JP3440791B2 - Road surface condition determination device - Google Patents

Description

Detailed Description of the Invention

[0001]

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to a road surface condition judging device, and more particularly to a road surface condition judging device for judging a road surface condition such as a friction coefficient μ of a road surface on which a vehicle is traveling.

[0002]

2. Description of the Related Art Conventionally, a road surface state determination device for determining the state of a road surface on which a vehicle is traveling based on road noise has been developed. For example, in Japanese Patent Laid-Open No. 7-156782, road noise generated from wheels is detected,
It is described that the road surface condition is determined by using a neural network from the pattern of each frequency component of the detected road noise.

[0003]

The conventional device uses a microphone to detect road noise, and is easily affected by external noise such as noise depending on the surrounding environment.
Therefore, the installation position of the microphone is limited,
It is difficult to determine the installation position. Further, since it is necessary to add a microphone as a new sensor, there is a problem that the number of constituent parts increases.

The present invention has been made in view of the above points, and by determining the road surface state from the result of the frequency analysis of the wheel speed signal, it is less susceptible to the influence of disturbance such as noise, and an accurate determination is made. Therefore, it is an object of the present invention to provide a road surface state determination device that can reduce the cost by using the existing wheel speed sensor.

[0005]

According to a first aspect of the present invention, there is provided a wheel speed detecting means for detecting a wheel speed, an analyzing means for performing a frequency analysis of the detected wheel speed signal, and a result of the analysis. And a determination unit that determines the road surface state based on the signal strength in a predetermined frequency region . Here, paying attention to the fact that the frequency analysis result of the wheel speed signal differs depending on the road surface condition, depending on the road surface condition of the frequency analysis result of the wheel speed signal.
Since the determination is performed in the frequency band in which a noticeable difference appears, the determination is performed based on the direct movement of the wheels, so that the determination is not likely to be affected by a disturbance such as noise and an accurate determination can be performed.

According to a second aspect of the present invention, in the road surface state determination device according to the first aspect, the determination means determines the road surface state based on a signal strength equal to or lower than a predetermined frequency. Therefore, the determination can be performed in the frequency band in which a significant difference appears depending on the road surface condition, the accurate determination can be performed, and the accuracy is improved. According to a third aspect of the present invention, in the road surface state determination device according to the first aspect, the determination means is
It determines the road surface condition based on the signal strength of the narrowband have frequency bands and centered a plurality of different frequencies from each other at a predetermined frequency or less.

Therefore, a large memory capacity is not required
The determination can be made simply and at low cost. Claim 4
The invention according to claim 1 is the road surface condition determining device according to claim 1 , wherein the determining means is an ununique determined by a wheel speed.
The road surface condition is determined based on the signal intensity of the fundamental wave and the harmonic wave of the formiti disturbance .

In a tire, a disturbance appears at a predetermined frequency determined by the wheel speed, the signal strength changes, and the degree of this change varies depending on the road surface condition. Therefore, it is possible to use this to determine the road surface condition. Invention according to claim 5, in road surface condition judging apparatus according to claim 1, wherein said determining means of the wheels by the front-rear direction of the spring vibration of the tire the solid
The road surface condition is determined based on the signal strength of a frequency substantially equal to the vibration frequency .

At a frequency substantially equal to the natural vibration frequency of the wheels, the signal strength changes depending on the road surface condition, and it is possible to determine the road surface condition by utilizing this. Claim 6
In the road surface condition determination device according to claim 5, the determination means normalizes a signal intensity of a frequency substantially equal to the natural vibration frequency of the wheel based on a signal intensity of a frequency only affected by a road surface disturbance. Then, the road surface condition is determined based on the normalized signal strength.

Since the signal strength changes due to the road surface disturbance due to the unevenness of the road surface, the signal strength is normalized based on the signal strength of the frequency that is influenced only by the road surface disturbance, so that the influence of the road surface disturbance can be eliminated and the road surface can be accurately measured. The state can be determined. According to a seventh aspect of the present invention, in the road surface state determination device according to the fifth aspect, navigation information of road surface vibration is provided.
The determination means has a storage means for storing it according to the traveling position obtained by the system, and the determination means has a signal strength of a frequency substantially equal to the vibration information according to the traveling position stored in the storage means and the natural vibration frequency of the wheel. The road surface condition is determined based on.

The vibration information of the road surface due to the unevenness of the road surface is stored in advance, and the road surface state is judged based on the vibration information according to the traveling position and the signal strength of the frequency substantially equal to the natural vibration frequency of the wheels, so that the influence of the road surface disturbance is determined. Can be eliminated and the road surface condition can be accurately determined. According to an eighth aspect of the present invention, in the road surface state determination device according to the fifth aspect, the natural vibration frequency used for the determination by the determination means based on the wheel speed is approximately
It has a judgment standard changing means for changing the relationship between the signal strength of the same frequency and the road surface condition .

Since the signal strength of the frequency substantially equal to the natural vibration frequency of the wheel changes depending on the wheel speed, the road surface condition can be accurately judged by changing the judgment standard based on the wheel speed. According to a ninth aspect of the present invention, in the road surface state determination device according to the fifth aspect, a spring in the front-rear direction of the tire used for the determination by the determination means based on the tire pressure.
It has frequency changing means for changing the frequency substantially equal to the natural vibration frequency of the wheel due to vibration .

Since the natural vibration frequency of the wheel changes depending on the tire air pressure, the road surface condition can be accurately determined by changing the frequency used for the determination based on the tire air pressure.

