Requirements Engineering for Automotive Perception Systems: an Interview Study
Abstract
Background: Driving automation systems (DAS), including autonomous driving and advanced driver assistance, are an important safety-critical domain. DAS often incorporate perceptions systems that use machine learning (ML) to analyze the vehicle environment. Aims: We explore new or differing requirements engineering (RE) topics and challenges that practitioners experience in this domain. Method: We have conducted an interview study with 19 participants across five companies and performed thematic analysis. Results: Practitioners have difficulty specifying upfront requirements, and often rely on scenarios and operational design domains (ODDs) as RE artifacts. 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. Conclusions: Our findings contribute to understanding how RE is practiced for DAS perception systems and the collected challenges can drive future research for DAS and other ML-enabled systems.
Keywords
Cite
@article{arxiv.2302.12155,
title = {Requirements Engineering for Automotive Perception Systems: an Interview Study},
author = {Khan Mohammad Habibullah and Hans-Martin Heyn and Gregory Gay and Jennifer Horkoff and Eric Knauss and Markus Borg and Alessia Knauss and Håkan Sivencrona and Polly Jing Li},
journal= {arXiv preprint arXiv:2302.12155},
year = {2023}
}