A requirements engineering approach in the development of an AI-based classification system for road markings in autonomous driving : a case study

Abstract: Background: Requirements engineering (RE) is the process of identifying, defining, documenting, and validating requirements. However, RE approaches are usually not applied to AI-based systems due to their ambiguity and is still a growing subject. Research also shows that the quality of ML-based systems is affected due to the lack of a structured RE process. Hence, there is a need to apply RE techniques in the development of ML-based systems.  Objectives: This research aims to identify the practices and challenges concerning RE techniques for AI-based systems in autonomous driving and then to identify a suitable RE approach to overcome the identified challenges. Further, the thesis aims to check the feasibility of the selected RE approach in developing a prototype AI-based classification system for road markings.  Methods: A combination of research methods has been used for this research. We apply techniques of interviews, case study, and a rapid literature review. The case company is Scania CV AB. A literature review is conducted to identify the possible RE approaches that can overcome the challenges identified through interviews and discussions with the stakeholders. A suitable RE approach, GR4ML, is found and used to develop and validate an AI-based classification system for road markings.  Results: Results indicate that RE is a challenging subject in autonomous driving. Several challenges are faced at the case company in eliciting, specifying, and validating requirements for AI-based systems, especially in autonomous driving. Results also show that the views in the GR4ML framework were suitable for the specification of system requirements and addressed most challenges identified at the case company. The iterative goal-oriented approach maintained flexibility during development. Through the system's development, it was identified that the Random Forest Classifier outperformed the Logistic Regressor and Support Vector Machine for the road markings classification.  Conclusions: The validation of the system suggests that the goal-oriented requirements engineering approach and the GR4ML framework addressed most challenges identified in eliciting, specifying, and validating requirements for AI-based systems at the case company. The views in the GR4ML framework provide a good overview of the functional and non-functional requirements of the lower-level systems in autonomous driving. However, the GR4ML framework might not be suitable for validation of higher-level AI-based systems in autonomous driving due to their complexity.