Automated generation of consistent models using qualitative abstractions and exploration strategies
Open access
Datum
2022-10Typ
- Journal Article
Abstract
Automatically synthesizing consistent models is a key prerequisite for many testing scenarios in autonomous driving to ensure a designated coverage of critical corner cases. An inconsistent model is irrelevant as a test case (e.g., false positive); thus, each synthetic model needs to simultaneously satisfy various structural and attribute constraints, which includes complex geometric constraints for traffic scenarios. While different logic solvers or dedicated graph solvers have recently been developed, they fail to handle either structural or attribute constraints in a scalable way. In the current paper, we combine a structural graph solver that uses partial models with an SMT-solver and a quadratic solver to automatically derive models which simultaneously fulfill structural and numeric constraints, while key theoretical properties of model generation like completeness or diversity are still ensured. This necessitates a sophisticated bidirectional interaction between different solvers which carry out consistency checks, decision, unit propagation, concretization steps. Additionally, we introduce custom exploration strategies to speed up model generation. We evaluate the scalability and diversity of our approach, as well as the influence of customizations, in the context of four complex case studies. Mehr anzeigen
Persistenter Link
https://doi.org/10.3929/ethz-b-000506675Publikationsstatus
publishedExterne Links
Zeitschrift / Serie
Software and Systems ModelingBand
Seiten / Artikelnummer
Verlag
SpringerThema
Model generation; Partial model; Graph solver; SMT-solver; Numeric solver; Exploration strategy; Test scenario synthesis