Vehicle Camera Calibration Using Traffic Sign Dimensions
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Solution Overview
Problem
Advanced driver assistance systems (ADAS) rely on camera calibration for accurate 3D measurement, but existing methods are inadequate as intrinsic camera parameters, such as focal length, can change with temperature, leading to inaccurate measurements.
Innovation Solution
Implement an automatic calibration process using processors to recognize traffic signs, calculate measurement errors, and determine calibration parameters based on the dimensions of recognized signs, allowing for continuous and precise adjustment of camera parameters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If camera parameters are calibrated using traditional methods, then initial calibration accuracy is achieved, but measurement precision deteriorates over time due to temperature changes affecting intrinsic parameters
Solution Approach 1:
The system continuously captures images of traffic signs, compares measured dimensions against known reference dimensions, calculates measurement errors, and uses these errors to iteratively adjust and recalibrate camera parameters. This closed-loop feedback mechanism ensures that calibration parameters are continuously optimized to compensate for temperature-induced changes, maintaining both measurement precision and reliability over time.
Solution Approach 2:
The camera system performs self-calibration by automatically detecting traffic signs in its field of view, calculating dimensional errors based on known sign specifications, and adjusting its own calibration parameters without external intervention. This self-service approach allows the system to autonomously maintain accurate calibration despite environmental variations.
2Measurement precision
If traffic sign recognition is used for continuous calibration, then measurement precision is maintained, but device complexity increases due to additional processing requirements
Solution Approach 1:
The traffic sign recognition system serves multiple functions: it detects traffic signs for navigation purposes, measures their dimensions for calibration, and provides reference objects for continuous parameter optimization. By reusing the same image processing pipeline for both navigation and calibration purposes, the system avoids the need for separate calibration hardware or procedures, thereby maintaining measurement precision without proportionally increasing device complexity.
Data Source
AI summary
In some examples, processor(s) of a vehicle may store specified dimensions of a plurality of first objects. The processor(s) may receive a first image from a camera onboard the vehicle, recognize a first object within the image, and determine, based on the first image, a distance to the first object from the camera, and a width and height of the first object. Further, the processor(s) may determine a first measurement error from the determined width of the first object and a specified width of the first object, and a second measurement error from the determined height of the first object and the specified height of the first object. Based on the first and second measurement error, the processor(s) may determine a third measurement error, may determine one or more calibration parameters of the camera based on the determined measurement errors, and may use the calibration parameters for subsequently received images.