Road surface type detection device

Abstract

Provided is a road-surface type detection device that can discriminate, in real time, an asphalt that is a standard road-surface type from other road-surface types by comparing the size and the frequency of a strain sensor signal waveform excellent in information detection in a low speed area. A road-surface type detection device 10 for estimating a plurality of road-surface types (asphalt, gravel road, grassy road) comprises: at least one of sensor elements 1, 2, 3 for detecting a physical quantity of a vehicle; a road-surface type estimation unit 4; and a storage unit 411 for storing output values that correspond to the plurality of road-surface types for sensor elements 1, 2, 3. One road-surface type (asphalt, gravel road, grassy road) from among the plurality of road-surface types is estimated from the size of the sensor signal waveform output by sensor elements 1, 2, 3. As a result, an asphalt can be discriminated from other road-surface types in real time.

Description

[Technical Field]

[0001] The present invention relates to a safe driving support device for a vehicle, and more particularly to a road surface type detection device for improving the control accuracy of a brake system or the like. [Background technology]

[0002] In recent years, in order to realize autonomous driving, tire sensor technology has been actively developed to provide safer driving conditions by detecting factors such as the slipperiness of the road surface and the load on the tires based on information obtained from the tires. This is because providing safer driving conditions can prevent accidents caused by insufficient brake control. To build such a safety control system, it is necessary to provide braking force that does not lock the tires, and to accurately detect road surface types, such as distinguishing between asphalt and gravel roads. Tire strain sensors can detect the load acting on the tire by detecting the strain and deformation of the tire, which is expected to prevent vehicle trouble and improve driving safety by detecting driving and road surface conditions.

[0003] A conventional technique for such a detection device is disclosed in Patent Document 1.

[0004] Patent Document 1 addresses the issue of "providing a lane keeping control system that can accurately detect the road surface condition or road surface μ of the road surface on which the vehicle is traveling and can perform more accurate lane keeping control based on the detection results," and describes the following technology: "A road surface condition estimation device is configured using a tire-side device 1 and a vehicle-side device 2, and the road surface condition can be grasped based on the road surface condition data sent from the tire-side device 1. This makes it possible to accurately detect the road surface condition or road surface μ of the road surface on which the vehicle is traveling and to perform more accurate lane keeping control based on the detection results. In particular, since the tire-side device 1 estimates the road surface condition by detecting vibrations of the tire's contact patch, it can estimate the road surface condition more accurately. Therefore, more accurate lane keeping control becomes possible."

[0005] In other words, the output vibration waveform of the vibration detection device (acceleration sensor) is frequency analyzed, and depending on the magnitude of the integrated amount of the output level, it is possible to detect whether the road surface is gravel or not. [Prior art documents] [Patent documents]

[0006] [Patent Document 1] Japanese Patent Application Laid-Open No. 2016-88429 Summary of the Invention [Problem to be solved by the invention]

[0007] In the technology described in Patent Document 1, the output vibration waveform of a vibration detection device (acceleration sensor) is frequency analyzed, and the road surface condition is detected based on the magnitude of the integrated amount of the output level. However, to obtain a valid vibration waveform from the acceleration sensor, a vehicle speed of 30 km / h or more is generally required, and no consideration is given to detecting road surface types at low speeds.

[0008] Furthermore, frequency analysis of vibration waveforms generally requires a large amount of data processing, which raises concerns that logical calculations will take a long time, and does not take into consideration real-time detection of road surface types.

[0009] In view of the above circumstances, the object of the present invention is to provide a road surface type detection device that can distinguish between the reference road surface type asphalt and other road surface types (such as gravel roads) in real time by comparing the magnitude and frequency of strain sensor signal waveforms, which are excellent at detecting information at low speeds. [Means for solving the problem]

[0010] In order to achieve the above object, the present invention is configured as follows. The road surface type detection device that estimates multiple road surface types is Tire distortion Detect Strain Sensor and 、storing output values ​​of the sensor element corresponding to the plurality of road surface types; The magnitude of the waveform of asphalt, which is one of the plurality of road surface types, is stored as the magnitude of the reference waveform. a storage unit for storing the a determination unit that compares a positive level where the signal waveform from the strain sensor changes to a positive value and a negative level where the signal waveform changes to a negative value with a peak value of the reference waveform stored in the memory unit to determine one of the plurality of road surface types; Equipped with The determination unit compares the peak value of the signal waveform output by the strain sensor with the peak value of the reference waveform, and when it is confirmed multiple times in succession that the peak value of the signal waveform output by the strain sensor is the same as the peak value of the reference waveform, it determines that the road surface type is asphalt, which is one of the multiple road surface types; when it is confirmed at least once that the peak value of the signal waveform output by the strain sensor is greater than or smaller than a certain value than the peak value of the reference waveform, it determines that the road surface type is a gravel road, which is another of the multiple road surface types; and when it is confirmed multiple times in succession that the peak value of the signal waveform output by the strain sensor is smaller than the peak value of the reference waveform, it determines that the road surface type is a grass road, which is another of the multiple road surface types. . In addition, in the brake control system, a road surface type detection device; The aforementioned Road surface type detection device is estimated The road Based on surface type The aforementioned a braking condition determination unit that determines a braking condition of the vehicle; The aforementioned and a brake control device that controls the braking force of the vehicle. [Effects of the Invention]

[0011] By comparing the magnitude and frequency of the strain sensor signal waveform, which is excellent at detecting information at low speeds, it is possible to provide a road surface type detection device that can distinguish between the reference road surface type of asphalt and other road surface types (such as gravel roads) in real time.

