Vehicle Lamp Degradation Detection via Pattern Analysis
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Solution Overview
Problem
Current methods for validating vehicle external lighting rely on limited internal circuit diagnostics and manual inspections, lacking an automated way to detect light degradation or failure independently and during vehicle operation.
Innovation Solution
A method that generates a distinct pattern for vehicle lights, using a camera to capture and analyze the pattern for health assessment, determining environmental luminescence, and sending alerts or storing diagnostic codes for light degradation or failure detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual inspection methods are used to validate vehicle lights, then the inspection can be performed with simple equipment, but the detection is time-consuming and labor-intensive
Solution Approach 1:
The system enables self-diagnosis of vehicle lights by the vehicle itself. The control module automatically commands lights to emit patterns and the camera captures and analyzes the emitted light, allowing the vehicle to self-validate its lighting system without external inspector intervention.
Solution Approach 2:
The patent replaces manual mechanical inspection with an automated optical-electronic system. A camera captures light patterns and a control module processes the images to detect degradation, substituting human visual inspection with automated image analysis technology.
2Reliability
If internal circuit diagnostics are used to detect light failure, then the detection method is simple, but it cannot detect degradation independently and during vehicle operation
Solution Approach 1:
The camera acts as an intermediary between the light source and the diagnostic system. Instead of directly monitoring electrical circuits, the system uses the camera to capture the actual light emission pattern, providing independent verification of light functionality that is not dependent on circuit diagnostic accuracy.
Solution Approach 2:
The system establishes a feedback loop where the control module commands a light pattern, the camera captures the actual emission, and the processed image is compared against the commanded pattern to detect degradation. This continuous feedback enables real-time monitoring during vehicle operation.
3Extent of automation
If automated camera-based detection is implemented, then independent and continuous monitoring is achieved, but the system complexity and cost increase
Solution Approach 1:
The system uses a camera, which is a common component in modern vehicles for other purposes (e.g., backup cameras, driver assistance), giving it multi-functionality. By repurposing existing camera infrastructure, the system reduces overall complexity and cost while achieving automated light detection.
Solution Approach 2:
The patent combines multiple functions into a single integrated system: the control module that commands lights, the camera that captures patterns, and the processing unit that analyzes images all work together as one unified automated diagnostic system, reducing the need for separate dedicated components.
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 automated, independent detection of vehicle light degradation or failure before, during, or after operation, improving safety and reducing manual inspection reliance.
Implementation Method 1
using a camera to capture and analyze the pattern
Data Source
AI summary
A method to automate detection of a vehicle light degradation or failure includes generating a distinct pattern to be executed by at least one vehicle light, and sending a command to a light control module operative to cause the at least one vehicle light to emit the distinct pattern. The method continues with extracting features from camera images of the distinct pattern emitted by the at least one vehicle light, and then comparing the features extracted from the camera images with features from the commanded distinct pattern to determine degradation or failure of the at least one vehicle light's state of health.