JP2002274357A - Road surface condition discriminating device and method and discriminating program for road surface condition - Google Patents

Abstract

PROBLEM TO BE SOLVED: To provide a road surface condition discriminating device identifying presently attached tires and capable of accurately setting a threshold for discriminating slipperiness. SOLUTION: The road surface condition discriminating device is provided with a rotational speed detecting means periodically detecting rotational speeds of the tires of four wheels of a vehicle, a first calculating means calculating a vehicle speed from a measurement value by the rotational speed detecting means, a second calculating means calculating acceleration/deceleration of the vehicle, a third calculating means calculating a slip ratio from the rotational speeds of the four wheels, a fourth calculating means subjecting the acceleration/ deceleration and the slip ratio of the vehicle to a moving average, a fifth calculating means determining a primary regression coefficient and a correlation between the acceleration/deceleration and the slip ratio of the vehicle subjected to the moving average, and a threshold setting means setting a threshold for road surface condition discrimination on the basis of the primary regression coefficient when the correlation is a predetermined value or more.

Description

DETAILED DESCRIPTION OF THE INVENTION

[0001]

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to a road condition determining apparatus and method, and a road condition determining program. More specifically, a road surface condition determination apparatus and method capable of identifying a currently mounted tire and setting a threshold value for determining slipperiness, for example, when a tire is replaced, and a determination of a road surface condition About the program.

[0002]

2. Description of the Related Art When a vehicle is suddenly accelerated or braked on a slippery road surface, there is a risk that tires may slip and spin. Also, sudden steering may cause the vehicle to skid or spin.

Accordingly, conventionally, before the braking force between the tire and the road surface exceeds the maximum value and the tire is locked, the brake torque acting on the wheel is reduced to prevent the locked state of the wheel, An anti-lock brake device that controls the number of rotations of a wheel at which a maximum braking force is obtained has been proposed (Japanese Patent Application Laid-Open No. 60-99757, Japanese Patent Application Laid-Open No.
249559, etc.).

For example, in the control of an anti-lock brake device, a slip ratio is calculated from a determined speed of a vehicle and a detected wheel speed (rotation speed), and the calculated slip ratio matches a predetermined reference slip ratio. The braking force is controlled so as to follow the maximum braking force.

In the control of such an ABS device or the like, the friction coefficient μ of the road surface is used. That is, according to the road friction coefficient μ (road surface μ), for example, the control content is changed between high μ and low μ to perform the optimal control.

In the device described in Japanese Patent Application Laid-Open No. 60-99759, the vehicle acceleration is obtained from the driven wheels at the time of occurrence of slip, and the road surface μ is determined using this acceleration. The acceleration of the driven wheel obtained by simply differentiating the rotation speed of the driven wheel is replaced with the vehicle acceleration, and is the road surface μ at the time when the vehicle acceleration is calculated, and is the road surface μ between the actual road surface and the tire? I don't know.

On the other hand, there is a method of estimating the slipperiness (coefficient of friction) between a tire and a road surface from information on the rotational speed of the running tire.

This method utilizes the fact that the rising slope of the μ-s curve between the tire and the road surface differs depending on the road surface friction coefficient μ. The slope that becomes the determination value is obtained by calculating the slip ratio and the acceleration of the driven wheel. Then, by comparing the obtained determination value with a preset threshold value, the slipperiness (road surface) μ is estimated. However, this threshold value needs to be changed depending on the stiffness of the tire and the load applied to the tire.

Therefore, there is a method in which a switch is provided for a vehicle in a summer tire mode, a winter tire mode, or the like, and different threshold values are set in advance for summer tires and winter tires having greatly different stiffness.

[0010]

However, in the case of the two types of mode switching as described above, even in summer tires (winter tires), there is a difference in the rising gradient when the maker or pattern is different, or even when the tires are worn, the rising slopes are different. There is a problem that the accuracy of discriminating the road surface state is inferior due to the difference in the gradient.

SUMMARY OF THE INVENTION In view of the above circumstances, the present invention identifies a tire currently mounted and identifies a road condition determining apparatus and method capable of accurately setting a threshold value for determining slipperiness. , And a road surface state determination program.

