Vehicle Light Source Classification via Diffraction Grating Spectrum
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Solution Overview
Problem
Existing vehicle control systems face challenges in simultaneously detecting and classifying various light sources, such as headlamps, taillights, and streetlights, due to limitations in spectral differentiation, which affects the performance of other driver assistance applications like lane departure warning and collision warning, and reduces imaging sensitivity with the use of color or infrared filters.
Innovation Solution
A spectral image processor equipped with a camera, a diffraction grating, and a processor that classifies light sources based on their spectral composition, using a spatial image sensor and a trained classifier, allowing for simultaneous operation with other driver assistance applications without sacrificing performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If spectral filters are used to distinguish light sources, then spectral differentiation capability is improved, but the availability of image frames for other driver assistance applications is reduced
Solution Approach 1:
The patent divides the camera system into two functional segments: a spectral camera with diffraction grating for light source classification, and a standard camera for general driver assistance applications. This segmentation allows both functions to operate simultaneously on different image frames without interference, resolving the contradiction between spectral differentiation and availability of frames for other applications.
Solution Approach 2:
The patent makes the camera system multi-functional by enabling it to perform both spectral analysis (for headlight control) and standard imaging (for lane departure warning, collision warning, etc.) using the same hardware platform. The processor can switch between spectral classification mode and standard image processing mode, allowing the system to serve multiple driver assistance applications simultaneously.
2Loss of information
If color filters are used in the camera, then color information is improved, but imaging sensitivity is reduced
Solution Approach 1:
The patent segments the imaging function into two specialized cameras: a spectral camera with diffraction grating that removes color filters to maximize sensitivity for spectral analysis, and a standard camera with color filters for color-based applications. This segmentation allows each camera to be optimized for its specific function without compromise.
Solution Approach 2:
The patent changes the optical parameters of the spectral camera by removing traditional color filters and replacing them with a diffraction grating. This parameter change transforms the camera from a color imaging device to a spectral analysis device, improving sensitivity by eliminating the light-blocking filters while maintaining spectral information through diffraction-based wavelength separation.
3Measurement precision
If exposure parameters are changed for headlight detection, then headlight control performance is improved, but other control systems relying on the same camera suffer performance degradation
Solution Approach 1:
The patent segments the camera resources by dedicating the spectral camera specifically to headlight detection and classification, while the standard camera handles other control systems like lane departure warning and collision warning. This segmentation eliminates resource contention and allows each system to operate with optimal parameters without affecting the other.
Solution Approach 2:
The patent creates a multi-functional camera system where the spectral camera is specialized for headlight control with optimized exposure parameters, while the standard camera provides universal support for multiple driver assistance applications. The processor coordinates both cameras to ensure each application receives appropriate image frames with suitable exposure settings.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enables accurate classification of light sources by their spectral characteristics, improving the performance of driver assistance systems like headlight control, lane departure warning, and collision warning, while maintaining imaging sensitivity and compatibility with multiple applications.
Implementation Method 1
A diffraction grating is disposed between the lens and the light source. The diffraction grating is adapted for providing a spectrum.
Data Source
AI summary
A system mounted in a vehicle for classifying light sources. The system includes a lens and a spatial image sensor. The lens is adapted to provide an image of a light source on the spatial image sensor. A diffraction grating is disposed between the lens and the light source. The diffraction grating is adapted for providing a spectrum. A processor is configured for classifying the light source as belonging to a class selected from a plurality of classes of light sources expected to be found in the vicinity of the vehicle, wherein the spectrum is used for the classifying of the light source. Both the image and the spectrum may be used for classifying the light source or the spectrum is used for classifying the light source and the image is used for another driver assistance application.