METHOD AND APPARATUS FOR DETERMINING ROAD SURFACE CONDITION

• US 20120330493A1
• Filed: 06/20/2012
• Published: 12/27/2012
• Est. Priority Date: 06/24/2011
• Status: Abandoned Application

• Current Assignee: Bridgestone Corporation, Inter-university Research Institute Corporation Research Organization of Information and Systems
• Original Assignee: Kabushiki Kaisha Bridgestone (Bridgestone Corporation), Inter-university Research Institute Corporation Research Organization of Information and Systems
• Inventors: HANATSUKA, Yasushi, HIGUCHI, Tomoyuki, MATSUI, Tomoko
• Application Number:
  US13/527,917
• Publication Number:
  US 20120330493A1
• Time in Patent Office: Days
• US Class Current: 701/29.1
• CPC Class Codes: B60T 2270/86 Optimizing braking by using ESP vehicle or tire model
• CPC Class Codes: B60T 8/172 Determining control parameters used in the regulation, e.g. by calculations involving measured or detected parameters B60T8/17551 takes precedence

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.

Claims

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.

2. The method for determining a road surface condition according to claim 1, wherein the feature vector is one, two or more, or all of:

vibration levels in specific frequency bands when a Fourier transform is performed on the time-series waveform,

vibration levels in specific frequency bands obtained by passing the time-series waveform through bandpass filters,

time-varying dispersions of the vibration levels in specific frequency bands, and

frequency cepstral coefficients of the time-series waveform.

3. The method for determining a road surface condition according to claim 1, wherein each of the hidden Markov models for the respective road surface conditions has seven states.

4. The method for determining a road surface condition according to claim 1, wherein the hidden Markov models for the respective road surface conditions include an extra-road-surface hidden Markov model, structured from a vibration waveform which is a vibration waveform other than that of the contact patch and whose vibration level is lower than a predetermined background level, and intra-road-surface hidden Markov models, structured from vibration waveforms which are vibration waveforms of the contact patch or those before and after the contact patch and whose vibration level is equal to or higher than the predetermined background level, and wherein the extra-road-surface hidden Markov model is provided either before or after or both before and after the intra-road-surface hidden Markov models.

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.