JP2011046256A - Method and device for estimating road surface state, and vehicle control method - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To provide a method and a device for estimating a road surface state during traveling with high accuracy, and to provide a method for controlling a travel state of a vehicle based on the state of a road surface. <P>SOLUTION: A road surface temperature T, vibration of a tire, tire noise, and a wheel velocity V during travel are measured. A vibration level ratio R that is a ratio between magnitude G<SB>L</SB>of a vibration component in a band of 1-2 kHz of a frequency spectrum of the vibration and magnitude G<SB>H</SB>of a vibration component in a band of 3-5 kHz in a pre-stepping area is calculated from data of the wheel velocity V and an output of an acceleration sensor 12 that show the vibration of the tire. A sound pressure signal that it an output of a microphone 13 is subjected to one Nth octave analysis, to calculate a sound pressure level ratio Q that is a ratio between a band power value P<SB>A</SB>of 500 Hz and a band power value P<SB>B</SB>of 8,000 Hz, from an octave distribution waveform. The road surface state is estimated by use of data on the road surface temperature T, the vibration level ratio R, the sound pressure level ratio Q, and the wheel velocity V. <P>COPYRIGHT: (C)2011,JPO&INPIT

Description

  The present invention estimates whether or not a running road surface is in a WET state, and whether the WET state is a shallow WET state or a deep WET state, or whether a hydroplaning phenomenon is predicted to occur. The present invention relates to a method, an apparatus thereof, and a vehicle control method.

  In order to improve the running stability of an automobile, it is required to accurately estimate the state of the road surface during running and feed it back to vehicle control. If it is possible to estimate the condition of the road surface during traveling, it is expected that, for example, more advanced control of the ABS brake and the like can be performed before the risk avoidance operation such as braking / driving and steering, and safety is further improved. Is done.

  As a method for estimating the road surface condition during traveling, for example, the vibration of the tire during traveling is detected, and the time region before the depression position (the region before depression) is detected from the time-series waveform of the detected tire vibration. The vibration level ratio R, which is the ratio of the magnitude of the vibration component in the high frequency region (3 kHz to 5 kHz band) to the magnitude of the vibration component in the low frequency region (1 kHz to 2 kHz band) of this vibration, is calculated. A method for estimating whether the road surface state is a high μ road surface or a low μ road surface from the vibration level ratio R has been proposed (see, for example, Patent Document 1).

  Further, the tire generation sound generated from the running tire is detected, the waveform of the detected tire generation sound is subjected to wavelet transform processing, and each wavelet component is subjected to time-frequency analysis, and the hopping noise component of the tire generation sound is detected. There is also a method to estimate whether the road surface is a dry asphalt road surface, a wet asphalt road surface, an icy road surface, a compressed snow road surface by comparing the size, the size of the rubber collision noise component, and the size of the tire body noise component. It has been proposed (see, for example, Patent Document 2).

WO 2006/135090 A1 JP 2004-168286 A

However, in the method of estimating the road surface condition from the vibration of the tire, it can be accurately estimated whether or not the road surface is a deeply wet WET road surface, but as shown in FIG. 4, a shallow WET where the road surface is slightly wetted. It was difficult to determine whether or not the road surface.
On the other hand, in the method of estimating the road surface condition from the tire generated sound, it can be accurately estimated whether or not the road surface is a shallow WET road surface, but on the WET road surface having a deep water depth, water is prevented from scattering to the sound detection device. In addition to this, as shown in FIG. 7, since the water jumps greatly on the deep WET road surface, the waveform is buried in white noise, and it is impossible to observe the characteristic sound on the road surface. There was a problem.

  The present invention has been made in view of the conventional problems, a method and apparatus for accurately estimating the road surface state during traveling, and a method for controlling the vehicle traveling state based on the road surface state. The purpose is to provide.

The invention according to claim 1 of the present application is a method for estimating the road surface state during traveling, the road surface temperature during traveling, the magnitude of vibration components in the 2 kHz to 10 kHz band of tire vibration, and the tire generated sound. Whether the road surface is in a state where the occurrence of a hydroplaning phenomenon is predicted is estimated from the magnitude of the sound pressure component in the 10 Hz to 10 kHz band.
As described above, the road surface condition is estimated from the road surface temperature, the tire vibration, and the tire generated sound, so that the hydroplaning phenomenon occurs on the road surface as compared with the estimation based on the tire vibration alone or the estimation based on the tire generated sound alone. It is possible to accurately estimate whether or not the predicted state exists.
Note that tire vibration is vibration that is input to the tire from a running road surface, and tire generated sound is sound that is generated near the tire ground contact surface when the tire contacts the road surface.
The hydroplaning phenomenon is a phenomenon in which when a vehicle runs at high speed on a road surface on which a water film is formed, water enters between the tire and the road surface, making it difficult for the steering wheel and brake to work. The WET state in which the occurrence of a phenomenon is predicted refers to a road surface state in which the water depth, which is the thickness of the water film, exceeds a predetermined value (1 mm to 3 mm).

The invention according to claim 2 is the road surface state estimation method according to claim 1, wherein the road surface temperature exceeds a preset reference temperature, and the vibration of the tire vibration in a 2 kHz to 10 kHz band is provided. When the magnitude of the component exceeds a preset vibration threshold, it is determined that the road surface is in the first road surface state that is a WET state in which the occurrence of the hydroplaning phenomenon is predicted, and the road surface temperature is set in advance. If the reference temperature is exceeded and the magnitude of the sound pressure component in the 10 Hz to 10 kHz band of the sound generated by the tire exceeds a preset sound pressure threshold, the road surface is hydroplaning. Is determined to be in the second road surface state that is an unpredictable WET state.
As a result, the WET state of the road surface can be clearly distinguished from the first road surface state where the water depth is deep and the hydroplaning phenomenon is likely to occur, and the second road surface state where the water depth is shallow and the occurrence of the hydroplaning phenomenon is not predicted. It is possible to reliably predict the occurrence of the hydroplaning phenomenon.

