Symbolic Model-based Design and Generation of Logical Scenarios for Autonomous Vehicles Validation

Abstract

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.