ADAS Object Detection with Stereo-Radar Bounding Box Filtering

The patent applies local quality by differentiating processing intensity based on object characteristics. High-confidence bounding boxes with clear features undergo minimal processing, while low-confidence or ambiguous detections receive enhanced processing including additional sensor data integration and re-evaluation. This selective processing approach maintains detection accuracy for critical objects while reducing overall computational burden.

The patent segments the object detection pipeline into distinct processing stages: initial candidate generation from individual sensors, confidence-based filtering, selective refinement processing, and final integration. This segmentation allows the system to process different subsets of candidate bounding boxes through different processing pathways, reducing the complexity of processing all candidates uniformly while maintaining comprehensive detection coverage.

The patent merges bounding box data from multiple sensors (cameras, radar, LIDAR) into a unified detection framework. By combining detections across sensor modalities and applying consistent filtering criteria, the system consolidates true positives while systematically eliminating false positives that do not appear across multiple sensor types or fail consistency checks.

The patent implements feedback mechanisms where detection results are continuously evaluated and used to adjust processing parameters. False positives are identified through consistency checks across frames and sensors, and the system adapts by adjusting confidence thresholds and filtering criteria based on observed error patterns, thereby reducing false positive rates while maintaining detection accuracy.

The patent performs preliminary filtering and confidence assessment on candidate bounding boxes before full risk evaluation. By pre-processing candidates to identify high-confidence detections and eliminate obvious false positives early in the pipeline, the system reduces the number of objects requiring computationally intensive risk assessment, thereby maintaining accurate risk notification while improving processing speed.

The patent dynamically adjusts processing depth based on contextual factors such as object confidence scores, object type, distance, and current driving conditions. High-risk scenarios trigger more thorough multi-step evaluation, while low-risk situations use streamlined processing. This dynamic approach ensures reliable risk assessment when necessary while maintaining high processing speed during normal operations.