Skip to main content
Top

2020 | OriginalPaper | Chapter

Human, Machine, Sensor, Infrastructure: All Together Against Cyberattacks in AV

Authors : Jonathan Petit, Victor Murray

Published in: Road Vehicle Automation 7

Publisher: Springer International Publishing

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

The Automated Vehicle’s ecosystem relies on four main components: Human, Machine, Sensor, and Infrastructure. Each component is designed with its own threat model and requirements in mind. However, to ensure end-to-end security and resilience against cyberattacks, we should consider the interoperability of each security model. In this chapter, we give an overview of the state-of-the-art of security in Human Factors, Machine Learning, Sensors, and Infrastructure, before highlighting the research challenges to solve in order to design end-to-end resilience in AVs.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Footnotes
1
One could think of offloading current system engineer work (located in the backseat) to the user.
 
2
And the system is considered to still be within its operational design domain.
 
3
Note that some approaches for generation of adversarial example use unbounded distance, which can be perceptible by humans.
 
Literature
1.
go back to reference Zhang, F., Petit, J., Roberts, S.C.: A simulator study on drivers’ response and perception towards vehicle cyberattacks. In: Proceedings of the Human Factors and Ergonomics Society Annual Meeting, vol. 63, pp. 1498–1502 (2019) Zhang, F., Petit, J., Roberts, S.C.: A simulator study on drivers’ response and perception towards vehicle cyberattacks. In: Proceedings of the Human Factors and Ergonomics Society Annual Meeting, vol. 63, pp. 1498–1502 (2019)
2.
go back to reference Neumeier, S., Wintersberger, P., Frison, A.K., Becher, A., Facchi, C., Riener, A.: Teleoperation: the holy grail to solve problems of automated driving? Sure, but latency matters. In: Proceedings of the 11th International Conference on Automotive User Interfaces and Interactive Vehicular Applications, pp. 186–197 (2019) Neumeier, S., Wintersberger, P., Frison, A.K., Becher, A., Facchi, C., Riener, A.: Teleoperation: the holy grail to solve problems of automated driving? Sure, but latency matters. In: Proceedings of the 11th International Conference on Automotive User Interfaces and Interactive Vehicular Applications, pp. 186–197 (2019)
3.
go back to reference Sitawarin, C., Bhagoji, A.N., Mosenia, A., Chiang, M., Mittal, P.: DARTS: deceiving autonomous cars with toxic signs. arXiv preprint arXiv:​1802.​06430 (2018) Sitawarin, C., Bhagoji, A.N., Mosenia, A., Chiang, M., Mittal, P.: DARTS: deceiving autonomous cars with toxic signs. arXiv preprint arXiv:​1802.​06430 (2018)
4.
go back to reference Mohseni, S., Pitale, M., Singh, V., Wang, Z.: Practical solutions for machine learning safety in autonomous vehicles. arXiv preprint arXiv:​1912.​09630 (2019) Mohseni, S., Pitale, M., Singh, V., Wang, Z.: Practical solutions for machine learning safety in autonomous vehicles. arXiv preprint arXiv:​1912.​09630 (2019)
5.
go back to reference Salay, R., Queiroz, R., Czarnecki, K.: An analysis of ISO 26262: using machine learning safely in automotive software. arXiv preprint arXiv:​1709.​02435 (2017) Salay, R., Queiroz, R., Czarnecki, K.: An analysis of ISO 26262: using machine learning safely in automotive software. arXiv preprint arXiv:​1709.​02435 (2017)
6.
go back to reference Gunning, D., Aha, D.W.: DARPA’s explainable artificial intelligence program. AI Mag. 40(2), 44–58 (2019) CrossRef Gunning, D., Aha, D.W.: DARPA’s explainable artificial intelligence program. AI Mag. 40(2), 44–58 (2019) CrossRef
7.
go back to reference Jha, S., Tsai, T., Hari, S., Sullivan, M., Kalbarczyk, Z., Keckler, S.W., Iyer, R.K.: Kayotee: A fault injection-based system to assess the safety and reliability of autonomous vehicles to faults and errors. arXiv preprint arXiv:​1907.​01024 (2019) Jha, S., Tsai, T., Hari, S., Sullivan, M., Kalbarczyk, Z., Keckler, S.W., Iyer, R.K.: Kayotee: A fault injection-based system to assess the safety and reliability of autonomous vehicles to faults and errors. arXiv preprint arXiv:​1907.​01024 (2019)
8.
go back to reference Petit, J., Stottelaar, B., Feiri, M., Kargl, F.: Remote attacks on automated vehicle sensors: experiments on camera and LiDAR. BlackHat Europe (2015) Petit, J., Stottelaar, B., Feiri, M., Kargl, F.: Remote attacks on automated vehicle sensors: experiments on camera and LiDAR. BlackHat Europe (2015)
Metadata
Title
Human, Machine, Sensor, Infrastructure: All Together Against Cyberattacks in AV
Authors
Jonathan Petit
Victor Murray
Copyright Year
2020
DOI
https://doi.org/10.1007/978-3-030-52840-9_12

Premium Partner