AV Sign Detection Using Spatial Validation of Reflections
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Solution Overview
Problem
Existing autonomous vehicle sign recognition technologies often misclassify real signs as reflections, leading to dangerous errors in navigation due to the inability to accurately distinguish between real signs and their mirror images in dynamic driving environments.
Innovation Solution
A system utilizing a combination of lidar and camera data, processed by machine-learning models, to classify candidate signs as real or reflections by evaluating spatial relationships and identifying mirror image counterparts, with spatial validation to confirm the presence of real signs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing autonomous vehicle sign recognition technologies are used, then sign detection speed is maintained, but classification accuracy deteriorates due to misclassification of real signs as reflections
Solution Approach 1:
The patent introduces an intermediary validation process that acts as a mediator between initial sign detection and final classification. This validation process uses spatial relationship analysis and mirror image detection to verify whether detected signs are real or reflections, thereby improving classification accuracy without compromising navigation safety
Solution Approach 2:
The patent applies preliminary spatial validation before final sign classification. By performing preliminary checks on spatial relationships, object positions, and potential mirror image counterparts, the system filters out false reflections before they can affect navigation decisions, thus improving both accuracy and reliability
2Measurement precision
If spatial validation and mirror image detection are implemented, then classification accuracy is improved, but system complexity increases
Solution Approach 1:
The patent segments the sign recognition system into distinct functional modules: initial detection module, spatial validation module, mirror image detection module, and final classification module. This segmentation allows each module to perform its specific function independently, improving overall accuracy while managing system complexity through modular design
Solution Approach 2:
The patent creates a multi-functional validation system that simultaneously performs spatial relationship analysis, object position verification, and mirror image detection using the same sensor data. This universal approach improves classification accuracy without requiring separate dedicated systems for each function, thereby controlling complexity
3Reliability
If comprehensive spatial validation is performed, then navigation safety is enhanced, but processing time increases
Solution Approach 1:
The patent implements partial spatial validation by focusing computational resources on critical verification steps rather than exhaustive analysis of all possible spatial relationships. The system performs essential checks on object positions and spatial consistency while skipping redundant validations, thereby maintaining navigation safety without excessive processing time
Solution Approach 2:
The patent merges multiple validation functions into a unified processing pipeline that simultaneously evaluates spatial relationships, object positions, and mirror image possibilities. By combining these functions into a single coordinated process rather than sequential steps, the system enhances navigation safety while minimizing total processing time
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach enables fast and accurate classification of real signs, reducing the risk of navigation errors and enhancing the safety and compliance of autonomous driving systems.
Implementation Method 1
the depth information includes at least one of a lidar data, a radar data, a stereo image data, or an ultrasonic data
Data Source
AI summary
The described aspects and implementations enable efficient identification of real and image signs in autonomous vehicle (AV) applications. In one implementation, disclosed is a method and a system to perform the method that includes obtaining, using a sensing system of the AV, a combined image that includes a camera image and a depth information for a region of an environment of the AV, classifying a first sign in the combined image as an image-true sign, performing a spatial validation of the first sign, which includes evaluation of a spatial relationship of the first sign and one or more objects in the region of the environment of the AV, and identifying, based on the performed spatial validation, the first sign as a real sign.