Feature Prediction Accuracy via Distance Transform Error Maps
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Solution Overview
Problem
Current methods for 3D road geometry modeling and feature detection in autonomous vehicle navigation are resource-intensive, time-consuming, and costly, with unreliable feature detection systems posing safety concerns and inefficiencies due to inaccurate or incomplete feature identification.
Innovation Solution
A method and apparatus that utilize pixel analysis to evaluate the precision and recall of feature prediction by comparing binary ground truth maps and prediction maps through distance transforms, generating an error map to assess accuracy, which can be used to improve the reliability of feature detection in autonomous driving systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional methods for 3D road geometry modeling and feature detection are used, then measurement accuracy can be maintained, but resource consumption increases significantly and time requirements extend
Solution Approach 1:
The patent replaces traditional mechanical measurement systems with pixel-based image analysis. Instead of using physical measurement tools and manual calculation methods, the system uses digital image processing techniques including distance transforms on binary maps to automatically compute feature detection accuracy, thereby substituting mechanical measurement with computational analysis to improve efficiency while maintaining precision
Solution Approach 2:
The patent creates binary ground truth maps and binary prediction maps as simplified digital representations of the physical environment. These binary maps serve as copies that capture essential feature information in a computationally efficient format, allowing accuracy assessment without processing complex original image data or physical measurements
2Reliability
If feature detection systems operate without accuracy evaluation, then system complexity is reduced, but reliability of autonomous navigation deteriorates
Solution Approach 1:
The patent implements a self-evaluation mechanism where the feature detection system automatically assesses its own accuracy by comparing binary prediction maps against binary ground truth maps. The system performs self-diagnosis through distance transform analysis without requiring external validation, thereby improving reliability while adding minimal complexity through automated internal verification
Solution Approach 2:
The patent establishes a feedback loop where accuracy metrics derived from distance transform comparisons are fed back into the autonomous navigation system. This feedback mechanism allows the system to adjust its feature detection parameters and improve reliability over time, with the complexity of feedback processing being offset by the automated nature of the evaluation
3Measurement precision
If pixel analysis methods are implemented for accuracy assessment, then measurement precision improves, but computational resources and processing time increase
Solution Approach 1:
The patent segments the accuracy assessment process into distinct computational stages: binary map generation, distance transform computation on ground truth map, distance transform computation on prediction map, and error calculation. This segmentation allows for optimized processing at each stage and enables selective application based on computational resource availability, reducing overall energy consumption while maintaining precision
Solution Approach 2:
The patent computes distance transforms only for pixels that are relevant to feature detection boundaries rather than processing the entire image uniformly. By focusing computational effort on critical regions where accuracy assessment is most needed, the system achieves high measurement precision without proportionally increasing energy consumption across the whole image
Data Source
AI summary
A method, apparatus, and computer program product are disclosed to estimate the accuracy of feature prediction in an image. Methods may include: receiving a binary ground truth map of pixels and a binary prediction map of pixels, where pixels corresponding to features of an environment are assigned a 1 or a 0, and pixels not corresponding to features of the environment are assigned the other of a 1 or a 0; computing a distance transform at each pixel of the binary ground truth map representing a distance to a closest pixel representing a feature; computing a distance transform at each pixel of the prediction map representing a distance to a closest pixel corresponding to a feature; and establishing accuracy of the prediction map of pixels using the error map of the distance transform for each pixel.