The Why, Where, When, What and How of Data for Data-driven Engineering of Automotive Systems

The use of data from real-world test drives and customer drives in development enables vehicle manufacturers to remain competitive and manage the increasing complexity of automotive systems, while meeting market demands and achieving faster, more customer-focused development cycles. Research has mainly taken a process view, looking at the potential of the data-driven engineering (DDE) process in the context of automotive systems engineering (ASE). A consolidated data-centric view has not been elaborated so far. In order to realise the full potential of using data and to understand the associated requirements for the data itself during ASE, this paper presents a framework that encompasses the Why, Where, When and What of data collection, as well as How data should be processed to gain actionable insights. Understanding the derived requirements for data in ASE can assist researchers and practitioners in understanding the critical aspects of data, thereby leading to more targeted outcomes.

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Publication Details

Published
2024-10-16
DOI
https://doi.org/10.1109/isse63315.2024.10741122
Citations
2
Primary Topic
Real-time simulation and control systems
Type
article
Field-Weighted Citation Impact
0.66
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The Why, Where, When, What and How of Data for Data-driven Engineering of Automotive Systems

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The Why, Where, When, What and How of Data for Data-driven Engineering of Automotive Systems

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article en
2 citations

Abstract

The use of data from real-world test drives and customer drives in development enables vehicle manufacturers to remain competitive and manage the increasing complexity of automotive systems, while meeting market demands and achieving faster, more customer-focused development cycles. Research has mainly taken a process view, looking at the potential of the data-driven engineering (DDE) process in the context of automotive systems engineering (ASE). A consolidated data-centric view has not been elaborated so far. In order to realise the full potential of using data and to understand the associated requirements for the data itself during ASE, this paper presents a framework that encompasses the Why, Where, When and What of data collection, as well as How data should be processed to gain actionable insights. Understanding the derived requirements for data in ASE can assist researchers and practitioners in understanding the critical aspects of data, thereby leading to more targeted outcomes.

Karlsruhe Institute of Technology (DE)
Industry, innovation and infrastructure
Openalex Percentile: Top 29%
Real-time simulation and control systems
0.66
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