Road Work Zone Detection Using Worker-Pedestrian Sensor Fusion
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Solution Overview
Problem
Current autonomous vehicle road work observation systems inaccurately distinguish between construction workers and regular pedestrians, leading to false positive active road work zone detections, which can cause unnecessary mode transitions and inconvenience.
Innovation Solution
A system that utilizes multiple sensors, including cameras and radar, to fuse data and apply machine learning and computer vision algorithms to accurately classify road work zones, determining a confidence level based on sensor fusion and sending alerts to vehicles.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Difficulty of detecting and measuring
If current RWO systems use sensor data to detect road work zones, then detection capability is provided, but accuracy in distinguishing construction workers from pedestrians deteriorates, leading to false positives
Solution Approach 1:
The patent introduces an intermediary classification system that processes sensor data between detection and final road work zone determination. This intermediary layer uses machine learning models to analyze visual features and distinguish construction workers from pedestrians, thereby resolving the contradiction by adding a mediating processing stage that improves accuracy without eliminating detection capability
Solution Approach 2:
The patent changes the parameters used for detection by incorporating multiple sensor types (camera, radar, LIDAR) and analyzing different feature parameters (visual appearance, motion patterns, spatial position). By changing from single-sensor detection to multi-sensor multi-parameter analysis, the system maintains detection capability while significantly improving classification accuracy
2Reliability
If the RWO system detects all road work zones including false positives, then comprehensive coverage is achieved, but unnecessary mode transitions and navigation changes occur, reducing user convenience
Solution Approach 1:
The patent implements feedback mechanisms where detection results are continuously validated and refined. The system provides feedback loops that allow operators to correct false positives, and the machine learning models learn from correction feedback to improve future detections. This feedback system maintains comprehensive coverage while reducing false alarms that would inconvenience users
Solution Approach 2:
The patent applies preliminary classification and validation actions before final road work zone determination. The system performs preliminary filtering of detected objects using multiple sensors and classification algorithms before triggering navigation changes or mode transitions. This preliminary action prevents unnecessary user alerts while maintaining comprehensive monitoring coverage
Data Source
AI summary
The disclosure provides a system, a method, and a computer program product for detecting active road work zones. The system obtains first sensor data associated with a detection of a road work zone, using a first sensor type and obtains second sensor data associated with presence of at least one individual in the road work zone, using a second sensor type. Further, the system determines a confidence level associated with the detection of the road work zone based on fusing of the first sensor data and the second sensor data. The system classifies the detected road work zone as at least one of the active road work zone or a non-active road work zone, based on the confidence level. The system updates map data associated with a map of a region based on the classification.