利用自适应压力测试和反向算法发现高保真仿真中的故障
Finding Failures in High-Fidelity Simulation using Adaptive Stress Testing and the Backward Algorithm
摘要：
验证自治系统的安全性通常需要使用高保真模拟器，以充分捕捉真实世界场景的变化。然而，它通常是不可行的，彻底搜索空间的仿真场景的故障。自适应压力测试（AST）是一种使用强化学习来发现系统最可能出现故障的方法。具有深度强化学习求解器的AST已被证明在发现一系列不同系统的故障方面是有效的。这种方法通常涉及运行许多模拟，当使用高保真模拟器时，这可能非常昂贵。为了提高效率，我们提出了一种方法，首先发现故障在低保真度的模拟器。然后，它使用反向算法，该算法使用单个专家演示来训练深度神经网络策略，以使低保真度故障适应高保真度。我们已经创建了一系列自动驾驶汽车验证案例研究，这些案例研究代表了低保真和高保真模拟器的一些不同之处，例如时间离散化。我们在各种案例研究中证明，这种新的AST方法能够找到故障，与直接以高保真度运行AST时所需的高保真度仿真步骤相比，所需的高保真度仿真步骤明显减少。作为概念验证，我们还在NVIDIA的DriveSim模拟器上演示了AST，这是一款业界最先进的高保真模拟器，可用于查找自动驾驶汽车中的故障。
英文摘要：
Validating the safety of autonomous systems generally requires the use of high-fidelity simulators that adequately capture the variability of real-world scenarios. However, it is generally not feasible to exhaustively search the space of simulation scenarios for failures. Adaptive stress testing (AST) is a method that uses reinforcement learning to find the most likely failure of a system. AST with a deep reinforcement learning solver has been shown to be effective in finding failures across a range of different systems. This approach generally involves running many simulations, which can be very expensive when using a high-fidelity simulator. To improve efficiency, we present a method that first finds failures in a low-fidelity simulator. It then uses the backward algorithm, which trains a deep neural network policy using a single expert demonstration, to adapt the low-fidelity failures to high-fidelity. We have created a series of autonomous vehicle validation case studies that represent some of the ways low-fidelity and high-fidelity simulators can differ, such as time discretization. We demonstrate in a variety of case studies that this new AST approach is able to find failures with significantly fewer high-fidelity simulation steps than are needed when just running AST directly in high-fidelity. As a proof of concept, we also demonstrate AST on NVIDIA's DriveSim simulator, an industry state-of-the-art high-fidelity simulator for finding failures in autonomous vehicles.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
