Pixel-Based Lane Prediction Accuracy Evaluation
Find Innovative SolutionsGenerate Solutions
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 data analysis.
Innovation Solution
A method and apparatus that analyze the accuracy of feature detection by comparing binary ground truth and prediction maps through pixel analysis, computing precision and recall, and generating reports to facilitate safe and efficient autonomous vehicle navigation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional methods for 3D road geometry modeling and feature detection are used, then comprehensive data analysis can be achieved, but the process becomes resource-intensive, time-consuming, and costly
Solution Approach 1:
The patent extracts only the essential pixel-level information needed for feature detection accuracy evaluation, rather than processing complete 3D geometric models. By comparing prediction maps with ground truth maps at the pixel level, the system isolates the critical data elements required for precision and recall calculation, thereby reducing computational resource requirements while maintaining detection reliability
Solution Approach 2:
The patent uses binary ground truth maps as simplified copies of the actual road geometry and feature data. Instead of processing complex 3D models and raw sensor data, the system works with binary pixel representations that capture the essential spatial information needed for evaluation, significantly reducing processing time and computational cost while preserving the necessary geometric relationships
2Reliability
If feature detection systems operate without accuracy evaluation, then processing speed is maintained, but reliability deteriorates due to undetected errors in feature detection
Solution Approach 1:
The patent implements a feedback mechanism by calculating precision and recall metrics from the comparison between prediction maps and ground truth maps. This quantitative evaluation provides feedback on the performance of the feature detection system, enabling operators to assess reliability and make informed decisions about system operation without requiring complex additional hardware or processing systems
Solution Approach 2:
The patent replaces complex mechanical or manual verification methods with automated computational comparison of binary maps. Instead of using sophisticated 3D modeling algorithms or manual inspection procedures to evaluate feature detection accuracy, the system uses straightforward pixel-level comparison operations that are computationally efficient and easy to implement
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 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; determining at least one feature within each of the ground truth map and the prediction map based on pixels including the at least one feature; computing the overlap of pixels of the at least one feature from the ground truth map with the pixels of the at least one feature from the prediction map; matching the at least one feature from the ground truth map with the at least one feature from the prediction map in response to the overlap of pixels satisfying a predetermined value; and establishing a precision of the prediction map based on the overlap of pixels.