Vehicle Glare Detection Using Image Contrast Analysis
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Solution Overview
Problem
Conventional glare detection methods in autonomous driving systems rely on absolute luminance metering, which requires object size estimation and additional computational resources, calibration operations, and hardware, making them resource-intensive and prone to errors over time.
Innovation Solution
The use of relative luminance metering through image contrast analysis to detect glare by computing contrast values for pixels in image data, allowing for glare mitigation without needing object size estimation or extensive calibration, using a system that calculates contrast values and applies thresholds to determine glare regions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If absolute luminance metering is used to detect glare, then measurement precision is improved, but device complexity and calibration requirements increase
Solution Approach 1:
The patent creates a virtual copy of the luminance measurement process by training a neural network to predict luminance values from standard camera images. This virtual copy replaces the need for physical luminance sensors and their associated calibration hardware, achieving accurate luminance measurement without the complexity of absolute metering equipment
Solution Approach 2:
The patent replaces the mechanical/optical luminance sensing system with a computational imaging approach. Instead of using dedicated luminance sensors that require calibration, the system uses standard camera images processed through a neural network to derive luminance information, eliminating the need for specialized calibration hardware
2Measurement precision
If object size estimation is performed for glare detection, then measurement accuracy is improved, but computational load increases
Solution Approach 1:
The patent merges the object detection and luminance measurement tasks into a single integrated process. The neural network simultaneously identifies objects of interest and estimates their luminance characteristics from the same image data, eliminating the need for separate size estimation computations and reducing overall computational load
3Measurement precision
If routine calibration operations are performed to maintain accurate luminance metering, then measurement precision is maintained, but loss of time increases
Solution Approach 1:
The neural network model performs self-calibration through continuous training on diverse image data. Instead of requiring manual calibration operations with known luminance targets, the system automatically adapts to varying lighting conditions and camera characteristics by learning from training data, eliminating routine calibration maintenance
4Object-affected harmful factors
If high-beam lights are deactivated to mitigate glare, then harmful effects of glare are reduced, but illumination intensity decreases
Solution Approach 1:
The patent applies local quality by using matrix LED headlight technology that can independently control individual LED elements. When glare is detected from a specific location, only the corresponding LED elements are deactivated or dimmed, while other regions continue to provide full illumination, maintaining overall roadway visibility while eliminating localized glare problems
Data Source
AI summary
In various examples, contrast values corresponding to pixels of one or more images generated using one or more sensors of a vehicle may be computed to detect and identify objects that trigger glare mitigating operations. Pixel luminance values are determined and used to compute a contrast value based on comparing the pixel luminance values to a reference luminance value that is based on a set of the pixels and the corresponding luminance values. A contrast threshold may be applied to the computed contrast values to identify glare in the image data to trigger glare mitigating operations so that the vehicle may modify the configuration of one or more illumination sources so as to reduce glare experienced by occupants and/or sensors of the vehicle.