Wheel Sensor Comparison for Tire Explosion Probability Estimation
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Solution Overview
Problem
Existing techniques fail to accurately detect and predict tire explosions in vehicles, leading to missed or false predictions, which can result in unsafe vehicle control and unnecessary mitigation actions.
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 techniques are used to detect and predict tire explosions, then the system can provide basic detection capability, but the accuracy is insufficient leading to missed predictions and 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 system reliability through cross-validation of multiple data sources.
Solution Approach 2:
The system implements feedback by continuously monitoring sensor data and comparing actual wheel behavior against expected behavioral patterns. The comparison results feed back into the prediction model to adjust probability estimates, improving both detection accuracy and prediction reliability through iterative refinement.
2Measurement precision
If existing prediction models are used, then some detection capability is provided, but false predictions lead to unnecessary mitigation actions
Solution Approach 1:
The system dynamically adjusts the probability of tire explosion based on real-time sensor data and behavioral comparisons. Rather than using fixed thresholds, the model continuously updates predictions based on changing vehicle conditions, improving accuracy while maintaining operational efficiency through adaptive decision-making.
Solution Approach 2:
The system changes parameters by analyzing multiple sensor measurements (wheel speed, acceleration, vertical force) simultaneously and comparing their relationships. By monitoring changes in these parameters and their interrelationships, the system achieves more accurate predictions and reduces false alarms that would trigger unnecessary mitigation actions.
3Reliability
If simple detection methods are used, then the system remains simple to operate, but missed predictions compromise vehicle safety
Solution Approach 1:
The system achieves multi-functionality by using a single integrated model that processes multiple sensor types (wheel speed, acceleration, vertical force) and performs multiple functions: real-time monitoring, anomaly detection, probability prediction, and event classification. This universal approach improves safety assurance without proportionally increasing system complexity.
Solution Approach 2:
The system performs self-service by automatically comparing sensor data against behavioral patterns and generating predictions without requiring external intervention. The model self-adjusts based on incoming data, providing reliable safety monitoring while maintaining operational simplicity through automated decision-making.
Data Source
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AI summary
A computer system is disclosed for dynamically estimating a probability of tire explosion for a vehicle. The computer system comprises 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. The sensor data pertains to one or more of: wheel speed, wheel acceleration, and wheel vertical force. 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. Corresponding vehicle, method, computer program product, and non-transitory computer-readable storage medium are also disclosed.