[0014]

FIG. 1 is a block diagram of a first embodiment of a road surface condition judging device of the present invention. In the figure, the wheel speed signal detected by the wheel speed sensor 10 is supplied to an ECU (electronic control unit) 12. Here, the wheel speed sensor 10 detects the wheel speed signal of one of the four wheels of the vehicle.
The wheel speed signals of the wheels are sequentially switched at predetermined time intervals and the ECU
It may be configured to be supplied to 12. F for the ECU 12
An FT (Fast Fourier Transform) 14 is connected.

FIG. 2 shows a flowchart of the first embodiment of the road surface condition determination processing executed by the ECU 12. This process is repeated every predetermined time. In the figure, first, step S
At 12, the wheel speed signal is read. Next, in step S14, it is determined whether or not the value of the counter n is less than the predetermined value Tn. n
<Tn, the wheel speed measured in step S16 is stored in the memory (built-in RAM), and the counter n is set to 1
Only increments and proceeds to step S12. When the value of the counter n is greater than or equal to Tn, the process proceeds to step S18, and the wheel speed data measured during the period corresponding to the predetermined value Tn is F.
It is supplied to the FT 14 to execute the Fourier transform, the gain at each frequency is calculated, and the frequency spectrum of the wheel speed is obtained.

In the meantime, when the frequency spectrum of the wheel speed signal is taken, on a wet road where the road surface is wet, the gain at a predetermined frequency (for example, 30 Hz) or less is large as shown by the broken line I in FIG. Thus, on a dry road where the road surface is dry, the gain below a predetermined frequency is generally reduced as shown by the solid line II in FIG. Therefore, in step S20, the total sum (integral value) SG1 of the gains at each of the discrete frequencies below the predetermined frequency (for example, 30 Hz) is obtained. Then, in step S22, the sum SG1 of the gains is compared with the threshold value TG1. This threshold T
For G1, the average value of the sum of the gains of the points on the broken line I and the sum of the gains of the points on the solid line II at a frequency of 30 Hz or less in FIG. 3 is used. Thereby, step S2
If SG1> TG1 in the comparison of 2, the process proceeds to step S24, it is determined that the road is wet, and if SG1 ≦ TG1,
In step S26, it is determined that the road is a dry road. After this,
In step S28, the counter n is reset to 0 and the processing cycle ends.

In this way, it is possible to determine the road surface state whether the road surface on which the vehicle is traveling is a wet road or a dry road, from the total sum of the gains of the wheel speed frequency spectrum below a predetermined frequency. The ECU 12 notifies the determination result of the road surface state to, for example, the antilock brake system, and the antilock brake system performs the brake control according to the road surface state. Since the anti-lock brake system requires the wheel speed sensor, the device of the present invention may use the wheel speed sensor signal of the anti-lock brake system, and does not need a new sensor such as a microphone.

FIG. 4 shows a block diagram of a second embodiment of the road surface condition judging device of the present invention. In the figure, the wheel speed signal detected by the wheel speed sensor 10 is supplied to the bandpass filter group 16. Here, the wheel speed signal of one of the four wheels of the vehicle is detected by the wheel speed sensor 10, but the wheel speed signals of the four wheels are sequentially switched at predetermined time intervals and supplied to the bandpass filter group 16. It may be configured.

The bandpass filter group 16 is composed of a plurality of bandpass filters. Each bandpass filter has a predetermined frequency (eg, 30 Hz) or less and different frequencies (eg, 5 Hz, 10 Hz, 15 Hz, 20 H).
z, 25 Hz) narrow frequency band (eg ±
It has a pass frequency band characteristic of passing a signal of several Hz). A signal that has passed through each of the plurality of bandpass filters of the bandpass filter group 16 is supplied to the ECU (electronic control unit) 12.

FIG. 5 shows a flow chart of a second embodiment of the road surface condition determination processing executed by the ECU 12. This process is repeated every predetermined time. In the figure, first, step S
At 30, the wheel speed signals passed through each of the plurality of bandpass filters of the bandpass filter group 16 are sequentially read. Next, the absolute value of the amplitude of the wheel speed signal read in step S32, that is, the gain is obtained. And step S
At 34, the sum SG2 of the gains of the respective bandpass filter outputs
Ask for. Then, in step S36, the sum SG2 of the gains is compared with the threshold value TG2.

This threshold value TG2 is defined by the pass center frequencies of the band pass filters of 5 Hz, 10 Hz, 15 Hz and 20 H.
Broken line of the frequency spectrum of FIG. 3 at z, 25 Hz
The average value of the sum of the gains of the points on I and the sum of the gains of the points on solid line II is used. As a result, if SG2> TG2 in the comparison in step S36, step S
The routine proceeds to step 38, where it is determined to be a wet road, and if SG2 ≦ TG2, the routine proceeds to step S40 where it is determined to be a dry road, and the processing cycle ends.

In this embodiment, the bandpass filter group 16
By using, the frequency spectrum of the wheel speed is simply obtained, there is no need to use an FFT with a large memory capacity, the cost is low, and the road surface on which the vehicle is traveling is a wet road or a dry road. The state can be determined. E
The CU 12 notifies the determination result of the road surface condition to, for example, the antilock brake system, and the antilock brake system performs the brake control according to the road surface condition. Since the anti-lock brake system requires the wheel speed sensor, the device of the present invention may use the wheel speed sensor signal of the anti-lock brake system, and does not need a new sensor such as a microphone.

