Per-Wheel Tire Explosion Probability Estimation Using Sensor Comparison
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Solution Overview
Problem
Existing techniques often fail to accurately detect and predict tire explosions in vehicles, leading to missed or false predictions that can result in unnecessary mitigation actions and safety risks.
Innovation Solution
A computer system that analyzes sensor data such as wheel speed, acceleration, and vertical force using a data behavioral model to estimate the probability of tire explosion by comparing sensor data across multiple wheels, distinguishing between different events like tire explosions, potholes, and speed bumps.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing detection techniques are used, then the system can detect tire explosions, but the detection accuracy is insufficient leading to missed or false predictions
Solution Approach 1:
The system segments the detection task by analyzing multiple individual sensor data streams (wheel speed, acceleration, vertical force) separately and then comparing them across wheels. This segmentation allows for more precise analysis of each parameter while maintaining overall detection reliability through cross-validation between sensors.
Solution Approach 2:
The system introduces a behavioral model as an intermediary that processes sensor data and generates probability estimates. This intermediary layer transforms raw sensor readings into meaningful detection probabilities, improving both measurement precision and prediction reliability by filtering noise and identifying patterns that direct comparison methods miss.
2Measurement precision
If existing detection techniques are used, then the system can identify tire explosion events, but false predictions lead to unnecessary mitigation actions
Solution Approach 1:
The system uses feedback by continuously comparing sensor data across multiple wheels and using the behavioral model to adjust probability estimates. The comparison between wheels provides feedback that helps distinguish true tire explosion events from false indicators, reducing false predictions and their harmful consequences.
Solution Approach 2:
The system changes parameters by analyzing multiple different sensor parameters (wheel speed, acceleration, vertical force) simultaneously rather than relying on a single parameter. This multi-parameter approach increases event identification accuracy while reducing false alarms, as true tire explosions will affect multiple parameters consistently.
3Loss of information
If per-wheel sensor data is analyzed individually, then detailed wheel-specific information is obtained, but comparison across wheels is needed to distinguish tire explosions from road events
Solution Approach 1:
The behavioral model serves as a universal processing mechanism that handles multiple sensor types and wheel configurations through the same analytical framework. This multi-functionality allows the system to maintain detailed wheel-specific information while using a unified comparison approach across all wheels, managing complexity through standardization.
Data Source
AI summary
A computer system is disclosed for dynamically estimating a probability of tire explosion for a vehicle, comprising processing circuitry configured to receive sensor data, analyze the received sensor data in relation to a data behavioral model, and provide a value for the probability of tire explosion based on the received sensor data as analyzed in relation to the data behavioral model. At least some of the sensor data is provided per wheel of the vehicle, and a difference metric specifies correspondence between the sensor data of two different wheels. The data behavioral model specifies a higher value for the probability of tire explosion of a specific wheel when the difference metric of the specific wheel relative each of the other wheel(s) exceeds a tire explosion threshold than when the difference metric of the specific wheel relative at least one of the other wheel(s) falls below the tire explosion threshold.