论文标题
基于模型学习的自主驾驶系统的安全分析
Safety Analysis of Autonomous Driving Systems Based on Model Learning
论文作者
论文摘要
我们提出了一种实用验证方法，用于对自主驾驶系统（ADS）的安全分析。主要思想是构建一个替代模型，该模型在指定的流量方案中定量描述了广告的行为。结果替代模型中证明的安全性适用于具有概率保证的原始广告。此外，我们探索了驾驶危险的交通情况的安全和不安全参数空间。我们通过评估文献中最先进的广告的安全性，并具有各种模拟的交通情况来证明拟议方法的实用性。
We present a practical verification method for safety analysis of the autonomous driving system (ADS). The main idea is to build a surrogate model that quantitatively depicts the behaviour of an ADS in the specified traffic scenario. The safety properties proved in the resulting surrogate model apply to the original ADS with a probabilistic guarantee. Furthermore, we explore the safe and the unsafe parameter space of the traffic scenario for driving hazards. We demonstrate the utility of the proposed approach by evaluating safety properties on the state-of-the-art ADS in literature, with a variety of simulated traffic scenarios.
