# Requirements Engineering for Automotive Perception Systems: an Interview Study

## TL;DR

This study investigates the specific requirements engineering challenges faced by practitioners developing perception systems for automotive driving automation, highlighting difficulties in scenario specification, ODD detection, and traceability.

## Contribution

It provides empirical insights into real-world RE practices and challenges in automotive perception systems through interviews with industry practitioners.

## Key findings

- Practitioners struggle with specifying upfront requirements.
- Reliance on scenarios and operational design domains (ODDs) as RE artifacts.
- Challenges include ODD detection, scenario realism, and requirement traceability.

## 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.