[0012] Problems, configurations, and effects other than those described above will become apparent from the following description of the embodiments. [Brief explanation of the drawings]

[0013] [Figure 1] 1 is a configuration diagram showing a vehicle equipped with a road surface type detection device according to a first embodiment. [Figure 2] 1 is a configuration diagram showing a road surface type detection device according to a first embodiment. [Figure 3] 4 is an explanatory diagram showing a sensor signal waveform of a strain sensor according to a rotation state of a tire according to the first embodiment. FIG. [Figure 4] FIG. 4 is a waveform diagram showing a sensor signal waveform of a strain sensor according to a rotation state of a tire according to the first embodiment. [Figure 5] FIG. 3 is an explanatory diagram showing a sensor signal waveform of a strain sensor in one period according to the first embodiment. [Figure 6] 4 is a flowchart for deriving a table of parameters mixed in a sensor signal waveform of the strain sensor according to the first embodiment. [Figure 7]FIG. 4 is an explanatory diagram showing an air pressure correlation table showing the correlation between the peak value of the positive level of the sensor signal waveform of the strain sensor according to the first embodiment and the air pressure. [Figure 8] FIG. 4 is an explanatory diagram showing a speed correlation table showing the correlation between the peak value of the positive level of the sensor signal waveform of the strain sensor according to the first embodiment and the speed. [Figure 9] FIG. 4 is an explanatory diagram showing a temperature correlation table indicating the correlation between the peak value of the positive level of the sensor signal waveform of the strain sensor according to the first embodiment and the temperature. [Figure 10] FIG. 4 is an explanatory diagram showing a load amount correlation table showing the correlation between the load amount and the peak value of the positive level of the sensor signal waveform of the strain sensor according to the first embodiment. [Figure 11] FIG. 2 is an explanatory diagram showing a table including various tables according to the first embodiment. [Figure 12] 4 is a flowchart for estimating a road surface type from a sensor signal waveform of a strain sensor according to the first embodiment. [Figure 13] 10 is a graph showing the results of verifying the feasibility of the road surface type estimation flowchart based on the measured sensor signal waveform of the strain sensor on a gravel road according to the first embodiment. [Figure 14] 10 is a graph showing the results of verifying the feasibility of the road surface type estimation flowchart based on the waveform of a sensor signal measured on an asphalt road surface by the strain sensor according to the first embodiment. [Figure 15A] 10 is a specific example of a road surface type detection logic flow according to the first embodiment. [Figure 15B] 1 is a graph for comparing the waveform of an asphalt road surface with the waveform of a gravel road surface. [Figure 16] 10 is a flowchart for estimating a soft road surface from a sensor signal waveform of a strain sensor according to a second embodiment. [Figure 17] 10 is a graph showing the results of verifying the feasibility of the road surface type estimation flowchart based on the soft road surface actual measurement sensor signal waveform of the strain sensor according to the second embodiment. [Figure 18] 10 is a specific example of a road surface type detection logic according to the second embodiment. [Figure 19A]FIG. 10 is a configuration diagram showing a brake control system according to a third embodiment. [Figure 19B] 11 is a graph showing the relationship between the braking force that does not lock and the road surface type and the road surface resistivity according to Example 3. DETAILED DESCRIPTION OF THE INVENTION

[0014] Hereinafter, embodiments of the present invention will be described in detail with reference to the drawings. In all drawings used to explain the embodiments, the same components are generally designated by the same reference numerals, and repeated explanations thereof will be omitted. The present invention should not be interpreted as being limited to the description of the embodiments shown below. Those skilled in the art will readily understand that the specific configuration can be changed within the scope of the idea or intent of the present invention.

[0015] The designations "first," "second," "third," etc. in this specification are used to identify components and do not necessarily limit the number or order. Furthermore, numbers used to identify components are used in different contexts, and numbers used in one context do not necessarily indicate the same configuration in another context. Furthermore, this does not prevent a component identified by a certain number from also serving the function of a component identified by another number.

[0016] In order to facilitate understanding of the invention, the position, size, shape, range, etc. of each component shown in the drawings etc. may not represent the actual position, size, shape, range, etc. Therefore, the present invention is not necessarily limited to the position, size, shape, range, etc. disclosed in the drawings etc.

[0017] As used herein, elements referred to in the singular are intended to include the plural unless the context clearly indicates otherwise. [Example]

[0018] Example 1 <Overall vehicle configuration> FIG. 1 is a schematic diagram showing a vehicle 100 equipped with a road surface type detection device 10 according to a first embodiment of the present invention.

