Camera Soiling Detection Using LIDAR-Depth Consistency Gating
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Solution Overview
Problem
Surround-view cameras on autonomous vehicles are vulnerable to soiling from environmental factors like rain, fog, snow, dust, and mud, which impairs their ability to accurately detect objects and navigate safely.
Innovation Solution
Utilizing LIDAR sensors to generate range images, combined with camera depth estimates, to create an attention map that highlights soiled regions, employing machine learning techniques for accurate soiling detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If surround-view cameras are used to provide 360-degree view for autonomous driving, then the vehicle's ability to detect obstacles and navigate safely is improved, but the cameras become vulnerable to soiling from rain, fog, snow, dust, and mud which degrades their performance
Solution Approach 1:
The patent introduces LIDAR sensors as an intermediary component to detect soiling on cameras. The LIDAR generates range images that are processed to create attention maps, which serve as a mediator to identify and locate soiled regions on the camera lenses without requiring direct contact with the cameras themselves.
Solution Approach 2:
The patent replaces physical inspection methods with optical and computational methods. Instead of mechanically checking camera clarity, the system uses LIDAR optical sensing and depth consistency analysis to detect soiling, eliminating the need for physical contact or manual inspection.
2Measurement precision
If LIDAR sensors and depth estimation algorithms are used to detect soiling, then the accuracy of soiling detection is improved, but the computational complexity and processing requirements increase
Solution Approach 1:
The patent performs preliminary actions by pre-processing LIDAR range images to fill sparse regions before depth estimation. This preparation step ensures that subsequent depth consistency comparisons are more accurate and reduces the need for complex iterative algorithms, thereby managing computational complexity while maintaining high detection accuracy.
Solution Approach 2:
The patent extracts only the essential features needed for soiling detection from the full LIDAR and camera data streams. By focusing specifically on depth consistency in attention map regions rather than processing all image data, the system achieves high accuracy with reduced computational burden.
3Reliability
If real-time soiling detection is implemented using LIDAR and depth consistency analysis, then the safety and reliability of autonomous driving systems are improved, but the energy consumption and computational resources required increase
Solution Approach 1:
The patent applies partial action by performing full soiling detection processing only when necessary - specifically when depth inconsistencies are detected in attention map regions. During normal operation, the system uses lighter monitoring, reducing energy consumption while maintaining safety through selective detailed analysis rather than continuous full processing.
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
Enhances the safety of autonomous driving by improving the accuracy of camera soiling detection, ensuring reliable obstacle recognition even in challenging conditions.
Implementation Method 1
LIDAR sensors uses laser light to measure the distance to objects in the environment. This allows a LIDAR sensor to create a three-dimensional (3D) map of the surrounding area
Implementation Method 2
The range image may be obtained by using a LIDAR sensor to measure the time it takes for laser pulses to travel to objects and back
Implementation Method 3
The filtering includes a filter configured to fill in one or more sparse regions in the first image
Data Source
AI summary
A method includes receiving a plurality of images, wherein a first image of the one or more images comprises a range image and a second image comprises a camera image and filtering the first image to generate a filtered first image. The method also includes generating a plurality of depth estimates based on the second image and generating an attention map by combining the filtered first image and the plurality of depth estimates. Additionally, the method includes generating a consistency score indicative of a consistency of depth estimates between the first image and the second image based on the attention map, modulating one or more features extracted from the second image based on the consistency score using a gating mechanism to generate modulated one or more features, and generating a classification of one or more soiled regions in the second image based on the modulated one or more features.