WO2005075959A1 - 摩擦係数推定方法及び装置 - Google Patents
摩擦係数推定方法及び装置 Download PDFInfo
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- WO2005075959A1 WO2005075959A1 PCT/JP2005/002010 JP2005002010W WO2005075959A1 WO 2005075959 A1 WO2005075959 A1 WO 2005075959A1 JP 2005002010 W JP2005002010 W JP 2005002010W WO 2005075959 A1 WO2005075959 A1 WO 2005075959A1
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- road surface
- target area
- measurement target
- friction coefficient
- temperature
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
- G01N21/55—Specular reflectivity
Definitions
- the present invention relates to a technology for measuring a road surface state during running of a vehicle.
- the present invention has been proposed in view of the above-described problems of the related art, and a detailed road surface friction coefficient is grasped by performing multifaceted measurements in consideration of the characteristics of the road surface to be measured. It is an object of the present invention to provide a method and apparatus for estimating a coefficient of friction that can be performed.
- Fig. 8 is a characteristic diagram in which vehicle speed is plotted on the horizontal axis and road surface friction coefficient is plotted on the vertical axis, and the road surface roughness, which is a nomometer, is divided into five levels of A-E and plotted for each roughness.
- the road surface surface water content and the road surface heat capacity can be considered.
- the road surface moisture content directly affects the adhesion state in the tire installation surface. That is, it is known that an increase in the water content on the road surface causes a decrease in the coefficient of friction, thereby increasing the braking distance.
- the heat capacity of the road surface is an important factor in determining the road surface friction coefficient in a cold region or the like because a sudden change in the friction coefficient near the triple point becomes a problem in a cold region. I have. In other words, it has been found that the heat balance of the snow and ice surface of the road contributes particularly to the occurrence of frozen road surfaces.
- the road surface heat capacity is obtained by detecting a difference in heat emissivity in each road surface state.
- the sensor fusion means the entire process of determining or estimating a specific numerical value using the measurement results of a plurality of sensors (in this case, the process of estimating the friction coefficient of the road surface).
- the friction coefficient estimating method of the present invention includes a step (S11) of measuring the light reflectance (albedo value a) of the measurement target area (road surface P) and a surface image (road surface image) of the measurement target area (road surface P). ) Is analyzed (S13), the process of measuring the temperature (road surface temperature) of the measurement target area (road surface P) (S15), and whether the force of the measurement target region (road surface P) is equal to or higher than the predetermined temperature (S18), and at least two parameters of the light reflectance, surface image analysis result, and temperature of the measurement target area (road surface P) are used to perform sensor fusion, and the friction coefficient of the measurement target area is determined. (S19 or S20) (claim 1).
- step of performing the sensor fusion if the temperature of the measurement target area (road surface P) is equal to or higher than a predetermined value, the albedo value (a) and the measurement target area (road surface P) Based on the surface image analysis results, sensor fusion is performed to estimate the friction coefficient of the measurement target area (road surface P) (S19) . If the temperature of the measurement target area (road surface P) is lower than a predetermined value, Sensor friction is performed based on the bed value (a) and the surface temperature of the measurement target area (road surface P) to estimate the friction coefficient of the measurement target area (road surface P) (S20) (Claim 2).
- the friction coefficient estimating apparatus (A) of the present invention is a light reflectance measuring means (albedo value measuring means 1) for measuring the light reflectance (albedo value a) of a measurement target area (road surface P).
- Image processing means road surface image processing means 2) for analyzing the surface image (road surface image) of the measurement target area (road surface P), and temperature measurement means for measuring the temperature (road surface temperature) of the measurement target area (road surface P) (Road surface temperature measuring means 3) and control means (road surface friction coefficient estimating means 5), and the control means (road surface friction coefficient estimating means 5) includes the light reflectance (Al) of the measurement target area (road surface P).
- Estimate the friction coefficient (S) of the measurement target area (road surface P) by performing sensor fusion using at least two parameters of the bed value a), the surface image analysis result (F), and the temperature (T). (Claim 3).
- the control means (5) determines whether the surface temperature of the measurement target area (road surface P) is equal to or higher than a predetermined value, and determines whether the temperature of the measurement target area (road surface P) is equal to or higher than the predetermined value.
