vehicles – Functional Safety (ISO, Geneva, Switzerland, 2011)
10. ISO, ISO/PRF PAS 21448, Road Vehicles – Safety of the Intended Functionality (ISO, Geneva,
Switzerland, 2018)
11. I. Goodfellow, Y. Bengio, A. Courville, Deep Learning, vol 1 (MIT Press, Cambridge, 2016)
12. IEEE, IEEE Standard Adoption of ISO/IEC 15026-1 – Systems and Software Engineering –
Systems and Software Assurance (IEEE, New York, 2014)
13. K. Varshney, Engineering safety in machine learning, ITA, 2016
14. D. Amodei, C. Olah, J. Steinhardt, P. Christiano, J. Schulman, D. Mane, Concrete problems in
AI safety, arXiv preprint, 2016
15. 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
16. A. Nguyen, J. Yosinski, J. Clune, Deep neural networks are easily fooled: High confidence
predictions for unrecognizable images, CVPR, 2015
17. A. Kurakin, I. Goodfellow, S. Bengio, Adversarial examples in the physical world, arXiv
preprint, 2016
18. J. Metzen, T. Genewein, B. Bischoff, On Detecting Adversarial Perturbations (ICLR, 2017).
[Online]. Available: https://openreview.net/forum?id=SJzCSf9xg
19. H. Lin, M. Tegmark, D. Rolnick, Why does deep and cheap learning work so well? J. Stat.
Phys. 168(6), 1223–1247 (2017)
20. 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)
21. K. Simonyan, A. Vedaldi, A. Zisserman, Deep inside convolutional networks: visualising
image classification models and saliency maps, arXiv preprint, 2013
22. L. Hendriks, Z. Akata, M. Rohrbach, J. Donahue, B. Schiele, T. Darrel, Generating visual
explanations, ECCV, 2016
23. S. Richter, V. Vineet, S. Roth, V. Koltun, Playing for data: Ground truth from computer games,
ECCV, 2016
24. K. Pei, Y. Cao, J. Yang, S. Jana, Deepxplore: Automated whitebox testing of deep learning
systems, SOSP, 2017
25. Y. Sun, X. Huang, D. Kroening, Testing deep neural networks, arXiv preprint, 2018
26. 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