提出路径级降雨模拟可信度评估方法，提升自动驾驶感知测试真实性。
From Nominal Intensity to Equivalent Rainfall: A Path-Based Credibility Evaluation Framework for Simulated Rainfall in Autonomous-Driving Perception Tests
- 以真实雨滴粒径和速度分布为基准，用路径等效强度与雨滴分布真实度评分评估模拟降雨
- 实测显示相同名义降雨量下空间不均匀，路径IV和VI表现最优（强度11.54±0.31、8.28±0.34 mm/h）
- 结合激光雷达点云与反射率实现感知一致性校正，适合自动驾驶雨天测试验证
可信的模拟降雨条件对识别自动驾驶感知系统边界及支持SOTIF导向的风险评估至关重要。然而，封闭场测试常仅以名义降雨强度或单点测量描述，难以使模拟降雨场与真实降雨对齐，也难以将测试结果映射至真实场景。本文提出一种基于路径的模拟降雨可信度评估方法。以真实降雨的雨滴粒径与速度联合分布为参考，每个候选路径用路径等效降雨强度、不确定区间及路径平均雨滴分布真实度（RRD）得分表征。进一步利用激光雷达目标点云数量与平均反射率进行感知一致性修正，量化各模拟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45个空间采样点（2.4 m × 7.2 m区域）。结果表明，相同名义条件下仍存在空间非均匀性，证实需路径级评估。方法识别出路径IV（11.54±0.31 mm/h，RRD=0.43）与路径VI（8.28±0.34 mm/h，RRD=0.46）为优选方案，其在降雨强度稳定性、雨滴谱真实性与感知一致性方面表现更均衡。该方法可支持降雨条件下自动驾驶感知测试的路径选择、条件描述与可信解读。
原文摘要 · Abstract (English)
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.
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