  The invention according to claim 3 is the road surface state estimation method according to claim 2, wherein the first road surface state is a state in which a water film having a thickness exceeding 2 mm is formed on the road surface, The road surface state is a state in which a water film having a thickness of 2 mm or less is formed on the road surface, whereby the correspondence between the magnitude of vibration components in the 2 kHz to 10 kHz band of tire vibration and the first road surface state Is further clarified, and the correspondence between the magnitude of the sound pressure component in the 10 Hz to 10 kHz band of the tire-generated sound and the second road surface state is further clarified, so that the estimation accuracy of the road surface state can be further improved. .

  According to a fourth aspect of the present invention, in the road surface state estimating method according to the second or third aspect, when the wheel speed is measured and it is determined that the road surface is in the first road surface state, The wheel speed is compared with a preset warning speed, and it is determined that the occurrence of a hydroplaning phenomenon is predicted when the wheel speed exceeds the warning speed. In this way, by setting the warning speed in advance, it is possible to perform control such as issuing a warning to the driver or suppressing the driving speed, as will be described later, thus improving the driving safety of the vehicle. Can be made.

  The invention according to claim 5 is the road surface state estimation method according to any one of claims 2 to 4, wherein after determining whether or not the road surface is in the first road surface state, the second road surface It is characterized by determining whether it is in a state. Thus, it is determined whether the road surface is in a state where the occurrence of the hydroplaning phenomenon is predicted when the determination based on the tire vibration is performed first to determine whether the road surface is in the first road surface state. Can be performed with high accuracy.

  The invention according to claim 6 is the road surface state estimation method according to any one of claims 1 to 5, wherein the magnitude of the vibration component of the 2 kHz to 10 kHz band of the tire vibration is determined based on the tire running. A signal in the time range corresponding to the pre-depression area before the depressing position of the vibration waveform of the vibration is extracted, and this extracted time range signal is calculated using the frequency spectrum obtained by frequency analysis. It is. As a result, the magnitude of the vibration component of the tire vibration having a high correlation with the road surface condition can be calculated with high accuracy, so that the road surface condition estimation accuracy is improved.

  The invention according to claim 7 is the road surface state estimation method according to any one of claims 1 to 5, wherein the magnitude of the sound pressure component in the 10 Hz to 10 kHz band of the tire generated sound is set to the sound pressure. The signal is calculated using a frequency spectrum obtained by analyzing the signal in 1 / N octave. Thereby, since the magnitude of the sound pressure component of the tire generated sound having a high correlation with the road surface condition can be calculated with high accuracy, the estimation accuracy of the road surface condition is improved.

  The invention according to claim 8 is a method for controlling the traveling state of the vehicle, and the vehicle is based on the road surface state estimated by the road surface state estimating method according to any one of claims 1 to 7. It is characterized by controlling the running state of the vehicle. Thus, if the running state of the vehicle is controlled by controlling the ABS brake or the like based on the estimated road surface state, the safety of the vehicle can be further enhanced.

The invention according to claim 9 is an apparatus for estimating a road surface condition during traveling, the means for measuring the road surface temperature during traveling, the means for detecting tire generated sound, the means for detecting tire vibration, Means for detecting the wheel speed; means for calculating the magnitude of a vibration component in the 2 kHz to 10 kHz band of the tire vibration; and means for calculating the magnitude of a sound pressure component in the 10 Hz to 10 kHz band of the tire generated sound; Means for estimating whether the road surface is in a state where occurrence of a hydroplaning phenomenon is predicted from the measured road surface temperature, the magnitude of the vibration component, and the magnitude of the sound pressure component; When the occurrence of hydroplaning phenomenon is predicted, the wheel speed is compared with a preset warning speed to determine whether the occurrence of hydroplaning phenomenon is predicted. Characterized in that a constant-estimating means.
By adopting such a configuration, it is possible to accurately estimate whether or not the occurrence of the hydroplaning phenomenon is predicted.

  A tenth aspect of the present invention is the road surface state estimating apparatus according to the ninth aspect, further comprising an alarm device that issues an alarm to the driver when it is estimated that the occurrence of a hydroplaning phenomenon is predicted. It is characterized by that. As a result, the driver can recognize the occurrence prediction of the hydroplaning phenomenon, so that the running stability of the vehicle can be improved.

  The summary of the invention does not list all necessary features of the present invention, and sub-combinations of these feature groups can also be the invention.

It is a functional block diagram which shows the structure of the road surface state estimation apparatus which concerns on this Embodiment. It is a figure which shows arrangement | positioning of a sensor. It is a figure which shows the area | region before stepping in in a tire vibration waveform, the contact surface area | region, and the area | region after kicking out. It is a figure which shows the frequency spectrum of the tire vibration in the area before depressing. It is a figure which shows the octave distribution waveform of the tire generated sound at the time of driving on a dry road surface. It is a figure which shows the octave distribution waveform of the tire generated sound at the time of shallow WET road surface running. It is a figure which shows the octave distribution waveform of the tire generation | occurrence | production sound at the time of deep WET road surface running. It is a flowchart which shows the estimation method of the road surface state which concerns on this Embodiment. It is a figure which shows the time change of the difference between the vibration level difference of the tire vibration at the time of dry road surface driving, and the dBG value of the vibration threshold, and the sound pressure level difference of the sound pressure signal. It is a figure which shows the time change of the difference between the vibration level difference of the tire vibration and the dBG value of the vibration threshold, and the sound pressure level difference of the sound pressure signal when running on a shallow WET road surface. It is a figure which shows the time change of the difference between the vibration level difference of the tire vibration and the dBG value of the vibration threshold when traveling on a deep WET road surface, and the sound pressure level difference of the sound pressure signal. It is a flowchart which shows the estimation method of the road surface state by this invention. It is a table | surface which shows the estimation result of the road surface state by the estimation method of the road surface state by this invention. It is a table | surface which shows the estimation result of the road surface state by the estimation method of the conventional road surface state.