[0019] 1, vehicle 100 includes four tires 101, one ECU 102, and one reporting unit 103. Vehicle 100 also includes four air pressure sensors 1, four temperature sensors 2, and four strain sensors 3. Note that vehicle 100 may be a two-wheeled vehicle or a four-wheeled vehicle that travels on road surface 20 (shown in FIG. 3).

[0020] The vehicle 100 travels on a road surface 20 by rotating four tires 101. A person rides in the vehicle 100.

[0021] The tire 101 is in contact with the road surface 20 and receives the load of the vehicle 100. The tire 101 rotates. The tire 101 is a rubber member.

[0022] The ECU 102 is a control unit that controls the vehicle 100. The ECU 102 has an arithmetic processing unit, a storage unit, and an input / output port electrically connected to various sensors, an arithmetic processing unit such as a CPU, a storage unit such as a memory, and the reporting unit 103.

[0023] The reporting unit 103 is a monitor of the car navigation system. The display screen of the reporting unit 103 is switched between a car navigation screen and a road surface type report screen by interrupt processing from the ECU 102. The display of the display screen of the reporting unit 103 is controlled based on the control of the ECU 102.

[0024] The air pressure sensor 1 acquires the air pressure of each tire 101 and outputs it to the ECU 102. The temperature sensor 2 acquires the temperature of each tire 101 and outputs it to the ECU 102. The strain sensor 3, which is a sensor element, acquires a sensor signal waveform 15 (shown in FIG. 3) in each tire 101 and outputs it to the ECU 102.

[0025] <Road surface type detection device 10> FIG. 2 is a configuration diagram showing a road surface type detection device 10 according to the first embodiment.

[0026] In FIG. 2, the road surface type detection device 10 relates to a safe driving support device for the vehicle 100, and in particular, is intended to prevent accidents caused by insufficient brake control by providing a safe driving state.

[0027] The road surface type detection device 10 is a device that detects the type of road surface that affects the grip of tires 101 mounted on a vehicle 100.

[0028] 2, the road surface type detection device 10 includes a distortion sensor 3, a road surface type estimation unit 4, and a reporting unit 103. The road surface type detection device 10 detects the road surface type based on the output signal waveform.

[0029] <Strain sensor 3> The strain sensor 3 is a sensor element. The strain sensor 3 is a semiconductor, and converts a change in resistance into a strain amount and outputs the converted amount. One strain sensor 3 is disposed in each tire 101. The strain sensor 3 outputs a sensor signal waveform 15 (shown in FIG. 3) having a reference level 151 (shown in FIG. 3), a positive level that changes more positively than the reference level 151, and a negative level that changes more negatively than the reference level 151.

[0030] <Road surface type estimation section 4> The road surface type estimation unit 4 performs the function of the road surface type estimation unit 4 by executing a program in the ECU 102. The road surface type estimation unit 4 receives the sensor signal waveform 15 output by the strain sensor 3.

[0031] The road surface type estimation unit 4 acquires the air pressure of the tire 101 from the air pressure sensor 1. The road surface type estimation unit 4 acquires the temperature of the tire 101 from the temperature sensor 2. The road surface type estimation unit 4 acquires the speed by subtracting the tire circumference from the output period of the sensor signal waveform 15.

[0032] The road surface type estimation unit 4 may acquire the speed from a speed sensor or the like. The road surface type estimation unit 4 corrects the sensor signal waveform output by the strain sensor 3 according to the acquired parameter conditions such as air pressure, temperature, speed, and load amount, and estimates the road surface type from the difference between this corrected signal and a reference waveform stored in the memory unit 411. The road surface type estimation unit 4 transmits the estimated road surface type to the reporting unit 103.

[0033] The road surface type estimation unit 4 includes a storage unit 411 , a signal waveform correction unit 412 , and a determination unit 413 .

[0034] The storage unit 411 stores output values ​​of the sensor element 3 etc. corresponding to a plurality of road surface types. The storage unit 411 has reference waveforms (peak values ​​at positive and negative levels) acquired under reference conditions (air pressure, temperature, speed, load) for the asphalt road surface type of the sensor signal waveform 15 output by the strain sensor 3, and a variation table 5 for the variation for each parameter condition.

[0035] The signal waveform correction unit 412 corrects the air pressure, speed, temperature, and load amount, which are parameters of the mixed signal mixed in the sensor signal waveform 15, to a signal waveform under specified conditions using the values ​​of the change amount table 5 stored in the memory unit 411 so as to cancel the difference from each reference condition, and transmits the corrected signal waveform to the judgment unit 413.

[0036] The determination unit 413 compares the corrected positive level peak value 152 (shown in FIG. 3) and negative level peak value 153 (shown in FIG. 3) sent by the signal waveform correction unit 412 with the reference waveforms stored in the variation table 5 stored in the memory unit 411, and if they are equal to each other, determines that the road surface is an asphalt road, and if they are larger or smaller than this, determines that the road surface is a gravel road or the like other than asphalt. The determination unit 423 sends the estimated road surface type to the report unit 103.

