Towards a Data Engineering Process in Data-Driven Systems Engineering
This paper aims to take a step towards the introduction of a data engineering process in data-driven automotive systems engineering by putting a spotlight on developing well-designed data sets as the central element for training and validating AI-based software.
Abstract
Highly Automated Driving (HAD) has become one of the leading trends in the automotive industry. Mandatory tasks like environment perception and scene understanding challenge existing rule-based methods. Thus, data-driven technologies and Artificial Intelligence (AI) have been introduced to automotive software development. Utilizing data in the development process has become essential as these systems are no longer developed with classical systems engineering methods, but rather by deriving requirements from and training the algorithms with recorded real-world data. This entails the introduction of data-driven workflows and data-management as new aspects of Automotive Systems Engineering (ASE). Tasks related to the development of Artificial Intelligence (AI) software differ from their classical engineering and programming counterparts. Thus, engineers require new tools and methods for developing safe and accurate AI-based software and handling data efficiently during ASE. Another important aspect of data-driven development is ensuring data quality throughout the systems engineering process. Hence, this paper aims to take a step towards the introduction of a data engineering process in data-driven automotive systems engineering. Putting a spotlight on developing well-designed data sets as the central element for training and validating AI-based software. Besides determining the quality of data sets, we present steps towards improving data and data set quality.