Dynamic Threshold Adjustment for Collision Detection in Location Sharing Systems
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Solution Overview
Problem
Existing collision detection systems for client devices, such as smartphones, suffer from high false positive and false negative rates due to the limited accuracy of location and accelerometer data, making it challenging to accurately detect physical collisions between devices.
Innovation Solution
Implementing a dynamic detection threshold adjustment mechanism, where the server temporarily lowers the detection threshold if the initial collision signatures do not meet the detection criteria, allowing for more accurate correlation of subsequent collision signatures and reducing false negatives while minimizing false positives.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Object-affected harmful factors
If a high detection threshold is used to reduce false positives, then false positive rate decreases, but false negative rate increases
Solution Approach 1:
The patent applies dynamics by making the detection threshold adjustable rather than fixed. The system dynamically adapts the detection threshold based on the correlation strength between collision signatures. When initial correlations are weak, the threshold is temporarily lowered to capture potential collisions that might otherwise be missed, thereby reducing false negatives while maintaining false positive control through subsequent verification.
Solution Approach 2:
The patent changes the detection threshold parameter adaptively. Instead of using a static threshold, the system modifies the threshold value based on the quality of correlation between collision signatures from different devices. This parameter change allows the system to optimize detection sensitivity in real-time, balancing false positive and false negative rates.
2Measurement precision
If sensor data accuracy is improved, then collision detection accuracy improves, but device complexity and cost increase
Solution Approach 1:
The patent introduces an intermediary processing layer (the server) that compensates for limited sensor accuracy. Instead of relying solely on high-precision sensors in each device, the system uses the server to perform sophisticated correlation analysis on collision signatures from multiple devices, effectively achieving high detection accuracy through software-based compensation rather than hardware upgrades.
Solution Approach 2:
The patent replaces the need for high-precision mechanical sensors with a software-based correlation system. Rather than requiring expensive and complex accelerometer and GPS hardware with high precision, the system substitutes this with algorithmic correlation of collision signatures, replacing mechanical measurement precision requirements with computational analysis.
3Reliability
If the detection threshold is lowered to reduce false negatives, then false negative rate decreases, but false positive rate increases
Solution Approach 1:
The patent applies periodic action through a two-stage detection process. First, an initial correlation is performed with a standard threshold. If this fails, the system periodically retries with a temporarily lowered threshold. This periodic re-attempt mechanism ensures that potential collisions aren't missed initially, while the temporary nature of threshold lowering prevents sustained false positive generation.
Solution Approach 2:
The system uses feedback by monitoring the correlation results and adjusting the detection threshold accordingly. When initial correlations don't meet the threshold, the system feeds this information back to trigger a threshold adjustment, allowing the detection process to adapt based on the actual quality of the collision signature correlation data.
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
This approach significantly improves the accuracy of collision detection by reducing false negatives and maintaining a low false positive rate, ensuring that collisions are reliably detected even with imperfect sensor data.
Implementation Method 1
acceleration data acquired by an accelerometer embedded in the phone
Implementation Method 2
a location of the phone
Implementation Method 3
a timestamp, a location of the phone, and acceleration data acquired by an accelerometer embedded in the phone
Data Source
AI summary
The invention provides methods, systems, and devices for detecting a physical collision between two client devices based on sensor data. A server computer receives a first collision signature from a first client device, and a second collision signature from a second client device. Based on determining that a correlation of the first collision signature and the second collision signature does not achieve a detection threshold, the server computer lowers, for a limited period of time, the detection threshold. If the server computer receives, within the limited period of time, a third collision signature from the first client device, and a fourth collision signature from the second client device, and determines that a correlation of the third collision signature and the fourth collision signature achieves the lowered detection threshold, the server computer detects a collision between the first and second client devices.