Traffic Face Detection via 3D Ray Summation
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Solution Overview
Problem
Autonomous vehicles face challenges in accurately detecting and mapping traffic faces in roadways, particularly due to issues like obscured views, triangulation errors, and insufficient image coverage, leading to missing or improperly labeled traffic faces in maps, which can affect navigation and safety.
Innovation Solution
A method using a computing system to analyze sensor data from multiple images and image capture locations, determining image areas associated with predicted traffic faces, projecting rays through 3D spaces, and identifying traffic face locations based on the sum of unit vectors, thereby improving the accuracy and efficiency of traffic face detection and mapping.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional image processing methods are used to detect traffic faces, then the system is simpler to implement, but detection accuracy and completeness deteriorate due to obscured views and insufficient image coverage
Solution Approach 1:
The patent transitions from 2D image processing to 3D spatial reasoning by projecting rays through multiple images captured at different locations and orientations. This dimensional extension allows the system to detect traffic faces that may be obscured in individual 2D images by utilizing spatial relationships and geometric projections across multiple viewing angles, thereby improving detection accuracy without requiring a completely new system architecture.
Solution Approach 2:
The patent merges information from multiple images captured at different locations and orientations into a unified 3D representation of the roadway environment. By combining data from these multiple sources and projecting rays through the 3D space, the system achieves more complete and accurate traffic face detection, overcoming the limitations of single-image approaches while maintaining computational efficiency.
2Reliability
If more images are captured from multiple locations to improve coverage, then detection completeness improves, but processing time and computational resources increase
Solution Approach 1:
The patent performs preliminary actions by pre-processing images to identify and extract only the relevant regions containing potential traffic faces before performing the computationally intensive 3D ray projection. This preliminary filtering of image data reduces the amount of information that needs to be processed in subsequent steps, thereby improving detection reliability through more comprehensive coverage while controlling processing time.
Solution Approach 2:
The patent applies local quality by focusing computational resources on specific regions of interest where traffic faces are likely to be detected, rather than uniformly processing all image data. By identifying and prioritizing regions with high probability of containing traffic faces based on local image characteristics, the system improves detection reliability in critical areas while reducing overall processing time through selective computation.
3Measurement precision
If traffic face detection is performed with high precision, then navigation safety improves, but the system becomes more sensitive to errors and outliers
Solution Approach 1:
The patent implements feedback mechanisms by continuously validating detected traffic face locations against the 3D projected ray data and comparing results across multiple images. This feedback loop allows the system to identify and correct erroneous detections, maintain high precision in traffic face location data, and reduce sensitivity to errors and outliers through cross-verification and consistency checking across the entire roadway environment.
Data Source
AI summary
Systems, devices, products, apparatuses, and/or methods, for detecting a missing traffic face, including: obtaining sensor data that includes a plurality of images associated with a geographic location including a roadway and associated with a plurality of image capture locations in the geographic location, determining in a subset of images of the plurality of images, a plurality of image areas associated with at least one predicted traffic face, determining a plurality of rays that project through 3-D spaces of the geographic location associated with the plurality of image areas from a subset of image capture locations of the plurality of image capture locations associated with the subset of images, and identifying at least one location of a traffic face in the geographic location based on a sum of unit vectors of the plurality of rays that project through the 3-D spaces of the geographic location at the at least one location of the traffic face.