Vehicle Camera Exposure Control for Roadway Coating Detection

The patent applies parameter changes by systematically varying the exposure time parameter of the camera system. Multiple images are captured with different exposure times (short, medium, long) to create a dataset that covers various lighting conditions. This allows the machine learning model to learn and adapt to different lighting scenarios, making the roadway coating detection independent of external lighting conditions.

The system applies self-service by using the camera system itself to capture multiple exposure images and then using machine learning algorithms to automatically process and analyze these images. The neural network model learns to identify roadway coatings by examining patterns across the different exposure images, enabling the system to self-diagnose and detect coatings without requiring complex manual processing or additional specialized sensors.

The patent applies preliminary action by pre-training machine learning models (such as neural networks) with large datasets of images captured under various lighting conditions and roadway coating scenarios. This pre-training process enables the model to learn robust features and patterns beforehand, so that during actual operation, the classification can be performed quickly and accurately without requiring extensive real-time computation.