[0037] <Sensor signal waveform 15> 3 is an explanatory diagram showing a sensor signal waveform 15 of the strain sensor 3 according to the rotation state of the tire 101 according to Example 1. As shown in Fig. 3, the strain sensor 3 disposed inside the tire 101 outputs a sensor signal waveform 15 that changes depending on the state of the rotating tire 101.

[0038] The strain sensor 3 outputs a sensor signal waveform 15 having a reference level 151, a positive level that changes to the positive side of the reference level 151, and a negative level that changes to the negative side of the reference level 151.

[0039] The strain sensor 3 maintains the reference level 151 of the sensor signal waveform 15 when not in contact with the ground. The strain sensor 3 outputs a peak value 152 of the positive level of the sensor signal waveform 15 when the tire 101 is in contact with the road surface 20. The strain sensor 3 outputs a peak value 153 of the negative level of the sensor signal waveform 15 at the moment when the tire 101 touches or separates from the road surface 20.

[0040] Here, the moment when the tire 101 contacts or separates from the road surface 20 is a sensor displacement point. The period between two sensor displacement points is a contact period when the tire 101 contacts the road surface 20.

[0041] In this way, the detected sensor signal waveform 15 changes depending on various physical quantities (load, air pressure, speed, temperature).

[0042] FIG. 4 is a waveform diagram showing a sensor signal waveform 15 of the strain sensor 3 according to the rotation state of the tire 101 according to the first embodiment.

[0043] As shown in Figures 4 and 3, as the tire 101 rotates, the sensor signal waveform 15 of the strain sensor 3 alternates between a reference level 151, a negative level that changes to a negative side relative to the reference level 151, a positive level that changes to a positive side relative to the reference level 151, and a negative level that changes to a negative side relative to the reference level 151.

[0044] The signal value of sensor signal waveform 15 can be expressed by signal amplitude. Sensor signal waveform 15 is also expressed by amplitude in Figures 3 and 4. The signal amplitude here may be any value that represents the amplitude of sensor signal waveform 15.

[0045] Sensor signal waveform 15 has a waveform in which a falling waveform is successively placed before and after a rising waveform, as shown in Figure 5. For example, the amplitude of the second falling waveform can be treated as the amplitude of sensor signal waveform 15. This will be assumed in the following.

[0046] Fig. 5 is an explanatory diagram showing a sensor signal waveform 15 of the strain sensor 3 in one period according to Example 1. Fig. 5 is an enlarged view of part A in Fig. 4. As shown in Fig. 5, both the peak value 152 of the positive level and the peak value 153 of the negative level contain information about the road surface type.

[0047] <How to create Change Table 5> FIG. 6 is a flowchart for deriving the variation table 5 that stores the minus level peak value 153 and the plus level peak value 152 under each condition including the reference condition of the sensor signal waveform 15 of the strain sensor 3 according to the first embodiment.

[0048] 6, in step S101, a predetermined control unit for the table creation test runs the vehicle 100 while maintaining the reference air pressure, temperature, speed, and load, and acquires the output of the strain sensor 3 for the asphalt road surface. The output waveform acquired in this manner is the reference waveform.

[0049] In step S102, a predetermined control unit for the table creation test acquires a relationship representing the change from the reference waveform of the sensor signal waveform 15 of the strain sensor 3 when the vehicle 100 is driven on an asphalt road surface while changing each of the reference air pressure, temperature, speed, and load amount.

[0050] In step S103, the control section stores the reference waveform and the amount of change thereof in the amount of change table 5 for the sensor signal waveform 15 acquired in step S102.

[0051] The change in sensor signal waveform 15 when each condition changes does not necessarily have to be expressed using the difference from the reference waveform and the difference from the reference signal value. However, since the absolute value of the signal value differs for each vehicle model and tire type, it becomes necessary to create data similar to change amount table 5 for each absolute value in advance, which would significantly increase the amount of data. Therefore, by describing the data using the difference from the reference value, the amount of data is reduced.

[0052] <Change Table 5> The correlations between the peak value 152 of the positive level of the sensor signal waveform 15 of the strain sensor 3 and the air pressure, temperature, speed, and load amount are stored in advance in the variation table 5 by varying these values.

[0053] 7 is an explanatory diagram of the peak value correction amount-air pressure correlation showing the correlation between the air pressure and the correction amount of the peak value at the positive level of the sensor signal waveform 15 of the strain sensor 3 according to Example 1. The correlation shown in Fig. 7 is such that as the air pressure increases, the correction amount of the peak value 152 at the positive level of the sensor signal waveform 15 decreases.

[0054] FIG. 8 is an explanatory diagram of the peak value correction amount-speed correlation showing the correlation between the correction amount of the peak value 152 at the positive level of the sensor signal waveform 15 of the strain sensor 3 according to the first embodiment and the speed.

