Vehicle Camera Blockage Cause Identification via Headlight Toggling
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Solution Overview
Problem
Existing methods for detecting camera blockages in vehicles do not identify the cause of the blockage, making it difficult to take corrective action to restore normal camera function.
Innovation Solution
A method that iteratively acquires images, detects blockages, and uses environmental sensors and time information to determine if the blockage is due to dark conditions, icing/fogging, sunset/sunrise, or uniform landscapes by toggling headlights, checking temperature and dew point, and analyzing lane markings and object presence.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If camera blockage detection is implemented, then camera reliability is improved, but the system cannot determine the cause of blockage
Solution Approach 1:
The blockage detection problem is segmented into multiple independent analysis dimensions: image sequence analysis, environmental sensor data (temperature, humidity), time information, and vehicle state information. Each segment analyzes a specific aspect and contributes to the overall cause determination, allowing comprehensive diagnosis without requiring a single complex detection system
Solution Approach 2:
The system uses a multi-functional approach where environmental sensors serve multiple purposes: temperature sensors detect both ambient conditions and potential icing conditions, humidity sensors monitor both atmospheric moisture and dew point conditions. The same sensor data is used for both blockage detection and cause identification, eliminating the need for dedicated sensors for each function
2Difficulty of detecting and measuring
If multiple sensors and environmental checks are added to identify blockage cause, then diagnostic capability is improved, but device complexity increases
Solution Approach 1:
The system merges multiple detection functions into a unified diagnostic framework. Environmental sensors that already exist in the vehicle (temperature, humidity) are combined with camera data and time information to create a comprehensive blockage diagnosis system. This integration allows cause identification without adding significant hardware complexity
Solution Approach 2:
The system uses existing vehicle resources to diagnose camera blockage causes. Time information from the vehicle's clock, environmental sensors already present for other functions, and the camera itself are all leveraged for self-diagnosis. The system serves itself by using its own operational data to identify problems, minimizing the need for external diagnostic equipment
3Measurement precision
If iterative image acquisition and analysis is performed, then blockage detection accuracy is improved, but processing time increases
Solution Approach 1:
The system performs preliminary analysis by continuously acquiring and pre-processing image sequences even before blockage is confirmed. Environmental parameters (temperature, humidity, dew point) are calculated in advance and stored ready for analysis. When blockage is detected, the system can immediately compare pre-computed data without waiting for additional processing time
Solution Approach 2:
The system uses partial image sequences rather than requiring complete iterative analysis for every decision. By analyzing a subset of the acquired images and using environmental context data, the system achieves sufficient diagnostic accuracy without processing every possible data point, reducing overall processing time while maintaining detection precision
Data Source
AI summary
A method for identifying a cause of blockage in a sequence of images provided by a camera of a vehicle, the method comprising iteratively:S10) acquiring an image of the camera; successively acquired images forming a sequence of images;S20) detecting a blockage in last images of the sequence of images;S60) determining whether it is day-time or night-time based on time information;if it is determined that it is night-time:S82) determining whether toggling front light(s) of the vehicle on/off causes a change in images acquired by the camera; andS84) in this case, determining that for the current iteration, the cause of the blockage is presumably the road being dark.A computer program for performing said method, a computer-readable recording medium containing such computer program, a driving assistance system capable of executing said method.