METHOD AND APPARATUS FOR DETERMINING ROAD SURFACE CONDITION
First Claim
1. A method for determining a road surface condition,comprising the steps of:
- detecting vibrations of a moving tire;
extracting time-series waveforms of tire vibrations in predetermined time widths from the tire vibrations detected;
calculating feature vectors from the time-series waveforms;
calculating likelihoods of the feature vectors respectively for a plurality of hidden Markov models (HMMS) structured to represent predetermined road surface conditions; and
comparing the likelihoods calculated respectively for the plurality of hidden Markov models with one another and determining a road surface condition corresponding to the hidden Markov model showing the highest likelihood to be the condition of the road surface on which the tire is running,wherein each of the feature vectors is vibration levels in specific frequency bands or a function of the vibration levels and wherein each of the hidden Markov models has at least four different states.
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Abstract
A method, featuring robustness against changes in tire size, is provided for determining a road surface condition by dividing a time-series waveform of tire vibrations into windows without resorting to detection of the peak positions or measurement of the wheel speed. A time-series waveform of tire vibrations detected by a tire vibration detecting unit is windowed by a windowing unit. Time-series waveforms are extracted from the respective time windows, feature vectors X are calculated therefor, and then likelihoods Z for road-surface HMMs (hidden Markov models) are calculated. The likelihoods Z1 to Z5 calculated for the respective road-surface HMMs are compared with one another, and a road surface condition corresponding to the road-surface HMM showing the highest likelihood is determined to be the condition of the road surface on which the tire is running.
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Citations
5 Claims
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1. A method for determining a road surface condition,
comprising the steps of: -
detecting vibrations of a moving tire; extracting time-series waveforms of tire vibrations in predetermined time widths from the tire vibrations detected; calculating feature vectors from the time-series waveforms; calculating likelihoods of the feature vectors respectively for a plurality of hidden Markov models (HMMS) structured to represent predetermined road surface conditions; and comparing the likelihoods calculated respectively for the plurality of hidden Markov models with one another and determining a road surface condition corresponding to the hidden Markov model showing the highest likelihood to be the condition of the road surface on which the tire is running, wherein each of the feature vectors is vibration levels in specific frequency bands or a function of the vibration levels and wherein each of the hidden Markov models has at least four different states. - View Dependent Claims (2, 3, 4)
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5. An apparatus for determining a road surface condition,
comprising: -
a tire vibration detecting unit disposed on the air chamber side of an inner liner portion of a tire tread for detecting vibrations of a moving tire; a windowing unit for windowing a time-series waveform of tire vibrations detected by the tire vibration detecting unit in predetermined time widths and extracting time-series waveforms of tire vibrations from the respective time windows; a feature vector calculating unit for calculating feature vectors, each having as components vibration levels in specific frequency bands or a function of the vibration levels, for the time-series waveforms extracted from the respective time windows; a storage unit for storing a plurality of hidden Markov models, each having at least four states, structured in advance for different road surface conditions; a likelihood calculating unit for calculating the likelihoods of the feature vectors for the plurality of hidden Markov models stored in the storage unit; and a determining unit for comparing the likelihoods calculated respectively for the plurality of hidden Markov models with one another and determining a road surface condition corresponding to the hidden Markov model showing the highest likelihood to be the condition of the road surface on which the tire is running.
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Specification