Vehicle Sensor Lens Self-Cleaning for Weather Obscurity Removal
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Solution Overview
Problem
Inclement weather conditions impair the performance of automotive object sensors, leading to inaccurate image capture and potential false object detection, which existing systems struggle to predict and correct.
Innovation Solution
A self-cleaning sensor system with multiple object sensors and lens treatment devices, utilizing a multi-task neural network to classify obscurities and apply appropriate remedies, such as heat, compressed gas, or pressurized liquid, to remove lens surface impurities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If object sensors are exposed to the environment for capturing images of external regions, then the sensors can detect objects and monitor the vehicle's surroundings, but the lens surface becomes susceptible to obscurities from inclement weather conditions such as rain, ice, snow, and dirt deposits
Solution Approach 1:
The system performs preliminary classification of obscurity types (ice, rain, snow, dirt, cracks) using a multi-task neural network before applying specific remedies. This allows the appropriate lens treatment device to be activated in advance with the correct treatment method, preventing severe impairment before it occurs rather than reacting after damage is done.
Solution Approach 2:
The sensor system performs self-diagnosis and self-cleaning through the multi-task neural network that automatically detects lens obscurities and triggers the appropriate lens treatment device. The system monitors its own lens surface condition and autonomously applies remedies without external intervention, maintaining continuous operational reliability.
2Adaptability or versatility
If multiple lens treatment devices with different remedies are implemented to address various obscurity classifications, then the system can effectively remove different types of impurities, but the device complexity increases
Solution Approach 1:
The multi-task neural network serves as a universal controller that handles multiple obscurity classifications (ice, rain, snow, dirt, cracks) and routes them to appropriate lens treatment devices. This centralized intelligent control allows diverse treatment functions to be coordinated through a single decision-making system, reducing overall complexity despite multiple specialized components.
Solution Approach 2:
The system changes operational parameters by selecting different treatment methods (heat application, compressed gas delivery, liquid delivery) based on the classified obscurity type. Each lens treatment device operates with optimized parameters specific to its intended remedy, allowing versatile obscurity removal while maintaining manageable device complexity through parameter-based differentiation rather than structural complexity.
3Reliability
If iterative attempts are made to remove obscurities by applying different remedies, then the system ensures complete lens clearance, but the time required for cleaning increases
Solution Approach 1:
The multi-task neural network performs preliminary classification of the obscurity type before treatment begins, allowing the system to select the most effective remedy from the start. This pre-assessment prevents trial-and-error approaches by matching the correct treatment method (heat, compressed gas, or liquid) to the specific obscurity type, reducing cleaning time while ensuring complete clearance.
Solution Approach 2:
The system employs feedback mechanisms where the neural network continuously monitors lens surface conditions during and after treatment application. Based on this real-time feedback, the system determines whether additional treatment attempts are necessary or if the lens is sufficiently clear, optimizing the balance between complete clearance and time efficiency by stopping treatment when adequate clarity is achieved.
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
Effectively removes obscurities on object sensors, ensuring accurate image capture and reducing false object detection by iteratively applying different remedies until the obscurity is fully cleared.
Implementation Method 1
a heat-based lens treatment device for applying heat to the lens surface to remove the obscurity formed on the lens surface of the associated object sensor
Implementation Method 2
applying heat to the lens surface to remove the obscurity formed on the lens surface
Implementation Method 3
a liquid-based lens treatment device for delivering a pressurized liquid to the lens surface to remove the obscurity formed on the lens surface of the associated object sensor
Implementation Method 4
a gas-based lens treatment device for delivering a compressed gas to the lens surface to remove the obscurity formed on the lens surface of the associated object sensor
Data Source
AI summary
A self-cleaning sensor system of a motor vehicle includes an object sensor having a lens surface facing a region located external to the motor vehicle. The object sensor generates a signal associated with an image or a video of the region. The system further includes multiple lens treatment devices for applying remedies for removing an obscurity formed on the lens surface. The system further includes a computer having one or more processors and a computer readable medium storing instructions. The processor is programmed to determine a classification of the obscurity, in response to the processor receiving the signal from the object sensor. The processor is further programmed to generate an actuation signal, and the associated lens treatment device applies the remedy, in response to the associated lens treatment device receiving the actuation signal from the processor.