JP7388000B2 - Road surface friction coefficient prediction system - Google Patents

Description

The present invention relates to a road surface friction coefficient prediction system that predicts the friction coefficient of a road surface.

BACKGROUND ART Conventionally, a road surface friction coefficient prediction system has been proposed that predicts a road surface friction coefficient based on the condition of a vehicle's tires (see, for example, Patent Document 1).

The road surface friction coefficient prediction system described in Patent Document 1 includes a vibration detection means for detecting vibration of a tire while driving, a vibration waveform detection means for detecting a time-varying waveform of tire vibration, and a road surface friction coefficient prediction system based on a time-varying waveform. The vehicle includes road surface condition determining means for determining the condition, and tire condition detecting means for acquiring tire condition information. The road surface condition determining means determines the road surface based on the tire condition information and a determination parameter for determining the road surface condition determined from the vibration waveform. According to this configuration, it is said that even if the tire condition changes, the road surface condition can be accurately determined.

Japanese Patent Application Publication No. 2018-004417

The road surface friction coefficient prediction system described in Patent Document 1 determines the road surface condition based on the vibration of the tires while driving, so if the road surface condition changes while driving, it is possible to accurately estimate the road surface friction coefficient. It may be unpredictable.

SUMMARY OF THE INVENTION Therefore, an object of the present invention is to provide a road surface friction coefficient prediction system that can accurately predict the friction coefficient of a road surface even when the road surface condition changes.

In order to achieve the above object, the present invention provides an external information acquisition means for acquiring external information regarding disturbance factors that affect the condition of the road surface on which a target vehicle runs, and an external information acquisition means for acquiring external information regarding a disturbance factor that affects the condition of a road surface on which a target vehicle runs, and and a friction coefficient predicting means for predicting a friction coefficient between the tire of the target vehicle and a road surface in front of the target vehicle in the traveling direction. , a road surface friction coefficient at the running position of the target vehicle calculated based on information regarding the running state of the target vehicle and a reference friction coefficient obtained from the external information, or friction between the tires and the road surface of a plurality of preceding vehicles. The tire information is obtained by comparing the coefficient with a reference friction coefficient obtained based on the coefficient, and the friction coefficient predicting means estimates the relationship between the tire and the road surface in front of the traveling direction based on the tire information and the external information. Provides a road surface friction coefficient prediction system that predicts the friction coefficient.

According to the road surface friction coefficient prediction system according to the present invention, it is possible to accurately predict the road surface friction coefficient even when the road surface condition changes.

1 is a configuration diagram showing a schematic configuration example of a driving support system according to an embodiment of the present invention. FIG. 1 is a block diagram showing a configuration example of a host vehicle according to an embodiment of the present invention. FIG. 2 is a block diagram showing a configuration example of an information collection server according to the present embodiment. (a) is an example of a reference map showing the relationship between the road surface condition and the friction coefficient, and (b) is an example of a statistical map showing the relationship between the tire condition coefficient and the friction coefficient. This is an example of collected data collected by the information collection server. 3 is a flowchart illustrating an example of the operation of the control unit of the host vehicle and the information collection server.

[Embodiment]
Embodiments of the present invention will be described with reference to FIGS. 1 to 6. The embodiments described below are shown as preferred specific examples for carrying out the present invention, and some portions specifically illustrate various technical matters that are technically preferable. However, the technical scope of the present invention is not limited to this specific embodiment.

FIG. 1 is a configuration diagram showing an example of a general configuration of the entire road surface friction coefficient prediction system according to an embodiment of the present invention.

As shown in FIG. 1, the road surface friction coefficient prediction system 100 communicates with a vehicle 1 whose tire condition is to be determined, a plurality of other vehicles 5, and a vehicle 1 and a plurality of other vehicles 5 via a wireless communication network 90. and an external device 9 communicatively connected to the external device 9.

In this embodiment, for convenience of explanation, a distinction is made between the own vehicle 1 and a plurality of other vehicles 5. However, the tire condition of a plurality of other vehicles 5 may be determined, and the own vehicle 1 The vehicle type, year, model, etc. of the plurality of other vehicles 5 are arbitrary and are not particularly limited. The host vehicle 1 and the plurality of other vehicles 5 correspond to the "target vehicle" in the present invention.

