We are interested in predicting failures of cyber-physical systems during their operation. Particularly, we consider stochastic systems and signal temporal logic specifications, and we want to calculate the probability that the current system trajectory violates the specification. The paper presents two predictive runtime verification algorithms that predict future system states from the current observed system trajectory. As these predictions may not be accurate, we construct prediction regions that quantify prediction uncertainty by using conformal prediction, a statistical tool for uncertainty quantification. Our first algorithm directly constructs a prediction region for the satisfaction measure of the specification so that we can predict specification violations with a desired confidence. The second algorithm constructs prediction regions for future system states first, and uses these to obtain a prediction region for the satisfaction measure. To the best of our knowledge, these are the first formal guarantees for a predictive runtime verification algorithm that applies to widely used trajectory predictors such as RNNs and LSTMs, while being computationally simple and making no assumptions on the underlying distribution. We present numerical experiments of an F-16 aircraft and a self-driving car.

翻译：我们致力于在信息物理系统运行过程中预测其故障。具体而言，针对随机系统与信号时序逻辑规范，我们旨在计算当前系统轨迹违反规范的概率。本文提出两种预测性运行时验证算法，通过当前观测到的系统轨迹预测未来系统状态。由于这些预测可能存在误差，我们利用保形预测这一不确定性量化统计工具构建预测区域，以量化预测不确定性。第一种算法直接针对规范的满足度构建预测区域，从而能够以期望置信度预测规范违反情况；第二种算法则先构建未来系统状态的预测区域，再据此得到满足度的预测区域。据我们所知，这是首个为预测性运行时验证算法提供形式化保证的方法，适用于RNN、LSTM等广泛使用的轨迹预测器，同时具有计算简单且不依赖底层分布假设的特点。我们针对F-16战斗机和自动驾驶汽车开展了数值实验。