This paper presents an adaptive verification framework for automated driving functions based on ontologies and data-mining techniques. Despite the recent rapid growth of driver assistance systems to consolidate road safety, they still have various challenges coping with dynamic traffic situations of daily life. Therefore, automotive systems engineering has established data- and knowledge-driven test methods to assure the required functional safety and reliability in a highly safety-critical context. However, the reliance on field testing is inadequate and, in particular, time- and cost-intensive when applied to the next generation of automated driving functions, e.g. collision-free emergency braking and vehicle platooning. The presented framework utilises an ontology-based test scenario synthesis to identify criticality margins using a Hardware-in-the-Loop co-simulation platform for automated driving functions. Additionally, we demonstrate a systematic process to complement virtual testing by extracting insights from field testing database using event-based time-series analysis. To this end, data mining techniques are used to obtain representative scenarios witnessed in real-world traffic. Agglomerative hierarchical clustering is performed to extract homogeneous groups (clusters) from recorded triggering events by proximity metrics using normalised cross-correlations. Extracted scenarios are subsequently used at earlier stages of development to effectively and efficiently ensure reliability and safety. In summary, the results show the benefits and some of the challenges of using the industry-proven framework, which enables a cost-effective extension of test domain vaidility throughout software product engineering.
Ontology-based adaptive testing for automated driving functions using data mining techniques
Transportation Research Part F: Psychology and Behaviour