Suspension system and estimation method

Abstract

To improve estimation of a state of a suspension.SOLUTION: There is provided a suspension system that is installed in a vehicle and estimates a state of a suspension installed in the vehicle. The suspension system comprises: a storage unit in which a plurality of neural networks is stored; an acquisition unit for acquiring weight-related data, which is data related to weight of objects loaded on the vehicle; and a vehicle state estimation unit for estimating a state of the suspension by inputting data related to traveling of the vehicle to any one of the neural networks stored in the storage unit which corresponds to the weight related data.SELECTED DRAWING: Figure 1

Description

[Technical field]

[0001] The present invention relates to a suspension system and an estimation method. [Background technology]

[0002] Suspension systems are widely used to absorb vibrations and shocks from the road surface. Patent Document 1 discloses a configuration for adjusting the noise of a vehicle's suspension and exhaust system in response to the characteristics of the occupants monitored via a camera and a microphone. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] US Patent Application Publication No. 2021 / 0114603 Summary of the Invention [Problem to be solved by the invention]

[0004] The invention described in Patent Document 1 leaves room for improvement in estimating the state of the suspension. [Means for solving the problem]

[0005] A suspension system according to a first aspect of the present invention is a suspension system that is mounted on a vehicle and estimates a state of a suspension mounted on the vehicle, and includes a memory unit in which a plurality of neural networks are stored, an acquisition unit that acquires weight-related data which is data related to the weight of cargo on the vehicle, and a vehicle state estimation unit that estimates the state of the suspension by inputting data related to the vehicle's traveling to any one of the neural networks stored in the memory unit that corresponds to the weight-related data. An estimation method according to a second aspect of the present invention is a method in which a computer mounted on a vehicle and having a memory unit in which a plurality of neural networks are stored estimates a state of a suspension mounted on the vehicle, the method including: acquiring weight-related data which is data relating to the weight of a load on the vehicle based on output of a sensor mounted on the vehicle; and estimating the state of the suspension by inputting data relating to the running of the vehicle to any one of the neural networks stored in the memory unit that corresponds to the weight-related data. Effect of the Invention

[0006] According to the present invention, the state of the suspension can be estimated with high accuracy. [Brief description of the drawings]

[0007] [Figure 1] FIG. 1 is a diagram showing the configuration of a vehicle according to a first embodiment. [Diagram 2] FIG. 1 is a diagram showing an example of a correspondence table. [Diagram 3] Schematic diagram of a neural network [Figure 4] Schematic showing the inputs and outputs to a neural network [Diagram 5] Flowchart showing the process of the reading unit [Figure 6] A diagram showing an example of the operation of the suspension ECU [Figure 7] A diagram showing the display on the instrument panel [Figure 8] Suspension ECU hardware configuration diagram [Figure 9] Vehicle configuration diagram in Modification 1 [Figure 10] FIG. 11 is a diagram showing a correspondence table in Modification 2. [Figure 11] FIG. 11 is a diagram showing a correspondence table in Modification 3. [Figure 12] FIG. 13 is a diagram showing a correspondence table in Modification 4. [Figure 13] FIG. 1 is a configuration diagram of a vehicle according to a second embodiment. [Figure 14]FIG. 1 is a diagram showing an example of a case in which a driver inputs the position of a load using an instrument panel; [Figure 15] FIG. 1 shows a display screen of a mobile terminal. [Figure 16] Schematic diagram showing input and output of the sensor processing unit [Figure 17] Neural network update system configuration DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0008] --First embodiment-- A first embodiment of a suspension system will now be described with reference to FIGS.

[0009] 1 is a configuration diagram of a vehicle 9 including a suspension ECU 107 that is a suspension control device. The vehicle 9 includes a weight measurement unit 103, an instrument panel 105, the suspension ECU 107, a steering ECU 108, an in-vehicle network 109, and a semi-active suspension 201. The in-vehicle network 109 is a known communication network, for example, IEEE802.3 or a Controller Area Network. The suspension ECU 107 and the steering ECU 108 are ECUs, i.e., electronic control units (Electronic Control Units).

