Autonomous vehicles are exposed to various weather during operation, which is likely to trigger the performance limitations of the perception system, leading to the safety of the intended functionality (SOTIF) problems. To efficiently generate data for testing the performance of visual perception algorithms under various weather conditions, a hierarchical-level rain image generative model, rain conditional CycleGAN (RCCycleGAN), is constructed. RCCycleGAN is based on the generative adversarial network (GAN) and can generate images of light, medium, and heavy rain. Different rain intensities are introduced as labels in conditional GAN (CGAN). Meanwhile, the model structure is optimized and the training strategy is adjusted to alleviate the problem of mode collapse. In addition, natural rain images of different intensities are collected and processed for model training and validation. Compared with the two baseline models, CycleGAN and DerainCycleGAN, the peak signal-to-noise ratio (PSNR) of RCCycleGAN on the test dataset is improved by 2.58 dB and 0.74 dB, and the structural similarity (SSIM) is improved by 18% and 8%, respectively. The ablation experiments are also carried out to validate the effectiveness of the model tuning.

通过构建一个基于生成对抗网络的多层级雨图生成模型，实现了在各种天气条件下测试视觉感知算法性能所需的数据生成，该模型能够生成不同强度的轻雨、中雨和大雨图像，并经过优化和调整来改善模式崩溃问题，相比于两个基准模型，测试数据集上 RCCycleGAN 的峰值信噪比 (PSNR) 提高了 2.58 dB 和 0.74 dB，结构相似性指数 (SSIM) 分别提高了 18% 和 8%，并进行了消融实验以验证模型调优的有效性。

基于GAN的分层等级雨图像生成模型