- the sensor fusion is performed based on the albedo value (a) and the surface image analysis result of the measurement target area (road surface P) to estimate the friction coefficient of the measurement target area (road surface P), and the measurement target area (road surface P) If the temperature is lower than the predetermined value, the albedo value (a) and the surface temperature of the measurement target area (road surface P) It is configured to estimate the coefficient of friction of the measurement target area (road surface P) by performing sensor fusion depending on the degree (claim 4).
- the light reflectance measuring means (albedo value measuring means 1), it is possible to accurately and accurately detect a change in the road surface friction coefficient caused by the roughness of the road surface.
- the light reflectance measuring means (albedo value measuring means 1) is a non-contact type, it is easy to handle and there is no fear of breakage.
- a change in road surface water content can be measured by the image processing means (road surface image processing means 2).
- the present invention has a movable temperature measuring means (road surface temperature measuring means 3), and detects a difference in heat emissivity in each road surface state to accurately determine the road surface state. It is possible to grasp.
- a friction coefficient estimating apparatus indicated by reference numeral 100 is an albedo value measuring means 1 for measuring an albedo value, a road surface for photographing a road surface and processing the photographed road surface image.
- image It comprises processing means 2 and road surface temperature measuring means 3 for measuring road surface temperature.
- an albedo value is used to detect the reflectance of the road surface related to the roughness of the road surface.
- the albedo value is a value defined as the ratio of the amount of reflected light to the amount of irradiated light.
- FIG. 9 is experimental data showing the relationship between the albedo value (the value of Reflection Value J on the horizontal axis in FIG. 9) and the road surface friction coefficient (the vertical axis "Road Friction" in FIG. 9). The coefficient also calculates the wheel lock braking test force.
- the albedo value is related to the road surface friction coefficient, and it is shown that measuring the albedo value is effective for accurately knowing the road surface friction coefficient.
- the albedo value measuring means 1 includes an irradiating means 11 for irradiating the road surface P with light (irradiation light) 1 and an irradiating light I reflected by the road surface P.
- the obtained albedo value a is transmitted from the albedo value measuring means 13 to the road surface friction coefficient estimating means 5.
- the road surface image processing means 2 includes an auxiliary light irradiating means 21 for irradiating the road surface P with auxiliary light in order to grasp a road surface state, and a road surface irradiated with the auxiliary light. It has a road surface image photographing means for photographing, for example, a CCD camera 22 and an analyzing means 23 for analyzing a road surface image based on information from the CCD camera 22.
- the analysis means 23 includes a brightness value determination means 231 for determining a brightness value of the road surface P based on information from the CCD camera, and a Fourier transform means 232 for performing a Fourier transform on the determined brightness value. And frequency analysis means 233 for frequency-analyzing the Fourier-transformed data.
- the frequency-analyzed data is transmitted from the frequency analysis means 233 to the road surface friction coefficient estimation means 5. Will be sent to
- Road surface image analysis is performed to detect changes in the amount of water on the road surface.
- the road surface illuminated by the auxiliary light emitted from the auxiliary light irradiation means 21 is photographed by the CCD camera 22.
- the road surface when the road surface is wet, the road surface is likely to be irregularly reflected by light because the water covers the asphalt so as not to exceed the unevenness of the asphalt. For this reason, in a running image, a line with higher luminance extends in the traveling direction as compared with an image on a dry road surface.
- a line with a high luminance sometimes extends in the traveling direction.
- the brightness value is taken in the direction perpendicular to the running direction, and the frequency analysis of the appearance degree of the height difference is performed.
- the line that takes the luminance value should be around the area where the auxiliary light source appears in the state of a water film.
- FIG. 10 is a diagram showing, as measurement examples, the results of image analysis on a dry road “Dry Surface”, a wet road “WetSurface”, and a water film road “Water film Surface”.
- one a2 is a dry road “0 Surface”, 10—bl, one b2 is a wet road “Wet Surface”, 10—c2, and one c2 are a water road “Water film Surface”.
- the horizontal axis is the spatial frequency based on the image width.