[0010] The suspension ECU 107 includes a vehicle state estimation unit 202, a control value calculation unit 203, a neural network storage unit 204, a readout unit 205, an acquisition unit 206, and an output unit 208. A correspondence table 2051 is stored in the readout unit 205. The weight measurement unit 103 is a weight sensor, such as a strain gauge and an amplifier, that measures the weight of the load on the vehicle 9. The load on the vehicle 9 includes the occupants of the vehicle 9 including the driver and baggage carried on the vehicle 9. Data related to the weight of the load on the vehicle 9 measured by the weight measurement unit 103 (hereinafter referred to as "weight-related data") is read by the acquisition unit 206. The acquisition unit 206 outputs the weight-related data measured by the weight measurement unit 103 to the readout unit 205 and the output unit 208. Note that the weight-related data in this embodiment is a total value of the weight of the load, but the weight-related data is a concept that may also include the distribution of weight.

[0011] The reading unit 205 refers to the correspondence table 2051 to identify the neural network corresponding to the weight-related data output by the acquisition unit 206, i.e., the weight of the load. The reading unit 205 then reads out the identified neural network from the neural network storage unit 204 and outputs it to the vehicle state estimation unit 202. The reading unit 205 operates with various signals received via the in-vehicle network 109 as triggers. For example, the reading unit 205 uses a door closing signal from a door sensor, an engine or motor start signal, and a signal indicating that a seat belt is fastened as triggers. Furthermore, the reading unit 205 may use an explicit instruction by an occupant of the vehicle 9, for example, pressing an operation command button as triggers.

[0012] The vehicle state estimation unit 202 performs inference using the neural network output by the reading unit 205. Time-series physical quantities related to the vehicle 9, such as the speed and acceleration of the vehicle 9, are input to this neural network. The vehicle state estimation unit 202 outputs the inference result to the control value calculation unit 203. The control value calculation unit 203 calculates a control value for controlling the damping force of the semi-active suspension 201 by a known method based on the physical quantities input from the vehicle state estimation unit 202.

[0013] The semi-active suspension 201 controls a suspension (not shown) mounted on the vehicle based on the output of the suspension ECU 107. A plurality of trained neural networks are stored in the neural network storage unit 204. The output unit 208 outputs the data on the weight of the loaded object output by the acquisition unit 206 to the instrument panel 105. The instrument panel 105 is a display device mounted on the vehicle 9, and outputs the information output by the output unit 208.

[0014] 1, the suspension ECU 107 is illustrated as one hardware device, but the suspension ECU 107 may be realized by a plurality of devices. Therefore, the suspension ECU 107 can also be called a suspension system.

[0015] 2 is a diagram showing an example of the correspondence table 2051 stored in the reading unit 205. The correspondence table 2051 shows the correspondence between the weight of the load on the vehicle 9 and the neural network used by the vehicle state estimation unit 202. Specifically, it shows that "N1" is used when the load is less than 100 kg, "N2" is used when the load is 100 kg or more and less than 300 kg, and "N3" is used when the load is 300 kg or more. The neural networks N1 to N3 are different in at least one of the configuration and parameters of the neural network.

[0016] 3 is a schematic diagram of a neural network N stored in the neural network storage unit 204. The neural network N shown in FIG. 3 represents, for example, N1 to N3 in FIG.

[0017] The neural network N includes an input layer N-IN, a hidden layer NH, and an output layer N-OUT. The input layer N-IN has a plurality of nodes, and time series values ​​of one or more physical quantities are input. Inputs to the input layer N-IN include, for example, the vehicle speed, the vehicle acceleration, and the yaw rate. The range of the time series is determined in advance. However, the range of the time series may differ for each physical quantity.

