TLDR: Researchers have developed a new method to generate more realistic synthetic camera data for autonomous driving by using solar altitude as a key variable. This approach, integrated into diffusion models via textual inversion, accurately captures varying lighting conditions and illumination-dependent image noise without needing extensive manual labeling, addressing a significant gap in current data generation techniques.
The development of safe and robust autonomous driving systems relies heavily on vast amounts of high-quality sensor data. However, collecting real-world data is expensive, labor-intensive, and limited by factors like safety protocols and diverse scenario coverage. This has led to a significant focus on generating synthetic camera sensor data.
A notable challenge in this field has been accurately representing variations in daytime illumination. Existing datasets often lack detailed labels for different times of day, making it difficult to simulate realistic lighting changes. Researchers Samed Do˘gan, Maximilian Hoh, Nico Leuze, Nicolas R.-PeËœna, and Alfred Sch¨ottl from the University of Applied Sciences Munich have addressed this gap in their paper, “Towards Solar Altitude Guided Scene Illumination”.
Their innovative approach introduces the solar altitude – the sun’s height in the sky – as a global conditioning variable for scene illumination. This is a crucial breakthrough because solar altitude can be easily computed from latitude-longitude coordinates and local time, eliminating the need for extensive manual labeling. This makes the data generation process much more scalable and efficient.
The team also developed a specialized normalization technique to account for the non-linear relationship between the sun’s movement and perceived light. For instance, small changes in solar altitude during sunrise and sunset can lead to rapid shifts in illumination, while changes around midday have a less noticeable effect. Their method ensures that both coarse lighting regions and fine-grained variations are accurately captured.
The researchers integrated this concept into diffusion models, which are powerful generative models capable of synthesizing complex data. They utilized textual inversion, a technique that allows a new concept (in this case, solar altitude) to be injected into the model’s vocabulary. This enables the model to generate images that not only reflect global lighting conditions but also the subtle image noise that varies with ambient light.
Their methodology involves a two-stage process: training a “structure token” to capture the general visual composition of camera images, and a separate, dynamically generated “context token” that adjusts based on the solar altitude. They demonstrated the effectiveness of their approach using the nuScenes dataset, showing promising results in capturing both overall lighting and illumination-dependent noise characteristics, even with the lightweight textual inversion method and limited data.
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This work represents a significant step towards generating more realistic and controllable synthetic camera data for autonomous driving. By automating the process of simulating diverse lighting conditions, it helps reduce the reliance on costly and time-consuming real-world data acquisition, paving the way for safer and more robust autonomous driving functions.
