Vehicle Camera Auto Exposure Using 3D Map Object Priority
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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 target values, and capturing images with these settings to ensure clear object detection and analysis.
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 or pedestrians), then detection accuracy for that class is improved, but detection of other object classes becomes challenging
Solution Approach 1:
The system applies different exposure settings to different regions of the image corresponding to different object classes. By identifying the class of object in the field of view and selecting exposure settings specific to that class, the system optimizes detection accuracy for each object type locally rather than using a single global exposure setting, thereby resolving the contradiction between specialized detection accuracy and general adaptability
Solution Approach 2:
The system dynamically adjusts exposure settings based on the detected object class in real-time. Rather than using fixed exposure parameters, the system changes exposure settings adaptively according to whether the detected object is a traffic signal, pedestrian, vehicle, or other class, enabling both specialized optimization and general versatility through dynamic reconfiguration
2Illumination intensity
If camera exposure settings are adjusted to capture low light areas, then visibility of objects in low light is improved, but details in high dynamic range areas are lost
Solution Approach 1:
The system applies different exposure settings to different spatial regions of the image based on local lighting conditions and object class. By tailoring exposure parameters to specific regions (low light areas versus high dynamic range areas), the system preserves visibility in dark regions while maintaining detail in bright regions, eliminating the need to choose between conflicting exposure requirements
Solution Approach 2:
The system segments the image into different regions with different exposure characteristics and applies appropriate exposure settings to each segment. This allows independent optimization of exposure for low light areas without compromising the quality of high dynamic range areas, as each region is processed with exposure parameters suited to its specific lighting conditions
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.