Data Handling for Assuring Production Quality of Image Intensive Autonomous Drive Systems: An Industrial Case Study

Abstract

Automotive software uses new Machine Learning (ML) algorithms in increased number of systems, including active safety ones. However, with this new paradigm, new challenges arise in the domain of safety-critical automotive software. This article reports on a case study of the development of ML-based vision perception systems at one vehicle Original Equipment Manufacturer (OEM). We investigate how image-intensive perception systems are developed, both from the perspective of ML development processes and automotive software development processes. We conducted interviews with four engineers who were involved in the development process and subsequently performed thematic coding to extract key findings. We focus on how the teams are involved in the process of assuring the production quality of the Society of Automotive Engineers (SAE) level 3 functionality in modern passenger cars. We examine how the ML development process phases (e.g., data collection, model training, and model validation) align with the automotive software development phases (prototype development, software development, validation, deployment). The study found that the development process for ML-based vision perception systems in active safety allows for flexibility to adapt to changes in data collection, and integrates ML model development into the software development process. The investigated approach combines the Agile SAFe model, ML-model development, and the standard automotive V-process model. This study shows an example of combining these development processes using an industrial case study and presents the essential alignment points between different phases of these processes. The study also recommends best practices for developing similar systems in other companies.