Logical Scenario Derivation by Clustering Dynamic-Length-Segments Extracted from Real-World-Driving-Data

Abstract

For the development of Advanced Driver Assistant Systems (ADAS) and Automated Driving Systems (ADS) a change from test case-based testing towards scenario-based testing can be observed. Based on current approaches to define scenarios and their inherent problems, we identify the need to extract scenarios including the static environment from recorded real-world-driving-data. We present an approach, that solves the problem to extract dynamic-length-segments containing a single scenario. These segments are enriched with a feature vector with information relevant for the feature under test. By clustering these scenarios a logical scenario catalog is created, containing all scenarios within the test data. Corner cases are represented as well as common scenarios. An accumulated total length can be calculated for each logical scenario, giving a brief understanding about existing test coverage of the scenario.