Road surface condition estimation apparatus and road surface condition estimation method using the same

A road surface condition estimation apparatus for a tire, the apparatus comprising:

• a sensor module mounted on the tire;
• a receiver module configured to receive sensing information measured by the sensor module;
• a processing module configured to extract a parameter for estimating a road surface condition by analyzing the sensing information received by the receiver module;
• and
• an estimation module configured to estimate the road surface condition by using the parameter extracted by the processing module,
• wherein the sensing information comprises an acceleration of the tire,
• wherein the processing module is configured to extract the parameter by analyzing acceleration vibration characteristics through an acceleration waveform graph, wherein the processing module is configured to extract a contact area of the tire from the acceleration waveform graph and extract the parameter through frequency analysis of the contact area,
• wherein the processing module is configured to analyze a power spectrum density in a high frequency range in accordance with road surfaces to select a frequency range of interest, and calculate signal energy within the selected frequency range of interest to determine the calculated signal energy as a parameter which is input to machine learning,
• wherein the signal energy is calculated by a following equation:
• y=∫f1f2|X(f)|2df
• wherein, y is the signal energy, f1 is a start point of the frequency range of interest, f2 is an end point of the frequency range of interest, and X(f) is the power spectrum density within the frequency range of interest.