FIG. 6 shows a block diagram of a third embodiment of the road surface condition judging device of the present invention. In the figure, the wheel speed signal detected by the wheel speed sensor 10 is a variable band pass filter group 18
Is supplied to. Here, the wheel speed signal of one of the four wheels of the vehicle is detected by the wheel speed sensor 10, but the wheel speed signals of the four wheels are sequentially switched at predetermined time intervals and supplied to the variable band pass filter group 18. It may be configured.

Here, the contact surface of the tire of the wheel is deformed to be flat and deformed, and the deflection of the tire causes ununiformity disturbance in the wheel speed signal. Tires have small slip loss on roads with a large road friction coefficient μ,
The peak of the fundamental wave of the ununiformity disturbance appears at a frequency equal to the rotation frequency of the tire, and its harmonics also appear. On the solid line II (dry path) in FIG. 3, the peak of the fundamental wave of the ununiformity disturbance appears at a frequency of 8 Hz, and its second, third, and fourth harmonics also appear. However, since the slip loss is large on a road surface having a small road friction coefficient μ, the peak of the fundamental wave of the ununiformity disturbance becomes broad and the gain becomes small. The harmonics also become broad and the gain becomes smaller. In the broken line I (wet path) in Fig. 3, the peak of the fundamental wave of the ununiformity disturbance with a frequency of 8 Hz becomes broad, and its secondary, tertiary, and
The second harmonic is also broad.

The variable bandpass filter group 18 is composed of a plurality of variable bandpass filters. Each of the plurality of variable bandpass filters band-separates the peak of the fundamental wave of the ununiformity disturbance having a frequency equal to the rotation frequency of the tire and its second, third, and fourth harmonics. The variable bandpass filter group 18 is E
The pass frequency band characteristic of each band pass filter is changed according to the control signal supplied from the CU 12, and the fundamental wave of the ununiformity disturbance and its second, third, and fourth harmonics are extracted to limit the band. It is supplied to the ECU 12 together with the wheel speed signal that is not being obtained.

FIG. 7 shows a flowchart of the third embodiment of the road surface condition determination processing executed by the ECU 12. This process is repeated every predetermined time. In the figure, first, step S
At 42, a wheel speed signal that is not band-limited to the four wheels is read to obtain a vehicle speed, and a control signal based on this vehicle speed is generated and supplied to the variable bandpass filter group 18. Fig. 8 shows the relationship between the vehicle speed and the fundamental wave of the ununiformity disturbance.
Shown in. As a result, the variable bandpass filter group 18
Changes the pass frequency band characteristic of each band pass filter to obtain the fundamental wave of ununiformity disturbance and its second order,
Each of the 3rd and 4th harmonics is extracted.

In step S44, the wheel speed signals that have passed through the variable bandpass filters from the variable bandpass filter group 18 are sequentially read. Next, the absolute value of the amplitude of the wheel speed signal read in step S46, that is, the gain is obtained. Then, in step S48, the sum SG3 of the gains of the outputs of the variable bandpass filters is obtained. Then, in step S50, the sum SG3 of the gains is compared with the threshold value TG3.

This threshold value TG3 is the broken line I of the frequency spectrum of FIG. 3 at the respective frequencies of the fundamental wave of the ununiformity disturbance and its second, third and fourth harmonics.
The average value of the sum of the gains of the upper points and the sum of the gains of the points on the solid line II is used. As a result, if SG3 ≦ TG3 in the comparison in step S50, step S5
The routine proceeds to step 2, where it is determined to be a wet road, and if SG3> TG3, the routine proceeds to step S52, where it is determined to be a dry road, and the processing cycle is ended.

In this embodiment, the variable bandpass filter group 16 is used to obtain the fundamental wave of the ununiformity disturbance and its second, third and fourth harmonics, and the road surface on which the vehicle is traveling is wet. Since the road surface state of a road or a dry road is determined, it is not necessary to use an FFT having a large memory capacity, and the cost is low. By the way, as shown in the tire model of FIG. 9, it is considered that the wheel 20 and the belt 22 are connected by a spring 24 in the rotational direction. When the tire is rotating, the belt 22
When the vehicle is grounded at 6, the rotational speed ω _{t of the} belt 22 is delayed with respect to the rotational speed ω _{w of the} wheel 20, and the spring 24 is pulled. When the belt 22 moves away from the road surface 26, the spring 24 contracts. Natural vibration is generated by spring vibration, and this natural vibration is included in the wheel speed signal. The frequency of this natural vibration is around 40 Hz.

Further, since the tire has a small slip loss on a road surface having a large road surface friction coefficient μ such as dry asphalt, the peak gain of the above-mentioned natural vibration is the solid line III in FIG.
As shown in Fig. 1, the slip loss is large on a road surface with a small road surface friction coefficient μ such as wet asphalt, so the peak gain of the natural vibration becomes small as shown by the chain line IV. Since the slip loss is very large on the road surface where the friction coefficient μ is smaller, the gain of the peak of the natural vibration becomes very small as shown by the broken line V.

Using this unique signal, the EC shown in FIG.
FIG. 11 shows a flowchart of the fourth embodiment of the road surface condition determination processing executed by U12. This process is repeated every predetermined time. In the figure, first, in step S112, the wheel speed signal is read. Next, in step S114, the counter n
It is determined whether the value of is less than the predetermined value Tn. If n <Tn, the wheel speed measured in step S116 is stored in the memory (built-in RAM), the counter n is incremented by 1, and the process proceeds to step S112. When the value of the counter n is greater than or equal to Tn, the process proceeds to step S118, and the wheel speed data measured during the period corresponding to the predetermined value Tn is FFT.
The frequency spectrum of the wheel speed is obtained by calculating the gain at each frequency by supplying it to 14 and executing the Fourier transform.

