Online Identification of Operational Design Domains of Automated Driving System Features

The Operational Design Domain (ODD) consists of operating conditions under which an Automated Driving System (ADS) feature is intended to be deployed and should satisfy safety and performance requirements. Creating human-interpretable and monitorable ODD specifications for ADS features, comprising black-box and non-deterministic Machine Learning (ML) components, is complicated owing to the unknown impact of possibly infinite operational contexts on system requirement fulfillment. Furthermore, these ML components may be updated to address unforeseen operational contexts encountered after feature deployment, thus necessitating further updates to the ODD. This paper proposes a novel approach for online ODD identification, i.e., discovering operating conditions wherein the ADS feature satisfies system requirements using fuzzy behavior oracles. Our data-driven approach involves human-interpretable representation of operational contexts, facilitating the semi-automatic generation of conditional ODD statements and updates to ODD post-feature deployment. The feasibility of our approach is validated with a case study on a Lane Change Assist ADS feature, which exhibits a 55% improvement in scalability, allowing its deployment in a broader ODD.