This paper addresses the problem of data-driven modeling and verification of perception-based autonomous systems. We assume the perception model can be decomposed into a canonical model (obtained from first principles or a simulator) and a noise model that contains the measurement noise introduced by the real environment. We focus on two types of noise, benign and adversarial noise, and develop a data-driven model for each type using generative models and classifiers, respectively. We show that the trained models perform well according to a variety of evaluation metrics based on downstream tasks such as state estimation and control. Finally, we verify the safety of two systems with high-dimensional data-driven models, namely an image-based version of mountain car (a reinforcement learning benchmark) as well as the F1/10 car, which uses LiDAR measurements to navigate a racing track.

翻译：本文针对基于数据驱动的感知驱动自主系统建模与验证问题展开研究。我们假设感知模型可分解为标准模型（基于第一性原理或仿真器获得）与噪声模型（包含真实环境引入的测量噪声）两部分。重点研究良性噪声与对抗性噪声两类噪声，分别采用生成模型与分类器构建对应的数据驱动模型。实验表明，基于状态估计与控制等下游任务的多种评估指标验证，所训练模型表现良好。最后，我们针对两个采用高维数据驱动模型的系统进行了安全性验证，包括基于图像的山地车系统（强化学习基准）以及利用激光雷达导航赛道的F1/10赛车系统。