Adaptive Threshold Determination for Vehicle Headlight Detection
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Solution Overview
Problem
Existing methods for distinguishing between oncoming vehicle lights and reflectors using camera sensors in automatic light control systems are unreliable due to variations in image sensor sensitivity over time and differences between sensors, leading to inconsistent detection of vehicle lights and reflectors.
Innovation Solution
A method that adapts threshold values by tracking and analyzing parameters such as average and maximum intensity, and lifespan of light points, using frequency distributions and decision theory to differentiate between vehicle lights and reflectors, and applies temporal filtering to maintain reliability despite sensor sensitivity changes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If fixed threshold values are used for distinguishing vehicle lights and reflectors, then the method is simple to implement, but the detection reliability deteriorates due to sensor sensitivity variations
Solution Approach 1:
The patent applies dynamics by transitioning from fixed threshold values to adaptive threshold values that automatically adjust based on sensor sensitivity changes. The system continuously monitors light point parameters and dynamically recalibrates thresholds to maintain detection reliability despite sensor degradation or environmental variations over time.
Solution Approach 2:
The patent implements parameter changes by modifying the threshold values based on observed parameter distributions from tracked light points. The system analyzes statistical parameters (mean, standard deviation) of light intensity and position over time, then adjusts threshold parameters to compensate for sensor sensitivity drift and maintain optimal detection performance.
2Reliability
If adaptive threshold adjustment is implemented, then detection reliability improves, but system complexity increases
Solution Approach 1:
The patent applies self-service by enabling the system to automatically calibrate and adjust its own threshold parameters without external intervention. The evaluation unit continuously tracks light point parameters, analyzes distribution changes, and autonomously recalibrates thresholds, making the system self-adapting to sensor sensitivity variations and eliminating the need for manual recalibration.
Solution Approach 2:
The patent implements feedback by using the tracked parameters of light points (intensity, position, duration) as feedback signals to adjust threshold values. The system continuously monitors the distribution of these parameters and uses the feedback information to automatically optimize threshold settings, creating a closed-loop control system that maintains detection reliability.
3Measurement precision
If multiple light point parameters are tracked and analyzed, then classification accuracy improves, but processing time increases
Solution Approach 1:
The patent applies partial action by selectively tracking and analyzing only the most discriminative light point parameters necessary for classification. Rather than processing all possible parameters, the system focuses on key parameters (intensity, position, duration) that provide sufficient differentiation between vehicle lights and reflectors, reducing processing overhead while maintaining classification accuracy.
Data Source
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AI summary
The invention relates to a method for determining at least one threshold value (S) for differentiating between reflectors and vehicle headlights in the dark, that are recorded as light spots by a camera sensor oriented towards the surroundings of a vehicle. The camera sensor records a series of images of the vehicle surroundings. At least one light spot in the series of images is monitored (tracked). At least one parameter such as the maximum intensity Imax of the light spot in the entire series of images, or the service life (t) of the light spot, is determined from measuring values such as the intensity of the light spot in each image, once the monitoring of a light spot is completed. The threshold value (S) is then adapted to the determined parameters. The determined parameter value of the light spot is received in a frequency distribution of the parameter values from previously monitored light spots. A new threshold value for differentiating between vehicle headlights and reflectors is determined from the updated frequency disribution of the parameter values. The threshold value for differentiating between reflectors and vehicle headlights is reset following a temporal filtering, especially a temporal low-pass filtering. The threshold value is adapted to the actual situation by means of the temporal filtering.