Modern autonomous vehicle systems use complex perception and control components. These components can rapidly change during development of such systems, requiring constant re-testing. Unfortunately, high-fidelity simulations of these complex systems for evaluating vehicle safety are costly. The complexity also hinders the creation of less computationally intensive surrogate models. We present GAS, the first approach for creating surrogate models of complete (perception, control, and dynamics) autonomous vehicle systems containing complex perception and/or control components. GAS's two-stage approach first replaces complex perception components with a perception model. Then, GAS constructs a polynomial surrogate model of the complete vehicle system using Generalized Polynomial Chaos (GPC). We demonstrate the use of these surrogate models in two applications. First, we estimate the probability that the vehicle will enter an unsafe state over time. Second, we perform global sensitivity analysis of the vehicle system with respect to its state in a previous time step. GAS's approach also allows for reuse of the perception model when vehicle control and dynamics characteristics are altered during vehicle development, saving significant time. We consider five scenarios concerning crop management vehicles that must not crash into adjacent crops, self driving cars that must stay within their lane, and unmanned aircraft that must avoid collision. Each of the systems in these scenarios contain a complex perception or control component. Using GAS, we generate surrogate models for these systems, and evaluate the generated models in the applications described above. GAS's surrogate models provide an average speedup of $3.7\times$ for safe state probability estimation (minimum $2.1\times$) and $1.4\times$ for sensitivity analysis (minimum $1.3\times$), while still maintaining high accuracy.

翻译：摘要：现代自动驾驶系统采用复杂的感知与控制组件。在系统开发过程中，这些组件可能快速迭代，需要持续重新测试。然而，用于评估车辆安全性的高保真仿真成本高昂，且复杂性也阻碍了计算成本较低的代理模型的构建。我们提出GAS，这是首个针对包含复杂感知和/或控制组件的完整自动驾驶系统（感知、控制与动力学）创建代理模型的方法。GAS的两阶段方法首先用感知模型替代复杂感知组件，随后利用广义多项式混沌（GPC）构建整车系统的多项式代理模型。我们展示了这些代理模型在两种应用中的使用：首先，估计车辆随时间进入不安全状态的概率；其次，对车辆系统相对于前一时刻状态进行全局敏感性分析。GAS的方法还允许在车辆开发过程中改变控制与动力学特性时复用感知模型，显著节省时间。我们考虑了五种场景：涉及不得碰撞相邻作物的农田管理车辆、需保持在车道内的自动驾驶汽车、以及必须避免碰撞的无人飞行器。每个系统中的组件均包含复杂感知或控制模块。利用GAS，我们为这些系统生成代理模型，并在上述应用场景中评估生成模型。GAS的代理模型在安全状态概率估计中平均加速3.7倍（最低2.1倍），在敏感性分析中加速1.4倍（最低1.3倍），同时保持高精度。