The external device 9 includes an information collection server 6 that collects vehicle information about the own vehicle 1 and a plurality of other vehicles 5, a weather information server 81, a road information server 82, and a GPS 83.

The weather information server 81 is a server that provides weather information for all of Japan obtained from meteorological observatories in various places. The road information server 82 is a server that provides information indicating the road conditions of unpaved roads, paved roads, etc. around the current location, based on the current location where the vehicle is traveling. The weather information provided by the weather information server 81 and the road information provided by the road information server 82 are examples of disturbance factors that affect the condition of the road surface on which the vehicle travels.

FIG. 2 is a block diagram showing the configuration of the host vehicle 1. As shown in FIG. FIG. 3 is a block diagram showing the configuration of the information collection server 6 of the external device 9. As shown in FIG. As shown in FIG. 2, the host vehicle 1 includes a communication unit 21 that transmits and receives information to and from a plurality of other vehicles 5 and an external device 9, a control unit 3 that controls the host vehicle 1, and a A display unit 22 that displays information, a driving information detection unit 23 that detects information regarding the driving state of the vehicle 1, and a position detection unit 24 that detects the current position of the vehicle 1 based on satellite signals from the GPS 83. It has a storage section 4 made of a storage element such as ROM or RAM. Note that the configurations of the plurality of other vehicles 5 are also similar to the own vehicle 1.

The communication unit 21 is an electronic control device that transmits and receives information by communicating with a communication target other than the own vehicle 1 via the wireless communication network 90 . The communication unit 21 is composed of a communication module such as a DCM (Data Communication Module), for example.

The display unit 22 displays information output from the control unit 3. The display section 22 is, for example, a display such as a liquid crystal display or an organic EL display, and may function as a display section of a car navigation system, or may be provided on a console panel of the vehicle.

The driving information detection unit 23 includes a plurality of sensors that acquire parameters related to the driving state of the host vehicle 1, such as a vehicle speed sensor that acquires the vehicle speed and a steering angle sensor.

The position detection unit 24 acquires position information indicating the current position of the own vehicle 1, and is a GPS receiver that acquires the current position information (longitude, latitude, etc.) of the own vehicle 1 using a satellite positioning system. It is.

The control unit 3 is composed of a CPU (arithmetic processing unit) and its peripheral circuits. By executing the program 41 stored in the storage unit 4, the control unit 3 causes each unit, such as a friction coefficient calculation unit 33, which will be described later, to function.

The control unit 3 includes an external information acquisition means 31, a position information acquisition means 32, a friction coefficient calculation means 33, a tire condition coefficient calculation means 34, a friction coefficient prediction means 35, a transmission means 36, and a statistical map acquisition means. 37, tire condition evaluation means 38, and notification means 39.

The external information acquisition means 31 estimates the road surface condition based on the weather information provided by the weather information server 81 of the external device 9. For example, when the weather information is sunny, the external information acquisition means 31 estimates that the road surface condition is dry, and when the weather information is rainy, the external information acquisition means 31 estimates that the road surface condition is wet. Further, the external information acquisition means 31 acquires information indicating the road condition (paved road or unpaved road) based on road information provided by the road information server 82. The position information acquisition means 32 acquires the position information generated by the position detection unit 24.

The friction coefficient calculating means 33 calculates the road surface friction coefficient at the driving position based on the driving information regarding the driving state of the vehicle detected by the driving information detecting section 23. This friction coefficient can be calculated, for example, based on the difference between the average rotational speed of the left and right front wheels and the average rotational speed of the left and right rear wheels in a steady running state.

The tire condition coefficient calculation means 34 calculates a tire condition coefficient by comparing the friction coefficient calculated by the friction coefficient calculation means 33 with a reference friction coefficient indicating the slipperiness of the tire according to the road surface condition.

