Obstacle Detection Probability Calculation for Small Targets
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Solution Overview
Problem
Existing obstacle detection techniques struggle to reliably determine small targets, such as parking blocks, due to their weak signal strength and intermittent detection, leading to inaccurate distance measurement and type classification.
Innovation Solution
An obstacle detection device with a result acquisition unit, probability calculation unit, and type determination unit that calculates detection probabilities for each reflection point and determines the target type based on these probabilities, rather than signal strengths, allowing for improved tracking and classification of small targets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If small targets are detected using conventional signal strength thresholding, then detection speed is maintained, but detection accuracy deteriorates due to weak and intermittent signals
Solution Approach 1:
The patent changes the detection parameter from signal strength to detection probability. Instead of using fixed signal strength thresholds that fail for small targets with weak reflections, the system calculates the probability of detection based on multiple measurement results, transforming the detection criterion to one that accounts for intermittent detection characteristics of small targets
Solution Approach 2:
The system implements feedback by repeatedly acquiring measurement results and using the detection probability calculated from previous measurements to inform subsequent detection decisions. The detection probability is updated based on the history of detection attempts, creating a feedback loop that improves reliability over time
2Measurement precision
If conventional obstacle detection is used, then processing load is reduced, but type determination accuracy deteriorates for small targets
Solution Approach 1:
The patent applies local quality by focusing processing resources on specific cells where detection probability is nonzero. Instead of uniformly processing all spatial cells, the system identifies and concentrates computational effort on regions where small targets are likely present, improving type determination accuracy while reducing overall processing load
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The device enhances the accuracy of detecting small targets by reducing environmental noise influence and improving reliability in type determination, while also reducing processing load by focusing on cells with nonzero detection probabilities.
Implementation Method 1
a sensor to transmit probe waves to the surroundings of the vehicle and receive the reflected waves from a target
Data Source
AI summary
A result acquisition unit repeatedly acquires measurement results from an environment monitoring sensor that emits probe waves to a probe region and measures the distance and the direction to a reflection point at which the probe waves are reflected. A probability calculation unit calculates a detection probability for each reflection point in accordance with the measurement results acquired by the result acquisition unit. A type determination unit determines the type of the target having the reflection point in accordance with the detection probability calculated by the probability calculation unit.