[0018] For convenience of drawing, only one hidden layer NH is shown in FIG. 3, but two or more layers may be used. The number of nodes in each layer of the hidden layer NH is arbitrary, and depending on the configuration of the neural network, there may be more nodes than the input layer N-IN or fewer nodes than the input layer N-IN. The hidden layer NH may or may not include a fully connected layer. The configuration of the neural network N including the hidden layer NH is not limited.

[0019] The two nodes of the output layer N-OUT indicate the instantaneous values ​​of two physical quantities. These two physical quantities are the vertical velocity of the upper part of the spring that constitutes the suspension (hereinafter also referred to as the "spring velocity"), and the relative velocity of the piston that constitutes the suspension with respect to the spring (hereinafter also referred to as the "piston relative velocity"). However, the two physical quantities of the output layer N-OUT may also be the sprung velocity and the vertical velocity of the lower part of the spring that constitutes the suspension (hereinafter also referred to as the "unsprung velocity").

[0020] The configuration of the neural network N is determined in advance, and the parameters, i.e., the weights between nodes, have been trained using previously collected data. The previously collected data is a plurality of pairs of input values ​​and output values ​​of the neural network N. A known method, such as the backpropagation method, can be used to train the parameters of the neural network N. Note that the configuration of the neural network N may be changed during the training process.

[0021] FIG. 4 is a schematic diagram showing the input and output to the neural network N. In the example shown in FIG. 4, the input to the neural network N is time-series data of the vehicle speed, and only the vertical speed of the sprung part is shown among the outputs. At time t2, the vehicle speed from time t1 to time t2 is input to the neural network N. The neural network N infers and outputs the vertical speed of the sprung part at time t2. If the period from time t1 to time t2 is 1 second and the sampling period of the vehicle speed is 10 ms, 100 vehicle speeds are input to the neural network N.

[0022] 5 is a flowchart showing the processing of the reading unit 205. First, in step S301, the reading unit 205 determines whether or not a trigger has been received. Examples of triggers include a door closing signal, an engine or motor start signal, and a signal indicating that a seat belt has been fastened. If the reading unit 205 determines that a trigger has been received, the process proceeds to step S302, and if the reading unit 205 determines that a trigger has not been received, the process remains in step S301.

[0023] In step S302, the reading unit 205 recognizes the weight of the load on the vehicle 9. For example, if there are multiple weight measuring units 103, the weights output by each weight measuring unit 103 are added together. In the following step S303, the reading unit 205 refers to the correspondence table 2051 and determines the neural network corresponding to the weight recognized in step S302 as the target to be read. In the following step S304, the reading unit 205 reads the target determined in step S303 from the neural network storage unit 204 and outputs it to the vehicle state estimation unit 202. Thereafter, the vehicle state estimation unit 202 performs inference using the neural network output in this step.

[0024] In the next step S305, the reading unit 205 determines whether or not the ignition switch is set to OFF. If the reading unit 205 determines that the ignition switch is set to OFF, the processing shown in Fig. 5 is terminated, and if the reading unit 205 determines that the ignition switch is not set to OFF, the processing returns to step S301.

[0025] FIG. 6 is a diagram showing an example of the operation of the suspension ECU 107 from time t10 to time t15. In FIG. 6, time progresses from left to right. FIG. 6 shows, from the top, the vehicle speed, trigger, weight condition, and neural network read into the vehicle state estimation unit 202 of the vehicle 9. At time t10, the vehicle 9 is stopped, and the weight of the load on the vehicle 9 is less than 100 kg. At time t11, the trigger is turned on due to the ignition switch being turned on or the like (S301: YES). Therefore, the reading unit 205 reads "N1" corresponding to a weight of less than 100 kg in the correspondence table 2051, and outputs it to the vehicle state estimation unit 202.

