Wheel Audio Sensing for Predicting Upcoming Road Friction
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing vehicle control systems lack reliable information about upcoming operating conditions, leading to inadequate planning and potential safety issues or under-utilization of control capacity.
Innovation Solution
A computer system using audio sensors mounted near vehicle wheels to measure sound and predict upcoming operating conditions, such as rolling resistance and tire-road friction, by processing audio data through an adjustable audio model.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If currently measured parameter values are used as estimation of upcoming parameter values, then the system is simple to operate, but the reliability of vehicle control deteriorates due to lack of accurate upcoming condition information
Solution Approach 1:
The audio sensor measures sound from the wheel-ground contact area before the wheel actually contacts that section of road, enabling prediction of upcoming operating conditions in advance. This preliminary measurement approach provides accurate forward-looking information without requiring complex real-time sensing during contact.
Solution Approach 2:
The patent replaces traditional mechanical or direct contact-based sensing methods with acoustic field-based measurement. By using sound waves to detect road surface characteristics and wheel interaction, the system obtains upcoming condition information without physical contact, improving both reliability and ease of operation.
2Reliability
If audio sensors and processing systems are added to predict upcoming operating conditions, then the reliability of vehicle control improves, but the device complexity increases
Solution Approach 1:
The patent extracts only the essential acoustic information needed for operating condition prediction from the complex audio signal environment. By focusing on specific sound characteristics related to wheel-ground contact and filtering out irrelevant noise, the system achieves reliable prediction without requiring overly complex processing infrastructure.
Solution Approach 2:
The audio sensor acts as an intermediary between the wheel-ground contact and the control system. Rather than directly measuring mechanical parameters, it converts physical contact characteristics into acoustic signals that are easier to process and interpret, simplifying the overall system while improving reliability.
3Measurement precision
If audio data processing with dynamically adjustable models is implemented, then the measurement precision of operating conditions improves, but the loss of computational resources increases
Solution Approach 1:
The audio processing model dynamically adjusts its parameters and complexity based on current driving conditions, road types, and sensor data quality. This dynamic adaptation allows the system to maintain high measurement precision when needed while reducing computational energy consumption during normal or predictable conditions.
Solution Approach 2:
The system changes processing parameters such as audio sampling rates, analysis window sizes, and model complexity thresholds based on the specific operating context. By adjusting these parameters dynamically, the system optimizes the balance between measurement precision and computational energy consumption for each situation.
Data Source
Figure 1~2
Figure 3~4A
Figure 4B~5B
AI summary
A method for determining upcoming operating conditions of a wheel of a vehicle is disclosed. The method comprises receiving measured audio data from an audio sensor mounted in vicinity of the wheel and configured to measure sound above a portion of the ground surface to be traveled by the wheel, and determining most likely audio-based operating conditions by processing the measured audio data based on an audio model. The audio model defines a plurality of operating condition classes and comprises a dynamically adjustable component. The most likely audio-based operating conditions corresponds to one of the operating condition classes. The method also comprises predicting upcoming operating conditions based on the most likely audio-based operating conditions, and updating the dynamically adjustable component of the audio model based on the measured audio data and the predicted operating conditions. Corresponding computer system, vehicle, computer program product, and non-transitory computer-readable storage medium are also disclosed.