Finding comprehensive and relevant scenarios is a major challenge for autonomous vehicles validation and SOTIF. A functional scenario, e.g. a cut-in, encloses many concrete variations. Formal methods help covering an intermediate level of scenario families, called logical, and capitalizing them in a scenario database. Families are generated from discrete and modular symbolic models through a new subsumption criterion which allows the identification of scenario suffixes which are redundant and eliminate them during the generation. The generation, including the implementation of the subsumption criterion, benefits from: i) the compact representation of models thanks to discretization and symbolic arithmetic, ii) dedicated symbolic execution techniques. Analysis is performed to verify how the generated scenarios cover real situations by confronting them to time series from the modeled system and identify potential gaps in the model. We formally define our approach, implement it in the symbolic execution tool DIVERSITY. Assessment is carried out on a real autopilot black box module from the project 3SA.

We present a visual analytics solution designed to address prevalent issues in the area of Operational Decision Management (ODM). In ODM, which has its roots in Artificial Intelligence (Expert Systems) and Management Science, it is increasingly important to align business decisions with business goals. In our work, we consider decision models (executable models of the business domain) as ontologies that describe the business domain, and production rules that describe the business logic of decisions to be made over this ontology. Executing a decision model produces an accumulation of decisions made over time for individual cases. We are interested, first, to get insight in the decision logic and the accumulated facts by themselves. Secondly and more importantly, we want to see how the accumulated facts reveal potential divergences between the reality as captured by the decision model, and the reality as captured by the executed decisions. We illustrate the motivation, added value for visual analytics, and our proposed solution and tooling through a business case from the car insurance industry.