  Hereinafter, the present invention will be described in detail through embodiments, but the following embodiments do not limit the invention according to the claims, and all combinations of features described in the embodiments are included. It is not necessarily essential for the solution of the invention.

FIG. 1 is a functional block diagram of a road surface state estimation apparatus 10 according to the present embodiment.
The road surface state estimating device 10 includes an infrared temperature sensor 11 as a road surface temperature measuring means, an acceleration sensor 12 as a vibration detecting means, a microphone 13 as a sound pressure signal detecting means, and a wheel speed sensor 14 as a rotational speed detecting means. And a vibration level calculation means 15, a sound pressure level calculation means 16, a road surface state estimation means 17, and an alarm means 18.
The infrared temperature sensor 11, the acceleration sensor 12, the microphone 13, and the wheel speed sensor 14 constitute a sensor unit 10 </ b> A of the road surface state estimation device 10. The vibration level calculation means 15, the sound pressure level calculation means 16, and the road surface condition estimation means 17 constitute an arithmetic processing unit 10B.

As shown in FIG. 2A, the infrared temperature sensor 11 is installed under the bumper 21 at the front of the vehicle body 20, and radiates from the road surface. Measure the temperature. The output of the infrared temperature sensor 11 is input to the road surface state estimating means 17.
As shown in FIG. 2B, the acceleration sensor 12 is arranged at a substantially central portion of the tire 22T of the front wheel 22F on the tire liner 24 side of the inner liner portion 23, and inputs vibrations to the tread 25 of the tire 22T. Is detected. In this example, the acceleration sensor 12 is arranged so that the detection direction is the tire circumferential direction, and the tread vibration in the tire circumferential direction input from the road surface is detected. The output of the acceleration sensor 12 is input to the vibration level calculation means 15.
In this example, the transmitter 12z that transmits the output of the acceleration sensor 12 to the arithmetic processing unit 10B on the vehicle body side is provided, and the receiver 10z is provided in the arithmetic processing unit 10B, so that the acceleration sensor 12 attached to the tire 22T is provided. The tire vibration data as an output is transmitted to the vibration level calculation means 15 of the arithmetic processing unit 10B by wireless transmission.
The transmitter 12z is disposed on the inner liner portion 23 integrally with the acceleration sensor 12, but the transmitter 12z may be attached to the valve 22v of the tire 22T.

As shown in FIG. 2A, the microphone 13 is attached to the lower portion of the frame 26 in front of the rear wheel 22R of the vehicle body 20, and the tire contact surface when the tire 22T of the rear wheel 22R contacts the road surface during traveling. A sound pressure signal of a tire generated sound generated in the vicinity is detected. The output of the microphone 13 is input to the sound pressure level calculation means 16.
The wheel speed sensor 14 detects the rotational speed of the wheel (hereinafter referred to as wheel speed), and in this example, a rotor having a gear formed on the outer peripheral portion thereof and rotating together with the wheel, and a yoke that constitutes the rotor and a magnetic circuit. And a coil that detects a change in magnetic flux of the magnetic circuit, and uses a known electromagnetic induction type wheel speed sensor that detects the rotation angle of the wheel (here, the front wheel 22F). The yoke and the coil are attached to a knuckle (not shown). The output of the wheel speed sensor 14 is input to the vibration level calculation means 15 and the road surface condition estimation means 17.
As the wheel speed sensor 14, a wheel speed sensor having another configuration such as a combination of a ring multipolar magnet and a magnetoresistive element may be used. Alternatively, the rotational speed of a transmission (not shown) may be detected and used as the wheel speed.

The vibration level calculation means 15, the sound pressure level calculation means 16, and the road surface state estimation means 17 that constitute the arithmetic processing unit 10B are each configured by microcomputer software. The arithmetic processing unit 10B is mounted on the frame 26 in front of the rear wheel 22R of the vehicle body 20.
The vibration level calculation means 15 includes a vibration waveform detection unit 151, a vibration level distribution calculation unit 152, a time domain signal extraction unit 153, a frequency analysis unit 154, a vibration level calculation unit 155, and a vibration level ratio calculation unit 156. The vibration level calculation value is obtained from tire vibration data which is the output of the acceleration sensor 12 transmitted from the transmitter 12z and received by the receiver 10z, and this is output to the road surface state estimating means 17.

The vibration waveform detection unit 151 obtains a vibration waveform in which vibration levels that are the magnitudes of the output signals of the acceleration sensor 12 are arranged in time series.
The vibration level distribution calculation unit 152 uses the output pulse from the wheel speed sensor 14 to convert the vibration waveform into a vibration waveform corresponding to a predetermined position of the tire, and the tire vibration level distribution as shown in FIG. Ask for.
The time domain signal extraction unit 153 specifies the exact depression point A and the kick point B of the tire 22T from the peak position of the tire vibration that appears in the vicinity of the tire contact surface, and the vibration level distribution data before depression. The data is divided into three areas, ie, the area, the contact surface area, and the area after kicking out, and the time series waveform of the vibration level in the area before stepping is extracted from these areas.

