Lane Detection Algorithm Using Sensor Fusion and Offline Refinement

The patent combines multiple sensors (IMU, GPS, camera) into an integrated lane detection system. The IMU provides motion data for dead reckoning, GPS provides position information, and the camera captures visual lane marking data. By merging these complementary data sources through sensor fusion algorithms, the system achieves both high precision and stable localization, overcoming the limitations of using differential GPS alone.

The patent introduces an intermediary processing layer that fuses data from multiple sensors and uses machine learning algorithms to interpret camera images. This intermediary layer processes raw sensor data, combines information from different sources, and produces refined lane detection results, thereby improving both precision and reliability compared to direct GPS-based localization.

The patent merges multiple sensor types (inertial sensors, GPS receiver, camera) into a unified lane detection system. Each sensor contributes its strengths: IMU provides high-frequency motion data, GPS provides absolute position, and camera provides visual lane marking information. The fusion of these sensors achieves superior detection precision that would be unattainable with any single sensor alone.

The patent creates a multi-functional sensor system where the same sensor suite serves multiple purposes: IMU data is used for both vehicle pose estimation and motion-based prediction, GPS provides both position and velocity information, and the camera captures both lane markings and environmental context. This universal approach maximizes the utility of each sensor while maintaining system precision.

The patent performs preliminary offline training of the lane detection algorithm using labeled ground truth data from multiple sensors. This preliminary action pre-computes optimal detection parameters and creates trained models that can be deployed for real-time operation. By doing the heavy computational lifting in advance, the system achieves high precision detection during actual use without real-time performance penalties.

The patent implements an offline refinement process where detected lane markings are compared against ground truth data, and the algorithm parameters are adjusted based on the feedback from these comparisons. This iterative feedback loop continuously improves detection precision while the trained model handles real-time detection efficiently, separating the time-consuming refinement process from operational detection.