Vehicle Blind Spot Detection Using Size-Based Region Segmentation
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Solution Overview
Problem
Existing technologies struggle to accurately determine and manage the blind spots of vehicles in real-time, particularly for large vehicles like buses and trucks, leading to frequent accidents due to undetected blind spots.
Innovation Solution
A method and system that utilizes an image sensor to acquire vehicle images, calculates the overall length and width of target vehicles, and divides their blind spots into multiple regions based on these dimensions to provide precise warning controls and cancellations, using a controller and warning transmitter to manage collision risks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If blind spot detection is performed for all vehicles regardless of size, then detection coverage is improved, but computational complexity and processing time increase
Solution Approach 1:
The system changes the detection parameter by using vehicle length as a threshold criterion. Vehicles with length greater than a predetermined value are subjected to blind spot detection, while smaller vehicles are excluded. This parameter-based filtering resolves the contradiction by maintaining detection accuracy for relevant large vehicles while reducing processing complexity for the overall system.
2Measurement precision
If blind spot range is calculated based on individual vehicle dimensions, then detection precision is improved, but real-time calculation capability deteriorates
Solution Approach 1:
The system segments the vehicle population into two groups: large vehicles (length > predetermined value) and small vehicles (length ≤ predetermined value). Blind spot detection and calculation are applied only to the large vehicle segment, while the small vehicle segment is handled with a simplified approach or excluded. This segmentation maintains precision for critical cases while improving overall real-time processing capability.
3Reliability
If multiple blind spot regions are detected and managed, then collision prevention coverage is improved, but system complexity increases
Solution Approach 1:
The system applies different levels of detection and management to different spatial regions around the host vehicle. Multiple blind spot regions (first, second, third regions) are detected with different characteristics and warning thresholds. Each region has locally optimized detection parameters and warning strategies, resolving the contradiction by providing comprehensive coverage through localized quality variations rather than uniform complex management across all regions.
Data Source
AI summary
A method for detecting a blind spot of a vehicle is provided The method for detecting a blind spot of a vehicle includes acquiring a vehicle image around a host vehicle using an image sensor, calculating an overall length and an overall width of a target vehicle included in the vehicle image, and detecting at least one region corresponding to a blind spot of the target vehicle using the overall length and the overall width of the target vehicle when the overall length of the target vehicle is greater than or equal to a predetermined length.