Using Knowledge Awareness to improve Safety of Autonomous Driving

We present a method, which incorporates knowledge awareness into the symbolic computation of discrete controllers for reactive cyber physical systems, to improve decision making about the unknown operating environment under uncertain/incomplete inputs. Assuming an abstract model of the system and the environment, we translate the knowledge awareness of the operating context into linear temporal logic formulas and incorporate them into the system specifications to synthesize a controller. The knowledge base is built upon an ontology model of the environment objects and behavioural rules, which includes also symbolic models of partial input features. The resulting symbolic controller support smoother, early reactions, which improves the security of the system over existing approaches based on incremental symbolic perception. A motion planning case study for an autonomous vehicle has been implemented to validate the approach, and presented results show significant improvements with respect to safety of state-of-the-art symbolic controllers for reactive systems

Combinatorial Interaction Testing with CITLAB

Abstract—In this paper the CITLAB tool for Combinatorial Interaction Testing is presented. The tool allows importing/exporting models of combinatorial problems from/to different application domains, by means of a common interchange syntax notation and a corresponding interoperable semantic metamodel. Moreover, the tool is a framework allowing embedding and transparent invocation of multiple, different implementations of combinatorial algorithms. CITLAB has been designed tightly integrated with the Eclipse IDE framework, by means of its plug-in extension mechanism. It is intended to easy the spread of CIT testing both in industrial practice and in academic research, by allowing users and researchers to apply multiple test suite generation algorithms, each with its peculiarities, on the same problem models, and let them compare the results in order to select the one that best fits their needs, while alleviating from the pain of knowing all the different details and notations of the underlying CIT tools. Index Terms—Combinatorial testing model, domain-specific language, Eclipse, XTEXT. I

Combinatorial testing for feature models using citlab

Abstract—Feature models are commonly used to represent product lines and systems with a set of features interrelated each others. Test generation from feature models, i.e. generating a valid and representative subset of all the possible product configurations, is still an open challenge. A common approach is to build combinatorial interaction test suites, for instance achieving pairwise coverage among the features. In this paper we show how standard feature models can be translated to combinatorial interaction models in our framework CITLAB, with all the advantages of having a combinatorial testing environment (in terms of a clear semantics, editing facilities, language for seeds and test goals, and generation algorithms). We present our translation which gives a precise semantics to feature models and it tries to minimize the number of parameter and constraints while preserving the original semantics of the feature model. Experiments show the advantages of our approach. I