The tire condition coefficient calculating means 34 can obtain the reference friction coefficient from, for example, a reference map 42 (shown in FIG. 4(a)) stored in the storage unit 4 in advance. This reference friction coefficient is a friction coefficient set for each road surface condition (dry, wet, snow), and is, for example, a value determined by performance evaluation at the time of factory shipment. The tire condition coefficient calculating means 34 obtains a reference friction coefficient by comparing the road surface condition information estimated by the external information obtaining means 31 with the reference map 42 . Note that the reference map 42 may have a reference friction coefficient set for each road condition in addition to the road surface condition. This makes it possible to prevent deviations in the reference friction coefficient due to differences in road conditions.

Here, the tire condition coefficient is an index value that affects the slipperiness of a vehicle due to the condition of the tires (tire wear, air pressure, etc.), and is tire information that indicates the condition of the tires of the vehicle. The tire condition coefficient can be obtained, for example, as a ratio of the reference friction coefficient to the estimated friction coefficient (tire condition coefficient=estimated friction coefficient/reference friction coefficient). In other words, the larger the value of the tire condition coefficient, the more difficult it is for the tires of the vehicle to slip. Note that the tire condition coefficient calculation means 34 is an example of "tire information acquisition means" in the present invention.

The friction coefficient prediction means 35 predicts the road surface friction coefficient ahead in the direction of travel of the vehicle based on the tire condition coefficient calculated by the tire condition coefficient calculation means 34 and the road surface condition or road condition acquired by the external information acquisition means 31. do.

More specifically, the friction coefficient prediction means 35 obtains a reference friction coefficient according to the road surface condition ahead in the direction of travel of the vehicle, and multiplies this reference friction coefficient by a tire condition coefficient to obtain a predicted friction coefficient in the front direction of the vehicle. calculate. The reference friction coefficient according to the road surface condition ahead in the direction of travel is obtained by acquiring information on the road surface condition (dry, wet, snow) at a position ahead in the direction of travel by external information acquisition means 31 and position information acquisition means 32. This information can be obtained by comparing the information on the road surface condition with the reference map 42.

In calculating the predicted friction coefficient by the friction coefficient prediction means 35, the road surface friction coefficients of a plurality of preceding vehicles that are of the same model as the own vehicle 1 and have traveled in the same position as the own vehicle 1 are used as the reference friction. It may also be used as a coefficient. The road surface friction coefficients of the plurality of preceding vehicles are, for example, statistical values (eg, average values, etc.) obtained by a statistical method.

The transmitting means 36 transmits information indicating the slipperiness of the tire to the information collection server 6 through the communication unit 21. In the present embodiment, the transmission means 36 transmits the predicted friction coefficient predicted by the friction coefficient prediction means 35, the tire condition coefficient calculated by the tire condition coefficient calculation means 34, and vehicle information regarding the own vehicle 1 (vehicle ID, vehicle type, The controller generates transmission data based on vehicle speed, etc.), location information, road conditions, and road surface conditions, and transmits the transmission data to the information collection server 6 of the external device 9. This transmission data corresponds to a part of the collected data 60 collected by the information collection server 6.

The information collection server 6 also receives data similar to the transmission data transmitted by the transmission means 36 from a plurality of other vehicles 5, and collects this received data as collected data 60. FIG. 5 is a table showing an example of the collected data 60. As shown in FIG. 5, the collected data 60 includes a vehicle ID for identifying the vehicle, information regarding the model of the vehicle, location information indicating the current location where the vehicle is traveling, and the road condition at the location indicated by the location information. and road surface conditions, friction coefficients and tire condition coefficients calculated for each vehicle as items. Note that the items of the collected data 60 are merely examples, and are not limited thereto; for example, information such as measurement date and time, mileage, etc. may be set as the items.

The statistical map acquisition means 37 of the control unit 3 acquires a statistical map 632 generated by the statistical map generation means 623 of the information collection server 6, which will be described later. FIG. 4B is a table showing an example of the statistical map 632. In the statistical map 632, a standard value O in which the tire condition coefficient and the friction coefficient are associated, and first and second threshold values S ₁ and S ₂ are set. Note that the statistical map 632 is not limited to this, as long as it can evaluate the slipperiness of the vehicle's tires. It may also be data in which vehicle speed and tire condition coefficient are associated.