[0055] The correlation shown in FIG. 8 is such that as the speed increases, the amount of correction for the peak value 152 at the positive level of the sensor signal waveform 15 increases.

[0056] FIG. 9 is an explanatory diagram of the peak value correction amount temperature correlation, which shows the correlation between the correction amount of the peak value 152 at the positive level of the sensor signal waveform 15 of the strain sensor 3 according to the first embodiment and the temperature.

[0057] The correlation shown in FIG. 9 is such that the correction amount for peak value 152 at the positive level of sensor signal waveform 15 increases as the temperature increases.

[0058] 10 is an explanatory diagram of the peak value correction amount-load amount correlation showing the correlation between the load amount and the correction amount of the peak value 152 at the positive level of the sensor signal waveform 15 of the strain sensor 3 according to Example 1. The correlation shown in Fig. 10 is such that the correction amount of the peak value 152 at the positive level of the sensor signal waveform 15 increases as the load amount increases.

[0059] Fig. 11 is an explanatory diagram showing a change amount table 5 that includes various reference tables 5-1 to 5-5 according to Example 1. As shown in Fig. 11, the change amount table 5 includes the reference table 5-5 of Example 1 and the reference tables 5-1 to 5-4 that indicate the various correlations shown in Figs. 7 to 10. Therefore, it is possible to estimate the amount of correction from the change amount table 5 stored in the storage unit 411 for the peak value 152 of the positive level of the sensor signal waveform 15 output by the strain sensor 3 in the vehicle 100 running on various road surfaces.

[0060] Similarly, a table for estimating the amount of correction for the minus level peak value 153 can be obtained in the same way as the change amount table 5.

[0061] <Road surface type detection method> FIG. 12 is a flowchart for estimating the type of road surface on which the tire 101 is traveling from the sensor signal waveform 15 of the strain sensor 3 according to the first embodiment.

[0062] The flowchart of the road surface type estimation method shown in FIG. 12 is repeatedly executed while the vehicle 100 is traveling.

[0063] When the road surface type estimation method is started, in step S201, the road surface type estimation unit 4 checks the driving conditions in the signal waveform correction unit 412 in the driving state of the vehicle 100. The driving conditions are conditions that match the driving conditions when the change amount table 5 is derived.

[0064] When the driving conditions are confirmed in the signal waveform correction unit 412 in step S201, the process proceeds to step S202.

[0065] In step S202, in order to correct the sensor signal waveform 15 output by the strain sensor 3 to a signal waveform under the same reference conditions as the reference waveform, the road surface type estimation unit 4 extracts a correction value that matches the conditions stored in the storage unit 411 and subtracts it from the sensor signal waveform 15 to obtain a corrected signal waveform. After processing in step S202, the process proceeds to step S203.

[0066] In step S203, the road surface type estimation unit 4 compares the peak value of the reference waveform with the positive level peak value 152 and the negative level peak value 153 of the corrected signal waveform obtained by correcting the sensor signal waveform 15 detected by the distortion sensor 3. After processing in step S203, the process proceeds to step S204.

[0067] In step S204, the road surface type estimation unit 4 determines the road surface type based on the result of comparing the reference waveform with the corrected signal waveform obtained by correcting the sensor signal waveform 15 output by the distortion sensor 3 detected in step S203.

[0068] If a peak value of the corrected signal waveform is greater than a positive peak value of the reference waveform, or a peak value smaller than a negative peak value of the reference waveform is detected even once, the road is determined to be a gravel road.

[0069] As a result, the road surface type estimation unit 4 estimates that the road surface is a gravel road. The estimated road surface type is transmitted to the reporting unit 103. If it is determined to be a gravel road, the process ends after the process of step S204.

[0070] On the other hand, if a peak value of the correction signal waveform that is greater than the positive peak value of the reference waveform or a peak value of the correction signal waveform that is smaller than the negative peak value of the reference waveform cannot be detected even once, processing proceeds from step S204 to step S205.

[0071] In step S205, the road surface type estimation unit 4 determines the road surface type based on the result of comparing the reference waveform with the corrected signal waveform in step S203. If a positive or negative peak value of the reference waveform and a peak value of the corrected signal waveform that are equal to each other are detected multiple times in succession, the road surface type is determined to be asphalt.

[0072] As a result, the road surface type estimation unit 4 estimates the road surface type to be asphalt. The estimated road surface type is transmitted to the reporting unit 103. If the road surface type is estimated to be asphalt, the process ends once after the process of step S205.

[0073] If the road is not estimated to be asphalt in step S205, the process returns from step S205 to step S204.

[0074] <Verification of road surface type detection method> FIG. 13 is a graph showing the results of verifying whether the road surface type can be estimated according to the flowchart of FIG. 12 based on an example of the waveform of a sensor signal actually measured by the strain sensor 3 according to the first embodiment.

[0075] Figure 13 is a graph showing the waveform obtained when traveling on a gravel road. When the positive and negative peak values ​​obtained when traveling on an asphalt road are plotted together with Criterion A (shown by the dashed line), it can be seen that the magnitude of the peaks in the waveform on the gravel road is characteristic.

