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：在自主系统部署前，发现其潜在失效至关重要。基于伪造的方法常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过高。通过识别被测系统的关键失效场景并降低仿真运行时间，可以加速验证过程。我们提出一种贝叶斯方法，将元学习策略与多臂老虎机框架相结合。该方法同时学习两类分布：易于触发被测系统失效的场景参数分布，以及能够实现快速精确仿真的保真度设置分布。遵循元学习理念，我们进一步评估所习得的保真度设置分布是否有助于加速新场景下场景参数分布的学习。我们采用前沿的3D驾驶仿真器进行验证，该仿真器集成了包含摄像头与激光雷达传感器的自动驾驶汽车系统的16种保真度设置。基于自动驾驶汽车预碰撞分类学评估了多种场景。结果表明，与仅依赖高保真仿真器的传统方法相比，我们的方法实现了高达18倍的显著加速比。