[0012]

SUMMARY OF THE INVENTION A road surface condition determining apparatus according to the present invention comprises a rotational speed detecting means for periodically detecting rotational speeds of four tires of a vehicle, and a vehicle speed based on a measured value by the rotational speed detecting means. , Second calculating means for calculating the acceleration / deceleration of the vehicle, third calculating means for calculating the slip ratio from the rotational speeds of the four wheels, acceleration / deceleration and slip of the vehicle. Fourth calculating means for moving and averaging the respective ratios, fifth calculating means for obtaining a first-order regression coefficient and a correlation coefficient between the acceleration / deceleration and the slip ratio of the moving-averaged vehicle, 1 if the number is greater than or equal to a predetermined value
Threshold value setting means for setting a threshold value for road surface state determination based on the following regression coefficient.

[0013] The method of determining a road surface condition according to the present invention includes the steps of periodically detecting the rotational speeds of the four tires of the vehicle, calculating the vehicle speed from the measured rotational speeds, and adjusting the vehicle speed. Calculating the speed, calculating the slip ratio from the rotational speeds of the four wheels, moving and averaging the acceleration / deceleration and the slip ratio of the vehicle, respectively; and calculating the acceleration / deceleration and slip of the moving averaged vehicle. Determining a primary regression coefficient and a correlation coefficient with the ratio; and setting a threshold value for road surface state determination based on the primary regression coefficient when the correlation coefficient is equal to or greater than a predetermined value. It is characterized by having.

Further, the road surface condition determination program according to the present invention comprises a first computing means for computing a vehicle speed from a measured value by a rotational speed detecting means, and a vehicle acceleration / deceleration to determine a road surface condition. Second calculating means,
Third calculating means for calculating the slip ratio from the rotational speeds of the four wheels, fourth calculating means for moving and averaging the acceleration / deceleration and the slip ratio of the vehicle, and acceleration / deceleration and the slip ratio of the moving averaged vehicle A fifth calculating means for calculating a first-order regression coefficient and a correlation coefficient with the above, and setting a threshold value for road surface state determination based on the first-order regression coefficient when the correlation coefficient is equal to or more than a predetermined value. Function as threshold setting means.

[0015]

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS A description will be given below of a road condition determining apparatus and method and a road condition determining program according to the present invention with reference to the accompanying drawings. FIG. 1 is a block diagram showing an embodiment of a road condition determining apparatus and method according to the present invention, FIG. 2 is a block diagram showing an electrical configuration of the road condition determining apparatus and method in FIG. 1, and FIG. FIG. 4 is a graph showing a change with time, FIG. 4 is a graph showing a rate at which a first-order regression coefficient becomes larger than a threshold value while traveling on a dry asphalt road, and FIG. FIG. 6 is a diagram showing a ratio at which the first-order regression coefficient becomes larger than a threshold value while traveling on an ice burn road.

As shown in FIG. 1, a road surface condition determining apparatus according to one embodiment of the present invention includes a tire F of a four-wheeled vehicle.
L, FR, RL, and RR are provided with rotation speed detection means for periodically detecting the rotation speed of the wheel tires. The output of the rotation speed detection means S is ABS
And transmitted to the control unit 2 which is a computer. Reference numeral 3 denotes an initialization switch operated by the driver, and reference numeral 4 denotes a low-μ road alarm display.

As the rotation speed detecting means, a wheel speed sensor 1 for generating a rotation pulse using an electromagnetic pick-up or the like and measuring the rotation speed from the number of pulses or power generation using rotation like a dynamo is used. An angular velocity sensor including one that measures the rotation speed from this voltage can be used.

As shown in FIG. 2, the control unit 2 has an I / O interface 2a required for transmitting and receiving signals to and from an external device, and a C functioning as a center of arithmetic processing.
A PU 2b, a ROM 2c in which a control operation program of the CPU 2b is stored, and a RAM 2d into which data or the like is temporarily written when the CPU 2b performs a control operation, and from which the written data is read. I have.

