Refining Environmental Requirements for Autonomous Driving Systems: Leveraging the FRAV Framework
Mahwish Kundi, Faraz Ahmad, Rosemary Monahan
Safety and reliability requirements are fundamental for any Autonomous Driving System (ADS) to ensure optimal performance, regulatory compliance, and overall safety. However, the standardization of ADS requirements remains a challenge due to the complexity of AI-based systems. This study initially focused on obtaining a textual description of the environmental requirements of ADS. A detailed literature review revealed a lack of structured information in this domain. Consequently, we identified the Functional Requirements for Automated Vehicles (FRAV) framework as a viable source for defining the environmental requirements of ADS. To evaluate the completeness of these requirements, we performed a comparative analysis of environmental attributes with the Safety Controller for Autonomous Driving (SCAD) case study and the FRAV framework. This comparison highlights how existing formal approaches address real-world environmental factors and identifies potential gaps that need further refinement. This study initiates the verification and validation process for ADS through the elicitation of relevant requirements. In the future, these requirements will be further refined by adopting a set of activities such as cognitive linguistics, crowd-based requirement engineering, and empirical validation with industry experts. In addition, traceability from the natural language requirements to their formal specification using the Formal Requirements Elicitation Tool (FRET) will be implemented to support the validation and verification of the ADS requirements. Our proposed approach can contribute to obtaining safer and more stable ADS requirements while also having the potential to serve as a standardized framework for eliciting AI-based requirements in future systems.