Autonomous Vehicle HDR Imaging for Flicker-Reduced Object Detection
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Solution Overview
Problem
Autonomous and semi-autonomous vehicles face challenges in accurately detecting objects under harsh or sub-optimal lighting conditions due to the production of noisy and artifact-filled images by standard camera systems, which can lead to failure in object detection and road condition recognition.
Innovation Solution
An enhanced sensor system and processing technique that utilizes high dynamic range (HDR) image sensors with multiple integration times to generate clearer images by applying autoexposure, selecting appropriate analog and digital gains, and interpolating color information to reduce artifacts such as flickering light effects and over/under-exposure issues, while also incorporating camera hardware improvements like anti-reflective coatings and hoods to minimize unwanted light interference.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If standard camera systems are used to detect objects, then the system is simple and cost-effective, but the image quality deteriorates under harsh lighting conditions with noise and artifacts
Solution Approach 1:
The patent divides the imaging process into multiple exposures with different integration times, capturing multiple images of the same scene at different light accumulation levels. This segmentation allows the system to process each exposure separately and combine them to eliminate flicker artifacts while maintaining object detection reliability under varying lighting conditions.
Solution Approach 2:
The patent adds the time dimension to the imaging process by capturing images at multiple different integration times rather than relying on a single exposure. This temporal dimension allows the system to separate flicker artifacts from actual scene information, improving detection reliability without requiring complex spatial filtering.
2Measurement precision
If longer integration time is used to improve image quality in low light, then signal-to-noise ratio improves, but flicker artifacts and over-exposure increase
Solution Approach 1:
The patent uses periodic action by capturing multiple images at different integration times that are synchronized with the flicker frequency of lighting sources. By sampling at specific periodic intervals, the system can identify and eliminate flicker artifacts while maintaining the signal-to-noise ratio benefits of longer exposures.
Solution Approach 2:
The patent changes the integration time parameter across multiple exposures, capturing images at short, medium, and long integration times. This parameter variation allows the system to select the optimal exposure for each scene region, maximizing signal-to-noise ratio while avoiding over-exposure and flicker artifacts through subsequent processing.
3Reliability
If multiple exposures with different integration times are captured, then image quality and artifact reduction improve, but processing complexity and time increase
Solution Approach 1:
The patent performs preliminary action by capturing all necessary multiple exposures with different integration times in rapid succession before the scene changes significantly. This preliminary capture of diverse temporal data allows subsequent processing to efficiently select and combine the best exposures without requiring extended processing time.
Solution Approach 2:
The patent creates multiple copies of the same scene at different integration times and then selects or combines the appropriate copies based on scene requirements. This copying approach allows the system to maintain high image quality while reducing processing time by choosing from pre-captured options rather than performing complex real-time 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
The solution provides clearer, artifact-reduced images that enhance object detection and driving safety under various lighting conditions, maximizing signal-to-noise ratio and minimizing flicker artifacts, thereby improving the reliability of autonomous driving systems.
Implementation Method 1
incorporating camera hardware improvements like anti-reflective coatings and hoods to minimize unwanted light interference
Data Source
AI summary
Systems and methods for enhanced vision processing and sensor system for autonomous vehicle. An example method includes obtaining a plurality of raw images of a real-world scene associated with different integration times for individual pixels, determining a lux estimate for the real-world scene, selecting an analog gain to be applied to the plurality of raw images, applying the analog gain to the plurality of raw images, selecting an integration time for individual pixels of the plurality of raw images, selecting a digital gain to be applied to the plurality of raw images, applying the digital gain to the plurality of raw images, forming an output image based on a combination of the plurality of raw images, wherein each pixel of the output image is based on a corresponding pixel of an individual raw image.