- processing is performed so that the section of 120 Hz is integrated, and the numerical value is divided by the average value of the luminance values in order to cancel the brightness of the screen.
- FIG. 11 is a characteristic diagram showing the relationship between the image analysis result (the amount obtained by dividing the Fourier transform by the average luminance value; horizontal axis) and the road surface friction coefficient (vertical axis).
- the image analysis well captures the change of the road surface friction coefficient due to the change of the road surface water content, and especially the wet road and the water film with a friction coefficient of about 0.5-0.7 (Fig. 11). It is effective for detecting the area enclosed by the symbol W)! /
- the road surface temperature measuring means 3 has a moving means 31 for measurement, an infrared radiation thermometer 32, and a measurement result creating means 33, as shown in detail in FIG. Then, the temperature data created by the measurement result creating means 33 is sent to the road surface friction coefficient estimating means 5 by a known means.
- the road surface temperature was measured using the infrared radiation thermometer 32, which is non-contact and easy to handle.
- FIGS. 12 and 13 show the results of temperature measurement in a cold region using the road surface temperature measurement means 3 described above.
- the measurement was performed by moving from a fresh snow road to an icy road with fresh snow, assuming a slippery road surface at the beginning of snowfall.
- FIG. 12 shows the measurement results of the albedo value (vertical axis) over time (horizontal axis)
- FIG. 13 shows the measurement results of the road surface temperature (vertical axis) over time (horizontal axis).
- Road surface temperature is affected by temperature fluctuations, and it is difficult to evaluate only its absolute value.
- the three measurement techniques that is, the method using the road surface light reflection technology (albedo value measurement technology), the method using the road surface image analysis, and the method using the road surface temperature have a single friction coefficient.
- discrimination is possible, assuming an actual traveling environment, it is effective to increase the accuracy of determining (estimating) the road surface by supplementing the characteristics of each.
- FIG. 14 is a diagram illustrating the concept of sensor fusion. That is, the measurement results of the albedo value “Albedo” a, the road surface image processing data “Road Image Processing Results” F, and the road surface temperature data “Road Temperature” T are obtained by orthogonal albedo values and road surface image processing data, respectively. This is reflected on the three-dimensional space D composed of each axis of the road surface temperature data, and the sensor fusion is most certain to find the (high accuracy!) Friction coefficient. By doing so, it became possible to calculate a frictional coefficient that could not be grasped with a single sensor.
- FIG. 15 is a three-dimensional characteristic diagram showing sensor fusion of albedo value a and image processing F.
- One of the horizontal axes is the albedo value a, the other one is the image processing (Fourier transform value) F, and the vertical axis is the road friction coefficient S.
- FIG. 16 is a three-dimensional characteristic diagram that is a sensor fusion of the albedo value a and the road surface temperature T.
- One of the horizontal axes is the albedo value a, the other one is the road surface temperature T, and the vertical axis is the road surface coefficient of friction S.
- the measured data Ddl gathers in a plane D1 (two-dimensional) composed of the albedo axis a and the road surface friction coefficient axis S, It cannot provide accurate results.
- the measurement data Ddl is valid because it is accurately represented as three-dimensional, and the detailed data of the dry path, the wet path, and the water path are detailed. A coefficient of friction is provided.
- measurement data Dd2 is effective because it is accurately represented as three-dimensional, and it is effective to improve the estimation accuracy of road surface friction coefficient S on snow and ice roads. Can be done.
- FIG. 17 shows the basic concept of the road surface friction coefficient estimation algorithm.
- estimate The road surface friction coefficient estimating means is based on the road surface friction coefficient estimating means, based on the measured values of the road surface a, the image analysis result F, and the road surface temperature T, using the fuzzy logic based on the fuzzy logic using the membership function.
- the fuzzy logic controller 50 provided in 5
- step S1 the albedo value a is measured by the albedo value measuring means 1 over a predetermined range (road surface P in the measurement range).
- step S2 the road surface friction coefficient estimating means 5 determines whether or not the measurement of the albedo value a has been completed.
- the process proceeds to step S3. If not completed (NO in step S2), the loop of step S2 is repeated until completed.
- step S3 image processing is performed on a predetermined range (road surface P in the measurement range) using road surface image processing means 2.
