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Challenges and Opportunities of Artificial Intelligence for Automated Driving

  • Benjamin WilschEmail author
  • Hala Elrofai
  • Edgar Krune
Conference paper
Part of the Lecture Notes in Mobility book series (LNMOB)

Abstract

The advancement of automated driving (AD) depends on a multitude of influencing factors, however, achieving higher levels of automation fundamentally hinges on the capabilities of Artificial Intelligence (AI) to perform driving tasks. Improvements in AI hardware and the availability of large amounts of data (Big Data) have fueled the rapid increase in AD-related research and development activities over the past decade and are thus also the key indicators for future development. The shift from humans to AI in vehicle control unlocks many of the well-established potentials of AD, but is also the root for many non-technical issues that affect its introduction. Starting from the state of the art of AI for AD this chapter discusses key challenges and opportunities that mark the development path.

Keywords

Artificial Intelligence Machine Learning Training Validation Hardware Big Data Vehicle automation Autonomous driving SCOUT CARTRE European Commission 

Notes

Acknowledgements

The authors are grateful for fruitful cooperation with the contractual partners of the Coordination and Support Actions “Safe and Connected Automation of Road Transport” (SCOUT) and “Coordination of Automated Road Transport Deployment for Europe” (CARTRE). The SCOUT and CARTRE projects have received funding from the EU’s Horizon 2020 programme under grant agreements No. 713843 and 724086, respectively. The section on AI hardware further draws from investigations carried out as part of the SCORE project, which has also received funding under the EU’s Horizon 2020 programme.

References

  1. 1.
    McCarthy, J.: Computer Controlled Cars, Essay (1969)Google Scholar
  2. 2.
    Touretzky, D., Pomerlau, D.: What’s hidden in the hidden layers? BYTE 14, 227–233 (1989)Google Scholar
  3. 3.
    Raina, R., Madhavan, A., Ng, A.: Large-scale deep unsupervised learning using graphics processors. In: Proceedings of the 26th Annual Conference on Machine Learning, ICML 2009, pp. 873–880 (2009)Google Scholar
  4. 4.
    Turing, A.M.: Computing machinery and intelligence. Mind 49, 433–460 (1950)MathSciNetCrossRefGoogle Scholar
  5. 5.
    Döbel, I., Leis, M., Vogelsang, M.M., et al.: Machine Learning - Competencies, Applications and Research Needs. Frauenhofer Society (2018). (in German)Google Scholar
  6. 6.
    Dally, W.: High-performance hardware for machine learning. NIPS Tutorial (2015)Google Scholar
  7. 7.
    Göhring, D., Latotzky, D., Wang, M., Rojas, R.: Semi-autonomous car control using brain computer interfaces. Advances in Intelligent Systems and Computing, vol. 94, pp. 393–408 (2013)Google Scholar
  8. 8.
    Shalev-Shwartz, S., Shammah, S., Shashua, A.: On a formal model of safe and scalable self-driving cars (2018). arXiv:1708.06374v5
  9. 9.
    Slusallek, P.: Understanding the world with AI: training & validating autonomous vehicles with synthetic data. Talk Presented at Interactive Symposium on Research and Innovation for CAD in Europe at Tech Gate, Vienna, 20 April 2018Google Scholar
  10. 10.
    Probst, L., Pedersen, B., Lefebvre, V., Dakkak-Arnoux, L.: USA-China-EU plans for AI: where do we stand? Digital Transformation Monitor of the European Commission (2018)Google Scholar
  11. 11.
    Ding, J.: Deciphering China’s AI Dream, Governance of AI Program. University of Oxford (2018)Google Scholar
  12. 12.
    Churchill, O.: Chinas AI dreams. Nature 553, S10–S12 (2018).  https://doi.org/10.1038/d41586-018-00539-yCrossRefGoogle Scholar
  13. 13.
    KI Bundesverband e.V.: Artificial Intelligence: State of the Art and Catalogue of Measures (2018). (in German)Google Scholar
  14. 14.
    Kalra, N., Paddock, S.M.: Driving to safety: how many miles of driving would it take to demonstrate autonomous vehicle reliability? RAND Corporation, Santa Monica (2016). https://www.rand.org/pubs/research_reports/RR1478.html
  15. 15.
    Schuman, C.D., et al.: A survey of neuromorphic computing and neural networks in hardware (2017). arXiv:1705.06963v1
  16. 16.
    Esser, S.K., et al.: Convolutional networks for fast, energy-efficient neuromorphic computing. PNAS 113(41), 11441–11446 (2016)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.VDI/VDE Innovation + Technik GmbHBerlinGermany
  2. 2.TNOHelmondThe Netherlands

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