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Monitoring and Forecasting of Key Functions and Technologies for Automated Driving

Author

Listed:
  • Christian Ulrich

    (German Aerospace Center (DLR), Institute of Vehicle Concepts, Pfaffenwaldring 38–40, 70569 Stuttgart, Germany)

  • Benjamin Frieske

    (German Aerospace Center (DLR), Institute of Vehicle Concepts, Pfaffenwaldring 38–40, 70569 Stuttgart, Germany)

  • Stephan A. Schmid

    (German Aerospace Center (DLR), Institute of Vehicle Concepts, Pfaffenwaldring 38–40, 70569 Stuttgart, Germany)

  • Horst E. Friedrich

    (German Aerospace Center (DLR), Institute of Vehicle Concepts, Pfaffenwaldring 38–40, 70569 Stuttgart, Germany
    Retired.)

Abstract

Companies facing transformation in the automotive industry will need to adapt to new trends, technologies and functions, in order to remain competitive. The challenge is to anticipate such trends and to forecast their development over time. The aim of this paper is to develop a methodology that allows us to analyze the temporal development of technologies, taking automated driving as an example. The framework consists of a technological and a functional roadmap. The technology roadmap provides information on the temporal development of 59 technologies based on expert elicitation using a multi-stage Delphi survey and patent analyses. The functional roadmap is derived from a meta-analysis of studies including 209 predictions of the maturity of automated driving functions. The technological and functional roadmaps are merged into a consolidated roadmap, linking the temporal development of technologies and functions. Based on the publication analysis, SAE level 5 is predicted to be market-ready by 2030. Contrasted to the results from the Delphi survey in the technological roadmap, 2030 seems to be too optimistic, however, as some key technologies would not have reached market readiness by this time. As with all forecasts, the proposed framework is not able to accurately predict the future. However, the combination of different forecast approaches enables users to have a more holistic view of future developments than with single forecasting methods.

Suggested Citation

  • Christian Ulrich & Benjamin Frieske & Stephan A. Schmid & Horst E. Friedrich, 2022. "Monitoring and Forecasting of Key Functions and Technologies for Automated Driving," Forecasting, MDPI, vol. 4(2), pages 1-24, May.
    • Handle: RePEc:gam:jforec:v:4:y:2022:i:2:p:27-500:d:820542
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    References listed on IDEAS

    as
    1. Daim, Tugrul & Lai, Kuei Kuei & Yalcin, Haydar & Alsoubie, Fayez & Kumar, Vimal, 2020. "Forecasting technological positioning through technology knowledge redundancy: Patent citation analysis of IoT, cybersecurity, and Blockchain," Technological Forecasting and Social Change, Elsevier, vol. 161(C).
    2. Sivarajah, Uthayasankar & Kamal, Muhammad Mustafa & Irani, Zahir & Weerakkody, Vishanth, 2017. "Critical analysis of Big Data challenges and analytical methods," Journal of Business Research, Elsevier, vol. 70(C), pages 263-286.
    3. Gao, Lidan & Porter, Alan L. & Wang, Jing & Fang, Shu & Zhang, Xian & Ma, Tingting & Wang, Wenping & Huang, Lu, 2013. "Technology life cycle analysis method based on patent documents," Technological Forecasting and Social Change, Elsevier, vol. 80(3), pages 398-407.
    4. Wang, Lili & Jiang, Shan & Zhang, Shiyun, 2020. "Mapping technological trajectories and exploring knowledge sources: A case study of 3D printing technologies," Technological Forecasting and Social Change, Elsevier, vol. 161(C).
    5. Roos, Michael & Siegmann, Marvin, 2020. "Technologie-Roadmap für das autonome Autofahren: Eine wettbewerbsorientierte Technik- und Marktstudie für Deutschland," Working Paper Forschungsförderung 188, Hans-Böckler-Stiftung, Düsseldorf.
    6. Rowe, Gene & Wright, George, 1999. "The Delphi technique as a forecasting tool: issues and analysis," International Journal of Forecasting, Elsevier, vol. 15(4), pages 353-375, October.
    7. Martin, Hilary & Daim, Tugrul U., 2012. "Technology roadmap development process (TRDP) for the service sector: A conceptual framework," Technology in Society, Elsevier, vol. 34(1), pages 94-105.
    8. David Levinson, 2015. "Climbing Mount Next: The Effects of Autonomous Vehicles on Society," Working Papers 000139, University of Minnesota: Nexus Research Group.
    9. Yuan Zhou & Fang Dong & Yufei Liu & Zhaofu Li & JunFei Du & Li Zhang, 2020. "Forecasting emerging technologies using data augmentation and deep learning," Scientometrics, Springer;Akadémiai Kiadó, vol. 123(1), pages 1-29, April.
    10. Huang, Lu & Zhang, Yi & Guo, Ying & Zhu, Donghua & Porter, Alan L., 2014. "Four dimensional Science and Technology planning: A new approach based on bibliometrics and technology roadmapping," Technological Forecasting and Social Change, Elsevier, vol. 81(C), pages 39-48.
    11. Samira Ranaei & Matti Karvonen & Arho Suominen & Tuomo Kässi, 2016. "Patent-based technology forecasting: case of electric and hydrogen vehicle," International Journal of Energy Technology and Policy, Inderscience Enterprises Ltd, vol. 12(1), pages 20-40.
    12. Jun, Seung-Pyo & Sung, Tae-Eung & Park, Hyun-Woo, 2017. "Forecasting by analogy using the web search traffic," Technological Forecasting and Social Change, Elsevier, vol. 115(C), pages 37-51.
    13. Park, Sang Yong & Kim, Jong Wook & Lee, Duk Hee, 2011. "Development of a market penetration forecasting model for Hydrogen Fuel Cell Vehicles considering infrastructure and cost reduction effects," Energy Policy, Elsevier, vol. 39(6), pages 3307-3315, June.
    14. Li, Shuying & Garces, Edwin & Daim, Tugrul, 2019. "Technology forecasting by analogy-based on social network analysis: The case of autonomous vehicles," Technological Forecasting and Social Change, Elsevier, vol. 148(C).
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