Radar Point Cloud Validity Scoring for False Alarm Filtering

The patent applies multi-functionality by integrating multiple sensor types (radar, LIDAR, cameras) into a unified autonomous perception system. Each sensor type compensates for the others' weaknesses: radar provides reliable detection in adverse weather, LIDAR offers precise distance measurement, and cameras deliver detailed environmental information. This multi-functional sensor fusion approach ensures the system maintains high reliability and measurement precision across diverse weather conditions.

The patent merges radar systems with light-based sensors (LIDAR and cameras) to create a hybrid perception system. The radar component ensures reliable operation in adverse weather conditions by detecting objects through rain, snow, and fog, while the integrated LIDAR and camera systems contribute high-precision distance and environmental detail data. This combination allows the system to maintain both reliability in challenging weather and measurement precision for accurate environmental perception.

The patent introduces an intermediary neural network-based filtering layer between the radar detection stage and the final target identification. This neural network intermediary processes the raw radar point cloud data, learns to distinguish true targets from false alarms through training, and outputs refined detection results. This approach maintains relatively simple device architecture while significantly reducing information loss by eliminating false alarm detections that would otherwise contaminate the target list.