The frequency analysis unit 154 analyzes the frequency of the time series waveform using, for example, an FFT analyzer, and obtains a frequency spectrum of tire vibration in the region before stepping as shown in the graph of FIG. The horizontal axis of the frequency spectrum is frequency (Hz), and the vertical axis is the vibration level (dBG). In the figure, the thin solid line is on a dry asphalt road surface, the broken line is a shallow WET road surface (in this case, a slightly wet road surface; semi-wet), and the thick solid line is a deep WET road surface (in this case, a 2 mm water surface). ) Frequency spectrum when driving.
The deep WET road surface is a road surface state corresponding to the first road surface state, and the shallow WET road surface is a road surface state corresponding to the second road surface state.
Vibration level calculation unit 155, from the frequency spectrum in the depression before the region shown in FIG. 4, in the low frequency band (e.g., 1-2 kHz bandwidth) of the vibration components size G _L and the high frequency band (e.g., 3～5KHz band) The magnitude _GH of the vibration component is calculated.
The vibration level ratio calculation unit 156 calculates a vibration level calculation value from the vibration component magnitude _{GL in the} low frequency band and the vibration component magnitude _{GH in} the high frequency band. In this example, the vibration level calculation value is the vibration level ratio R which is the ratio of the vibration component magnitude _{GH in} the high frequency band to the magnitude _GL of the vibration component in the low frequency band. R = (G _H / G _L ).
Note that when expressed the G _L and G _H in decibels (DBG) the vibration level ratio R becomes the difference between the DBG value of DBG value and G _H of G _L.

The sound pressure level calculation means 16 includes a frequency analysis unit 161, a sound pressure level calculation unit 162, and a sound pressure level ratio calculation unit 163. The sound pressure level calculation unit 16 generates sound from the sound pressure signal data of the tire generated sound output from the microphone 13. A pressure level calculation value is obtained and output to the road surface state estimating means 17.
The frequency analysis unit 161 analyzes the sound pressure signal of the tire-generated sound by 1 / N octave to obtain a sound pressure signal distribution waveform (octave distribution waveform) as shown in FIGS. The octave distribution waveform is obtained by measuring the sound pressure level (band power) for each octave band divided into 1 / N octave bands. In this example, N = 3.
The horizontal axis of the octave distribution waveform is frequency (Hz), and the vertical axis is band power (dB). FIG. 5 shows an octave distribution waveform when running on a dry road surface, FIG. 6 shows a semi-wet road surface (shallow WET road surface), and FIG. 7 shows a water depth 2 mm road surface (deep WET road surface).

Sound pressure level calculator 162, the octave distribution waveform, a low frequency band (e.g., 500 Hz) band power values P _A and the high frequency band (e.g., 8000 Hz) calculates a band power value P _B with.
Sound pressure level ratio calculating unit 163 calculates a sound pressure level calculated value from a band power value P _B of band power values P _A and the high frequency band of the low frequency band. In this example, the sound pressure level calculation value is a sound pressure level ratio Q which is a ratio of the band power value P _{B in the} high frequency band to the band power value P _A in the low frequency band. Q = (P _B / P _A ).
When P _A and P _B are expressed in decibel units (dB), the sound pressure level ratio Q is the difference between the dB value of P _{B and} the dB value of P _A.

The road surface state estimating means 17 is data of the road surface temperature T inputted from the infrared temperature sensor 11, data of the vibration level ratio R inputted from the vibration level calculating means 15, and sound inputted from the sound pressure level calculating means 16. Using the data of the pressure level ratio Q and the data of the wheel speed V input from the wheel speed sensor 14, it is determined whether the road surface is not in the WET state, shallow WET state, or deep WET state. At the same time, the determination result is output to the vehicle control device 30 that controls the driving state of the vehicle by controlling the ABS brake or the like.
Further, when the road surface state estimating means 17 determines that the road surface is in a deep WET state, the wheel speed is compared with a preset warning speed to determine whether or not the occurrence of a hydroplaning phenomenon is predicted. The determination result is output to the alarm means 18.
The determination procedure will be described later.

When the warning means 18 is installed in the vicinity of the driver's seat and a signal that the occurrence of the hydroplaning phenomenon is predicted is input, the warning LED is turned on or blinked to cause the driver to generate the hydroplaning phenomenon. Recognize what is expected.
Note that an alarm buzzer may be driven to cause the driver to recognize that the occurrence of a hydroplaning phenomenon is predicted by an alarm sound, or an alarm buzzer and an LED may be used in combination.
Also, a warning LED or warning LED is provided, and when a signal indicating that the road surface is in a shallow WET state is input, the attention LED is turned on to prompt attention and a signal that the road surface is in a deep WET state May be emitted by turning on a warning LED.

Next, a method for estimating the road surface state during traveling using the road surface state estimation device 10 according to the present embodiment will be described.
First, by each sensor (infrared temperature sensor 11, acceleration sensor 12, microphone 13, and wheel speed sensor 14) of the sensor unit 10A, the road surface temperature T, the vibration of the tire 22T, the tire generated sound, and the wheel speed V are calculated. Measure each.
The road surface temperature T data is directly output to the road surface state estimating means 17.
The output of the acceleration sensor 12 which is vibration data of the tire 22T is transmitted from the transmitter 12z, received by the receiver 10z of the arithmetic processing unit 10B, and input to the vibration level calculation means 15.
The output of the microphone 13 that is sound data in the vicinity of the tire contact surface including the tire generated sound is output to the sound pressure level calculation means 16.
The wheel speed V data is output to the vibration level calculation means 15 and the road surface condition estimation means 17.