[0026] Thereafter, the vehicle 9 starts traveling, and at time t12 the vehicle speed becomes zero again. At this time, for example, multiple passengers are loaded and the weight of the cargo exceeds 300 kg. However, since the trigger has not yet occurred, there is no change in the neural network of the vehicle state estimation unit 202 at this timing. At time t13, a trigger is generated by closing the door, etc., and the reading unit 205 reads "N3" in the correspondence table 2051, which corresponds to a weight of 300 kg or more. Therefore, in FIG. 6, the neural network is set to "N3" after time t13. After this, no trigger is generated and the vehicle 9 continues traveling until time t15.

[0027] 7 is a diagram showing a display on the instrument panel 105 based on the output of the output unit 208. In this figure, the positions where the weight is detected by the weight measuring unit 103 are shown with hatched circles. Note that in this figure, all positions where the weight is detected are displayed uniformly, but the display may be different depending on the magnitude of the weight. For example, the color, shape, and density of the icon may be changed depending on the magnitude of the weight.

[0028] 8 is a hardware configuration diagram of the suspension ECU 107. The suspension ECU 107 includes a CPU 41 which is a central processing unit, a ROM 42 which is a read-only storage device, a RAM 43 which is a readable and writable storage device, a storage device 44 which is a non-volatile recording medium, and a communication device 45. The CPU 41 loads a program stored in the ROM 42 into the RAM 43 and executes it, thereby realizing a vehicle state estimation unit 202, a control value calculation unit 203, a reading unit 205, an acquisition unit 206, and an output unit 208.

[0029] The suspension ECU 107 may be realized by a field programmable gate array (FPGA) that is a rewritable logic circuit or an application specific integrated circuit (ASIC) that is an integrated circuit for a specific application, instead of the combination of the CPU 41, the ROM 42, and the RAM 43. Furthermore, the suspension ECU 107 may be realized by a combination of different configurations, for example, a combination of the CPU 41, the ROM 42, the RAM 43, and an FPGA, instead of the combination of the CPU 41, the ROM 42, and the RAM 43.

[0030] The storage device 44 is, for example, a hard disk drive, and realizes a neural network storage unit 204. The communication device 45 is a communication interface with the in-vehicle network 109. The communication device 45 is used by the vehicle state estimation unit 202, the control value calculation unit 203, the reading unit 205, the acquisition unit 206, and the output unit 208, which require input and output of data from and to the outside of the suspension ECU 107.

[0031] According to the above-described first embodiment, the following advantageous effects can be obtained. (1) The suspension ECU 107 is mounted on the vehicle 9 and estimates the state of a semi-active suspension 201 mounted on the vehicle 9. The suspension ECU 107 includes a neural network storage unit 204 in which a plurality of neural networks are stored, an acquisition unit 206 that acquires weight-related data which is data related to the weight of a load on the vehicle 9, and a vehicle state estimation unit 202 that estimates the state of the suspension by inputting data related to the traveling of the vehicle to any one of the neural networks stored in the neural network storage unit 204 that corresponds to the weight-related data. Therefore, the suspension ECU 107 uses different neural networks depending on the weight of the vehicle 9, and can estimate the state of the suspension with high accuracy.

[0032] (2) The suspension ECU 107 includes a read unit 205 that reads out a neural network corresponding to the weight-related data from the neural network storage unit 204 and outputs the neural network to the vehicle state estimation unit 202. The vehicle state estimation unit 202 estimates the state of the suspension using the neural network output by the read unit 205. Therefore, only data necessary for calculation is output to the vehicle state estimation unit 202, so that the vehicle state estimation unit 202 does not need to be equipped with an excessive memory.

[0033] In a conventional suspension system, an acceleration sensor is installed on a semi-active suspension mounted on each of the four wheels of a vehicle body to measure the vertical speed of the sprung mass and the relative speed of the piston stroke, and the suspension control value is calculated by referring to the measurement results. In this case, the number of steps for mounting and assembling the acceleration sensor is increased, and brackets and harnesses are required, which increases the cost of the sensor itself and other items. In contrast, in this embodiment, the vehicle state estimation unit 202 estimates physical values ​​related to suspension control by referring to specific information, such as wheel speed and longitudinal acceleration, lateral acceleration, and yaw rate, which indicate vehicle behavior, among the data constantly transmitted to the CAN 109. Then, by transferring the estimation results to the control value calculation unit 203 at the subsequent stage, the acceleration sensor is not required, and cost reduction can be achieved.