In step S120, the gain at the frequency of natural vibration (near 40 Hz) is read from the frequency spectrum of the wheel speed. Then, step S122
Then, the road surface friction coefficient μ is estimated by referring to the map with the above gain. This map is based on FIG. 10 and shows the gain and road friction coefficient μ at the frequency of natural vibration (near 40 Hz).
And are related. After this, step S128
Then, the counter n is reset to 0 and the processing cycle is ended.

In this way, the road surface friction coefficient μ of the road surface on which the vehicle is traveling can be estimated from the gain at the frequency of natural vibration (near 40 Hz) of the frequency spectrum of the wheel speed. FIG. 12 is a flowchart of a fifth embodiment of the road surface condition determination processing executed by the ECU 12 shown in FIG.
Shown in. This process is repeated every predetermined time. In the figure, first, in step S130, the bandpass filter group 1
The wheel speed signal supplied from the band-pass filter that passes the frequency of natural vibration (near 40 Hz) out of 6 is read. Next, the absolute value of the amplitude of the wheel speed signal read in step S132, that is, the gain is obtained. Then, in step S134, the road surface friction coefficient μ is estimated by referring to the map with the gain. This map relates the gain and the road surface friction coefficient μ at the frequency of natural vibration (near 40 Hz) based on FIG. After that, the processing cycle is ended.

In this embodiment, the bandpass filter group 16
By using, the natural vibration component of the wheel speed is simply extracted, and the road surface friction coefficient μ of the road surface on which the vehicle is traveling can be estimated at low cost. by the way,
Since the road surface disturbance is small on a smooth road surface such as dry asphalt, the overall gain of the frequency spectrum of the wheel speed signal is small as shown by the solid line in FIG. 13, and the road surface disturbance is large on an uneven road surface such as a snow-covered road. This road surface disturbance becomes an offset, and the frequency spectrum of the wheel speed signal has a large overall gain as shown by the broken line in FIG. In this case, if the frequency gain of the natural vibration is used as it is, the road surface friction coefficient μ will be erroneously estimated.

The following embodiment solves this problem. Sixth of the road surface condition determination processing executed by the ECU 12 shown in FIG.
A flow chart of the embodiment is shown in FIG. 14, the same steps as those in FIG. 11 are designated by the same reference numerals. This process is repeated every predetermined time. In FIG. 14, first, in step S112, the wheel speed signal is read. Next, in step S114, it is determined whether or not the value of the counter n is less than the predetermined value Tn. If n <Tn, the wheel speed measured in step S116 is stored in the memory (built-in RAM), the counter n is incremented by 1, and the step S112 is performed.
Go to. When the value of the counter n is Tn or more, step S
Proceeding to 118, the wheel speed data measured during the period corresponding to the predetermined value Tn is supplied to the FFT 14 to execute the Fourier transform, and the gain at each frequency is calculated to obtain the frequency spectrum of the wheel speed. In step S120, the gain at the frequency of natural vibration (near 40 Hz) is read from the frequency spectrum of the wheel speed.

Next, in step S125, the average value H _AVE of the gains of the specific frequency (for example, 20 Hz) where only the road surface disturbance exists is calculated. This is calculated by holding the gain of a specific frequency for a certain period of time in the past. Next, in step S126, the reference value H _REF and the average value H _AVE set in advance are set.
And the difference D (= H _AVE −H _REF ) is calculated, and step S12
In step 7, the gain of the natural vibration frequency (near 40 Hz) in the frequency spectrum obtained this time is multiplied by the difference D to normalize the gain of the natural vibration frequency.

Then, in step S122, the road friction coefficient μ is estimated by referring to the map with the normalized gain. After that, the counter n is reset to 0 in step S124, and the processing cycle ends. In this way, the gain of the natural vibration frequency is normalized, and the road surface friction coefficient μ is estimated by referring to the map with the normalized gain, so that the accurate road surface friction coefficient can be obtained without error due to the influence of road surface disturbance. It is possible to estimate μ.

FIG. 15 shows a flowchart of the seventh embodiment of the road surface condition determination processing executed by the ECU 12 shown in FIG. This process is repeated every predetermined time. This process is repeated every predetermined time. In the figure, first, step S
At 140, the wheel speed signal supplied from the bandpass filter that passes the frequency of natural vibration (near 40 Hz) and the specific frequency (for example, 20 Hz) in which only road surface disturbance exists in the bandpass filter group 16 is read. Next, the absolute value of the amplitude of the wheel speed signals of the two frequencies read in step S142, that is, the gain is obtained.

Next, in step S144, the average value H _AVE of the gains of the specific frequency (for example, 20 Hz) in which only the road surface disturbance exists is calculated. This is calculated by holding the gain of a specific frequency for a certain period of time in the past. Next, in step S146, the reference value H _REF and the average value H _AVE set in advance are set.
And the difference D (= H _AVE −H _REF ) is calculated, and step S14
In step 8, the gain of the natural vibration frequency (near 40 Hz) obtained this time is multiplied by the difference D to normalize the gain of the natural vibration frequency.

