Scenario-based testing for automated driving systems (ADS) must be able to simulate 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 required micro-simulation, while also supporting behavior reuse and test reproducibility for a wide range of interactive scenarios. To fill this gap between scenario design and execution, we propose the Simulated Driver-Vehicle (SDV) model to represent and simulate vehicles as dynamic entities with their behavior being constrained by scenario design and goals set by testers. The model combines driver and vehicle as a single entity. It is based on human-like driving and the mechanical limitations of real vehicles for realistic simulation. The model leverages behavior trees to express high-level behaviors in terms of lower-level maneuvers, affording multiple driving styles and reuse. Furthermore, optimization-based maneuver planners guide the simulated vehicles towards the desired behavior. Our extensive evaluation shows the model's design effectiveness using NHTSA pre-crash scenarios, its motion realism in comparison to naturalistic urban traffic, and its scalability with traffic density. Finally, we show the applicability of our SDV model to test a real ADS and to identify crash scenarios, which are impractical to represent using predefined vehicle trajectories. The SDV model instances can be injected into existing simulation environments via co-simulation.

翻译：自动驾驶系统（ADS）的场景化测试需能模拟依赖于与其他车辆交互的交通场景。尽管已有多种高级场景建模语言被提出，但它们缺乏精确可靠地控制所需微观仿真的功能，同时也难以在广泛的交互场景中支持行为复用与测试可复现性。为填补场景设计与执行之间的鸿沟，我们提出模拟驾驶员-车辆（SDV）模型，将车辆表征和模拟为动态实体，其行为受场景设计及测试人员设定的目标约束。该模型将驾驶员与车辆融合为单一实体，基于类人驾驶行为与真实车辆的机械限制以实现逼真仿真。模型利用行为树以底层机动动作为基础表达高层行为，支持多种驾驶风格与行为复用。此外，基于优化的机动规划器引导模拟车辆实现预期行为。我们通过NHTSA预碰撞场景验证了模型设计的有效性，对比自然城市交通数据证明了运动仿真的真实性，并通过交通密度测试验证了模型的可扩展性。最后，我们展示了SDV模型在测试真实ADS及识别碰撞场景方面的适用性——这些场景使用预定义车辆轨迹难以有效表征。SDV模型实例可通过协同仿真注入现有仿真环境。