- step S4 the road surface friction coefficient estimating means 5 determines whether or not the image processing has been completed, and if completed (YES in step S4), proceeds to step S5. If not completed (NO in step S4), the loop of step S4 is repeated until completed.
- step S5 the road surface temperature is measured for a predetermined range (road surface P in the measurement range) by using the road surface temperature measuring means 3.
- step S6 the road surface friction coefficient estimating means 5 determines whether or not the measurement of the road surface temperature has been completed, and if completed (YES in step S6), proceeds to step S7. If not completed (NO in step S6), the loop of step S6 is repeated until completed.
- step S7 the road surface friction coefficient estimating means 5 completes the measurement of the albedo value a, the road surface image processing, and the road surface temperature for the predetermined range (road surface P in the entire measurement range to be measured). It is determined whether or not the power is good.
- step S7 If the albedo value a, the road surface image processing, and the measurement of the road surface temperature have all been completed (YES in step S7), proceed to the next step S8, and if not completed (step S7). (NO), return to step SI, and repeat step SI and subsequent steps again.
- step S8 the road surface friction coefficient estimating means 5 performs sensor fusion based on the albedo value a, the road surface image processing result, and the road surface temperature data as described above, and estimates the road surface friction coefficient.
- FIG. 19 is a characteristic diagram showing the correlation between the friction coefficient (vertical axis) estimated by the above-described method and the road surface friction coefficient (horizontal axis) confirmed by a lock braking test using an actual vehicle.
- the correlation R shows a very high correlation of 0.98.
- the albedo value measuring unit 1 having the light irradiating unit 11 and the light receiving unit 12 allows the road surface friction coefficient due to the roughness of the road surface to be increased. Can be accurately and accurately detected.
- the albedo value measuring means is a non-contact type, it is easy to handle and there is no fear of breakage.
- the road surface image processing means 2 can measure a change in the water content of the road surface. By measuring the change in the water content of the road surface, it is possible to grasp the tendency of irregular reflections on the road surface depending on the extent to which water covers the unevenness caused by the road surface roughness. Then, by following the change in the luminance value due to the irregular reflection, it is possible to accurately grasp the road surface.
- a movable road surface temperature measuring means is provided, and by detecting a difference in thermal emissivity in each road surface state, the road surface state can be accurately grasped.
- the configuration of the device is substantially the same, and only the estimation process is different.
- step S11 the albedo value a is measured by the albedo value measuring means 1 over a predetermined range (road surface in the measurement range).
- step S12 the friction coefficient estimating means 100 determines whether or not the measurement of the albedo value a has been completed. When the measurement has been completed (YES in step S12), the process proceeds to step S13. If not completed (NO in step S12), repeat the loop of step SI2 until completed.
- step S13 image processing is performed on a predetermined range (road surface in the measurement range) using the road surface image processing means 2.
- step S14 the friction coefficient estimating means 100 determines whether or not the image processing has been completed, and if completed (YES in step S14), proceeds to step S15. If not completed (NO in step S14), the loop of step S14 is repeated until the processing is completed.
- step S15 the road surface temperature is measured for a predetermined range (road surface in the measurement range) by using the road surface temperature measuring means 3.
- step S16 the friction coefficient estimating means 100 determines whether or not the measurement of the road surface temperature has been completed. If the measurement has been completed (YES in step S16), the process proceeds to step S17. If not completed (NO in step S16), repeat the loop in step S16 until completed
- step S17 the road surface friction coefficient estimating means 5 completes the measurement of the albedo value a, the road surface image processing F, and the road surface temperature T for a predetermined range (the road surface in the entire measurement range to be measured). Judge your strength! /
- step S17 If all the measurements of the albedo value a, the road surface image processing F, and the road surface temperature T have been completed (YES in step S17), the process proceeds to the next step S18, and if not completed (step S17). NO), returning to step S11, and repeating step S11 and subsequent steps again.
- step S18 the road surface friction coefficient estimating means 5 determines whether or not the road surface temperature is equal to or higher than a predetermined value (for example, 5 ° C.). If it is less than the predetermined value (NO in step S18), the flow proceeds to step S20.