Then, in step S150, the road surface friction coefficient μ is estimated by referring to the map with the above gain. This map is based on Fig. 10 and shows the frequency of natural vibration (40Hz
This is a relation between the gain in the vicinity) and the road surface friction coefficient μ. After that, the processing cycle is ended. In this way, the gain of the natural vibration frequency is normalized, and the road surface friction coefficient μ is estimated by referring to the map with the normalized gain, so that the accurate road surface friction coefficient can be obtained without error due to the influence of road surface disturbance. It is possible to estimate μ.

It is experimentally known that the gain of the frequency of natural vibration changes depending on the vehicle speed. On a road surface such as a snow-covered road, as shown in FIG. 16A, the gain decreases in the order of the gain b at the medium speed and the gain c at the high speed with respect to the gain a of the frequency of the natural vibration at the low speed. Similarly, on a road surface such as dry asphalt, as shown in FIG.
The gain decreases in the order of the gain a at the frequency of natural vibration at low speed, the gain b at medium speed, and the gain c at high speed.
In this case, if the frequency gain of the natural vibration is used as it is, the road surface friction coefficient μ will be erroneously estimated.

The next embodiment solves this problem. Eighth of road surface condition determination processing executed by the ECU 12 shown in FIG.
A flowchart of the embodiment is shown in FIG. 17, the same steps as those in FIG. 11 are designated by the same reference numerals. This process is repeated every predetermined time. In FIG. 17, first, in step S112, the wheel speed signal is read. Next, in step S114, it is determined whether or not the value of the counter n is less than the predetermined value Tn. If n <Tn, the wheel speed measured in step S116 is stored in the memory (built-in RAM), the counter n is incremented by 1, and the step S112 is performed.
Go to. When the value of the counter n is Tn or more, step S
Proceeding to 118, the wheel speed data measured during the period corresponding to the predetermined value Tn is supplied to the FFT 14 to execute the Fourier transform, and the gain at each frequency is calculated to obtain the frequency spectrum of the wheel speed. In step S120, the gain at the frequency of natural vibration (near 40 Hz) is read from the frequency spectrum of the wheel speed.

Next, in step S128, the vehicle speed is calculated from the average of the wheel speeds of the four wheels, and a map is selected based on this wheel speed. This map relates the gain at the frequency of natural vibration (near 40 Hz) and the road surface friction coefficient μ for each vehicle speed based on FIG. Then, in step S122, the road surface friction coefficient μ is estimated with reference to the map selected by the gain. After this, step S1
At 24, the counter n is reset to 0 and the processing cycle map ends.

As described above, the map is selected based on the vehicle speed, and the road surface friction coefficient μ is estimated with reference to the map selected by the gain of the frequency of the natural vibration. The coefficient μ can be estimated. FIG. 18 shows a flowchart of the ninth embodiment of the road surface condition determination processing executed by the ECU 12 shown in FIG. Figure 1
8, the same steps as in FIG. 12 are assigned the same numbers. This process is repeated every predetermined time. This process is repeated every predetermined time. In FIG. 18, first, in step S130, the wheel speed signal supplied from the bandpass filter that passes the natural vibration frequency (near 40 Hz) in the bandpass filter group 16 is read. Next, the absolute value of the amplitude of the wheel speed signal read in step S132, that is, the gain is obtained.

Next, in step S136, the vehicle speed is calculated from the average of the wheel speeds of the four wheels, and a map is selected based on this wheel speed. This map relates the gain at the frequency of natural vibration (near 40 Hz) and the road surface friction coefficient μ for each vehicle speed based on FIG. Then, in step S134, the road surface friction coefficient μ is estimated with reference to the map selected by the gain. After that, the processing cycle is ended.

As described above, since the road surface friction coefficient μ is estimated by selecting the map based on the vehicle speed and referring to the map selected by the gain of the frequency of the natural vibration, the accurate road surface friction can be obtained without any error due to the influence of the vehicle speed. The coefficient μ can be estimated. Further, the tire air pressure changes the constant of the spring 24 of the model shown in FIG. 9 to change the frequency of natural vibration. On a road surface such as a snow-covered road, as shown in FIG. 19A, when the tire air pressure is low, the natural vibration frequency fb is low when the tire pressure is medium to the natural vibration frequency fa when the tire pressure is high. Frequency fc of natural vibration of
The frequency decreases in the order of. Similarly, on a road surface such as dry asphalt, as shown in FIG. 19B, the natural vibration frequency fb when the tire pressure is medium to the natural vibration frequency fa when the tire pressure is high, and the tire pressure are The frequencies decrease in the order of the natural vibration frequency fc when the frequency is low. That is, if the frequency of the natural vibration is fixed when the tire air pressure fluctuates, if the gain of that frequency is used as it is, the road surface friction coefficient μ will be erroneously estimated.

The next embodiment solves this problem. First of the road surface state determination processing executed by the ECU 12 shown in FIG.
FIG. 20 shows a flowchart of the 0th embodiment. However, in FIG.
The tire pressure detected by the tire pressure sensor is supplied to the ECU 12. 20, the same steps as those in FIG. 11 are designated by the same reference numerals. This process is repeated every predetermined time. In FIG. 20, first, in step S112, the wheel speed signal is read. Next, in step S114, it is determined whether or not the value of the counter n is less than the predetermined value Tn. If n <Tn, the wheel speed measured in step S116 is stored in the memory (built-in RAM), the counter n is incremented by 1, and the process proceeds to step S112. When the value of the counter n is greater than or equal to Tn, the process proceeds to step S118, and the wheel speed data measured during the period corresponding to the predetermined value Tn is F.
It is supplied to the FT 14 to execute the Fourier transform, the gain at each frequency is calculated, and the frequency spectrum of the wheel speed is obtained.

