Perception systems operate as a subcomponent of the general autonomy stack, and perception system designers often need to optimize performance characteristics while maintaining safety with respect to the overall closed-loop system. For this reason, it is useful to distill high-level safety requirements into component-level requirements on the perception system. In this work, we focus on efficiently determining sets of safe perception system performance characteristics given a black-box simulator of the fully-integrated, closed-loop system. We combine the advantages of common black-box estimation techniques such as Gaussian processes and threshold bandits to develop a new estimation method, which we call smoothing bandits. We demonstrate our method on a vision-based aircraft collision avoidance problem and show improvements in terms of both accuracy and efficiency over the Gaussian process and threshold bandit baselines.

翻译：感知系统作为自主驾驶完整系统中的子组件，其设计者通常需要在保持全局闭环系统安全性的前提下优化性能特征。因此，将高层安全需求转化为感知系统的组件级需求具有重要意义。本研究聚焦于在给定全集成闭环系统的黑盒仿真器时，如何高效确定安全的感知系统性能特性集合。我们融合了高斯过程与阈值赌博机等常见黑盒估计技术的优势，提出了一种名为平滑赌博机的新估计方法。通过在基于视觉的飞机防撞问题上的实验验证，该方法在准确性和效率方面均优于传统高斯过程与阈值赌博机基线方法。