Camera Auto Exposure Using 3D Maps for Traffic Signal Detection
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Solution Overview
Problem
Autonomous vehicles face challenges in accurately detecting and classifying traffic signals and other objects in varying lighting conditions and high dynamic range scenes, leading to information loss and difficulties in AI-based processing.
Innovation Solution
A method for an autonomous vehicle to identify objects of interest by using map data and sensor information to select optimal automatic exposure settings for its camera, adjusting luminance levels to match a target, and capturing images with these settings to preserve details in images, thereby enhancing object detection and classification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If automatic exposure settings are optimized to capture images of specific classes of targets (such as traffic signals), then detection accuracy for those targets is improved, but detection of other object classes becomes challenging
Solution Approach 1:
The patent applies local quality by implementing region-of-interest (ROI) based exposure control. Different exposure settings are applied to different regions of the image: traffic signal regions receive optimized exposure settings for accurate detection, while other regions maintain standard exposure. This allows the system to prioritize detection accuracy for critical objects (traffic signals) without completely sacrificing detection capability for other object classes, thus resolving the contradiction between measurement precision and adaptability.
2Reliability
If exposure settings are adjusted to capture details in low light areas, then detection of objects in low light is improved, but high dynamic range areas may suffer from information loss
Solution Approach 1:
The system implements local quality by applying different exposure settings to different spatial regions of the image based on their lighting characteristics. Low light regions (such as tunnel interiors or shadowed areas) receive increased exposure to preserve detail and improve detection reliability, while high dynamic range regions (such as bright outdoor scenes) maintain appropriate exposure to prevent overexposure and information loss. This regional differentiation resolves the contradiction between reliability in low light and information preservation in high dynamic range areas.
Solution Approach 2:
The system performs preliminary action by using map data and location information to predict upcoming regions of interest before the vehicle actually reaches them. This allows the system to pre-adjust exposure settings in anticipation of entering low light areas (such as tunnels) or approaching specific objects, ensuring optimal exposure is already in place before the scene is captured. This predictive approach helps maintain detection reliability while minimizing information loss across varying lighting conditions.
3Manufacturing precision
If camera captures images with high dynamic range settings, then detail preservation in bright areas is improved, but low light areas may lose detail
Solution Approach 1:
The patent applies local quality by implementing spatially-varying exposure control where different regions of the image receive different exposure settings based on their local lighting conditions. Bright high dynamic range regions use settings optimized for detail preservation, while low light regions use settings optimized for signal-to-noise ratio and detail visibility. This regional approach resolves the contradiction between detail preservation in bright areas and detail maintenance in low light areas, as each region receives exposure settings tailored to its specific characteristics.
Data Source
AI summary
Systems and methods are provided for operating a vehicle, is provided. The method includes, by a vehicle control system of the vehicle, identifying map data for a present location of the vehicle using a location of the vehicle and pose and trajectory data for the vehicle, identifying a field of view of a camera of the vehicle, and analyzing the map data to identify an object that is expected to be in the field of view of the camera. The method further includes, based on (a) a class of the object, (b) characteristics of a region of interest in the field of view of the vehicle, or (c) both, selecting an automatic exposure (AE) setting for the camera. The method additionally includes causing the camera to use the AE setting when capturing images of the object, and using the camera, capturing the images of the object.