Vehicle Signal Object Integrity Detection Using Map-Guided Classification
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Solution Overview
Problem
Current methods for monitoring signal object integrity in roadways are labor-intensive, unpredictable, and often untimely, leading to compromised safety and efficiency due to undetected damage or obstruction of signal objects.
Innovation Solution
Automated vehicles equipped with sensors and processing circuitry identify and report signal object damage by comparing high-definition maps with real-time observations, using classifiers to determine the type and degree of damage, and communicate results to maintenance authorities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual monitoring methods are used to check signal object integrity, then labor costs and time consumption increase, but detection capability and response timeliness remain insufficient
Solution Approach 1:
The patent replaces manual mechanical inspection methods with automated optical sensing systems. Image-capture sensors mounted on vehicles automatically detect and classify signal objects, substituting human labor with machine-based optical detection to improve both detection precision and response timeliness
Solution Approach 2:
The system enables self-service monitoring where vehicles automatically capture images, process sensor data through neural networks, identify signal objects, assess their integrity, and report findings without human intervention. This automated self-monitoring eliminates the need for dedicated manual inspection resources
2Measurement precision
If comprehensive sensor coverage is implemented to detect all signal objects, then detection accuracy improves, but system complexity and processing requirements increase
Solution Approach 1:
The patent segments the complex detection task into distinct processing stages: image capture by sensors, preliminary processing of sensor data, neural network-based object identification, integrity assessment, and result reporting. This segmentation allows each component to be optimized independently while managing overall system complexity
Solution Approach 2:
The patent introduces map data as an intermediary reference to verify detected signal objects. By comparing sensor data against known map information, the system improves recognition accuracy while using the map data as a mediating layer to reduce false positives and validate detections
Data Source
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AI summary
System and techniques for vehicle-based measurement of signal object integrity are described herein. Sensor data of an environment of the vehicle is obtained. A bound for the sensor data for the sensor data is also obtained. Here, the bound corresponds to a signal object (e.g., sign, light, road marking, etc.). A classifier for the signal object is invoked on the sensor data based on the bound. If the classifier produces a result that indicates a problem with the signal object, a representation of the result is communicated.