[0034] (3) Weight-related data refers to the weight of the cargo loaded on the vehicle.

[0035] (4) The reading unit 205 reads out any one of the neural networks from the neural network storage unit 204 based on the weight-related data every time it detects any one of the following: closure of the door of the vehicle 9, fastening of a seat belt in the vehicle 9, and an instruction from an occupant of the vehicle 9, as shown in Fig. 6. Therefore, the suspension ECU 107 can reread the neural network at an appropriate timing.

[0036] (5) The output unit 208 outputs the weight-related data acquired by the acquisition unit 206 to the instrument panel 105, which is a display device provided in the vehicle 9. Therefore, the passengers of the vehicle 9 can confirm the weight inside the vehicle 9 recognized by the suspension ECU 107.

[0037] (Variation 1) FIG. 9 is a configuration diagram of vehicle 9 in Modification 2. This modification is significantly different from the first embodiment in that correspondence table 2051 is stored in vehicle state estimation unit 202. In this modification, reading unit 205 reads out all of the neural networks stored in neural network storage unit 204 to vehicle state estimation unit 202. Also, in this modification, information on the weight of the loaded object acquired by acquisition unit 206 is output to vehicle state estimation unit 202, not to reading unit 205. In addition to the processing in the first embodiment, vehicle state estimation unit 202 further selects a neural network, which was executed by reading unit 205 in the first embodiment.

[0038] According to the first modification, the following advantageous effects can be obtained. (6) A plurality of neural networks are loaded into the vehicle state estimation unit 202. The vehicle state estimation unit 202 selects a neural network corresponding to the weight-related data from the plurality of loaded neural networks, and estimates the state of the suspension.

[0039] (7) Each time the vehicle state estimation unit 202 detects any of the following: closure of the door of the vehicle 9, fastening of a seat belt in the vehicle 9, and an instruction from an occupant of the vehicle 9, the vehicle state estimation unit 202 reselects the neural network corresponding to the weight-related data.

[0040] (Variation 2) 10 is a diagram showing a correspondence table 2051 in Modification 2. In this correspondence table 2051, a neural network is set in advance for each wheel of the vehicle 9 corresponding to the weight-related data. For example, when the weight is "less than 100 kg", it is shown that "N1-LF" is used to estimate the state of the suspension for the left front tire, "N1-RF" is used to estimate the state of the suspension for the right front tire, "N1-LB" is used to estimate the state of the suspension for the left rear tire, and "N1-RB" is used to estimate the state of the suspension for the right rear tire.

[0041] The reading unit 205 reads out four neural networks corresponding to the weight of the load on the vehicle 9 from the neural network storage unit 204, and outputs them to the vehicle state estimation unit 202. The vehicle state estimation unit 202 uses a different neural network for each wheel to estimate the state of the suspension corresponding to that wheel. The weight on each tire of the vehicle 9 is not necessarily uniform and has a bias, so this weight bias can be reflected in the estimation of the suspension state.

[0042] According to the second modification, the following advantageous effects can be obtained. (8) The vehicle state estimation unit 202 estimates the state of the suspension for each wheel of the vehicle 9. Therefore, the state of each suspension can be estimated with high accuracy. Note that instead of providing a neural network for each wheel, a neural network that outputs eight physical quantities may be used. The eight physical quantities are the sprung mass velocity and the piston relative velocity for each of the four wheels.

