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.

翻译：可信的模拟降雨条件对于识别自动驾驶感知系统边界及支撑面向SOTIF的风险评估至关重要。然而，封闭场地测试通常仅通过标称降雨强度或单点测量值进行描述，这使得难以将模拟降雨场与真实降雨对齐，并难以将测试结果映射至真实场景。本文提出一种面向自动驾驶感知测试中模拟降雨的路径级可信度评估方法。以真实降雨的雨滴粒径与速度联合分布为基准，每条候选路径均可由路径等效降雨强度、不确定性带以及路径平均雨滴分布真实性（RRD）得分表征。进一步利用激光雷达目标点云计数与平均反射率进行感知一致性校正，量化各模拟降雨路径对真实降雨感知效果的代理能力。实验基于约10,000个真实降雨雨滴谱样本、728个RainSense感知样本及2.4米×7.2米模拟降雨区域内的45个空间采样点开展。结果表明：相同标称条件下仍存在空间非均匀性，证实了路径级评估的必要性。该方法识别出路径IV与路径VI为优选候选路径，其结果分别为11.54±0.31毫米/小时（RRD=0.43）与8.28±0.34毫米/小时（RRD=0.46）。这两条路径在降雨强度稳定性、雨滴谱真实性及感知一致性方面表现更为均衡。所提方法可支持降雨条件下自动驾驶感知测试的路径选择、条件描述及可信解释。