In the present embodiment, the control unit 2
First calculating means for calculating the vehicle speed from the value measured by the wheel speed sensor 1, second calculating means for calculating the traveling distance, and third calculating means for calculating the acceleration / deceleration of the vehicle. A fourth calculating means for calculating a slip ratio (a ratio between the wheel speed of the front wheel tires and the wheel speed of the rear wheel tires) from the rotational speeds of the four wheels, and a moving average of the acceleration / deceleration and the slip ratio of the vehicle. Fifth calculating means, accumulating and calculating data of acceleration / deceleration and slip ratio of the moving averaged vehicle until the calculated travel distance reaches a predetermined distance,
Sixth calculating means for obtaining a mutual primary regression coefficient and a correlation coefficient, and a threshold value for road surface state determination based on the primary regression coefficient when the correlation coefficient is equal to or more than a predetermined value. Threshold setting means. In the present embodiment,
The third arithmetic means for calculating the acceleration / deceleration of the vehicle accumulates and calculates the data of the acceleration / deceleration and the slip ratio of the moving averaged vehicle until the calculated traveling distance reaches a predetermined distance, and calculates the primary and secondary data of each other. A description will be given of obtaining a regression coefficient and a correlation coefficient.However, in the present invention, the present invention is not limited to this, and instead of the travel distance, moving average is performed using an accumulation time or the number of accumulated data. It is also possible to obtain a first-order regression coefficient and a correlation coefficient between the acceleration / deceleration of the vehicle and the slip ratio. In this case, a moving average is provided by using an accumulation time or the number of accumulated data instead of the sixth computing means, and an arithmetic means for computing the accumulation time or the number of accumulated data is provided instead of the second arithmetic means. Calculating means for calculating a first-order regression coefficient and a correlation coefficient between the acceleration / deceleration of the vehicle and the slip ratio.

The program for determining the road surface condition according to the present embodiment includes a control unit 2 for calculating a vehicle speed based on a value measured by a rotational speed detecting unit, and a second calculating unit for calculating a vehicle acceleration / deceleration. Calculating means for calculating the slip ratio from the rotational speeds of the four wheels, fourth calculating means for moving and averaging the acceleration / deceleration and the slip ratio of the vehicle, and adjusting the speed of the moving averaged vehicle. Fifth calculating means for calculating a primary regression coefficient and a correlation coefficient between the speed and the slip ratio, and a threshold value for road surface state determination based on the primary regression coefficient when the correlation coefficient is equal to or more than a predetermined value Function as threshold value setting means for setting.

Further, the control unit 2 calculates the first-order regression coefficient and the correlation coefficient from the data of the acceleration / deceleration and the slip ratio of the moving averaged vehicle until the traveling distance of the vehicle reaches a predetermined distance. If so, it is made to further function as a calculating means for calculating the traveling distance of the vehicle.

Generally, winter tires are tires with different tread patterns and materials so that they can run on snowy roads. For example, "SNOW", "M + S",
“STUDESS”, “ALL WEATHER”,
A tire having an indication such as "ALL SEASON", and a summer tire is a tire having no such indication on a sidewall portion unlike a winter tire, but is referred to as a summer tire in this specification. Differences in winter tires include not only the presence or absence of such display but also differences in the tread pattern rigidity. That is, a tire having a large pattern stiffness that affects vehicle control and the accuracy of detecting the internal pressure of the tire is a summer tire, and a tire having a small pattern stiffness is a winter tire.

In this embodiment, the rotation speed of the four tires is detected in 0.1 seconds or less, preferably in 0.05 seconds or less. The vehicle speed and travel distance are calculated from the rotational speeds of the four wheels and the dynamic load radius of the tire. Although the acceleration / deceleration of the vehicle can be measured by a G sensor, it is preferable to calculate by differentiating the speed of the vehicle from the viewpoint of cost.

Next, the slip ratio and the acceleration / deceleration of the vehicle are obtained by moving and averaging the data for a certain period of time, for example, data of at least 0.1 second or more for each sampling time.

Further, the data of the acceleration / deceleration and the slip ratio of the moving averaged vehicle are accumulated until the traveling distance reaches a predetermined distance, and the accumulated data is used to store the data.
First-order regression coefficients and correlation coefficients of the slip ratio and the acceleration / deceleration of the vehicle are obtained. Here, when the acceleration / deceleration of the vehicle obtained by the moving average is below a certain value (for example,-
It is desirable not to use it for the calculation of the regression coefficient during braking (in the case of 0.03 G or less) or during braking.
This is because a braking force is applied to the four wheels during deceleration, particularly during braking, and an accurate slip ratio cannot be obtained.

Hereinafter, the operation of the road surface condition discriminating apparatus according to the present embodiment will be described in accordance with the following procedures.

From the rotational speeds of the four-wheel tires FL, FR, RL, and RR, the wheel speeds (V1 _n , V1
2 _n , V 3 _n , V 4 _n ).

For example, the wheel speed data at a certain point in time of each wheel tire FL, FR, RL, RR of the vehicle obtained from a sensor such as an ABS sensor is converted into wheel speeds V1 _n , V2 _n , V3.
_n , V4 _n .