Then, from the output of the acceleration sensor 12 input to the vibration level calculation means 15 and the wheel speed V data, a time series waveform of the vibration level in the pre-depression region is extracted and subjected to frequency analysis to obtain a frequency spectrum. from the frequency spectrum, after calculating the magnitude G _H vibration components of the size G _L and 3~5kHz band vibration component 1~2kHz band, vibration components in the low frequency band magnitude G _L and the high frequency band The vibration level ratio R = (G _H / G _L ), which is the ratio to the magnitude of the vibration component _GH , is calculated. The vibration level ratio R is output to the road surface condition estimating means 17.

On the other hand, the sound pressure signal that is the output of the microphone 13 is subjected to 1 / N octave analysis to obtain an octave distribution waveform. From this octave distribution waveform, the band power value P _{A of} the reference point A (500 Hz) and the reference point B (8000 Hz). after calculating the band power value P _B) of, which is the ratio of the band power value P _B of band power values P _a and the high frequency band of the low frequency band sound pressure level ratio Q = (P _a / P _B) Calculate. The sound pressure level ratio Q is output to the road surface condition estimating means 17.

The road surface state estimation means 17 estimates the road surface state using the road surface temperature T data, the vibration level ratio R data, the sound pressure level ratio Q data, and the wheel speed V data.
Hereinafter, a road surface state estimation method in the road surface state estimation means 17 will be described with reference to a flowchart of FIG. 8 (hereinafter referred to as flow 1).
First, it is determined whether or not the water on the road surface is frozen. Specifically, the road surface temperature T data is compared with a preset reference temperature T0 (step S11). In this example, the reference temperature T0 is T0 = −3 ° C.
When the measured road surface temperature T is equal to or lower than the reference temperature T0, it is determined that the road surface is frozen, that is, not in the WET state.
If the measured road surface temperature T exceeds the reference temperature T0, the process proceeds to step S12.

In step S12, it is determined from the tire vibration whether the road surface is in a deep WET state. Specifically, the ratio between the size G _H is the vibration level ratio of the vibration component in 3~5kHz band to the size G _L of the vibration component of 1~2kHz bands calculated from the frequency spectrum of the depression front region R = ( _GH / _GL ) is compared with a preset vibration threshold value Kp, and the ratio of the vibration level ratio R = ( _GH / _GL ) to the preset vibration threshold value Kp exceeds 1. It is determined that the state is a deep WET state.
In this example, as shown in the graph of FIG. 4, since the magnitude _GL of the vibration component in the low frequency band and the magnitude _GH of the vibration component in the high frequency band are both set in the dBG unit, the vibration level ratio R is used instead. The vibration level difference R ′ (dBG) = G _H (dBG) −G _L (dBG) is compared with the vibration threshold value Kp, and the difference between the vibration level difference R ′ and the dBG value of the vibration threshold value Kp exceeds zero. It is determined that the state is a deep WET state. In this example, the Kp dBG value was set to -20 (dBG).

  The upper graphs of FIGS. 9 to 11 show the difference between the tire vibration level difference R ′ and the dBG value of the vibration threshold value Kp during dry asphalt road running, semi-wet road running, and water depth 2 mm road running. As can be seen by comparing each graph, the difference between the vibration level difference R ′ and the Kp dBG value is shown in the frequency spectrum on the road surface with a water depth of 2 mm in the deep WET state. It is far above 0dBG. In contrast, when the road surface state is a shallow WET state (semi-wet) and when the road surface state is a dry road, the difference between the vibration level difference R 'and the Kp dBG value does not reach 0 dBG. Therefore, it is possible to determine whether or not the road surface state is a deep WET state from the difference between the vibration level difference R ′ and the dBG value of the vibration threshold value Kp set in advance.

When the road surface state is a deep WET state, a hydroplaning phenomenon may occur. Therefore, if it is determined that the road surface state is a deep WET state, the process proceeds to step S13 to determine whether or not the occurrence of a hydroplaning phenomenon is predicted.
That is, when the vehicle travels on a road surface in a deep WET state where the water depth is 2 mm or more, the drainage capacity of the tire is reduced, and water enters between the tire 22T and the back surface, so that the steering wheel and the brake are not effective. The so-called hydroplaning phenomenon (also called aquaplaning phenomenon) may occur. It is known that this hydroplaning phenomenon is more likely to occur as the wheel speed V increases.
Therefore, in step S13, the measured wheel speed V is compared with a warning speed V0 which is a preset wheel speed. In this example, the warning speed V0 is V0 = 40 km / h.
When the measured wheel speed V exceeds the warning speed V0, it is determined that the occurrence of the hydroplaning phenomenon is predicted, and a signal that the occurrence of the hydroplaning phenomenon is predicted is output to the alarm means 18, Make the driver recognize that the hydroplaning phenomenon is expected by turning on or blinking the alarm LED.
On the other hand, when the measured wheel speed V is equal to or lower than the warning speed V0, it is determined that the hydroplaning phenomenon is not predicted although the road surface state is in the deep WET state. However, since there is a possibility that the driver may increase the speed, it is preferable to output a signal that the road surface is in a wet state to the alarm means and turn on the warning LED to issue a warning.

