Reference Image Generation for Vehicle Guidance Lighting Tests

The patent uses a generative adversarial network (GAN) to create virtual copies of light sources and their lighting effects. Instead of physically placing and controlling numerous light sources in the environment, the system generates synthetic lighting conditions by translating images between different lighting domains (e.g., daytime to nighttime, different streetlight conditions). This virtual copying approach maintains the ability to control lighting parameters while eliminating the need for complex physical setups.

The patent replaces the mechanical/physical system of actual light sources with a computational system. The GAN-based image translation approach substitutes physical light emission mechanisms with algorithmic image processing, where lighting conditions are manipulated through neural network transformations rather than physical light sources. This substitution dramatically reduces device complexity while preserving lighting control capabilities.

The patent applies preliminary action by pre-training the generative adversarial network on diverse lighting conditions and scene types. Once trained, the GAN can rapidly generate test images with various lighting conditions without requiring actual physical data collection for each scenario. This preliminary training phase enables fast generation of statistically sufficient test data, reducing the time needed for actual testing while maintaining evaluation validity.

The system creates virtual copies of diverse scenes with different lighting conditions through image translation. Instead of capturing and storing vast amounts of real-world footage under various lighting scenarios, the GAN generates synthetic variations of scenes with controlled lighting parameters. This copying approach provides comprehensive testing coverage without the time-consuming process of collecting and curating large datasets of real scenes.