Abstract
The inflection point in the development of some core technologies enabled the Autonomous Vehicles (AV). The unprecedented growth rate in Artificial Intelligence (AI) and Machine Learning (ML) capabilities, focusing only on AVs, is expected to shift the transportation paradigm and bring relevant benefits to the society, such as accidents reduction. However, recent AVs accidents resulted in life losses. This paper presents a viewpoint discussion based on findings from a preliminary exploratory literature review. It was identified an important misalignment between AI and Safety research communities regarding the impact of AI on the safety risks in AV. This paper promotes this discussion, raises concerns on the potential consequences and suggests research topics to reduce the differences between AI and system safety mindsets.
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Notes
- 1.
“a mindset is a set of assumptions, methods or notations held by one or more people or groups of people which is so established that it creates a powerful incentive within these people or groups to continue to adopt or accept prior behaviors, choices, or tools. …” [17]
References
Singh, S.: Critical reasons for crashes investigated in the National Motor Vehicle Crash Causation Survey, Washington, DC (2015)
Dogan, E., Chatila, R., Chauvier, S., Evans, K., Hadjixenophontos, P., Perrin, J.: Ethics in the design of automated vehicles: the AVEthics project. In: EDIA@ ECAI, pp. 10–13 (2016)
Meltz, D., Guterman, H.: RobIL—Israeli program for research and development of autonomous UGV: performance evaluation methodology. In: IEEE International Conference on the Science of Electrical Engineering (ICSEE), pp. 1–5 (2016)
Lugano, G.: Virtual assistants and self-driving cars. In: 2017 15th International Conference on ITS Telecommunications (ITST), pp. 1–5 (2017)
Hamdi, S., Faiedh, H., Souani, C., Besbes, K.: Road signs classification by ANN for real-time implementation. In: 2017 International Conference on Control, Automation and Diagnosis (ICCAD), pp. 328–332 (2017)
Yoneda, K., Kuramoto, A., Suganuma, N.: Convolutional neural network based vehicle turn signal recognition. In: 2017 International Conference on Intelligent Informatics and Biomedical Sciences (ICIIBMS), pp. 204–205 (2017)
Chen, Z., Huang, X.: Accurate and reliable detection of traffic lights using multiclass learning and multiobject tracking. IEEE Intell. Transp. Syst. Mag. 8, 28–42 (2016)
Habermann, D., Vido, C.E.O., Osório, F.S., Ramos, F.: Road junction detection from 3D point clouds. In: 2016 International Joint Conference on Neural Networks (IJCNN), pp. 4934–4940 (2016)
Hossai, M.R.T., Shahjalal, M.A., Nuri, N.F.: Design of an IoT based autonomous vehicle with the aid of computer vision. In: International Conference on Electrical, Computer and Communication Engineering (ECCE), pp. 752–756 (2017)
Bock, J., Beemelmanns, T., Klösges, M., Kotte, J.: Self-learning Trajectory Prediction with Recurrent Neural Networks at Intelligent Intersections (2017)
Lopez Pulgarin, E.J., Herrmann, G., Leonards, U.: Drivers’ Manoeuvre classification for safe HRI. In: Gao, Y., Fallah, S., Jin, Y., Lekakou, C. (eds.) TAROS 2017. LNCS (LNAI), vol. 10454, pp. 475–483. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-64107-2_37
Koopman, P.: Challenges in autonomous vehicle validation: keynote presentation abstract. In: Proceedings of the 1st International Workshop on Safe Control of Connected and Autonomous Vehicles, p. 3 (2017)
McAllister, R., et al.: Concrete problems for autonomous vehicle safety: advantages of Bayesian deep learning. In: Proceedings of the 26th International Joint Conference on Artificial Intelligence, IJCAI 2017 (2017)
NTSB: Collision Between a Car Operating with Automated Vehicle Control Systems and a Tractor-Semitrailer Truck Near Williston, Florida 7 May 2016. https://www.ntsb.gov/investigations/AccidentReports/Pages/HAR1702.aspx
NTSB: Preliminary Report Highway: HWY18MH010. https://www.ntsb.gov/investigations/AccidentReports/Pages/HWY18MH010-prelim.aspx
NTSB: Driver Errors, Overreliance on Automation, Lack of Safeguards, Led to Fatal Tesla Crash. https://www.ntsb.gov/news/press-releases/Pages/PR20170912.aspx
Leveson, N.G.: The use of safety cases in certification and regulation (2011)
Knight, W.: The Dark Secret at the Heart of AI. https://www.technologyreview.com/s/604087/the-dark-secret-at-the-heart-of-ai/
Mirnig, N., Stollnberger, G., Miksch, M., Stadler, S., Giuliani, M., Tscheligi, M.: To err is robot: how humans assess and act toward an erroneous social robot. Front. Robot. AI. 4, 21 (2017)
Clarke, R.: Big data, big risks. Inf. Syst. J. 26, 77–90 (2016)
Liu, K., Sun, L., Dix, A., Narasipuram, M.: Norm-based agency for designing collaborative information systems. Inf. Syst. J. 11, 229–247 (2001)
Committee on Legal Affairs: REPORT with recommendations to the Commission on Civil Law Rules on Robotics (2017)
Delcker, J.: Europe divided over robot “personhood,” (2018)
King, W.R.: Editor’s comment decision support systems, artificial intelligence, and expert systems. MIS Q. 8, iv–v (1984)
Garret, O.: 10 Million Self-Driving Cars Will Hit The Road By 2020. https://www.forbes.com/sites/oliviergarret/2017/03/03/10-million-self-driving-cars-will-hit-the-road-by-2020-heres-how-to-profit/#3af8be847e50
Harris, M.: Google reports self-driving car mistakes: 272 failures and 13 near misses (2016)
Wakabayashi, D.: Self-Driving Uber Car Kills Pedestrian in Arizona, Where Robots Roam (2018)
BBC: Tesla in fatal California crash was on Autopilot (2018)
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This work is supported by the Research, Development and Innovation Center, Ericsson Telecomunicações S.A., Brazil.
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Nascimento, A.M. et al. (2018). Concerns on the Differences Between AI and System Safety Mindsets Impacting Autonomous Vehicles Safety. In: Gallina, B., Skavhaug, A., Schoitsch, E., Bitsch, F. (eds) Computer Safety, Reliability, and Security. SAFECOMP 2018. Lecture Notes in Computer Science(), vol 11094. Springer, Cham. https://doi.org/10.1007/978-3-319-99229-7_42
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