Automotive Perception Software Development: An Empirical Investigation into Data, Annotation, and Ecosystem Challenges

Heyn, Hans-Martin; Habibullah, Khan Mohammad; Knauss, Eric; Horkoff, Jennifer; Borg, Markus; Knauss, Alessia; Li, Polly Jing

doi:10.1109/cain58948.2023.00011

Cited by 2 publications

References 37 publications

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Requirements and software engineering for automotive perception systems: an interview study

Habibullah,

Heyn,

Gay

et al. 2024

Requirements Eng

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Driving automation systems, including autonomous driving and advanced driver assistance, are an important safety-critical domain. Such systems often incorporate perception systems that use machine learning to analyze the vehicle environment. We explore new or differing topics and challenges experienced by practitioners in this domain, which relate to requirements engineering (RE), quality, and systems and software engineering. We have conducted a semi-structured interview study with 19 participants across five companies and performed thematic analysis of the transcriptions. Practitioners have difficulty specifying upfront requirements and often rely on scenarios and operational design domains (ODDs) as RE artifacts. RE challenges relate to ODD detection and ODD exit detection, realistic scenarios, edge case specification, breaking down requirements, traceability, creating specifications for data and annotations, and quantifying quality requirements. Practitioners consider performance, reliability, robustness, user comfort, and—most importantly—safety as important quality attributes. Quality is assessed using statistical analysis of key metrics, and quality assurance is complicated by the addition of ML, simulation realism, and evolving standards. Systems are developed using a mix of methods, but these methods may not be sufficient for the needs of ML. Data quality methods must be a part of development methods. ML also requires a data-intensive verification and validation process, introducing data, analysis, and simulation challenges. Our findings contribute to understanding RE, safety engineering, and development methodologies for perception systems. This understanding and the collected challenges can drive future research for driving automation and other ML systems.

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