Discovering potential failures of an autonomous system is important prior to deployment. Falsification-based methods are often used to assess the safety of such systems, but the cost of running many accurate simulation can be high. The validation can be accelerated by identifying critical failure scenarios for the system under test and by reducing the simulation runtime. We propose a Bayesian approach that integrates meta-learning strategies with a multi-armed bandit framework. Our method involves learning distributions over scenario parameters that are prone to triggering failures in the system under test, as well as a distribution over fidelity settings that enable fast and accurate simulations. In the spirit of meta-learning, we also assess whether the learned fidelity settings distribution facilitates faster learning of the scenario parameter distributions for new scenarios. We showcase our methodology using a cutting-edge 3D driving simulator, incorporating 16 fidelity settings for an autonomous vehicle stack that includes camera and lidar sensors. We evaluate various scenarios based on an autonomous vehicle pre-crash typology. As a result, our approach achieves a significant speedup, up to 18 times faster compared to traditional methods that solely rely on a high-fidelity simulator.

翻译：发现自主系统潜在故障对于部署前评估至关重要。基于伪造的方法常被用于评估此类系统的安全性，但运行大量精确仿真的成本可能很高。通过识别被测系统的关键故障场景并减少仿真运行时间，可以加速验证过程。我们提出一种将元学习策略与多臂赌博机框架相结合的贝叶斯方法。该方法涉及学习易触发被测系统故障的场景参数分布，以及实现快速精确仿真的保真度设置分布。遵循元学习理念，我们进一步评估所学保真度设置分布是否有助于更快学习新场景的场景参数分布。我们使用前沿的3D驾驶模拟器展示方法，针对包含摄像头和激光雷达传感器的自主驾驶系统堆栈设置了16种保真度等级，并基于自主驾驶车辆预碰撞分类学评估多种场景。结果表明，与仅依赖高保真模拟器的传统方法相比，我们的方法实现了高达18倍的加速比。