The safety of autonomous driving systems (ADS) depends on accurate perception across distance and driving conditions. The outputs of AI perception algorithms are stochastic, which have a major impact on decision making and safety outcomes, including time-to-collision estimation. However, current perception evaluation metrics do not reflect the stochastic nature of perception algorithms. We introduce the Perception Characteristics Distance (PCD), a novel metric incorporating model output uncertainty as represented by the farthest distance at which an object can be reliably detected. To represent a system's overall perception capability in terms of reliable detection distance, we average PCD values across multiple detection quality and probabilistic thresholds to produce the average PCD (aPCD). For empirical validation, we present the SensorRainFall dataset, collected on the Virginia Smart Road using a sensor-equipped vehicle (cameras, radar, and LiDAR) under different weather (clear and rainy) and illumination conditions (daylight, streetlight, and nighttime). The dataset includes ground-truth distances, bounding boxes, and segmentation masks for target objects. Experiments with state-of-the-art models show that aPCD captures meaningful differences across weather, daylight, and illumination conditions, which traditional evaluation metrics fail to reflect. PCD provides an uncertainty-aware measure of perception performance, supporting safer and more robust ADS operation, while the SensorRainFall dataset offers a valuable benchmark for evaluation. The SensorRainFall dataset is publicly available at https://www.kaggle.com/datasets/datadrivenwheels/sensorrainfall, and the evaluation code is available at https://github.com/datadrivenwheels/PCD_Python.

翻译：自动驾驶系统的安全性依赖于在不同距离和驾驶条件下的精确感知。人工智能感知算法的输出具有随机性，这对决策制定和安全结果（包括碰撞时间估计）具有重大影响。然而，当前的感知评估指标未能反映感知算法的随机性本质。本文提出了感知特性距离，这是一种新颖的度量指标，它融合了以目标物体可被可靠检测到的最远距离所表示的模型输出不确定性。为了从可靠检测距离的角度表征系统的整体感知能力，我们在多个检测质量和概率阈值上对PCD值进行平均，得到平均PCD。为了进行实证验证，我们提出了SensorRainFall数据集，该数据集在弗吉尼亚智能道路上使用配备传感器（摄像头、雷达和激光雷达）的车辆，在不同天气（晴朗和雨天）和光照条件（日光、街灯照明和夜间）下采集。数据集包含目标物体的真实距离、边界框和分割掩码。使用最先进模型进行的实验表明，aPCD能够捕捉到天气、日光和光照条件之间具有实际意义的差异，而传统评估指标则无法反映这些差异。PCD提供了一种考虑不确定性的感知性能度量方法，有助于实现更安全、更鲁棒的自动驾驶系统运行，同时SensorRainFall数据集为评估提供了一个有价值的基准。SensorRainFall数据集公开发布于https://www.kaggle.com/datasets/datadrivenwheels/sensorrainfall，评估代码发布于https://github.com/datadrivenwheels/PCD_Python。