Next, the average wheel speed (Vf _n , Vd _n ) of the driven wheel and the drive wheel is calculated.

In the case of front-wheel drive, the average wheel speed Vf _n of the following wheels and the driving wheels at a certain time, the Vd _n of the following formula (1),
Determined by (2). _{Vf n = (V3 n + V4} n) / 2 ··· (1) Vd n = (V1 n + V2 n) / 2 ··· (2)

Next, the traveling distance per unit time of the vehicle is calculated by the following equation (3). DIST = Vf _n × Δt (3)

Here, Δt is a time interval (sampling time) between the average wheel speeds Vf _n and Vf _n−1 of the driven wheels calculated from the wheel speed data.

Next, the average wheel acceleration / deceleration of the driven wheels (ie, the acceleration / deceleration of the vehicle) Af _n is calculated.

[0034] from the average wheel speed Vf _n 1 preceding wheel speed data from the driven wheel, when the average wheel speed Vf _n-1, the average wheel acceleration Af _n each of the following formula driven wheel (4) Desired. Af _n = (Vf _n -Vf _n-1 ) / Δt / g (4)

Here, Δt is a time interval (sampling time) between the wheel speeds Vf _n and Vf _n−1 calculated from the wheel speed data, and g is a gravitational acceleration. The sampling time needs to be 0.1 second or less in order to reduce the variation in data and make a determination in a short time. More preferably, the time is 0.05 seconds or less.

[0036] followed in response to said value of the acceleration and deceleration Af _n of the vehicle, it calculates the slip ratio.

[0037] First, in the acceleration state, when the drive wheels are the vehicle slips in the locked state and _{(Vd n = 0, Vf n} ≠ 0), in a deceleration state, the vehicle drive wheels causes a wheel spin is stopped and when that _{(Vf n = 0, Vd n} ≠ 0) include, but are not occur, the slip ratio S _n the following formula (5),
Calculate from (6). If it is af _n ≧ 0 and _{Vd n ≠ 0, S n =} (Vf n -Vd n) / Vd n ··· (5) if it is af _n <0 and _{Vf n ≠ 0, S n =} (Vf _n− Vd _n ) / Vf _n (6) In other cases, S _n = 1.

Then, the data of the slip ratio and the acceleration / deceleration of the vehicle are subjected to a moving average process for each sampling time.

Regarding the slip ratio, MS _n = (S ₁ + S ₂ +... + S _n ) / N (7) MS _{n + 1} = (S ₂ + S ₃ +... + S _{n + 1} ) / N (8) MS _{n + 2} = (S ₃ + S ₄ +... + S _{n + 2} ) / N (9)

Regarding the acceleration / deceleration of the vehicle, MAf _n = (Af ₁ + Af ₂ +... + Af _n ) / N (10) MAf _{n + 1} = (Af ₂ + Af ₃ +... + Af _{n + 1} ) / N (11) MAf _{n + 2} = (Af ₃ + Af ₄ +... + Af _{n + 2} ) / N (12) The vehicle travels at the moving average slip ratio and the vehicle acceleration / deceleration. The distance is accumulated for each predetermined distance. When estimating the road surface condition, since the road surface condition during traveling changes every moment,
For example, it is necessary to estimate within a few seconds or less, but when setting a threshold value for determining the road surface condition, it may not be so early. Therefore, data for a predetermined distance that is relatively long is accumulated, and a first-order regression coefficient and a correlation coefficient are obtained.

[0041]

(Equation 1)

[0042]

[Table 1]

The correlation coefficient RS is as follows: RS = KS1 × KS2 (15)

According to the above procedure, the first-order regression coefficient KS1 when the correlation coefficient RS is a predetermined value, for example, 0.9 or more.
Calculates the threshold value L from

When the correlation coefficient RS is 0.9 or more, it means a high μ road. Because
On a high μ road such as asphalt, since the road surface condition is stable, even if data for a relatively long distance is accumulated, the data does not vary and the correlation coefficient is high. However, on low μ roads such as snow-covered roads and ice-burn roads, the road surface conditions are not stable,
This is because when data of a relatively long distance is accumulated, the data varies and the correlation coefficient decreases. As described above, by using the first-order regression coefficient KS1 when the road is recognized as a high μ road as the reference value, it is possible to set a threshold value specific to the currently mounted tire. L = 6KS1 ² + 0.4KS1 + 0.04 (16)

This equation (16) was calculated by an experiment.
The threshold value L obtained by equation (16) is
Average and update with.

