Lane Feature Quality Estimation via Region Confidence Classification
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Solution Overview
Problem
Autonomous driving systems face challenges in accurately localizing vehicles due to the insufficient precision of existing lane feature detection methods, particularly in varying environmental conditions and lane marking quality, which affects the reliability of navigation and route planning.
Innovation Solution
A computer-implemented method and apparatus that processes input images to detect lane features, determines confidence values for image regions, and classifies them into confidence levels to estimate the quality of lane features, using machine learning classifiers like convolutional neural networks to assess the visibility and integrity of lane markings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional lane detection methods are used, then the system is simple and fast, but the detection precision and reliability are insufficient under varying environmental conditions
Solution Approach 1:
The patent introduces confidence values as an intermediary metric that bridges the gap between simple detection and reliable quality assessment. These confidence values serve as a mediator that quantifies the reliability of detected lane features without requiring complex additional verification systems, thus improving measurement precision while maintaining reasonable system complexity
Solution Approach 2:
The patent changes the parameter of detection output from binary (detected/not detected) to continuous (confidence values representing probability). This parameter transformation enables nuanced quality assessment of lane features, allowing the system to distinguish between high-quality and low-quality detections, thereby improving measurement precision through probabilistic reasoning
2Measurement precision
If confidence values are calculated for all image regions, then the quality estimation becomes more accurate, but the computational time and processing complexity increase
Solution Approach 1:
The patent segments the image into multiple regions and calculates confidence values for each region separately. This segmentation approach allows parallel processing of different regions, improving quality estimation accuracy through region-specific analysis while managing computational time by dividing the overall processing task into smaller, concurrent units
Solution Approach 2:
The patent applies confidence value calculation selectively to regions where lane features are detected or suspected, rather than uniformly processing the entire image. This partial action approach focuses computational resources on relevant areas, achieving accurate quality estimation where needed while reducing unnecessary processing time in irrelevant regions
Data Source
AI summary
An approach is provided for estimating a quality of lane features of a roadway. The approach involves processing, by a computer vision system, an input image to detect the lane features of the roadway. The approach also involves determining respective confidence values associated with a plurality of regions of the input image used to detect the lane features. The respective confidence values represent a probability of predicting the lane features from each of the plurality of regions. The approach further involves performing a classification of the plurality of regions into a plurality of confidence levels based on the respective confidence values. The approach further involves determining the estimated quality of the lane features based on the classification of the plurality of regions.