Automotive Perception Software Development: An Empirical Investigation into Data, Annotation, and Ecosystem Challenges
Paper in proceeding, 2023

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.

requirements specification

machine learning

data

ecosystems

annotations

accountability

Author

Hans-Martin Heyn

University of Gothenburg

Khan Mohammad Habibullah

University of Gothenburg

Eric Knauss

University of Gothenburg

Jennifer Horkoff

University of Gothenburg

M. Borg

RISE Research Institutes of Sweden

Alessia Knauss

Zenseact AB

Polly Jing Li

Kognic

Proceedings - 2023 IEEE/ACM 2nd International Conference on AI Engineering - Software Engineering for AI, CAIN 2023

13-24
9798350301137 (ISBN)

2nd IEEE/ACM International Conference on AI Engineering - Software Engineering for AI, CAIN 2023
Melbourne, Australia,

Very Efficient Deep Learning in IOT (VEDLIoT)

European Commission (EC) (EC/H2020/957197), 2020-11-01 -- 2023-10-31.

Subject Categories

Software Engineering

DOI

10.1109/CAIN58948.2023.00011

More information

Latest update

10/26/2023