Semantic Map-Based Adaptive Auto-Exposure for Vehicle Object Detection
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Solution Overview
Problem
Automated vehicles face challenges in accurately detecting objects in complex environments due to poor image quality caused by auto exposure features, which reduce camera sensitivity in bright conditions, leading to dim or undetected objects of interest.
Innovation Solution
A vehicle system that determines appropriate camera exposure configurations based on object types and environmental conditions, using sensor data for localization and object detection, and applies these configurations to improve image quality and object detection accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Illumination intensity
If auto exposure feature is used to control light reaching camera sensor, then exposure settings are determined based on environmental light, but camera sensitivity is reduced in bright conditions causing dim or undetected objects of interest
Solution Approach 1:
The system applies different exposure configurations to different regions or aspects of the imaging process. Instead of using a single global exposure setting, the system determines object-specific exposure configurations based on semantic map data, allowing each object type to be captured with optimized exposure parameters tailored to its characteristics and environmental conditions.
Solution Approach 2:
The system dynamically adjusts exposure parameters (such as exposure time, gain, or aperture) based on the detected object type and environmental conditions. By changing these parameters adaptively rather than using fixed auto-exposure settings, the system maintains camera sensitivity while still controlling overall exposure levels.
2Reliability
If camera sensitivity is reduced in bright conditions to control exposure, then overexposure is prevented, but objects of interest become dim or undetected
Solution Approach 1:
The system uses semantic map data to identify and locate objects of interest before capturing the image. This preliminary identification allows the system to pre-calculate appropriate exposure configurations for each detected object, ensuring that when the image is captured, the exposure settings are already optimized for detecting these objects even in bright conditions.
Solution Approach 2:
The system introduces an intermediate processing layer that uses semantic map data and object detection algorithms to determine exposure configurations. This intermediary process bridges the gap between automatic exposure control and object detection requirements, allowing the system to maintain both exposure reliability and detection precision.
3Ease of operation
If standard auto exposure is used, then overall image exposure is controlled, but image quality for specific objects deteriorates in complex environments
Solution Approach 1:
The system transitions from static, fixed exposure settings to dynamic, adaptive exposure configurations. By continuously updating exposure parameters based on real-time object detection and semantic map data, the system maintains ease of automatic operation while significantly improving image quality for specific objects in complex environments.
Data Source
AI summary
In one embodiment, a method includes receiving sensor data of an environment of the vehicle generated by one or more sensors of the vehicle, the sensors comprising a camera, identifying, based on the sensor data, one or more objects in a field of view of the camera and one or more object types that correspond to the one or more objects, determining one or more target histograms that correspond to the object types, generating a processed image based on an image captured by the camera, wherein the processed image has a histogram based on the target histograms, and using the processed image to determine state information associated with the objects. The processed image may be generated by processing the image captured by the camera using a histogram matching algorithm to generate the histogram of the processed image based on the target histograms.