Next, in step S129, the tire air pressure is detected, and the natural vibration frequency is determined from the tire air pressure. In the next step S120, the gain at the determined natural vibration frequency is read from the wheel speed frequency spectrum. Then, in step S122, the road surface friction coefficient μ is estimated by referring to the map with the gain. This map relates the gain at the frequency of natural vibration and the road surface friction coefficient μ. After that, the counter n is reset to 0 in step S124, and the processing cycle map ends.

As described above, since the frequency of the natural vibration is determined based on the tire pressure and the road surface friction coefficient μ is estimated by referring to the map with the gain of the frequency of the determined natural vibration, the error of the tire pressure causes an error. It is possible to accurately estimate the road surface friction coefficient μ. ECU shown in FIG.
FIG. 21 shows a flowchart of the eleventh embodiment of the road surface state determination processing executed by 12. However, the tire air pressure detected by the tire air pressure sensor is supplied to the ECU 12 in FIG. 21, the same steps as those in FIG. 12 are designated by the same reference numerals. This process is repeated every predetermined time. This process is repeated every predetermined time. In FIG. 21, first,
In step S138, the tire air pressure is detected, and the natural vibration frequency is determined from the tire air pressure. In the next step S130, the wheel speed signal supplied from the bandpass filter that passes the frequency of the natural vibration determined in the bandpass filter group 16 is read. Next, step S1
The absolute value of the amplitude of the wheel speed signal read in 32, that is, the gain is obtained.

Next, in step S136, the vehicle speed is obtained from the average of the wheel speeds of the four wheels, and a map is selected based on this wheel speed. This map relates the gain at the frequency of natural vibration (near 40 Hz) and the road surface friction coefficient μ for each vehicle speed based on FIG. Then, in step S134, the road surface friction coefficient μ is estimated with reference to the map selected by the gain. After that, the processing cycle is ended.

In this way, the frequency of the natural vibration is determined based on the tire air pressure, and the road surface friction coefficient μ is estimated by referring to the map with the gain of the determined natural vibration frequency. It is possible to accurately estimate the road surface friction coefficient μ. Furthermore, the constant of the spring 24 of the model shown in FIG. 9 changes according to the environmental temperature, and the frequency of natural vibration changes. On a road surface such as a snow-covered road, as shown in FIG. 22 (A), the natural vibration frequency fb when the temperature is medium to the natural vibration frequency fa when the temperature is high, and the natural vibration when the temperature is low. The frequency increases in the order of the frequency fc. Similarly, on a road surface such as dry asphalt, as shown in FIG. 19 (B), the frequency fb of the natural vibration when the temperature is medium to the frequency fa of the natural vibration when the temperature is high, and the frequency fa when the temperature is low. The frequencies increase in the order of the natural vibration frequency fc. That is, if the frequency of the natural vibration is fixed when the temperature fluctuates, the road surface friction coefficient μ will be erroneously estimated if the gain of that frequency is used as it is.

Also in this case, in the embodiment of FIG. 20, the temperature is detected in step S129, and the frequency of the natural vibration is determined from this detected temperature. In the embodiment of FIG. 21, the temperature is detected in step S138. It is possible to deal with the problem by determining the frequency of the natural vibration from the detected temperature, and the accurate road surface friction coefficient μ can be estimated.

As described above, since the road surface disturbance is small on a smooth road surface such as dry asphalt, the frequency spectrum of the wheel speed signal has a small overall gain as shown by the solid line in FIG. Since the road surface disturbance is large on an uneven road surface, this road surface disturbance becomes an offset, and the overall gain of the frequency spectrum of the wheel speed signal becomes large as shown by the broken line in FIG. In this case, if the frequency gain of the natural vibration is used as it is, the road surface friction coefficient μ will be erroneously estimated.

Another embodiment for solving this will be described. FIG. 23 shows a block diagram of a third embodiment of the road surface condition judging device of the present invention. In the figure, the wheel speed signal detected by the wheel speed sensor 10 is supplied to an ECU (electronic control unit) 12. Here, the wheel speed sensor 10 detects the wheel speed signal of one of the four wheels of the vehicle, but the wheel speed signal of the four wheels may be sequentially switched at predetermined time intervals and supplied to the ECU 12. . An FFT (Fast Fourier Transform) 14 is connected to the ECU 12, and a navigation system 30 and a memory 32 are also connected to the ECU 12. The navigation system 30 detects the traveling position of the vehicle and supplies it to the ECU 12. The memory 32 stores in advance a map in which the gain at the frequency of natural vibration (near 40 Hz) and the road surface friction coefficient μ are associated with each other corresponding to the traveling position.

FIG. 24 shows a flowchart of the fourth embodiment of the road surface condition determination processing executed by the ECU 12 shown in FIG. In FIG. 24, the same steps as those in FIG. 11 are given the same numbers. This process is repeated every predetermined time. In FIG. 24,
First, in step S112, the wheel speed signal is read. Next, in step S114, it is determined whether or not the value of the counter n is less than the predetermined value Tn. If n <Tn, step S1
The wheel speed measured in 16 is stored in the memory (built-in RAM), the counter n is incremented by 1, and the process proceeds to step S112. When the value of the counter n is equal to or more than Tn, the process proceeds to step S118, the wheel speed data measured in the period corresponding to the predetermined value Tn is supplied to the FFT 14, the Fourier transform is executed, the gain at each frequency is calculated, and the wheel is calculated. Find the fast frequency spectrum. Step S1
At 20, the gain at the frequency of natural vibration (near 40 Hz) is read from the frequency spectrum of the wheel speed.