[0076] In the example shown in Figure 13, there is a mixture of peaks that are both larger and smaller than criterion A, making it easy to make a judgment. It can be seen that a gravel road can be judged as such when a positive or negative peak, or a peak value that is larger or smaller than criterion A, is detected at least once.

[0077] Figure 14 shows the waveform obtained when driving on an asphalt road. Both positive and negative peaks are repeated at similar levels, making it easy to judge. It can be seen that asphalt can be judged if the average level of positive or negative peaks can be detected about five times in a row.

[0078] The average level may be determined using the average value of the past 10 times. The determination standard may also be set to a range such as ±10%.

[0079] <Example of a specific road surface type detection logic flow> 15A is a specific example of a road surface type detection logic flow for the processing (road surface type determination) of step S204 and step S205 in the flowchart of the road surface type detection method shown in Fig. 12 according to Example 1. Note that absolute values ​​are used for comparison of magnitude with each of thresholds A, B, C, and D.

[0080] This is the specific judgment logic when a range of ±10% is specified for the average level judgment criterion in Figure 14. This logic judges a road to be gravel when either the positive peak or the negative peak has a peak value that is greater or smaller than the asphalt judgment criterion.

[0081] Therefore, thresholds A (-440), B (-360), C (-540), and D (660) are set as the criteria for determining asphalt within a range of ±10%, and a counter is used to count the number of times these conditions are met, and if it is detected even once, it is determined to be a gravel road.On the other hand, if the above conditions are not met, it will fall within the asphalt determination range, and if this is detected five times in a row, it will be determined to be an asphalt road surface.

[0082] FIG. 15B is a graph for comparing the waveform of an asphalt road surface with the waveform of a gravel road surface.

[0083] <Effects> As described above, according to the first embodiment, the magnitude of the reference waveform (asphalt road surface) stored in the memory unit 411 is compared with the magnitude of the sensor signal waveform output by the strain sensor 3, and when similar magnitudes are confirmed multiple times in succession, the road surface is estimated to be an asphalt road surface, and when the magnitude of the sensor signal waveform is confirmed at least once to be larger or smaller than the reference waveform, the road surface is estimated to be a gravel road, thereby achieving the effect of detecting the road surface type in real time even at low speeds.

[0084] Since the road surface type is determined based on the magnitude of the waveform, simple arithmetic logic processing is sufficient, the amount of calculation data required is small, processing speed can be increased, and the road surface type can be determined in real time regardless of vehicle speed.

[0085] Therefore, according to the first embodiment, it is possible to provide a road surface type detection device that can distinguish the road surface type in real time even in a low speed range.

[0086] <Example 2> Next, a second embodiment of the present invention will be described.

[0087] In the following description, since the configuration is similar to that of the first embodiment, the configuration diagram and description are omitted, and only the characteristic parts are described.

[0088] <Road surface type detection device 10> Although not shown in the figure, the road surface type detection device 10 of Example 2, like Example 1, relates to a safe driving support device for the vehicle 100, and in particular, is intended to prevent accidents caused by insufficient brake control, etc., by providing a safe driving condition.

[0089] The road surface type detection device 10 is a device that detects the road surface type that affects the grip force of the tires 101 mounted on the vehicle 100, and is equipped with a strain sensor 3, a road surface type estimation unit 4, and a reporting unit 103, and detects the road surface type based on the output signal waveform.

[0090] <Road surface type detection method> FIG. 16 is a flowchart for estimating the type of road surface on which the tire 101 is traveling from the sensor signal waveform 15 of the strain sensor 3 according to the second embodiment.

[0091] The flowchart of the road surface type detection method shown in FIG. 16 is repeatedly executed while the vehicle 100 is traveling.

[0092] When the road surface type detection method is performed, in step S301, the road surface type estimation unit 4 checks the driving conditions in the signal waveform correction unit 412 in the driving state of the vehicle 100. The driving conditions are conditions that match the driving conditions when deriving the table 5. Once the driving conditions are checked in the signal waveform correction unit 412 in step S301, the process proceeds to step S302.

[0093] In S302, in order to correct the sensor signal waveform 15 output by the strain sensor 3 to a signal waveform under the same reference conditions as the reference waveform, the road surface type estimation unit 4 extracts a correction value that matches the conditions stored in the storage unit 411 and subtracts it from the sensor signal waveform 15 to obtain a corrected signal waveform. After processing in step S302, the process proceeds to step S303.

[0094] In step S303, the road surface type estimation unit 4 compares the peak value of the reference waveform with the positive level peak value 152 and the negative level peak value 153 of the corrected signal waveform obtained by correcting the sensor signal waveform 15 detected by the distortion sensor 3. After the processing of step S303, the processing proceeds to step S304.

