Vehicle Headlight Calibration with Inverse Pattern Imaging
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing methods for calibrating vehicle headlights struggle with misalignment due to factors like mechanical tension, thermal expansion, and stepper motor step losses, leading to safety issues such as dazzling oncoming traffic and reduced vision, especially under uncontrolled environmental conditions, and are imprecise in high-resolution lighting systems.
Innovation Solution
A method involving at least three recordings of calibration patterns, including a first with a calibration pattern, a second without, and a third with an inverse pattern, followed by subtracting the second from the first and third to isolate the pattern, using circles with known positions, and employing straight line approximation to calculate circle centers, allowing for robust and accurate headlight adjustment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional headlight calibration methods using edge and maximum detections are used, then the calibration process can be performed, but the measurement precision and reliability cannot reach safety-relevant accuracies of 0.1% due to imprecise feature extraction under uncontrolled environmental conditions
Solution Approach 1:
The patent uses a projected calibration pattern (a copy of a known geometric structure) instead of relying on natural scene features. This synthetic calibration pattern provides precisely known reference points that are immune to environmental variations, enabling accurate and reliable measurement of headlight alignment at 0.1% precision levels.
Solution Approach 2:
The patent transforms the calibration approach by changing from detecting arbitrary natural features to using a structured calibration pattern with precisely controlled parameters. The known geometric parameters of the projected pattern enable accurate calculation of projection geometry and headlight alignment, achieving safety-relevant precision.
2Illumination intensity
If high-resolution lighting systems with complex light distributions (LCD, DMD, μLED) are implemented, then illumination quality improves, but the device complexity and difficulty of calibration increase due to increasingly complex light distributions that require more sophisticated calibration methods
Solution Approach 1:
The patent separates the calibration process into distinct stages: projecting a structured calibration pattern, capturing the pattern with the camera, and independently calculating projection geometry. This segmentation allows the complex high-resolution lighting system to be calibrated using a simplified mathematical model based on the known calibration pattern, reducing calibration complexity despite the complexity of the light distribution system.
Solution Approach 2:
The calibration pattern acts as an intermediary between the complex lighting system and the calibration camera. This intermediary provides a known reference structure that simplifies the calibration process, allowing the system to handle complex light distributions from LCD, DMD, or μLED technologies without increasing calibration difficulty.
3Ease of operation
If automatic headlight calibration is performed in uncontrolled environmental conditions, then productivity and ease of operation improve, but measurement precision deteriorates due to variations in lighting conditions, surface structures, and positioning that affect feature extraction accuracy
Solution Approach 1:
The system performs automatic calibration without requiring controlled environmental conditions or manual intervention. The calibration pattern is projected and captured automatically, and the geometry is calculated using the known pattern structure, enabling the system to self-calibrate in any environmental condition while maintaining high precision through the robustness of the mathematical model.
Data Source
AI summary
Vehicle headlights are calibrated by identifying a misalignment of a predefined light distribution by analyzing images from at least one vehicle camera. In a first step, at least three recordings are preformed, which include a first recording with recording of a calibration pattern, a second recording without calibration pattern, and a third recording with recording of the inverse calibration pattern. In a second step the second recording is subtracted from the first and third recordings to clean the recordings from a present scene.