The standard value O is, for example, a value determined based on performance evaluation of the vehicle at the time of factory shipment. The first and second threshold values S ₁ and S ₂ are values determined, for example, by mapping the tire condition coefficient and friction coefficient of the collected data 60 and using a statistical method on the mapped data. The first and second threshold values S ₁ and S ₂ deviate from the standard value O by a predetermined value. The first and second threshold values S ₁ and S ₂ have values of friction coefficients relative to tire condition coefficients that are smaller than the standard value O, and the second threshold value S ₂ deviates from the standard value O more than the first threshold value S ₂ . ing.

Here, in the statistical map 632, the area where the friction coefficient is smaller than the second threshold value _S2 is defined as the first area A, the area surrounded by the first threshold value _S1 and the second threshold value S2 is defined as the second area B, and the area surrounded by the first threshold value S1 and the second threshold value _S2 is defined as the second area B. The area surrounded by one threshold value S1 _and the standard value O is defined as a third area C, and the area where the friction coefficient is larger than the standard value O is defined as a fourth area D. For example, if the value of the friction coefficient corresponding to the tire condition coefficient exists in the first area A, it means that the vehicle is in a slippery state because the friction coefficient is smaller than the standard value O. If it exists in area D, it means that the vehicle is in a state where it is difficult to slip because the coefficient of friction is larger than the standard value O.

The tire condition evaluation means 38 determines rank information, which is an evaluation index indicating the slipperiness of the tires of the own vehicle 1, by referring to the acquired statistical map 632. More specifically, the tire condition evaluation means 38 maps the friction coefficient calculated by the friction coefficient calculation means 33 and the tire condition coefficient calculated by the tire condition coefficient calculation means 34 onto a statistical map 632, and determines whether mapped data exists. The rank information is determined based on the area on the statistical map 632.

For example, if the mapped data exists in the first area A, rank A (slip level: large) is set in the rank information, and the target data exists in the second area B. In this case, rank B (slip level: medium) is set in the rank information, and when the target data exists in the third area C, rank C (slip level: small) is set in the rank information. In this way, the tire condition evaluation means 38 determines the slipperiness of the own vehicle 1 based on the statistical map 632 generated from the collected data 60 regarding a plurality of other vehicles 5, so the tire condition evaluation means 38 determines the slipperiness of the own vehicle 1. It is possible to objectively evaluate the

The notification means 39 notifies the driver of rank information as a determination result obtained by the tire condition evaluation means 38. The notification means 39 may notify the driver by, for example, displaying caution information to the effect that the tires are slippery on the display unit 22, or may notify the driver as audio information. Thereby, it is possible to encourage the driver to drive safely.

As shown in FIG. 3, the information collection server 6 includes a communication unit 61 that transmits and receives information between the own vehicle 1 and a plurality of other vehicles 5, and a communication unit 61 that performs calculations based on information collected via the communication unit 61. It has a control section 62 that executes processing, and a storage section 63 that stores a program 631 that the control section 62 executes.

The control unit 62 of the information collection server 6 includes a receiving unit 621 that receives collected data 60 via the communication unit 61, a collected data extraction unit 622 that extracts data from the collected data 60 under predetermined extraction conditions, and a collected data extraction unit 622 that extracts data from the collected data 60 under predetermined extraction conditions. A statistical map generation means 623 that generates a statistical map 632 based on the extracted data extracted by the means 622, a storage means 624 that stores the generated statistical map 632 in a storage unit 63, and a storage means 624 that transmits the statistical map 632 to the vehicle. It has a transmitting means 625.

The collected data extraction means 622 extracts from the collected data 60 data that has the same vehicle type and location information as the own vehicle 1, and has the same road surface conditions. Note that the extraction conditions are not limited to these; for example, the collected data extraction means 622 may extract data only for the same vehicle type as the host vehicle 1, or may extract data only for the same location as the host vehicle 1. .

The statistical map generation means 623 generates a statistical map 632 based on the tire condition coefficients of the plurality of extracted data extracted by the collected data extraction means 622 and the tire condition coefficients.

