The field of autonomous driving is moving towards more robust and reliable perception systems, with a focus on simulating real-world scenarios and generating high-quality synthetic data. Researchers are exploring new methods to simulate refractive distortions, weather-induced artifacts, and scene illumination, which is crucial for developing safe and efficient autonomous vehicles. The use of data augmentation techniques is also gaining attention, particularly in the context of traffic light detection and panoramic street-view generation. These advancements have the potential to improve the performance and reliability of autonomous driving systems, especially in challenging environments. Noteworthy papers include:

• One that presents a procedural augmentation pipeline to simulate refractive distortions and weather-induced artifacts in low-cost monocular dashcam footage, providing a benchmark for image restoration models.
• Another that proposes a panoramic generation method enabling coherent generation of panoramic data with control signals, overcoming inherent information loss caused by the pinhole sampling process.