Challenges in homologation process of vehicles with artificial intelligence
Abstract
The traditional automotive homologation processes aim to ensure the safety of vehicles on public roads. Autonomous Vehicles (AV) with Artificial Intelligence (AI) are difficult to account for in these conventional processes. This research aims to map and attempt to close the gaps in the areas of testing and approval of such automated and connected vehicles. During our research into the homologation process of traditional vehicles; functional safety issues, challenges of AI in safety critical systems, along with questions of cyber security were investigated. Our process focuses on the integration of the already existing functions and prototypes into new products safely. As a key result, we managed to identify the main gaps between Information and Communication Technology (ICT) and automotive technology: the rigidity of the automotive homologation process, functional safety, AI in safety critical areas and we propose a solution.
Keyword : autonomous vehicle, safety critical systems, artificial intelligence, functional safety, homologation
How to Cite
Zöldy, M., Szalay, Z., & Tihanyi, V. (2020). Challenges in homologation process of vehicles with artificial intelligence. Transport, 35(4), 447-453. https://doi.org/10.3846/transport.2020.12904
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
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Zöldy, M. 2018. Investigation of autonomous vehicles fit into traditional type approval process, in ICTTE 2018: International Conference on Traffic and Transport Engineering, 27–28 September 2018, Belgrade, Serbia, 428–432.
Buttyán, L.; Szijj, A.; Szalay, Z. 2015. Hacking Cars in the Style of Stuxnet. https://doi.org/10.5446/18858
EC. 2020. The EU Cybersecurity Act. European Commission, Brussels, Belgium. Available from Internet: https://ec.europa.eu/digital-single-market/en/eu-cybersecurity-act
ECOSOC. 1968. Convention on Road Traffic: Done at Vienna on 8 November 1968. Economic and Social Council (ECOSOC), United Nations.
Edelstein, S. 2015. Ford’s New GT has More Lines of Code than a Boeing Jet Airliner. Digital Trends, Designtechnica Corporation. Available from Internet: https://www.digitaltrends.com/cars/the-ford-gt-uses-more-lines-of-code-than-a-boeing-787
Esser, F.; Schindler, C. 2016. Assisted, automated and autonomous driving (“Triple A”) for railway traffic, in XVII Scientific-Expert Conference on Railways RAILCON’16, 13–14 October 2012, Niš, Serbia, 17–20.
Fraszczyk, A.; Brown, P.; Duan, S. 2015. Public perception of driverless trains, Urban Rail Transit 1(2): 78–86. https://doi.org/10.1007/s40864-015-0019-4
GlobalAutoRegs. 2020. WP.1 Agreement: 1968 Vienna Convention on Road Traffic. Available from Internet: https://globalautoregs.com/rules/157-1968-vienna-convention-on-road-traffic
Godoy, J.; Pérez, J.; Onieva, E.; Villagrá, J.; Milanés, V.; Haber, R. 2015. A driverless vehicle demonstration on motorways and in urban environments, Transport 30(3): 253–263. https://doi.org/10.3846/16484142.2014.1003406
Goodfellow, I.; Bengio, Y.; Courville, A. 2016. Deep Learning. MIT Press. 800 p.
Grzywaczewski, A. 2017. Training AI for Self-Driving Vehicles: the Challenge of Scale. Available from Internet: https://devblogs.nvidia.com/training-self-driving-vehicles-challenge-scale
Hirz, M.; Walzel, B. 2018. Sensor and object recognition technologies for self-driving cars, Computer-Aided Design and Applications 15(4): 501–508. https://doi.org/10.1080/16864360.2017.1419638
ISO 26262. Road Vehicles – Functional Safety.
