Generic Detection and Search-based Test Case Generation of Urban Scenarios based on Real Driving Data

Abstract

This study enhances automated driving scenario-based safety assessment methods previously developed for highways, and enables their application to urban areas. First, we propose a methodology for matching open source map data with naturalistic driving data recorded with test vehicles. The methodology proposed proved feasible detecting various geometry-related scenarios and can contribute to overcome the difficulties to create representative real driving urban scenario databases that cover such geometries. Second, a search-based test case generation methodology previously developed to fulfill requirements of severity, exposure and realism with a focus on highways, is further developed and adapted to active urban scenarios. Active scenarios require an active maneuver decision of the Vehicle under Test and have not been considered in related work so far. To show the feasibility of the methodologies proposed, we apply them to a set of Left Turn Across Path / Opposite Direction scenarios, extracted from an existing urban driving database. The map matching and the search-based test case generation methodology succeeded in deriving test cases, which equally account for exposure and coverage criteria for normal driving situations in urban settings.