Modern autonomous vehicle systems use complex perception and control components and must cope with uncertain data received from sensors. To estimate the probability that such vehicles remain in a safe state, developers often resort to time-consuming simulation methods. This paper presents an alternative methodology for analyzing autonomy pipelines in vehicular systems, based on Generalized Polynomial Chaos (GPC). We also present GAS, the first algorithm for creating and using GPC models of complex vehicle systems. GAS replaces complex perception components with a perception model to reduce complexity. Then, it constructs the GPC model and uses it for estimating state distribution and/or probability of entering an unsafe state. We evaluate GAS on five scenarios used in crop management vehicles, self driving cars, and aerial drones - each system uses at least one complex perception or control component. We show that GAS calculates state distributions that closely match those produced by Monte Carlo Simulation, while also providing 2.3x-3.0x speedups.
翻译:现代自主车辆系统使用复杂的感知和控制组件,并必须应对从传感器收到的不确定数据。为了估计这些车辆是否仍然处于安全状态,开发商往往采用耗时的模拟方法。本文介绍了基于通用多式多式混乱(GPC)分析车辆系统中自主管道的替代方法。我们还提供了GAS,这是创建和使用GPC复杂车辆系统模型的首个算法。GAS用一个感知模型取代复杂的感知组件,以减少复杂性。然后,它建造了GPC模型,并用来估计国家分布和/或进入不安全状态的可能性。我们评估了在作物管理车辆、自驾驶汽车和空中无人驾驶飞机中使用的GAS,每个系统至少使用一个复杂的感知或控制组件。我们显示GAS计算了与Monte Carlo Simulate所生成的相近的分布,同时提供2.3x-3.0x加速器。