Vehicle Sensor AI Contamination Detection
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Solution Overview
Problem
Environment sensor systems in autonomous vehicles are frequently impaired by contamination such as insects, water droplets, or dust, leading to potential unfavorable driving situations and reduced driving comfort due to undetected defects.
Innovation Solution
A method utilizing artificial intelligence to perform a setpoint-actual comparison of image data from a vehicle environment, allowing for the detection of sensor contamination or defects by analyzing image data against standard data, and enabling the deactivation of defective pixels and extrapolation of missing data to maintain accurate image recognition.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If environment sensor systems are used for autonomous driving, then driving automation capability is improved, but sensor contamination and defects occur more frequently leading to reduced reliability
Solution Approach 1:
The system performs preliminary detection of sensor contamination and defects by comparing current image data with reference image data before the contamination significantly impacts driving safety. This early detection allows for timely corrective actions such as cleaning or replacing sensors, maintaining reliability in automated driving systems.
Solution Approach 2:
The system establishes a feedback mechanism where image data from environment sensors is continuously analyzed against reference data, and when deviations indicating contamination are detected, the system generates alerts or automatically adjusts sensor operations. This closed-loop feedback ensures reliable operation of automated driving functions despite environmental challenges.
2Reliability
If sensor monitoring is performed continuously to detect contamination early, then detection reliability is improved, but system complexity and computational load increase
Solution Approach 1:
The monitoring system is segmented into modular components: image data acquisition modules, reference data storage modules, comparison algorithms, and alert generation modules. This segmentation allows the complex monitoring task to be distributed across independent functional units, making the system more manageable and maintainable while achieving continuous reliable monitoring.
Solution Approach 2:
Instead of performing complex real-time analysis of all sensor data, the system creates simplified reference copies of normal operating conditions (reference image data) and compares current data against these copies. This copying approach reduces computational complexity while maintaining high detection reliability for contamination events.
3Manufacturing precision
If defective pixels are deactivated to prevent faulty data propagation, then data quality is improved, but loss of sensing information occurs
Solution Approach 1:
The system extracts and removes only the specific defective pixels or contaminated regions from the image data while preserving the rest of the functional sensor data. This selective extraction approach maintains high data quality by eliminating faulty information sources while minimizing information loss by keeping all operational pixels active.
Solution Approach 2:
When pixels are deactivated due to contamination, the system merges data from adjacent functional pixels or alternative sensors to compensate for the lost information. This combining approach reconstructs the missing sensing information from reliable sources, maintaining overall data quality and information completeness.
Data Source
AI summary
A method for operating an environment sensor system of a vehicle, in particular of an autonomous motor vehicle, and an environment sensor system. The method includes the steps: producing first image data from a first image of a vehicle environment with the aid of a first sensor, performing a setpoint-actual comparison of the first image data with the second image data, and recognizing an operability of the environment sensor system based on the setpoint-actual comparison, the second image data including standard image data and/or image information from a second image of the vehicle environment, and the operability being recognized with the aid of a first artificial intelligence.