[0095] In step S304, the road surface type estimation unit 4 determines the road surface type based on the result of comparing the reference waveform in step S303 with the corrected signal waveform obtained by correcting the sensor signal waveform 15 output by the detected distortion sensor 3. If a peak value of the corrected signal waveform that is smaller within a specific range than the positive or negative peak value of the reference waveform is detected even once, the road surface type is determined to be a grass road (soft road).

[0096] As a result, the road surface type estimation unit 4 estimates the road surface to be a grass road. The estimated road surface type is sent to the reporting unit 103. If it is determined to be a grass road in the processing of step S304, the processing is temporarily terminated. Note that the detection determination may be changed from once to multiple times in succession.

[0097] On the other hand, if step S304 does not detect even one peak value of the corrected signal waveform that is smaller within the specific range than positive peak value 152 or negative peak value 153 of the reference waveform, the process proceeds to step S305.

[0098] In step S305, the road surface type estimation unit 4 determines the road surface type based on the result of comparing the reference waveform with the corrected signal waveform in step S303. If a peak value equal to the positive peak value 152 or negative peak value 153 of the reference waveform and the peak value of the corrected signal waveform is detected multiple times in succession, the road surface type is determined to be asphalt.

[0099] As a result, the road surface type estimation unit 4 estimates the road surface type to be asphalt. The estimated road surface type is transmitted to the reporting unit 103. If the road surface type is determined to be asphalt in the processing of step S305, the processing is temporarily terminated. If the road surface type is not determined to be asphalt in the processing of step S305, the processing returns to step S304.

[0100] <Verification of road surface type detection method> Fig. 17 is a diagram showing the results of verifying whether the road surface type can be estimated according to the flowchart of Fig. 16 based on an example of the sensor signal waveform actually measured by the strain sensor 3 according to Example 2. Fig. 17 shows the waveform when traveling on a grass road surface.

[0101] If the positive and negative peak values ​​when traveling on an asphalt road are plotted alongside Criterion A (dotted line), it can be seen that the magnitude of the peaks in the waveform on grass roads is characteristic.

[0102] In the case of grass roads, the peaks are smaller on average than in judgment criterion A, making it easy to determine whether the road is a grass road. It can be seen that a grass road can be determined when a positive or negative peak is detected within a specific range, even if only one small peak value is detected, relative to judgment criterion A.

[0103] The average level may be determined using the average value of the past 10 times. The determination criteria may also be set to a range such as 75%±10% of the reference waveform.

[0104] <Specific example of road surface type detection logic> Fig. 18 shows a specific example of road surface type detection logic for the processing (road surface type determination) of steps S304 and S305 in the flowchart of the road surface type detection method shown in Fig. 16 according to the second embodiment. This is specific determination logic when a range of ±10% is specified for the average level determination in Fig. 17. This logic determines a grass road when both the positive peak and the negative peak are smaller than the determination criterion for asphalt and the peak values ​​are detected within the range of ±10%.

[0105] Therefore, thresholds are set based on the criteria for determining asphalt (+540 and -360) and a range of ±10% (+360 and -240), and a grass road is determined if these conditions are met.

[0106] <Effects> As described above, according to the second embodiment, the magnitude of the sensor signal waveform output by the strain sensor 3 is compared with the reference waveform (asphalt) stored in the memory unit 411, and a grass road is estimated when the magnitude of the sensor signal waveform is smaller than the reference waveform and a peak value is detected once or multiple times continuously within a specified range, thereby achieving the effect of detecting road surface types other than asphalt and gravel roads in real time even at low speeds.

[0107] Therefore, according to the second embodiment, similarly to the first embodiment, it is possible to provide a road surface type detection device that can distinguish the road surface type in real time even in a low speed range.

[0108] Example 3 Next, a third embodiment of the present invention will be described.

[0109] 19A is a diagram illustrating a configuration example of a brake control system 20 according to a third embodiment of the present invention. It is an image diagram in which the road surface type detection device 10 according to the first or second embodiment is applied to the brake control system 20 of a vehicle.

[0110] The brake control system 20 includes a brake control device 51 and a road surface type detection device 10.

[0111] As in Example 1, the road surface type detection device 10 relates to a safe driving support device for the vehicle 100, and in particular is a device for preventing accidents caused by insufficient brake control, etc., by providing a safe driving condition.

[0112] The road surface type detection device 10 is a device that detects the road surface type that affects the grip force of the tires 101 mounted on the vehicle 100, and is equipped with a strain sensor 3, a road surface type estimation unit 4, and a brake control device 51.The road surface type estimation result based on the output signal waveform is sent to a braking condition determination unit 511 within the brake control device 51, and the braking conditions are determined within the system.

[0113] FIG. 19B is a graph showing the relationship between the braking force that does not lock and the road surface type and the road surface resistivity according to the third embodiment of the present invention.

[0114] The vertical axis of Figure 19B represents the braking force required to prevent locking, and the horizontal axis represents the coefficient of friction μ. Comparing the coefficient of friction μ on frozen roads, gravel roads, and asphalt, the coefficient of friction μ is smallest on frozen roads and largest on asphalt. Gravel roads are roughly halfway between frozen roads and asphalt. The braking force required to prevent locking increases as the coefficient of friction μ increases.