When it is determined that the road surface state is not in the deep WET state, the process proceeds to step S14 to determine whether or not the road surface state is in the shallow WET state. Specifically, the sound pressure level ratio Q, which is a calculated value of the detected sound pressure level, is compared with a preset sound pressure threshold value Kq. In this example, the sound pressure threshold value is Kq = 1, that is, in this example, the sound pressure level ratio Q itself is used to determine whether or not the road surface state is a shallow WET state.
When the road surface is a dry asphalt road surface, as shown in FIG. 5, the band power value P _A at the reference point A (500 Hz) is higher than the band power value P _{B at the} reference point B (8000 Hz). The ratio Q is 1 or more. On the other hand, when traveling on a semi-wet road surface, the sound pressure level ratio Q is less than 1 as shown in FIG. Therefore, it can be determined from the sound pressure level ratio Q whether or not the road surface is a shallow WET road surface.
If the road surface is a deep WET road surface, the spectrum is filled with white noise as shown in FIG.

The middle graphs in FIGS. 9 to 11 show the sound pressure level difference measured in dB for the sound pressure level ratio Q of the tire-generated sound during dry asphalt road running, semi-wet road running, and water depth 2 mm road running. This shows the time variation of Q ′, and as can be seen by comparing the respective graphs, when the road surface state is a dry road, the sound pressure level difference Q ′ exceeds 0 dBG. On the other hand, when the road surface state is a shallow WET state (semi-wet), the sound pressure level difference Q ′ does not reach 0 dBG. Therefore, it can be determined whether or not the road surface state is a shallow WET state from the sound pressure level difference Q ′. Note that when the road surface is a deep WET road surface, the spectrum is filled with white noise, so the sound pressure level difference Q ′ also varies around 0 dBG, and correct determination cannot be made.
Even if the road surface is in a shallow WET state, if it continues to rain, the road surface state may shift to a deep WET state. Therefore, a signal indicating that the road surface is in a shallow WET state is output to the alarm means, It is preferable to call attention by turning on the LED.

As described above, in the present embodiment, the infrared temperature sensor 11, the acceleration sensor 12, the microphone 13, and the wheel speed sensor 14 respectively change the road surface temperature T during running, the vibration of the tire 22T, the tire generated sound, and the wheel speed V. While measuring, from the output of the acceleration sensor 12 which is the vibration data of the tire 22T and the data of the wheel speed V, the magnitudes G _L and 3 of the vibration component in the 1-2 kHz band of the frequency spectrum of the vibration in the pre-depression region The vibration level ratio R = (G _H / G _L ), which is the ratio with the magnitude of vibration component _GH in the 5 kHz band, is calculated, and the sound pressure signal that is the output of the microphone 13 is analyzed by an octave of N. it is the ratio of the band power value P _B of band power values P _a and reference point B of the reference point a from octave distribution waveform (500Hz) (8000Hz) sound pressure level The ratio Q = calculates the (P _A / P _B), using the data of the surface temperature T, the data of the vibration level ratio R, and the data of the sound pressure level ratio Q, and the data of the wheel speed V, road conditions Therefore, the road surface condition can be estimated with high accuracy.

Specifically, the road surface temperature T data is compared with a preset reference temperature T0 to determine whether or not the road surface is in the WET state. It is determined whether or not it is in a state. When the road surface state is a deep WET state, the measured wheel speed V is compared with a warning speed V0 that is a preset wheel speed to determine whether or not the occurrence of the hydroplaning phenomenon is predicted. Determine whether. When the occurrence of the hydroplaning phenomenon is predicted, the driver is made aware that the occurrence of the hydroplaning phenomenon is predicted by turning on or blinking an alarm LED.
When it is determined that the road surface state is not in the deep WET state, it is determined from the tire generated sound whether the road surface is a shallow WET road surface.
Therefore, if the road surface state information estimated by the road surface state estimation device 10 of the present invention is output to the vehicle control means 30 to control the running state of the vehicle, more advanced control of the ABS brake or the like is possible. Thus, the safety of the vehicle can be further improved.

In the above embodiment, the vibration component in the low frequency band and the vibration component in the high frequency band are calculated from the frequency spectrum in the region before stepping obtained by FFT analysis of the time series waveform of the vibration level in the region before stepping. The time series waveform is passed through a band pass fill for extracting a low frequency band vibration component and a high frequency band vibration component respectively, and the power value of the low frequency vibration level and the high frequency vibration level in the region before kicking out Each power value may be calculated.
In the above example, the road surface state is estimated from the magnitude G _L of the vibration component in the 1-2 kHz band calculated from the frequency spectrum of the region before the stepping and the magnitude G _H of the vibration component in the 3-5 kHz band. The frequency band used for estimating the road surface condition is not limited to this. Further, three or more frequency bands used for estimating the road surface condition may be used. Also in this case, the relationship between the magnitude of the vibration component in three or more frequency bands and the road surface condition is obtained in advance, and the road surface condition can be estimated from the ratio of the magnitude of the vibration component in these frequency bands. Good.

It is also possible to compare the magnitude G _H with a preset threshold KH vibration components in 3~5kHz band calculated from the frequency spectrum of the depression front region. In this case, since the vibration level changes with the wheel speed V, the threshold value KH is preferably variable according to the wheel speed V.
In the above example, the road surface condition is estimated by extracting the time series waveform of the area before depression from the time series waveform of the tire vibration, but the signal in the time range corresponding to the depression area after the depression position is extracted. The road surface condition may be estimated. Alternatively, the road surface state may be estimated by extracting signals in a time range corresponding to the area before the stepping on and the contact surface area.

