Collision Avoidance Sensing with Nested Radar Zones and Image Fusion
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Solution Overview
Problem
Conventional collision avoidance systems (CAS) face challenges in accurately determining the speed or distance of approaching objects due to interference from environmental noise in radar-based systems and inaccuracies in image recognition, leading to unreliable collision detection.
Innovation Solution
A collision avoidance system that integrates radar and image recognition technologies to detect objects, calculates collision probabilities based on detection areas and lighting states, and determines the need for alarm messages using a processor to enhance accuracy and reliability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Area of stationary object
If radar technology is used for detecting objects, then the detection range is extended, but the accuracy deteriorates due to environmental noise interference
Solution Approach 1:
The patent combines radar detection with image recognition technology to create a hybrid detection system. The radar provides detection range while the image recognition module compensates for noise interference, achieving both extended range and maintained accuracy through data fusion of multiple detection modalities.
Solution Approach 2:
The image recognition module serves as an intermediary that validates and refines radar detection results. By cross-referencing radar data with visual information, the system filters out false positives caused by environmental noise, thereby improving measurement precision while preserving the extended detection range provided by radar.
2Loss of information
If image recognition technology is used for detecting objects, then the visual identification capability is improved, but the speed and distance determination accuracy deteriorates
Solution Approach 1:
The patent merges image recognition with radar technology where each moditor compensates for the other's weaknesses. Image recognition provides superior visual identification while radar supplies accurate speed and distance measurements, achieving comprehensive detection performance through integration.
Solution Approach 2:
The system uses feedback loops where radar measurements of speed and distance are cross-validated with image recognition data. The processor continuously refines object characterization by comparing temporal changes in image data with radar range and velocity measurements, improving overall measurement precision.
3Device complexity
If a single detection area is used, then the system complexity is reduced, but the collision probability calculation reliability deteriorates
Solution Approach 1:
The patent divides the detection space into multiple nested detection areas (first detection area and second detection area) with different radii. This segmentation allows the system to calculate collision probabilities more reliably by considering the spatial distribution of detected objects across different zones, with each zone contributing differently to the overall risk assessment.
Data Source
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AI summary
A CAS (100) and a collision avoidance method are provided. The collision avoidance method includes: detecting, by a radar (130, 131, 132, 133, 134, 135, 136, 137, 138), a first detection area (10) and a second detection area (20) to generate a detection result of a target object (300, 400), where the second detection area (20) includes the first detection area (10) and is greater than the first detection area (10); determining whether the target object (300, 400) invades the first detection area (10) or the second detection area (20) based on the detection result; in response to determining that the target object (300, 400) invades the second detection area (20) but does not invade the first detection area (10), calculating a first collision probability based on a first weight and the detection result; in response to determining that the target object (300, 400) invades the first detection area (10), calculating the first collision probability based on a second weight and the detection result; determining whether to output an alarm message based on the first collision probability.