Enhancing Autonomous Driving Perception Under Complex Weather Conditions Through Cyclegan-Based Driving Scene Generation

Complex and dynamic weather conditions pose significant challenges to the perception capabilities of autonomous driving systems. Existing datasets often lack sufficient cover-age of adverse weather scenarios, resulting in degraded performance of vision-based modules. To address this limitation, we propose a weather-aware driving scene generation framework based on CycleGAN. Our enhanced architecture integrates a multi-scale discriminator, feature matching loss, and channel-level structural similarity (SSIM) loss to generate high-fidelity driving images under various weather conditions. To evaluate the effectiveness of our approach, we perform cross-validation using three widely adopted detection models: YOLOv8, Faster R-CNN, and EfficientDet. The results demonstrate that the generated multi-weather images significantly improve detection accuracy and enhance the robustness and reliability of the perception system in complex environments. In addition to improving dataset diversity, our method preserves fine-grained structural details and enhances the visual quality of the generated scenes.