The transmitting means 625 reads the statistical map 632 from the storage unit 63 and transmits transmission data generated based on the read statistical map 632 to the own vehicle 1 through the communication unit 61.

Next, the processing contents of the road surface friction coefficient prediction system 100 according to the present embodiment will be explained with reference to FIG. 6. FIG. 6 is a flowchart showing an example of processing by the control unit 3 of the own vehicle 1 and the control unit 62 of the information collection server 6.

The control unit 3 of the own vehicle 1 first obtains position information indicating the current position of the own vehicle 1 generated by the position detection unit 24 (step S10). Then, the control unit 3 estimates the road surface condition (dry, wet, snow) based on the weather information acquired from the weather information server 81 of the external device 9, and also estimates the road surface condition (paved, unfinished) acquired from the road information server 82. The road condition is estimated based on the road condition (pavement) (step S11).

Next, the control unit 3 of the host vehicle 1 calculates the road surface friction coefficient at the travel position based on the travel information indicating the travel state of the host vehicle 1 acquired from the travel information detection unit 23 (step S12), and calculates the road surface friction coefficient at the travel position (step S12). A tire condition coefficient is calculated based on the coefficient and the reference friction coefficient of the reference map 42 stored in the storage unit 4 (step S13). Then, the control unit 3 acquires the road surface condition at a position ahead in the direction of travel of the vehicle, and predicts the friction coefficient of the road surface ahead in the direction of travel based on the reference friction coefficient and tire condition coefficient corresponding to the acquired road surface condition. (Step S14). The control unit 3 transmits the estimated friction coefficient and tire condition coefficient calculated above as well as vehicle information regarding the host vehicle 1 (vehicle ID, vehicle type, vehicle speed, etc.) to the information collection server 6 of the external device 9 (step S15).

The control unit 62 of the information collection server 6 receives collected data 60 transmitted from the own vehicle 1 and a plurality of other vehicles 5 (step S20), and generates a statistical map 632 based on the collected data 60 (step S21). ). The control unit 62 stores the generated statistical map 632 in the storage unit 63 (step S22), and transmits the statistical map 632 to the vehicle (step S23). This ends the processing of the information collection server 6.

The control unit 3 acquires the statistical map 632 transmitted from the information collection server 6 (step S16), and refers to the acquired statistical map 632 to determine the rank, which is an evaluation index of the slipperiness of the tire condition of the host vehicle 1. Information is obtained (step S17). If the rank information is the aforementioned rank A or rank B (step S18), the driver is notified of caution information indicating that the tires are in a slippery condition (step S19). Note that the determination condition in step S17 is not limited to this, and for example, caution information may be notified only when the rank information is A, or caution information with different content may be notified for rank A and rank B. You may also make a notification. On the other hand, if the rank information is not rank A or rank B, the process ends immediately.

(Actions and effects of embodiments)
According to the embodiment described above, the friction coefficient between the tires and the road surface in the forward direction of the vehicle is predicted based on the tire condition coefficient indicating the tire condition and the road surface condition, so that the road surface condition at the vehicle traveling position is predicted. Even if the road surface friction coefficient changes, it is possible to accurately calculate the road surface friction coefficient.

Further, according to the present embodiment, the tires of the host vehicle 1 are determined based on the statistical map 632 generated from the tire condition coefficients and friction coefficients of a plurality of other vehicles 5 of the same vehicle type as the host vehicle 1 and traveling in the same location. Rank information can be obtained as an index value indicating slipperiness. Thereby, it is possible to inform the driver of objective information indicating the slipperiness of the tire condition of the own vehicle 1.

(Additional note)
Although the present invention has been described above based on the embodiments, these embodiments do not limit the invention according to the claims. Furthermore, it should be noted that not all combinations of features described in the embodiments are essential for solving the problems of the invention.

In the above embodiment, a case has been described in which the friction coefficient prediction means 35 predicts the friction coefficient of the road surface at a position ahead of the own vehicle 1 in the traveling direction of the own vehicle 1. The vehicle may be configured to predict the road surface friction coefficient at a stop position within the vehicle.

