Nocturnal Fog Detection Using Headlight Halo Elliptic Curve Analysis
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Solution Overview
Problem
Current methods for detecting fog at night are ineffective due to the difficulty in processing images under nocturnal conditions, as they rely on parameters that are less marked and are adapted for daytime fog detection, and existing systems like LIDAR are expensive and not feasible for conventional vehicles.
Innovation Solution
A method that detects nocturnal fog by acquiring images of the road scene, extracting and approximating light halos using elliptic curves, and determining the presence of fog based on the halo's form, which is then used to calculate the visibility distance, utilizing image processing techniques such as binarization and parameter estimation of the elliptic curve.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If LIDAR is used to detect fog presence, then measurement precision is improved, but device complexity and cost increase significantly
Solution Approach 1:
The patent replaces expensive LIDAR systems with inexpensive image processing methods using standard cameras and headlight systems already present in vehicles. The solution uses readily available components (camera, headlight, image processor) rather than specialized expensive equipment, making fog detection accessible for mass-market vehicles.
Solution Approach 2:
The patent substitutes active LIDAR sensing with passive image capture and processing. Instead of using mechanical/LIDAR-based active sensing systems, the invention uses optical image capture combined with digital image processing algorithms to detect fog conditions, thereby simplifying the hardware architecture.
2Ease of operation
If daytime fog detection methods are used at night, then ease of operation is maintained, but measurement precision deteriorates due to less marked parameters
Solution Approach 1:
The patent adapts the detection parameters specifically for nighttime conditions by utilizing the headlight beam pattern and light scattering characteristics that are prominent at night. The system processes image data with algorithms tuned to detect fog-specific optical patterns in low-light environments, changing the detection parameters from daytime sky-based metrics to nighttime headlight-based metrics.
Solution Approach 2:
The patent focuses analysis on specific local regions of the image where fog effects are most pronounced, such as the headlight beam area and specific sky regions. By concentrating processing on these localized areas with characteristic fog signatures, the system maintains simplicity while improving detection precision for nighttime conditions.
3Ease of operation
If fog lights are switched on manually, then ease of operation is improved, but reliability deteriorates when driver forgets to switch them on
Solution Approach 1:
The patent implements an automatic feedback system where the image processing unit continuously monitors environmental conditions and provides feedback to the control unit. When fog is detected, the system automatically activates fog lights without requiring driver intervention, ensuring reliable operation based on actual environmental conditions rather than driver memory or attention.
Solution Approach 2:
The system performs self-service by automatically detecting fog conditions and controlling the fog lights without driver involvement. The vehicle's own imaging and processing systems serve to monitor and respond to environmental conditions, making the lighting system self-regulating based on detected fog presence.
4Illumination intensity
If fog lights are used in rain or without fog, then visibility is improved, but object-generated harmful factors increase due to nuisance to other drivers
Solution Approach 1:
The system uses continuous environmental monitoring with feedback control to activate fog lights only when fog is actually detected. The image processing unit provides real-time feedback on atmospheric conditions, enabling the control unit to switch lights on or off based on actual fog presence rather than driver assumption, thereby preventing unnecessary light activation that would cause nuisance to other drivers.
Solution Approach 2:
The patent changes the operational parameters of the lighting system by using automated environmental sensing to determine when fog lights should be active. Instead of manual driver control or continuous operation, the system dynamically adjusts lighting parameters based on detected atmospheric conditions, activating lights only when fog parameters are detected through image analysis.
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 detection of fog and determination of visibility distance at night, improving road visibility and reducing the risk of accidents by automatically adapting lighting and vehicle speed according to the fog conditions.
Implementation Method 1
extracting, from the image of the road scene, at least one light halo produced by a lighting device of the vehicle
Data Source
AI summary
The invention concerns a system and method for detecting, at night, the presence of an element such as fog interfering with the visibility of a road scene situated in front of a vehicle, comprising the following operations: acquiring an image of the road scene, extracting, from the image of the road scene, at least one light halo produced by a lighting device of the vehicle, approximating a form of this light halo by an elliptic curve, comparing the form of this light halo with the elliptic curve obtained in order to deduce therefrom the presence or absence of an element interfering with visibility.