Road Surface State Determination Using Tire Vibration and Braking Force Models
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Solution Overview
Problem
Existing methods for determining road surface state, such as those using hidden Markov models and kernel functions, fail to accurately identify road surfaces during acceleration, deceleration, or on slopes due to neglecting the category configuration of braking/driving forces applied to the tire.
Innovation Solution
A method that detects tire vibration, extracts time series waveforms, calculates feature vectors, and determines road surface state using multiple road surface models constructed based on varying braking/driving forces, estimating the force applied and selecting the appropriate model for accurate determination.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a single road surface model is used for determination, then the device complexity is low, but the determination accuracy decreases during acceleration or deceleration or on slopes
Solution Approach 1:
The patent divides the road surface determination system into multiple models (first road surface model for when braking/driving force is applied, and second road surface model for when it is not applied). This segmentation allows each model to be optimized for specific operating conditions, improving determination accuracy while managing complexity through conditional selection.
Solution Approach 2:
The patent implements dynamic model selection based on real-time detection of braking/driving force application. The system transitions between different road surface models depending on the current operational state, allowing the determination mechanism to adapt dynamically to changing conditions rather than using a static single model.
2Measurement precision
If multiple road surface models are constructed according to braking/driving force magnitudes, then the determination accuracy improves during acceleration or deceleration, but the device complexity increases
Solution Approach 1:
The patent applies local quality by creating specialized road surface models tailored to specific braking/driving force conditions. Each model has localized optimization for its intended condition (e.g., first model for force application, second model for no force application), ensuring high accuracy for each specific scenario without requiring a single overly complex universal model.
Solution Approach 2:
The patent changes the parameter of model selection based on the presence or absence of braking/driving force application. By detecting this parameter change and switching between corresponding models, the system achieves high determination accuracy across different operating conditions while maintaining manageable complexity through parameter-based model selection.
3Measurement precision
If road surface determination is performed without considering braking/driving force, then the device complexity is low, but the determination accuracy on uphill slope or downhill slope decreases
Solution Approach 1:
The patent performs preliminary detection of braking/driving force application status before conducting road surface determination. This preliminary action allows the system to pre-select the appropriate road surface model, ensuring that the determination process uses the most suitable model for current conditions, thereby improving accuracy on slopes without adding significant complexity to the overall system.
Data Source
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AI summary
A time series waveform of detected vibration of a tire during travel is multiplied by a window function of a prescribed time width and a time series waveform for each time window is extracted to calculate a feature vector from the time series waveform for each time window. Thereafter, when determining the state of the road surface during travel using the feature vector for each time window and road surface models, a plurality of the aforementioned road surface models is constructed depending on the magnitude of a braking/driving force, the braking/driving force acting on the aforementioned tire is estimated, and the state of the road surface is determined using the road surface models, which depend on the aforementioned feature vector and the magnitude of the estimated braking/driving force. The aforementioned road surface models are constructed with learning data comprising time series waveform data of tire vibration obtained by causing a vehicle mounted with a tire provided with an acceleration sensor to travel on road surfaces in multiple road surface states.