Surround Vision Driver Assistance for Safe Zone Estimation
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Solution Overview
Problem
Advanced driver assistance systems require high processing capabilities to analyze visual data from multiple frames per second, which can lead to reduced frame rates and delayed threat detection in vehicles, especially when traveling at high speeds.
Innovation Solution
A driver assistance system utilizing a two-camera setup with embedded processors to analyze image data, employing probabilistic distribution curves and region-of-interest analysis to efficiently detect vehicles and determine threats, thereby reducing computational resources needed for threat analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If image data from multiple cameras is analyzed at high frame rates, then threat detection accuracy is improved, but processing time increases and system complexity increases
Solution Approach 1:
The patent segments the image processing task by dividing the field of view into multiple regions of interest (ROIs) corresponding to different lanes and vehicle positions. Each ROI is processed independently and in parallel, allowing the system to analyze multiple areas simultaneously without requiring sequential processing of the entire image, thus reducing overall processing time while maintaining detection accuracy.
Solution Approach 2:
The system applies partial action by focusing computational resources only on specific regions of interest rather than analyzing the entire image frame. By identifying and processing only the relevant portions of the image (such as areas where vehicles are detected or lanes are relevant), the system reduces the total computational load and processing time while maintaining threat detection accuracy.
2Speed
If high processing capabilities are used to analyze image data at high frame rates, then threat detection speed is improved, but device complexity and computational resource requirements increase
Solution Approach 1:
The processing system is segmented into multiple independent processing units that can operate in parallel on different regions of interest. This segmentation allows the system to achieve high threat detection speed through parallel processing while keeping each individual processing unit relatively simple, thereby reducing overall device complexity.
Solution Approach 2:
The system performs preliminary actions by pre-identifying regions of interest and pre-processing image data before full analysis. By preparing and segmenting the data in advance, the system reduces the computational complexity of the main processing stage while maintaining high detection speed.
3Reliability
If the entire image data is processed to detect vehicles and threats, then detection completeness is improved, but processing time and computational resources increase
Solution Approach 1:
The system applies local quality by assigning different processing levels and priorities to different regions of the image based on their importance. Critical regions (such as areas with detected vehicles or potential threats) receive more detailed analysis, while less critical regions receive minimal or no processing. This ensures detection completeness for important areas while maintaining high processing efficiency overall.
Solution Approach 2:
The system performs partial action by processing only the necessary portions of the image data required for threat detection. By identifying and analyzing only the relevant regions (such as vehicle locations, lane boundaries, and potential conflict zones), the system achieves sufficient detection completeness without the computational overhead of processing the entire image.
Data Source
AI summary
A driver assistance system including at least a first camera. The first camera can be configured to obtain image data of a region external to the host vehicle. Lane detection analysis can be performed on the image data to identify a region of interest in the image data. The region of interest can be divided into portions. Each portion can be analyzed to determine whether it includes the characteristics of an under-vehicle region. When an under-vehicle region is detected a portion of the image adjacent an under-vehicle region can be analyzed to determine whether it includes an image of another vehicle. A distance between the another vehicle and the host vehicle can be determined using the image data. A threat analysis can be performed to provide a level of threat posed by the another vehicle to the host vehicle.