Sensor Self-Calibration Using Neural Network Feature Extraction

The sensor system performs self-calibration by automatically detecting calibration features in the environment and computing calibration parameters without human intervention. The system uses its own sensors to identify features, extract data, and calculate extrinsic parameters, making the calibration process autonomous and practical for normal operation.

The patent replaces manual mechanical calibration procedures with an automated computational approach. Instead of physically positioning calibration bodies and manually adjusting sensors, the system uses neural networks and algorithms to automatically detect features and compute calibration parameters from sensor data streams.

The calibration system uses general environmental features (traffic signs, buildings, terrain) that serve dual purposes: they are part of the normal operational environment for sensor function and simultaneously serve as calibration targets. This eliminates the need for separate calibration blocks and enables calibration during normal operation.

The system transitions from static calibration blocks requiring specific positioning to dynamic environmental features that can be detected during vehicle motion. The calibration process adapts to the moving environment, allowing calibration to occur continuously during normal operation rather than requiring stationary conditions.

The patent extracts the calibration function from separate physical calibration blocks and integrates it into the normal sensor operation. The calibration features are extracted from environmental data streams that the sensors already capture during operation, eliminating the need for dedicated calibration equipment.

Instead of using physical calibration blocks, the system creates virtual calibration models of environmental features. Neural networks generate synthetic representations of calibration features that can be compared with actual sensor data, enabling calibration without physical objects.