[0115] For example, if you brake on a gravel road with braking force equivalent to that on asphalt, the wheels will lock up, and the ABS will repeatedly try to avoid locking up, which will increase the braking distance.

[0116] On the other hand, if it can determine that the road is gravel, it can apply braking force that will prevent the vehicle from locking from the start, allowing for more efficient braking. In other words, by detecting changes in the road surface, the brake system can apply braking that is appropriate for the road surface.

[0117] The relationship between the friction coefficient μ and the braking force that does not lock the wheels, shown in FIG. 19B, is stored in the wheel lock condition table 6 that the brake control device 51 has.

[0118] The determination unit 413 calculates the friction coefficient μ of the road surface and outputs it to the braking condition determination unit 511. The braking condition determination unit 511 determines the braking force that will not lock the wheels based on the relationship between the friction coefficient μ stored in the wheel lock condition table 6 and the braking force that will not lock the wheels. The brake control device 51 controls the brakes so that the braking force determined by the braking condition determination unit 511 is achieved, using the vehicle speed and obstacle information as input information.

[0119] <Effects> As described above, by applying the road surface type detection device 10 according to the third embodiment of the present invention to the brake control system 20, highly accurate brake control becomes possible, and there is an effect that safer brake control becomes possible. As another example of the above-mentioned embodiments, there is an example in which the road surface type determination flow of Example 1 (FIG. 12) and the road surface type determination flow of Example 2 (FIG. 16) are combined to determine three types of road surface: asphalt, grass road, and gravel road.

[0120] The present invention is not limited to the above-described embodiments, but includes various modifications. For example, it is possible to replace part of the configuration of one embodiment with the configuration of another embodiment, or to add the configuration of another embodiment to the configuration of one embodiment. Furthermore, it is possible to add, delete, or replace part of the configuration of each embodiment with the configuration of another embodiment.

[0121] Furthermore, in the above-described embodiment, any one of a plurality of road surface types is estimated from the waveform of the sensor signal output by the strain sensor 3, but it is also possible to estimate any one of a plurality of road surface types by processing the waveform of the sensor signal output by the air pressure sensor 1 or the temperature sensor 2 in the same manner as the output signal waveform of the strain sensor 3. The air pressure sensor 1, the temperature sensor 2, and the strain sensor 3 can be collectively referred to as sensor elements that detect physical quantities of the vehicle. [Explanation of symbols]

[0122] 1...Air pressure sensor, 2...Temperature sensor, 3...Distortion sensor, 4...Road surface type estimation unit, 5...Change amount table, 5-1 to 5-4...Correlation table, 5-5...Reference table, 6...Wheel lock condition table, 10...Road surface type detection device, 20...Brake control system, 15...Sensor signal waveform, 51...Brake control device, 100...Vehicle, 101...Tire, 102...ECU, 103...Reporting unit, 151...Reference level, 152...Peak value of positive level, 153...Peak value of negative level, 411...Memory unit, 412...Signal waveform correction unit, 413...Determination unit, 511...Braking condition determination unit

Claims

1. In a road surface type detection device that estimates a plurality of road surface types, a strain sensor that detects strain in a tire of a vehicle; a storage unit that stores output values ​​of the strain sensor corresponding to the plurality of road surface types and holds the waveform magnitude of asphalt, which is one of the plurality of road surface types, as the magnitude of a reference waveform; a determination unit that compares a positive level where the signal waveform from the strain sensor changes to a positive value and a negative level where the signal waveform changes to a negative value with a peak value of the reference waveform stored in the memory unit to determine one of the plurality of road surface types; Equipped with The determination unit The peak value of the signal waveform output by the strain sensor is compared with the peak value of the reference waveform, and when the peak value of the signal waveform output by the strain sensor is confirmed to be the same as the peak value of the reference waveform multiple times in succession, the strain sensor determines that the road surface type is asphalt, which is one of the multiple road surface types; When it is confirmed at least once that the peak value of the signal waveform output by the strain sensor is greater than or smaller than a certain value than the peak value of the reference waveform, the road surface is determined to be a gravel road, which is another road surface type among the plurality of road surface types; A road surface type detection device characterized by the fact that when the peak value of the signal waveform output by the strain sensor is smaller than the peak value of the reference waveform and this is confirmed multiple times in succession, the device determines that the road is a grass road, which is a different road surface type from the multiple road surface types.

2. A road surface type detection device according to claim 1; a brake control device having a braking condition determination unit that determines a braking condition of the vehicle based on the road surface type estimated by the road surface type detection device, and that controls a braking force of the vehicle; A brake control system comprising:

3. 3. The brake control system according to claim 2, The brake control system is characterized in that the brake control device has a wheel lock condition table that stores the relationship between the friction coefficient of the road surface type and the braking force that will not lock the wheels of the vehicle, and controls the braking force of the vehicle with a braking force that will not lock the wheels depending on the road surface type.

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