Monitoring and Forecasting of Key Functions and Technologies for Automated Driving
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 allo...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-05-01
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| Series: | Forecasting |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2571-9394/4/2/27 |
| _version_ | 1797487471785672704 |
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| author | Christian Ulrich Benjamin Frieske Stephan A. Schmid Horst E. Friedrich |
| author_facet | Christian Ulrich Benjamin Frieske Stephan A. Schmid Horst E. Friedrich |
| author_sort | Christian Ulrich |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-09T23:48:09Z |
| format | Article |
| id | doaj.art-cfdf1ec7ba6444449ae08ce3ae6cbcf2 |
| institution | Directory Open Access Journal |
| issn | 2571-9394 |
| language | English |
| last_indexed | 2024-03-09T23:48:09Z |
| publishDate | 2022-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Forecasting |
| spelling | doaj.art-cfdf1ec7ba6444449ae08ce3ae6cbcf22023-11-23T16:39:37ZengMDPI AGForecasting2571-93942022-05-014247750010.3390/forecast4020027Monitoring and Forecasting of Key Functions and Technologies for Automated DrivingChristian Ulrich0Benjamin Frieske1Stephan A. Schmid2Horst E. Friedrich3German Aerospace Center (DLR), Institute of Vehicle Concepts, Pfaffenwaldring 38–40, 70569 Stuttgart, GermanyGerman Aerospace Center (DLR), Institute of Vehicle Concepts, Pfaffenwaldring 38–40, 70569 Stuttgart, GermanyGerman Aerospace Center (DLR), Institute of Vehicle Concepts, Pfaffenwaldring 38–40, 70569 Stuttgart, GermanyGerman Aerospace Center (DLR), Institute of Vehicle Concepts, Pfaffenwaldring 38–40, 70569 Stuttgart, GermanyCompanies 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.https://www.mdpi.com/2571-9394/4/2/27technology monitoringtechnology roadmaptechnology forecastingtechnology readiness level (TRL)manufacturing readiness level (MRL)autonomous vehicles |
| spellingShingle | Christian Ulrich Benjamin Frieske Stephan A. Schmid Horst E. Friedrich Monitoring and Forecasting of Key Functions and Technologies for Automated Driving Forecasting technology monitoring technology roadmap technology forecasting technology readiness level (TRL) manufacturing readiness level (MRL) autonomous vehicles |
| title | Monitoring and Forecasting of Key Functions and Technologies for Automated Driving |
| title_full | Monitoring and Forecasting of Key Functions and Technologies for Automated Driving |
| title_fullStr | Monitoring and Forecasting of Key Functions and Technologies for Automated Driving |
| title_full_unstemmed | Monitoring and Forecasting of Key Functions and Technologies for Automated Driving |
| title_short | Monitoring and Forecasting of Key Functions and Technologies for Automated Driving |
| title_sort | monitoring and forecasting of key functions and technologies for automated driving |
| topic | technology monitoring technology roadmap technology forecasting technology readiness level (TRL) manufacturing readiness level (MRL) autonomous vehicles |
| url | https://www.mdpi.com/2571-9394/4/2/27 |
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