Then, in step S160, the traveling position of the vehicle is read from the navigation system 30, and the natural vibration frequency (40H) corresponding to the traveling position is read from the memory 32.
A map in which the gain in the vicinity of z) and the road surface friction coefficient μ are related is read. In the next step S122, the road surface friction coefficient μ is estimated by referring to the map with the above gain. After that, the counter n is reset to 0 in step S128, and the processing cycle ends.

In the above embodiment, it is necessary to prepare a map in which the gain at the frequency of natural vibration (near 40 Hz) corresponding to the position of any road is associated with the road surface friction coefficient μ. Therefore, the cost is high. In order to avoid this, as shown in FIG. 25, the operation switch 34 is provided in the ECU 12 so that the driver can operate the operation switch 34 such as dry asphalt, wet asphalt, or a snowy road.
Alternatively, the road condition (road friction coefficient μ) may be stored in the memory 32 in accordance with the traveling position of the vehicle from the navigation system 30, and learning may be performed.

Further, as shown in FIG. 26, from the road-vehicle communication system 36, the natural vibration frequency (4
A map in which the gain and the road surface friction coefficient μ in the vicinity of 0 Hz) are related to each other may be received, and the road condition (road surface friction coefficient μ) may be stored in the memory 32 corresponding to the traveling position of the vehicle. The ECU in FIGS. 25 and 26 described above
The flowchart shown in FIG. 24 may be used for the road surface state determination processing executed by the control unit 12.

[0059]

As described above, the invention according to claim 1 is
Paying attention to the fact that the frequency analysis result of the wheel speed signal differs depending on the road surface condition,
The judgment is performed in the frequency band where a significant difference appears.
Therefore, since the determination is made based on the direct movement of the wheels, the influence of external disturbance such as noise is less likely to occur, and the determination can be performed accurately. Moreover, the existing wheel speed sensor can be used, and the cost can be kept low.

In the invention described in claim 2, the judging means is:
The road surface condition is determined based on the signal strength below a predetermined frequency. Therefore, the determination can be performed in the frequency band in which a significant difference appears depending on the road surface condition, the accurate determination can be performed, and the accuracy is improved. In the invention described in claim 3, the determining means determines the road surface condition based on the signal strength of the narrowband have frequency bands and centered multiple <br/> frequencies differ from each other by less than a predetermined frequency. Therefore, it is possible to easily make a determination at low cost without requiring a large memory capacity.

In the invention according to claim 4, the judging means is a vehicle.
Fundamental wave of ununiformity disturbance determined by wheel speed
And the road surface condition is determined based on the signal strength of the harmonics .
In a tire, a disturbance appears at a predetermined frequency determined by the wheel speed, the signal strength changes, and the degree of this change varies depending on the road surface condition. Therefore, it is possible to use this to determine the road surface condition. According to a fifth aspect of the present invention, the determining means is the natural vibration of the wheel due to the spring vibration of the tire in the front-back direction.
The road surface condition is determined based on the signal strength of a frequency substantially equal to the frequency . At a frequency substantially equal to the natural vibration frequency of the wheels, the signal strength changes depending on the road surface condition, and thus it is possible to determine the road surface condition by utilizing this.

According to a sixth aspect of the invention, the determining means normalizes the signal strength of the frequency substantially equal to the natural vibration frequency of the wheel based on the signal strength of the frequency affected only by the road surface disturbance, and normalizes the signal. The road surface condition is determined based on the strength. Since the signal strength changes due to road surface disturbances due to the unevenness of the road surface, the signal strength is normalized based on the signal strength of the frequency that is affected only by the road surface disturbance, and the influence of the road surface disturbance is eliminated and the road surface condition is accurately determined. can do.

The invention according to claim 7 has a storage means for storing the vibration information of the road surface according to the traveling position obtained by the navigation system, and the judging means stores the traveling position stored in the storage means. The road surface condition is determined based on the corresponding vibration information and the signal strength at a frequency substantially equal to the natural vibration frequency of the wheel. The road surface vibration information due to the unevenness of the road surface is stored in advance, and the road surface condition is judged based on the vibration information according to the traveling position and the signal strength of the frequency substantially equal to the natural vibration frequency of the wheels, so the influence of road surface disturbance is eliminated. Therefore, the road surface condition can be accurately determined.

The invention described in claim 8 is substantially equal to the natural vibration frequency used for the determination by the determination means based on the wheel speed.
It has a judgment standard changing means for changing the relationship between the signal strength of a new frequency and the road surface condition . Since the signal strength of the frequency substantially equal to the natural vibration frequency of the wheel changes depending on the wheel speed,
The road surface condition can be accurately determined by changing the determination standard based on the wheel speed. The invention according to claim 9 is
Wherein used for the determination by said determining means based on the tire air pressure
Natural frequency of wheel due to tire front-back spring vibration
It has frequency changing means for changing the frequency substantially equal to the number . Since the natural vibration frequency of the wheel changes depending on the tire pressure, the road surface condition can be accurately determined by changing the frequency used for the determination based on the tire pressure.

[Brief description of drawings]

FIG. 1 is a block diagram of a first embodiment of a road surface state determination device of the present invention.

FIG. 2 is a flowchart of a first embodiment of road surface condition determination processing.

