Monitoring and Forecasting of Key Functions and Technologies for Automated Driving

Christian Ulrich, Benjamin Frieske, Stephan A. Schmid and Horst E. Friedrich
Additional contact information
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

Forecasting, 2022, vol. 4, issue 2, 1-24

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.

Keywords: technology monitoring; technology roadmap; technology forecasting; technology readiness level (TRL); manufacturing readiness level (MRL); autonomous vehicles; Delphi survey; publication analysis; patent analysis (search for similar items in EconPapers)
JEL-codes: A1 B4 C0 C1 C2 C3 C4 C5 C8 M0 Q2 Q3 Q4 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.mdpi.com/2571-9394/4/2/27/pdf (application/pdf)
https://www.mdpi.com/2571-9394/4/2/27/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jforec:v:4:y:2022:i:2:p:27-500:d:820542

Access Statistics for this article

Forecasting is currently edited by Ms. Joss Chen

More articles in Forecasting from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().