- a predetermined value for example, 5 ° C.
- step S19 the road surface friction coefficient estimating means 5 performs sensor fusion using the albedo value a and the image processing result F to obtain a friction coefficient, and then ends the estimation control.
- step S20 the road surface friction coefficient estimating means 5 performs sensor fusion based on the albedo value a and the road surface temperature T to obtain the friction coefficient S, and then ends the estimation control.
- the illustrated embodiment is merely an example, and is not intended to limit the technical scope of the present invention.
- FIG. 1 is a block diagram showing a configuration of a friction coefficient estimation device according to a first embodiment of the present invention.
- FIG. 2 is a block diagram showing a configuration of an albedo value measuring unit according to the first embodiment of the present invention.
- FIG. 3 is a block diagram showing a configuration of a road surface image processing means according to the first embodiment of the present invention.
- FIG. 4 is a block diagram showing a configuration of a road surface temperature measuring means according to the first embodiment of the present invention.
- FIG. 5 is a flowchart illustrating a friction coefficient estimation method according to the first embodiment of the present invention.
- FIG. 6 is a flowchart illustrating a friction coefficient estimation method according to a second embodiment of the present invention.
- FIG. 7 is a block diagram conceptually showing road surface characteristics.
- FIG. 8 is a characteristic diagram showing a relationship between road surface roughness and friction coefficient.
- FIG. 9 is a characteristic diagram showing a relationship between an albedo value and a road surface friction coefficient.
- FIG. 10 is a characteristic diagram showing a road surface image analysis result in the embodiment of the present invention.
- FIG. 11 is a characteristic diagram showing a relationship between a road surface image analysis result and a road surface friction coefficient.
- FIG. 12 is a measurement result in a cold region according to an embodiment of the present invention, showing measurement data showing a temporal change of an albedo value.
- FIG. 13 is a measurement result in a cold region according to the embodiment of the present invention, showing measurement data showing a temporal change of a road surface temperature.
- FIG. 14 is a conceptual diagram of a sensor fusion according to the embodiment of the present invention.
- FIG. 15 shows an albedo value and sensor fusion of image processing according to the embodiment of the present invention.
- FIG. 16 is a sensor fusion of albedo value and road surface temperature according to the embodiment of the present invention.
- FIG. 17 is a block diagram showing a road friction coefficient estimation algorithm according to the embodiment of the present invention.
- FIG. 18 is a table showing memberships and rules used in a road surface friction coefficient estimation algorithm according to the embodiment of the present invention.
- FIG. 19 is a correlation diagram showing a correlation between a result estimated by the embodiment of the present invention and an actual measurement location. Explanation of symbols
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Cited By (6)
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JP2008249560A (ja) * | 2007-03-30 | 2008-10-16 | Bridgestone Corp | タイヤ性能予測方法、地盤シミュレーション方法、タイヤ設計方法、記録媒体及びタイヤ性能予測プログラム |
JP2009083765A (ja) * | 2007-10-02 | 2009-04-23 | Jtekt Corp | 車両制御装置 |
WO2009074690A1 (de) * | 2007-12-13 | 2009-06-18 | Technische Universität Ilmenau | Vorrichtung und verfahren zur ermittlung des reibungszustandes einer fahrbahnoberfläche sowie dessen verwendung |
WO2011158306A1 (ja) * | 2010-06-18 | 2011-12-22 | 本田技研工業株式会社 | 路面反射率分類のためのシステム |
CN102297849A (zh) * | 2010-06-22 | 2011-12-28 | 株式会社Pfu | 摩擦系数估计设备和摩擦系数估计方法 |
WO2020195231A1 (ja) * | 2019-03-25 | 2020-10-01 | 株式会社デンソー | 車両における路面状態判定装置、運転支援システムおよび路面状態判定方法 |
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WO2020195231A1 (ja) * | 2019-03-25 | 2020-10-01 | 株式会社デンソー | 車両における路面状態判定装置、運転支援システムおよび路面状態判定方法 |
US11970171B2 (en) | 2019-03-25 | 2024-04-30 | Denso Corporation | Road surface condition determination device for vehicle, driving assistance system, and road surface condition determination method |
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