从名义强度到等效降雨：自动驾驶感知测试中模拟降雨的基于路径的可信度评估框架
From Nominal Intensity to Equivalent Rainfall: A Path-Based Credibility Evaluation Framework for Simulated Rainfall in Autonomous-Driving Perception Tests
AI总结：
提出基于路径的可信度评估方法，通过路径等效降雨强度、不确定性带和雨滴分布真实度评分，结合激光雷达点云计数和平均反射率进行感知一致性校正，实现模拟降雨与真实降雨的对齐及测试结果映射。
摘要：
可信的模拟降雨条件对于识别自动驾驶感知系统边界和支持面向SOTIF的风险评估至关重要。然而，封闭场地测试通常仅用名义降雨强度或单点测量来描述，这使得模拟降雨场难以与真实降雨对齐，并将测试结果映射到真实场景。本文提出了一种基于路径的自动驾驶感知测试中模拟降雨的可信度评估方法。以真实降雨的雨滴尺寸和速度联合分布为参考，每条候选路径由路径等效降雨强度、不确定性带和路径平均雨滴分布真实度（RRD）评分表示。进一步利用激光雷达目标点云计数和平均反射率进行感知一致性校正，量化每条模拟降雨路径对真实降雨感知效果的代理能力。实验使用了约10,000个真实降雨雨滴谱样本、728个RainSense感知样本以及2.4 m x 7.2 m模拟降雨区域内的45个空间采样点。结果表明，在相同名义条件下空间非均匀性仍然存在，证实了基于路径评估的必要性。该方法识别出路径IV和路径VI为优选候选路径，结果分别为11.54 +/- 0.31 mm/h、RRD = 0.43和8.28 +/- 0.34 mm/h、RRD = 0.46。这些路径在降雨强度稳定性、雨滴谱真实性和感知一致性方面表现出更均衡的性能。所提方法支持降雨条件下自动驾驶感知测试的路径选择、条件描述和可信解释。
英文摘要：
Credible simulated-rainfall conditions are essential for identifying perception-system boundaries and supporting SOTIF-oriented risk assessment in automated driving. However, closed-field tests are often described only by nominal rainfall intensity or single-point measurements, making it difficult to align simulated rain fields with real rainfall and map test results to real-world scenarios. This paper proposes a path-based credibility evaluation method for simulated rainfall in autonomous-driving perception tests. Using the drop size and velocity joint distribution of real rainfall as the reference, each candidate path is represented by path-equivalent rainfall intensity, an uncertainty band, and a path-averaged Realism of Raindrop Distribution (RRD) score. Lidar target point-cloud count and mean reflectivity are further used for perception-consistency correction, quantifying the proxy capability of each simulated-rainfall path for real-rainfall perception effects. Experiments are conducted using about 10,000 real-rainfall raindrop-spectrum samples, 728 RainSense perception samples, and 45 spatial sampling points in a 2.4 m x 7.2 m simulated-rainfall area. Results show that spatial non-uniformity remains under the same nominal condition, confirming the need for path-based evaluation. The method identifies Path IV and Path VI as preferable candidates, with results of 11.54 +/- 0.31 mm/h, RRD = 0.43, and 8.28 +/- 0.34 mm/h, RRD = 0.46, respectively. These paths show more balanced performance in rainfall-intensity stability, raindrop-spectrum realism, and perception consistency. The proposed method supports path selection, condition description, and credible interpretation of autonomous-driving perception tests under rainfall.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
