Stereo Vision Height Clearance Detection for Overhead Obstacles
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Solution Overview
Problem
Traditional front height clearance warning systems in vehicles suffer from limited accuracy and robustness due to sensor data quality, algorithm robustness, failure to detect occluded or complex-shaped obstacles, and false positives from noise and lighting conditions.
Innovation Solution
A stereo vision-based system using two cameras to capture images from different perspectives, creating a 3D depth map for obstacle detection, and processing the images to determine height clearance, providing visual and audible warnings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional single-camera or simple sensor systems are used for height clearance detection, then the device complexity is low, but the measurement precision and reliability are limited
Solution Approach 1:
The patent transitions from 2D image data to 3D spatial understanding by computing depth maps from stereo image pairs. The disparity between corresponding points in left and right images is converted into depth information, enabling accurate height clearance measurement that accounts for the vehicle's three-dimensional position and orientation.
Solution Approach 2:
The patent introduces an intermediary processing layer that includes depth map generation, road surface modeling, and coordinate transformation systems. These intermediary components bridge the gap between raw stereo camera data and final height clearance calculations, improving measurement precision through multiple processing stages.
2Reliability
If traditional algorithms are used for obstacle detection, then the processing speed is faster, but the reliability is reduced due to false positives from noise and lighting conditions
Solution Approach 1:
The patent segments the image processing task into distinct stages: stereo matching for depth map generation, road surface segmentation, obstacle detection, and height clearance calculation. This segmentation allows each stage to be optimized independently, improving overall reliability while managing processing time through efficient algorithm selection at each stage.
Solution Approach 2:
The patent implements feedback mechanisms where the depth map and road surface model inform subsequent obstacle detection and classification. The system uses the computed three-dimensional information to validate detected obstacles and reduce false positives by cross-referencing multiple data sources and processing stages.
3Measurement precision
If traditional height clearance systems are used, then the device complexity is low, but the measurement precision is limited by sensor data quality
Solution Approach 1:
The patent replaces traditional mechanical or simple optical height sensing systems with a computational vision system using stereo cameras and image processing algorithms. This substitution enables more precise measurements by leveraging computational methods to extract three-dimensional information from visual data, overcoming limitations of simpler sensors.
Solution Approach 2:
The patent computes the vehicle's three-dimensional position and orientation relative to the road surface by analyzing disparities in stereo images. This dimensional transformation from 2D images to 3D spatial understanding enables accurate height clearance measurement that accounts for vehicle pitch, roll, and position on the road.
4Reliability
If traditional obstacle detection methods are used, then the ease of operation is maintained, but the reliability is reduced due to failure to detect occluded or complex-shaped obstacles
Solution Approach 1:
The patent uses stereo vision to generate depth maps that provide three-dimensional information about obstacles, including occluded or complex-shaped objects. By computing depth from disparity, the system can detect obstacles that may not be fully visible in single 2D images, improving detection reliability for overhead obstacles with complex geometries.
Solution Approach 2:
The patent introduces depth map generation and three-dimensional obstacle modeling as intermediary processing steps between image capture and obstacle detection. These intermediary components enable the system to infer the presence and characteristics of occluded obstacles by analyzing depth information and spatial relationships across multiple image points.
Data Source
AI summary
Aspects of the subject disclosure relate to stereo vision-based height clearance detection, of which a device includes a processor that obtains first data and second data from different cameras of a vehicle. The processor generates a depth map based on a disparity between the first data and the second data. The processor determines a road segmentation based at least in part on the depth map. The processor determines road surface height information for corresponding portions of the road segmentation. The processor determines a height estimation of a non-road object corresponding to at least one portion of the road segmentation. The processor provides an indication of a height clearance estimate for the at least one portion of the road segmentation based on the road surface height information and the height estimation of the non-road object.