Further, in the above example, the sound pressure signal of the tire-generated sound is analyzed by an octave of N to obtain an octave distribution waveform, and the band power value P _A and the reference point B (the reference point A (500 Hz) of the octave distribution waveform are obtained. The sound pressure level ratio Q is calculated from the band power value P _B of 8000 Hz), but the sound pressure signal is FFT-analyzed and the sound pressure level at the reference point A (500 Hz) and the sound at the reference point B (8000 Hz) are calculated. The sound pressure level ratio Q may be calculated by calculating the pressure level.
In the above example, the road surface state is estimated from the band power values of two reference points, but the road surface state may be estimated from a ratio of three or more band power values.

Note that the road surface condition can be estimated by comparing the band power value P _{A of} the reference point A (500 Hz) or the band power value P _B of the reference point B (8000 Hz) with a preset threshold value Kr. Since the sound other than the tire generated sound is also input to the microphone 13, the estimation accuracy is improved by estimating from the ratio of the band power values of the two reference points as in this example.
If the reference point is set to one, the sound pressure level changes depending on the wheel speed V. Therefore, the threshold value Kr is preferably variable according to the wheel speed V.
Further, N in obtaining the octave distribution waveform is not limited to N = 3, but N = 3 is preferable from the relationship between the uniformity of the sound pressure distribution and the resolution.

In the above example, after determining whether or not the road surface is in a deep WET state from tire vibration, it is determined from the sound pressure signal whether or not the road surface state is in a shallow WET state. As shown in the flow 2), after determining whether or not the road surface state is a shallow WET state from the sound pressure signal, it is possible to determine whether or not the road surface is a deep WET state from the tire vibration. Good.
In this flow 2, first, the road surface temperature data is compared with a preset reference temperature T0 to determine whether or not the road surface is in the WET state (step S21).
If it is determined that the road surface is in the WET state, the process proceeds to step S22, where the sound pressure level ratio Q is compared with a preset sound pressure threshold value Kq to determine whether or not the road surface state is in a shallow WET state. Determine.
When it is determined that the road surface state is not in the shallow WET state, the process proceeds to step S23, and it is determined from the tire vibration whether the road surface is in the deep WET state. Then, when the road surface state is a deep WET state, there is a possibility that a hydroplaning phenomenon may occur. Therefore, the process proceeds to step S24, where the measured wheel speed V and the reference wheel speed are set in advance. The warning speed V0 is compared, and if the measured wheel speed V exceeds the warning speed V0, it is determined that the occurrence of the hydroplaning phenomenon is predicted, and a signal that the occurrence of the hydroplaning phenomenon is predicted is generated. The alarm is output to the alarm means 18 so that the driver recognizes that the occurrence of the hydroplaning phenomenon is predicted by turning on or blinking the alarm LED.
On the other hand, when the measured wheel speed V is equal to or lower than the warning speed V0, it is determined that the road surface state is in the deep WET state.
If it is determined in step S23 that the road surface is not in a deep WET state, the road surface is neither frozen nor in a shallow WET state or a deep WET state (here, a dry road surface). Therefore, it is determined that the state is other than WET.
Note that the details of the determination method are the same as in the case of the flow 1 and are omitted.
[Example]

An infrared temperature sensor is attached to the bumper of a test vehicle, a four-wheel drive vehicle, an acceleration sensor is attached to the left front wheel inner liner, and a microfilm is attached to the lower part of the vehicle in front of the left rear wheel. FIGS. 9 to 11 show results obtained by running the WET road surface and the deep WET road surface and estimating the road surface state using the flow 1 in FIG. 8. The tire size is 265 / 65R17, and the running speed is 60 km / h.
The dry road surface and the shallow WET road surface (semi-wet road) were ordinary roads in Hokkaido, and the deep WET road surface was a test course with a water depth of 2 mm.
In each figure, the number of measurement points when driving on a shallow WET road surface is 216 points, the number of measurement points when driving on a deep WET road surface is 157 points, the number of measurement points when driving on a road surface other than WET (dry road surface) Is 251 points. Further, the determination rate (%) indicating the probability that the estimation result estimated at these measurement points is correct is summarized in the table of FIG. For reference, an estimation result estimated using the flow 2 of FIG. 12 is shown in the table of FIG.

When running on the dry road surface shown in FIG. 9, the difference between the vibration level difference R ′ obtained from tire vibration and the dBG value of the vibration threshold Kp is all 0 dBG or less, and the sound pressure level difference Q ′ is all 0 dBG or more. . That is, as shown in FIG. 13A, all determination results are other than WET. As a result, it was confirmed that the road surface other than the WET can be reliably determined to be other than the WET. The result estimated using flow 2 is the same.
When the semi-wet road surface shown in FIG. 10 is used, the difference between the vibration level difference R ′ obtained from tire vibration and the dBG value of the vibration threshold Kp is almost 0 dBG or less, and the sound pressure level difference Q ′ is all 0 dBG or less. It is. Looking at this in the table of FIG. 13 (a), it was confirmed that the determination rate for correctly determining shallow WET was 97.7%, and it was possible to accurately estimate that the road surface was shallow. In addition, when it estimated using the flow 2, the determination rate was 100%.

When traveling on a road surface with a water depth of 2 mm (deep WET road surface) shown in FIG. 11, the difference between the vibration level difference R ′ obtained from the tire vibration and the dBG value of the vibration threshold Kp exceeds 0 dBG. On the other hand, the sound pressure level difference Q ′ is slightly less than 0 dBG. In the flow 1, since it is determined whether or not the deep WET state is first, in the table of FIG. 13A, the determination rate for correctly determining the deep WET is 100%. Therefore, it was confirmed that it is possible to accurately estimate that the road surface is in a deep WET state.
On the other hand, in the flow 2 for determining whether the state is the shallow WET state first, as shown in the table of FIG. 13B, the determination rate for the deep WET state is 88.5%, which is an estimation accuracy compared to the flow 1. Since it was low, it is understood that it is preferable to first determine whether or not the state is a deep WET state.

