Road Surface Condition Detection with Camera-LiDAR Feature Fusion

The patent combines data from multiple sensor types (e.g., optical camera and lidar sensor) to determine road surface conditions. By merging the visual information from the camera with the depth and spatial data from the lidar, the system achieves more accurate and reliable road surface condition estimation than would be possible with a single sensor type, while managing complexity through integrated processing.

The system employs a multi-functional sensor arrangement where different sensor types (camera, lidar) contribute to a unified road surface condition determination process. Each sensor serves multiple purposes: the camera provides visual texture and color information while the lidar provides spatial and depth data, and both are integrated to create a comprehensive assessment of road conditions, enhancing reliability without requiring separate dedicated systems for each function.

The system extracts specific relevant features from the raw sensor data using feature extraction techniques. Instead of processing all raw data from multiple sensors, the system identifies and extracts key features (such as texture patterns from camera images and spatial characteristics from lidar point clouds) that are most indicative of road surface conditions. This extraction process reduces the dimensionality of the problem and focuses computational resources on the most discriminative features, improving accuracy while managing computational complexity.