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Automotive Percepti...
Automotive Perception Software Development : An Empirical Investigation into Data, Annotation, and Ecosystem Challenges
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- Heyn, Hans-Martin, 1987 (author)
- Gothenburg University,Göteborgs universitet,Institutionen för data- och informationsteknik (GU),Department of Computer Science and Engineering (GU),University of Gothenburg, Sweden
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- Habibullah, Khan Mohammad, 1990 (author)
- Gothenburg University,Göteborgs universitet,Institutionen för data- och informationsteknik, datorteknik (GU),Department of Computer Science and Engineering, Computer Engineering (GU),University of Gothenburg, Sweden
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- Knauss, Eric, 1977 (author)
- Gothenburg University,Göteborgs universitet,Institutionen för data- och informationsteknik (GU),Department of Computer Science and Engineering (GU),University of Gothenburg, Sweden
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- Horkoff, Jennifer, 1980 (author)
- Gothenburg University,Göteborgs universitet,Institutionen för data- och informationsteknik (GU),Department of Computer Science and Engineering (GU),University of Gothenburg, Sweden
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- Borg, Markus (author)
- RISE,RISE Research Institutes of Sweden
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- Knauss, Alessia, 1983 (author)
- Zenseact AB, Sweden
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- Li, Polly J (author)
- Kognic AB, Sweden
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(creator_code:org_t)
- Institute of Electrical and Electronics Engineers Inc. 2023
- 2023
- English.
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In: Proceedings - 2023 IEEE/ACM 2nd International Conference on AI Engineering - Software Engineering for AI, CAIN 2023. - : Institute of Electrical and Electronics Engineers Inc.. - 9798350301137 ; , s. 13-24
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Abstract
Subject headings
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- Software that contains machine learning algorithms is an integral part of automotive perception, for example, in driving automation systems. The development of such software, specifically the training and validation of the machine learning components, requires large annotated datasets. An industry of data and annotation services has emerged to serve the development of such data-intensive automotive software components. Wide-spread difficulties to specify data and annotation needs challenge collaborations between OEMs (Original Equipment Manufacturers) and their suppliers of software components, data, and annotations.This paper investigates the reasons for these difficulties for practitioners in the Swedish automotive industry to arrive at clear specifications for data and annotations. The results from an interview study show that a lack of effective metrics for data quality aspects, ambiguities in the way of working, unclear definitions of annotation quality, and deficits in the business ecosystems are causes for the difficulty in deriving the specifications. We provide a list of recommendations that can mitigate challenges when deriving specifications and we propose future research opportunities to overcome these challenges. Our work contributes towards the on-going research on accountability of machine learning as applied to complex software systems, especially for high-stake applications such as automated driving.
Subject headings
- NATURVETENSKAP -- Data- och informationsvetenskap -- Programvaruteknik (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Software Engineering (hsv//eng)
Keyword
- accountability
- annotations
- data
- ecosystems
- machine learning
- requirements specification
- Application programs
- Automation
- Automotive industry
- Large dataset
- Learning algorithms
- Machine components
- Software design
- Annotation
- Automotives
- Data annotation
- Empirical investigation
- Machine learning algorithms
- Machine-learning
- Requirements specifications
- Software-component
- Specifications
Publication and Content Type
- ref (subject category)
- kon (subject category)
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