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Scenario-based testing for automated driving systems (ADS) must be able tosimulate traffic scenarios that rely on interactions with other vehicles.Although many languages for high-level scenario modelling have been proposed,they lack the features to precisely and reliably control the requiredmicro-simulation, while also supporting behavior reuse and test reproducibilityfor a wide range of interactive scenarios. To fill this gap between scenariodesign and execution, we propose the Simulated Driver-Vehicle Model (SDV) torepresent and simulate vehicles as dynamic entities with their behavior beingconstrained by scenario design and goals set by testers. The model combinesdriver and vehicle as a single entity. It is based on human-like driving andthe mechanical limitations of real vehicles for realistic simulation. Thelayered architecture of the model leverages behavior trees to expresshigh-level behaviors in terms of lower-level maneuvers, affording multipledriving styles and reuse. Further, optimization-based maneuver planner guidesthe simulated vehicles towards the desired behavior. Our extensive evaluationshows the model's design effectiveness using NHTSA pre-crash scenarios, itsmotion realism in comparison to naturalistic urban traffic, and its scalabilitywith traffic density. Finally, we show the applicability of SDV model to test areal ADS and to identify crash scenarios, which are impractical to representusing predefined vehicle trajectories. The SDV model instances can be injectedinto existing simulation environments via co-simulation.

A Driver-Vehicle Model for ADS Scenario-based Testing