Vehicle Camera Region Settings for Road Line Detection
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Solution Overview
Problem
Existing onboard cameras for vehicles face challenges in detecting road lines in adverse conditions such as tunnels or rainfall, and there is a lack of proposals for cameras specifically designed for vehicles in mixed automatic and manual driving environments.
Innovation Solution
An image capture device for vehicles with a setting unit that adjusts image capture conditions for each region based on the vehicle's state and external conditions, including frame rate and pixel decimation ratio, to enhance detection accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a fixed image capture condition is used for the entire image capture unit, then the device complexity is reduced, but the detection precision of road lines in adverse conditions deteriorates
Solution Approach 1:
The image capture unit is divided into multiple regions, and different image capture conditions (such as frame rate, pixel decimation ratio) are set for each region based on its specific requirements. This segmentation allows the system to optimize detection precision for road lines in adverse conditions without uniformly increasing complexity across the entire image capture unit.
Solution Approach 2:
Different regions of the image capture unit are assigned different image capture conditions tailored to their specific functions. For example, regions dedicated to road line detection use higher frame rates and lower pixel decimation ratios, while other regions use different settings. This local quality approach improves detection precision where needed without unnecessarily increasing overall device complexity.
2Speed
If the frame rate is increased for all regions, then the detection speed of moving objects is improved, but the energy consumption increases
Solution Approach 1:
The image capture unit is segmented into multiple regions with different frame rates. Regions requiring high-speed detection (such as those monitoring moving vehicles or pedestrians) operate at higher frame rates, while regions with static or less critical content use lower frame rates. This segmentation improves detection speed for moving objects without uniformly increasing energy consumption across the entire system.
Solution Approach 2:
Different frame rates are applied to different regions based on their specific detection requirements. High-frame-rate regions are localized to areas where fast-moving objects need to be tracked, while other regions use lower frame rates to conserve energy. This local quality approach optimizes the balance between detection speed and energy consumption.
3Measurement precision
If pixel decimation ratio is reduced for all pixels, then the image quality is improved, but the processing time increases
Solution Approach 1:
The image capture unit is divided into multiple regions with different pixel decimation ratios. Regions requiring high image quality (such as those focused on road lines or critical obstacles) use lower decimation ratios, while regions with less critical content use higher decimation ratios. This segmentation improves image quality where needed without uniformly increasing processing time across the entire image capture unit.
Solution Approach 2:
Different pixel decimation ratios are applied to different regions based on their specific quality requirements. High-quality regions are localized to areas where detailed image information is critical for detection, while other regions use higher decimation to reduce processing time. This local quality approach optimizes the balance between image quality and processing efficiency.
Data Source
AI summary
An image capture device is mounted in a vehicle. The image capture device includes: an image capture unit; and a setting unit that sets an image capture condition for each region of the image capture unit each having a plurality of pixels, or for each pixel, based upon at least one of a state exterior to the vehicle and a state of the vehicle.