For comparison, the results for the case where the determination is made only by sound and the case where the determination is made only by tire vibration are shown in the tables of FIGS.
As shown in the table of FIG. 14 (a), the determination of only the sound can surely determine the shallow WET state, but the determination rate for the deep WET state is as low as 88.5%.
On the other hand, as shown in the table of FIG. 14B, the determination of only the tire vibration can surely determine the deep WET state, but the determination rate is 97.7% for the shallow WET state. Therefore, it was confirmed that the estimation accuracy was improved by estimating the road surface condition using both the sound and the tire vibration rather than the determination of only the sound or the determination of only the tire vibration.

  As mentioned above, although this invention was demonstrated using embodiment, the technical scope of this invention is not limited to the range as described in the said embodiment. It will be apparent to those skilled in the art that various modifications or improvements can be added to the embodiment. It is apparent from the claims that the embodiments added with such changes or improvements can also be included in the technical scope of the present invention.

  As described above, according to the present invention, the road surface state during traveling can be accurately estimated. Therefore, if the vehicle traveling state is controlled based on the estimated road surface state, the vehicle The driving safety of the vehicle can be improved.

10 road surface state estimation device, 10A sensor unit, 10B arithmetic processing unit,
10z receiver, 11 infrared temperature sensor, 12 acceleration sensor, 12z transmitter, 13 microphone, 14 wheel speed sensor, 15 vibration level calculation means,
151 Vibration waveform detector, 152 Vibration level distribution calculator,
153 time domain signal extraction unit, 154 frequency analysis unit, 155 vibration level calculation unit,
156 vibration level ratio calculation unit, 16 sound pressure level calculation means, 161 frequency analysis unit,
162 sound pressure level calculation unit, 163 sound pressure level ratio calculation unit, 17 road surface state estimation means,
18 alarm means, 20 vehicle body, 21 bumper, 22F front wheel, 22R rear wheel,
22T tire, 23 inner liner, 24 tire chamber, 25 tread,
26 frames, 30 vehicle control device.

Claims (10)

  The road surface is predicted to generate a hydroplaning phenomenon based on the road surface temperature during traveling, the magnitude of the vibration component of the 2 kHz to 10 kHz band of the tire vibration, and the magnitude of the sound pressure component of the 10 Hz to 10 kHz band of the tire generated sound. A road surface state estimating method characterized by estimating whether or not the vehicle is in a road. When the road surface temperature exceeds a preset reference temperature and the magnitude of the vibration component in the 2 kHz to 10 kHz band of the tire vibration exceeds a preset vibration threshold, the road surface is hydroplaning. It is determined that the vehicle is in the first road surface state that is a WET state in which the occurrence of
When the road surface temperature exceeds a preset reference temperature and the magnitude of the sound pressure component in the 10 Hz to 10 kHz band of the tire generated sound exceeds a preset sound pressure threshold, the road surface is The road surface state estimation method according to claim 1, wherein the road surface state is determined to be in a second road surface state that is a WET state in which occurrence of a hydroplaning phenomenon is not predicted.   The first road surface state is a state in which a water film having a thickness exceeding 2 mm is formed on the road surface, and the second road surface state is a state in which a water film having a thickness of 2 mm or less is formed on the road surface. The road surface state estimating method according to claim 2, wherein:   When the wheel speed is measured and it is determined that the road surface is in the first road surface state, the wheel speed is compared with a preset warning speed, and the wheel speed exceeds the warning speed. 4. The road surface state estimating method according to claim 2, wherein it is determined that occurrence of a hydroplaning phenomenon is predicted.   The road surface state according to any one of claims 2 to 4, wherein it is determined whether or not the road surface is in a second road surface state after determining whether or not the road surface is in a first road surface state. Estimation method. The magnitude of the vibration component in the 2 kHz to 10 kHz band of the tire vibration is
Extract a signal in the time range corresponding to the pre-depression region before the depressing position of the vibration waveform of the tire vibration during driving, and calculate using the frequency spectrum obtained by frequency analysis of this extracted time range signal The road surface state estimation method according to any one of claims 1 to 5, wherein the road surface condition is estimated. The magnitude of the sound pressure component in the 10 Hz to 10 kHz band of the tire generated sound is as follows:
6. The road surface state estimating method according to claim 1, wherein the road surface condition is calculated using a frequency spectrum obtained by performing an octave analysis of the sound pressure signal.   A vehicle control that controls the running state of the vehicle based on the road surface state estimated by the road surface state estimating method according to any one of claims 1 to 7 when controlling the running state of the vehicle. Method. Means for measuring the road surface temperature while traveling;
Means for detecting tire-generated sound;
Means for detecting tire vibrations;
Means for detecting wheel speed;
Means for calculating the magnitude of the vibration component of the 2 kHz to 10 kHz band of the tire vibration;
Means for calculating the magnitude of the sound pressure component of the 10 Hz to 10 kHz band of the tire generated sound;
Means for estimating whether the road surface is in a state where occurrence of a hydroplaning phenomenon is predicted from the measured road surface temperature, the magnitude of the vibration component, and the magnitude of the sound pressure component;
When the road surface is in a state where the occurrence of a hydroplaning phenomenon is predicted, the wheel speed is compared with a preset warning speed to estimate whether the occurrence of the hydroplaning phenomenon is predicted. And a road surface state estimating device.   The road surface state estimating device according to claim 9, further comprising an alarm device that issues an alarm to a driver when it is estimated that the occurrence of a hydroplaning phenomenon is predicted.

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