# Requirements Engineering for Automotive Perception Systems: an Interview Study

arXiv: https://arxiv.org/abs/2302.12155 · HTML: https://mlsanity.com/paper/2302.12155/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Feb 2023.

## Built on by (most cited first, 1 of 1)

- [How Mature is Requirements Engineering for AI-based Systems? A Systematic Mapping Study on Practices, Challenges, and Future Research Directions](https://mlsanity.com/paper/2409.07192/) (2024)

## Abstract (arXiv)

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.