FIG. 3 is a diagram showing a frequency spectrum of a wheel speed signal.

FIG. 4 is a block diagram of a second embodiment of the road surface condition determination device of the present invention.

FIG. 5 is a flowchart of a second embodiment of a road surface state determination process.

FIG. 6 is a block diagram of a third embodiment of the road surface condition determination device of the present invention.

FIG. 7 is a flowchart of a third embodiment of road surface condition determination processing.

FIG. 8 is a diagram showing a relationship between a vehicle speed and a fundamental wave of ununiformity disturbance.

FIG. 9 is a diagram showing a tire model.

FIG. 10 is a diagram showing a relationship between a gain of natural vibration and a road surface condition.

FIG. 11 is a flowchart of a fourth embodiment of the road surface condition determination processing.

FIG. 12 is a flowchart of a fifth embodiment of road surface condition determination processing.

FIG. 13 is a diagram showing a relationship between a gain of natural vibration corresponding to a road surface disturbance and a road surface state.

FIG. 14 is a flowchart of a sixth embodiment of road surface condition determination processing.

FIG. 15 is a flowchart of a seventh embodiment of the road surface condition determination processing.

FIG. 16 is a diagram showing a relationship between a gain of natural vibration and a road surface state according to a vehicle speed.

FIG. 17 is a flowchart of an eighth embodiment of road surface condition determination processing.

FIG. 18 is a flowchart of a ninth embodiment of the road surface condition determination processing.

FIG. 19 is a diagram showing a relationship between a natural vibration gain according to tire pressure and a road surface condition.

FIG. 20 is a flowchart of a tenth embodiment of the road surface state determination processing.

FIG. 21 is a flowchart of an eleventh embodiment of road surface condition determination processing.

FIG. 22 is a diagram showing a relationship between a gain of natural vibration according to temperature and a road surface state.

FIG. 23 is a block diagram of a third embodiment of the road surface condition determination device of the present invention.

FIG. 24 is a flowchart of a twelfth embodiment of road surface condition determination processing.

FIG. 25 is a block diagram of a road surface state determination device according to a fourth embodiment of the present invention.

FIG. 26 is a block diagram of a fifth embodiment of the road surface condition determination device of the present invention.

[Explanation of symbols]

10 Wheel speed sensor 12 ECU (electronic control unit) 14 FFT (Fast Fourier Transform) 16 bandpass filters 18 Variable bandpass filter group 20 wheels 22 belt 24 spring 26 road surface 30 navigation system 32 memory 34 Operation switch 36 Road-to-vehicle communication system

Continuation of front page (56) Reference JP-A-7-260637 (JP, A) JP-A-9-54020 (JP, A) JP-A-6-258196 (JP, A) JP-A-8-184533 (JP , A) JP 6-50878 (JP, A) JP 7-98325 (JP, A) JP 8-156537 (JP, A) JP 8-101085 (JP, A) JP 6-174543 (JP, A) JP-A-7-156782 (JP, A) JP-A-8-334454 (JP, A) JP-A-6-138018 (JP, A) (58) Fields investigated (Int. Cl. ⁷ , DB name) G01M 17/007 G01N 19/02 G01P 3/44 B60T 8/58

Claims (9)

(57) [Claims] 1. A wheel speed detecting means for detecting a wheel speed, an analyzing means for performing a frequency analysis of the detected wheel speed signal, and a road surface condition determined from the result of the analysis based on a signal intensity in a predetermined frequency region. A road surface condition determination device comprising: 2. The road surface state determination device according to claim 1, wherein the determination means determines the road surface state based on a signal strength equal to or lower than a predetermined frequency. 3. A road surface condition judging apparatus of the mounting according to claim 1, wherein the determining means, based on the signal strength of the narrowband have frequency bands and centered multiple <br/> frequencies differ from each other by less than a predetermined frequency A road surface condition determination device characterized by determining a road surface condition. 4. The road surface state determination device according to claim 1, wherein the determination means determines the road surface state based on signal strengths of a fundamental wave and a harmonic wave of an ununiformity disturbance determined by a wheel speed. Road surface condition determination device. 5. The road surface condition determination device according to claim 1, wherein the determination means determines the road surface condition based on a signal intensity of a frequency substantially equal to a natural vibration frequency of the wheel due to spring vibration of the tire in the front-rear direction. A road surface condition determination device. 6. The road surface condition determining apparatus according to claim 5, wherein the determining unit normalizes a signal intensity of a frequency substantially equal to a natural vibration frequency of the wheel based on a signal intensity of a frequency only affected by a road surface disturbance. A road surface state determination device characterized by determining a road surface state based on the normalized signal strength. 7. The road surface state determination device according to claim 5, further comprising a storage unit that stores road surface vibration information according to a traveling position obtained by a navigation system, and the determination unit is stored in the storage unit. A road surface state determination device, characterized in that the road surface state is determined based on vibration information corresponding to a running position and a signal intensity of a frequency substantially equal to a natural vibration frequency of a wheel. 8. The road surface condition determination device according to claim 5, wherein the criterion is changed to change the relationship between the signal strength of the frequency substantially equal to the natural vibration frequency used for the determination by the determination means based on the wheel speed and the road surface condition. A road surface condition determination device having means. 9. The road surface condition determination device according to claim 5, wherein a frequency change is performed to change a frequency substantially equal to a natural vibration frequency of a wheel due to spring vibration in the front-rear direction of the tire, which is used for the determination by the determination means based on the tire pressure. A road surface condition determination device having means.