Koopman, P.; Wagner, M. 2016. Challenges in autonomous vehicle testing and validation, SAE International Journal of Transportation Safety 4(1): 15–24. https://doi.org/10.4271/2016-01-0128
Li, Z. R.; Chitturi, M. V.; Yu, L.; Bill, A. R.; Noyce, D. A. 2015. Sustainability effects of next-generation intersection control for autonomous vehicles, Transport 30(3): 342–352. https://doi.org/10.3846/16484142.2015.1080760
Martins, H. 2010. Type Approval Homologation and Self Certification. Ford Motor Company. 9 p. https://doi.org/10.13140/RG.2.2.31708.39041
Maurer, M.; Gerdes, J. C.; Lenz, B.; Winner, H. 2016. Autonomous Driving: Technical, Legal and Social Aspects. Springer. 706 p. https://doi.org/10.1007/978-3-662-48847-8
Pinchon, N.; Cassignol, O.; Nicolas, A.; Bernardin, F.; Leduc, P.; Tarel, J.-P.; Brémond, R.; Bercier, E.; Brunet, J. 2019. All-weather vision for automotive safety: which spectral band?, in J. Dubbert, B. Müller, G. Meyer (Eds.). Advanced Microsystems for Automotive Applications 2018: Smart Systems for Clean, Safe and Shared Road Vehicles, 11–12 September 2018, Berlin, Germany, 3–15. https://doi.org/10.1007/978-3-319-99762-9_1
Reschka, A. 2016. Safety concept for autonomous vehicles, in M. Maurer, J. C. Gerdes, B. Lenz, H. Winner (Eds.). Autonomous Driving: Technical, Legal and Social Aspects, 473–496. https://doi.org/10.1007/978-3-662-48847-8_23
Shabtai, A.; Moskovitch, R.; Elovici, Y.; Glezer, C. 2009. Detection of malicious code by applying machine learning classifiers on static features: a state-of-the-art survey, Information Security Technical Report 14(1): 16–29. https://doi.org/10.1016/j.istr.2009.03.003
Szalay, Z. 2016. Structure and architecture problems of autonomous road vehicle testing and validation, in Proceedings of the 15th Mini Conference on Vehicle System Dynamics, Identification and Anomalies: VSDIA2016, 7–9 November 2016, Budapest, Hungary, 229–236.
Szalay, Z.; Tettamanti, T.; Esztergár-Kiss, D.; Varga, I.; Bartolini, C. 2018. Development of a test track for driverless cars: vehicle design, track configuration, and liability considerations, Periodica Polytechnica Transportation Engineering 46(1): 29–35. https://doi.org/10.3311/PPtr.10753
Tettamanti, T.; Varga, I.; Szalay, Z. 2016. Impacts of autonomous cars from a traffic engineering perspective, Periodica Polytechnica Transportation Engineering 44(4): 244–250. https://doi.org/10.3311/PPtr.9464
Török, Á.; Pauer, G. 2018. Optimization of linear traffic distribution problem in terms of the road toll structure assuming an autonomous transportation system, International Journal for Traffic and Transport Engineering 8(1): 112–124. https://doi.org/10.7708/ijtte.2018.8(1).08
Trujillo, A.; Dunlop, C. 2016. ISO 26262 Software Compliance with Parasoft: Achieving Functional Safety in the Automotive Industry. Parasoft Corporation, Monrovia, CA, US, 11 p. Available from Internet: https://blog.parasoft.com/hs-fs/hub/ 69806/file-15282344-pdf/docs/iso_26262_software_compliance.pdf
Valasek, C.; Miller, C. 2014. A Survey of Remote Automotive Attack Surfaces. IOActive, Inc. 90 p. Available from Internet: https://ioactive.com/a-survey-of-remote-automotive-attack-surfaces
Wahlström, M.; Forster, D.; Karvonen, A.; Puustinen, R.; Saariluoma, P. 2019. Perspective-taking in anticipatory maritime navigation – implications for developing autonomous ships, in 18th Conference on Computer and IT Applications in the Maritime Industries COMPIT’19, 25–27 March 2019, Tullamore, Ireland, 191–200.
Wang, M.; Cui, Y.; Wang, X.; Xiao, S.; Jiang, J. 2018. Machine learning for networking: workflow, advances and opportunities, IEEE Network 32(2): 92–99. https://doi.org/10.1109/MNET.2017.1700200
Zöldy, M. 2018. Investigation of autonomous vehicles fit into traditional type approval process, in ICTTE 2018: International Conference on Traffic and Transport Engineering, 27–28 September 2018, Belgrade, Serbia, 428–432.