For example, if the initially obtained threshold value is 0.
At 122, the next time a new threshold value of 0.118 is obtained, the threshold value is (0.122 + 0.118) / 2 = 0.
120. When a new threshold value 0.126 is obtained, the threshold value becomes (0.122 + 0.118 + 0.
126) /3=0.122.

Thus, it is possible to cope with the aging of the tire and the like. That is, when the winter tire is worn or when the hardness of the tread rubber increases due to aging, the threshold value can be set accordingly. Since the averaging is performed at the time of setting the threshold value, even if an inappropriate first-order regression coefficient is obtained once or twice, the effect is very small and converges to the vicinity of the most frequent value. become.

On the other hand, since the determination of the road surface condition must be performed in a short time, the moving average slip ratio and the acceleration / deceleration of the vehicle are accumulated for a predetermined time, for example, 2 seconds, and the first-order regression coefficient KS1A is obtained. Find the correlation coefficient RSA. Then, the correlation coefficient RSA is a predetermined value, for example, 0.7
If this is the case, the primary regression coefficient KS1A is compared with the threshold L, and the primary regression coefficient KS1
If A is larger, it is determined that the vehicle is slippery and a warning is issued to the driver.

[0050]

Next, the present invention will be described based on examples, but the present invention is not limited to only these examples.

First, as a four-wheel tire of a front wheel drive vehicle, two kinds of winter tires to be mounted on a vehicle are a glass pick DS-1 (Example 1) manufactured by Sumitomo Rubber Industries, Ltd. and a winter sport (Winter sports) manufactured by Dunlop of Germany.
M2 (Example 2). The rubber hardness of the winter tire is about 50 degrees for the former and about 60 degrees for the latter.

As the running conditions, the sampling time of the wheel speed of the wheels was set to 40 ms because, for example, one second is too long in sampling time in order to increase the number of data and to eliminate variations and measurement errors.

Then, according to the above-mentioned procedures (1) and (2), the wheel speed was taken in based on the wheel speed pulse, the acceleration / deceleration of the vehicle and the slip ratio of the front and rear wheels were calculated every 40 ms, and the moving average processing was performed. Then, the threshold value is set by setting a first-order regression coefficient KS1 and a correlation coefficient R of the acceleration / deceleration of the vehicle with respect to the slip ratio at every traveling distance of 1000 m.
S was determined. Then, the threshold value L was calculated from the first-order regression coefficient KS1 when the correlation coefficient RS was 0.9 or more, using the above equation (16). The new threshold value is updated by averaging with the threshold value L so far.

On the other hand, the road surface condition is determined by determining the first-order regression coefficient KS1A and the correlation coefficient RSA from the moving averaged slip ratio and the acceleration / deceleration data of the vehicle for two seconds. The value of the primary correlation coefficient KS1A at the time described above was compared with a threshold value L.

FIG. 3 shows the change over time of the threshold value when the two types of tires (Examples 1 and 2) run on a dry asphalt road, a snow-covered road, an ice-burn road or the like for about 2 hours. As is clear from FIG. 3, in the case of the first embodiment, the threshold value is constantly in the range of 0.117 to 0.123, whereas the threshold value of the second embodiment is 0.061.
６５0.065, which indicates that the difference between the two tires is clear. Further, it can be seen that the threshold value stably changes at almost the same value, and that the threshold value is determined with high accuracy.

Next, using the updated threshold value, the primary regression coefficient KS1A of the acceleration / deceleration of the vehicle with respect to the slip ratio when the vehicle travels on a dry asphalt road, a snow-covered road, and an ice-burn road for about 30 minutes, respectively. FIGS. 4 to 6 show the ratio at which the ratio becomes larger than the threshold value (the ratio at which a slip alarm occurs). 4 to 6, SL is a ratio below the threshold and SU is a ratio above the threshold.

As shown in FIGS. 4 (a) and 4 (b), both the first and second embodiments use the dry asphalt road.
The first-order regression coefficient K1 hardly became larger than the threshold value. That is, no alarm was issued. On the other hand, as shown in FIGS. 5 (a) and 5 (b), on the snowy road, the ratio of the first embodiment is about 51% and that of the second embodiment is about 61%. The regression coefficient KS1A became large, and an alarm was issued. As shown in FIGS. 6 (a) and 6 (b), the first-order regression is about 66% in the first embodiment and about 81% in the second embodiment on the ice-burn road. The coefficient KS1A has increased and an alarm has been issued. This indicates that the threshold value is appropriate and the difference in the slipperiness of the road surface is well identified.

