Automated vehicles (AV) heavily depend on robust perception systems. Current methods for evaluating vision systems focus mainly on frame-by-frame performance. Such evaluation methods appear to be inadequate in assessing the performance of a perception subsystem when used within an AV. In this paper, we present a logic -- referred to as Spatio-Temporal Perception Logic (STPL) -- which utilizes both spatial and temporal modalities. STPL enables reasoning over perception data using spatial and temporal operators. One major advantage of STPL is that it facilitates basic sanity checks on the functional performance of the perception system, even without ground-truth data in some cases. We identify a fragment of STPL which is efficiently monitorable offline in polynomial time. Finally, we present a range of specifications for AV perception systems to highlight the types of requirements that can be expressed and analyzed through offline monitoring with STPL.

翻译：自动驾驶车辆（AV）高度依赖于鲁棒的感知系统。当前评估视觉系统的方法主要关注逐帧性能。此类评估方法在评估感知子系统在AV中的表现时显得不足。本文提出一种逻辑——称为时空感知逻辑（STPL）——该逻辑同时运用空间与时间模态。STPL能够通过时空算子对感知数据进行推理。STPL的一大优势在于，即使在某些情况下缺乏真值数据，也能对感知系统的功能性能进行基本合理性检查。我们识别出STPL的一个可在多项式时间内高效离线监控的子片段。最后，我们提出了一系列适用于AV感知系统的规范，以展示通过STPL离线监控可表达和分析的需求类型。