Vehicle Camera Settings Tuned for Ambient-Light Conditions
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Solution Overview
Problem
Existing systems, such as autonomous vehicles, face challenges in accurately interpreting environmental images due to suboptimal camera settings, leading to unsafe or less than ideal vehicle behavior, particularly in varying light conditions.
Innovation Solution
An automated mechanism for determining image processor settings based on ambient light conditions, involving a two-step optimization process that includes testing with controlled lighting and using a perception component to refine settings, ensuring accurate object classification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If automated optimization of image processor settings is implemented, then image quality and object classification accuracy improve, but device complexity and manufacturing cost increase
Solution Approach 1:
The patent applies preliminary action by pre-optimizing image processor settings offline using controlled test images with known object characteristics. The optimization process determines optimal settings for various lighting conditions beforehand, storing them for later use. This eliminates the need for complex real-time optimization during actual operation, thereby improving object classification accuracy while avoiding the complexity burden of runtime optimization systems.
Solution Approach 2:
The patent uses copying by creating test images that replicate real-world scenarios with known ground truth information. These synthetic test images contain embedded objects with verified characteristics, allowing the optimization algorithm to learn from idealized copies of actual operating conditions. This approach enables accurate setting determination without requiring complex analysis of every real-world image.
2Adaptability or versatility
If two-step optimization process is used, then image processor settings are more accurate for different light conditions, but processing time and computational resources increase
Solution Approach 1:
The two-step optimization process applies preliminary action by performing the computationally intensive setting optimization offline during manufacturing or system initialization. The first step optimizes settings using controlled test images, and the second step refines them using real-world test data. These optimized settings are then stored and rapidly applied during actual operation based on detected lighting conditions, achieving high adaptability without real-time processing delays.
Solution Approach 2:
The system applies dynamics by adapting image processor settings dynamically based on detected ambient lighting conditions. The optimization process creates multiple setting profiles for different lighting scenarios (daylight, twilight, nighttime, etc.), and the system selectively applies the appropriate profile based on current conditions. This dynamic adaptation achieves versatility while maintaining fast operation through pre-computed settings.
3Manufacturing precision
If image processor settings are optimized for specific light conditions, then image quality improves in those conditions, but the system complexity for managing multiple settings increases
Solution Approach 1:
The patent applies universality by creating a unified optimization framework that handles multiple lighting conditions through a single systematic process. The same optimization algorithm and test image generation methodology work across all lighting scenarios, producing a universal set of optimized settings that can be applied to any condition. This universal approach improves image quality across diverse conditions while avoiding the complexity of developing separate optimization systems for each scenario.
Solution Approach 2:
The system applies parameter changes by adjusting image processor settings (exposure, gain, white balance, etc.) based on detected lighting conditions. The optimization process determines the optimal parameter values for different light environments, and the system dynamically modifies these parameters according to current conditions. This parameter-based approach achieves high image quality while maintaining manageable system complexity through continuous parameter adjustment rather than discrete mode switching.
Data Source
AI summary
Techniques are provided comprising illuminating a camera test target according to a first light setting associated with an ambient light condition experienced by a vehicle traversing an environment; determining, based at least in part on the first image and on a known characteristic of the camera test target, a first image quality indicator; and determining, based at least in part on the first image quality indicator, a first image processor setting associated with the first light setting. Respective image processor settings can be determined for each of multiple ambient light conditions. The image processor settings may be transmitted to a vehicle controlled based at least in part on an ambient light detection associated with the environment through which the vehicle is traversing being associated with the first light setting or the second light setting.