[0058]

As described above, according to the present invention,
The threshold value can be automatically set according to the currently mounted tire, and the road surface condition can be determined with high accuracy.

[Brief description of the drawings]

FIG. 1 is a block diagram showing an embodiment of a road surface condition determination device of the present invention.

FIG. 2 is a block diagram showing an electrical configuration of the road surface condition determination device in FIG.

FIG. 3 is a diagram showing a change with time of a threshold value.

FIGS. 4 (a) and 4 (b) are diagrams each showing a rate at which a first-order regression coefficient becomes larger than a threshold value while traveling on dry asphalt roads of Examples 1 and 2. FIG.

FIGS. 5 (a) and 5 (b) are diagrams each showing a rate at which a first-order regression coefficient becomes larger than a threshold value while traveling on a snow-covered road in Examples 1 and 2.

FIGS. 6 (a) and 6 (b) are diagrams each showing a rate at which a first-order regression coefficient becomes larger than a threshold value while traveling on an ice burn road in Examples 1 and 2. FIG.

[Explanation of symbols]

 DESCRIPTION OF SYMBOLS 1 Wheel speed sensor (rotation speed detecting means) 2 Control unit 3 Initialization switch 4 Low μ road warning indicator FL, FR, RL, RR Tire

Claims (6)

[Claims] 1. A rotation speed detection means for periodically detecting rotation speeds of four tires of a vehicle, a first calculation means for calculating a vehicle speed from a value measured by the rotation speed detection means, Second calculating means for calculating acceleration / deceleration;
Third calculating means for calculating the slip ratio from the rotation speeds of the four wheels, fourth calculating means for moving and averaging the acceleration / deceleration and the slip ratio of the vehicle, and acceleration / deceleration of the moving averaged vehicle. A fifth calculating means for calculating a primary regression coefficient and a correlation coefficient with the slip ratio, and a threshold value for road surface state determination based on the primary regression coefficient when the correlation coefficient is equal to or more than a predetermined value. A road surface condition determination device comprising: a threshold value setting means for setting. 2. The method according to claim 1, wherein the first-order regression coefficient and the correlation coefficient are obtained from data of acceleration / deceleration and a slip ratio of the moving average of the vehicle until the traveling distance of the vehicle reaches a predetermined distance. The road surface state determination device according to claim 1, further comprising a calculation means for calculating a travel distance of the road surface. A step of periodically detecting rotation speeds of four tires of the vehicle; a step of calculating a vehicle speed from the measured rotation speed; a step of calculating acceleration / deceleration of the vehicle; Calculating a slip ratio from the rotational speed of the wheel;
A step of moving and averaging the acceleration / deceleration and the slip ratio of the vehicle, and a step of obtaining a first-order regression coefficient and a correlation coefficient between the acceleration / deceleration and the slip ratio of the moving-averaged vehicle,
Setting a threshold value for road surface state determination based on a first-order regression coefficient when the correlation coefficient is equal to or greater than a predetermined value. 4. The method according to claim 1, wherein the first-order regression coefficient and the correlation coefficient are obtained from acceleration / deceleration and slip ratio data of the moving average of the vehicle until the travel distance of the vehicle reaches a predetermined distance. 4. The road surface state determination method according to claim 3, further comprising a step of calculating a travel distance of the road surface. 5. A computer for judging a road surface condition, comprising: first computing means for computing a vehicle speed from a value measured by a rotational speed detecting means; second computing means for computing acceleration / deceleration of the vehicle; Third calculating means for calculating the slip ratio from the rotation speed, fourth calculating means for moving and averaging the acceleration / deceleration and the slip ratio of the vehicle, and one of the acceleration / deceleration and the slip ratio of the moving averaged vehicle. Fifth calculating means for calculating a next regression coefficient and a correlation coefficient, a threshold value setting for setting a threshold value for road surface state determination based on a first regression coefficient when the correlation coefficient is equal to or more than a predetermined value A road surface condition determination program for functioning as a means. 6. The method according to claim 1, wherein the first-order regression coefficient and the correlation coefficient are obtained from data of acceleration / deceleration and slip ratio of the moving average of the vehicle until the traveling distance of the vehicle reaches a predetermined distance. 6. The road surface state determination program according to claim 5, wherein said program is used as a calculating means for calculating a traveling distance of the vehicle.