In this case, the friction coefficient prediction means 35 predicts the friction coefficient at the stopped position of the own vehicle 1 based on the road surface condition at the current position acquired by the external information acquisition means 31 immediately before the own vehicle 1 starts. For example, when the own vehicle 1 is turned on from a stopped state with the ignition OFF, the friction coefficient prediction means 35 obtains from the reference map 42 a reference friction coefficient according to the weather information at the time when the ignition is turned on. By multiplying this reference friction coefficient by the tire condition coefficient, the road surface friction coefficient at the stopping position can be predicted. Thereby, even if the road surface condition changes while the vehicle is stopped, it is possible to accurately predict the road surface friction coefficient. Note that the road surface condition at the time the ignition was turned on is compared with the road surface condition immediately before the ignition was turned off, and if the comparison results are the same, the friction coefficient predicted at the time the ignition was turned off is used as the predicted friction. It may also be applied as a coefficient.

Further, in the above embodiment, only the control section 3 of the own vehicle 1 among the own vehicle 1 and the plurality of other vehicles 5 has been described, but the plurality of other vehicles 5 also have a control section with a similar configuration, and the tire condition It shall be possible to determine the In this case, if there is a vehicle whose rank information is rank A or rank B among the plurality of other vehicles 5, the host vehicle 1 may be configured to detect this information. Thereby, by notifying the driver of the own vehicle 1 of the presence of dangerous slippery vehicles in the surrounding area, it is possible to prevent accidents.

Further, in the above embodiment, the thresholds set in the statistical map 632 are two thresholds, the first and second thresholds S ₁ and S ₂ , but the number of thresholds is not limited to this, and for example, the thresholds are There may be three, or even six.

Further, in the above embodiment, the control unit 3 of the own vehicle 1 determines the rank information using the tire condition evaluation means, but the control unit 62 of the information collection server 6 may determine the rank information. In this case, the transmitting means 625 of the control section 62 of the information collection server 6 transmits only the rank information to the own vehicle 1, and the notifying means 39 of the control section 3 of the own vehicle 1 drives the vehicle based on the received rank information. Notify people of caution information.

Further, in the present invention, the information collection server 6 of the external device 9 is not an essential component. That is, in the above embodiment, the information collection server 6 of the external device 9 generates the statistical map 632 based on the collected data 60, but when the host vehicle 1 side receives the collected data 60 from a plurality of other vehicles 5. , a statistical map 632 may be generated.

Furthermore, in the above embodiment, the control unit 3 acquires driving information from the driving information detection unit 23 composed of a plurality of sensors. , parameters related to the driving state may be acquired.

1... own vehicle 3... control unit 5... other vehicle 6... information collection server 9... external device 31... external information acquisition means 32... position information acquisition means 33... friction coefficient calculation means 34... tire condition coefficient calculation means 35... friction coefficient Prediction means 81... Weather information server 82... Road information server 100... Road surface friction coefficient prediction system

Claims (3)

External information acquisition means for acquiring external information regarding disturbance factors that affect the condition of the road surface on which the target vehicle runs; Tire information acquisition means for acquiring tire information indicating the slipperiness status of the tires of the target vehicle; Friction coefficient prediction means for predicting a friction coefficient between the road surface in front of the target vehicle in the traveling direction and the tires of the target vehicle,
The tire information acquisition means includes a road surface friction coefficient at the running position of the target vehicle calculated based on information regarding the running state of the target vehicle, a reference friction coefficient obtained from the external information, or a road surface friction coefficient in a plurality of preceding vehicles. Obtaining the tire information by comparing it with a reference friction coefficient obtained based on the friction coefficient between the tire and the road surface;
The friction coefficient prediction means predicts a friction coefficient between the tire and a road surface in front of the traveling direction based on the tire information and the external information.
Road surface friction coefficient prediction system. The external information includes at least one of weather information and road information.
The road surface friction coefficient prediction system according to claim 1. The friction coefficient prediction means predicts the friction coefficient of the road surface at the stop position of the target vehicle based on the external information acquired immediately before the target vehicle starts.
The road surface friction coefficient prediction system according to claim 1 or 2 .

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