Autonomous Driving Image Exposure Synthesis for Tunnel Contrast
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Solution Overview
Problem
Autonomous driving vehicles face challenges in accurately recognizing geographic features in environments with high brightness contrast, such as tunnels, which can hinder safe predictive driving due to difficulties in understanding the illumination levels inside tunnels compared to outside entrances or exits.
Innovation Solution
An image processing method that recognizes target objects like tunnel entrances, exits, and vehicle lights, adjusts the exposure of subsequent frames based on brightness differences, and generates synthesized images to enhance the visibility of road regions, thereby improving the vehicle's ability to navigate through varying lighting conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Illumination intensity
If the vehicle uses standard image capture without exposure adjustment, then the image capture process is simple and fast, but the visibility of road regions in high brightness contrast environments (such as tunnels) is poor
Solution Approach 1:
The system performs preliminary recognition of target objects (tunnel entrances, exits, vehicle lights) in the first frame before capturing the second frame. Based on this preliminary recognition, the exposure of the second frame is pre-adjusted to ensure proper visibility of road regions in high brightness contrast environments.
Solution Approach 2:
The system applies different exposure settings to different regions of the image based on local brightness characteristics. By identifying target objects and their surrounding regions, the system adjusts exposure locally rather than uniformly across the entire image, optimizing visibility where needed while maintaining simplicity in other areas.
2Measurement precision
If the vehicle adjusts exposure and synthesizes images to improve visibility, then the accuracy of surrounding estimation is improved, but the computational resource usage increases
Solution Approach 1:
The system performs exposure adjustment and image synthesis only partially - specifically, only when target objects such as tunnel entrances, exits, or vehicle lights are detected in the first frame. This selective application reduces computational resource usage compared to processing all frames, while still improving measurement precision in critical situations.
Solution Approach 2:
The system changes the exposure parameter of the second frame based on the brightness characteristics of target objects identified in the first frame. By dynamically adjusting the exposure parameter rather than using fixed settings, the system improves surrounding estimation accuracy while managing computational resources efficiently through targeted parameter modification.
Data Source
AI summary
A processor implemented image processing method includes recognizing a target object in a first frame of an input image; adjusting an exposure of a second frame of the input image based on a brightness of the target object; and generating a synthesized image by synthesizing the first frame and the second frame.