[0043] (Variation 3) 11 is a diagram showing a correspondence table 2051 in the third modified example. The weight-related data in this modified example is a combination of weight and position, or a combination of weight and distribution. In this correspondence table 2051, a neural network corresponding to each combination of weight and position is determined in advance. The reading unit 205 reads out the neural network corresponding to the combination of weight and position from the neural network storage unit 204, and outputs it to the vehicle state estimation unit 202.

[0044] According to the third modification, the following advantageous effects can be obtained. (9) The weight-related data refers to the weight and position of the load loaded on the vehicle 9. Therefore, the suspension state can be estimated with high accuracy using an optimal neural network that takes into account not only the weight but also the position of the load.

[0045] (Variation 4) 12 is a diagram showing a correspondence table 2051 in the fourth modified example. The weight-related data in this modified example is a combination of weight and position, or a combination of weight and distribution. In this correspondence table 2051, a neural network is set in advance for each wheel of the vehicle 9 for each combination of weight and position. This modified example is, so to speak, a combination of the second modified example and the third modified example.

[0046] The reading unit 205 reads out four neural networks corresponding to combinations of weight and position from the neural network storage unit 204, and outputs them to the vehicle state estimation unit 202. The vehicle state estimation unit 202 uses a different neural network for each wheel to estimate the state of the suspension corresponding to that wheel. Since the weight applied to each tire of the vehicle 9 is not necessarily uniform but has a bias, this weight bias can be reflected in the estimation of the suspension state.

[0047] --Second embodiment-- A second embodiment of the suspension system will be described with reference to Figs. 13 to 16. In the following description, the same components as those in the first embodiment are given the same reference numerals, and differences will be mainly described. Points that are not particularly described are the same as those in the first embodiment. This embodiment differs from the first embodiment mainly in that the state inside the vehicle is estimated instead of directly measuring weight. However, at least a part of instrument panel 105 in this embodiment is a touch panel and also has an input function.

[0048] 13 is a configuration diagram of a vehicle 9A in the second embodiment. The difference from the first embodiment is that the vehicle 9A does not include a weight measuring unit 103, and includes a seat belt state detecting unit 104, a camera 106, and a communication unit 111. The suspension ECU 107A in this embodiment further includes a sensor processing unit 207. The instrument panel 105 in this embodiment receives input from a user and outputs the input to the sensor processing unit 207.

[0049] The sensor processing unit 207 receives outputs from the seat belt state detection unit 104, the camera 106, and the communication unit 111. The sensor processing unit 207 estimates the weight of the load on the vehicle 9 and outputs it to the acquisition unit 206. The seat belt state detection unit 104 detects the fastening state of the seat belt of each seat in the vehicle 9, and outputs the detected seat data to the sensor processing unit 207. It can be assumed that an occupant is sitting in a seat where the seat belt is fastened.

[0050] The camera 106 outputs a captured image obtained by capturing an image of the interior of the vehicle 9 to the sensor processing unit 207. The angle of view of the camera 106 is adjusted to include each seat of the vehicle 9. The communication unit 111 communicates with a mobile terminal 1206 carried by a user, receives data related to the occupants input by the user, and outputs the data to the sensor processing unit 207.

[0051] 14 is a diagram showing an example of a case where the driver inputs the position of the cargo using the instrument panel 105. The instrument panel 105 is, for example, a touch panel, and the driver inputs the presence or absence of passengers and luggage at each position by touch operation. In this example, passengers are shown as circles as indicated by reference numeral 1204, and luggage is shown as squares as indicated by reference numeral 1205. In this example, it is shown that there are passengers in the driver's seat and the passenger seat, and luggage is present at the rear right.

[0052] 15 is a diagram showing a display screen of a mobile terminal 1206 that is carried by a user and into which data related to occupants is input. An occupant setting application is running on the mobile terminal 1206. This application includes a number of people input field 1208 and a weight input field 1209. When the user inputs numerical values ​​into the number of people input field 1208 and the weight input field 1209, the mobile terminal 1206 transmits the numerical data to the communication unit 111. The communication unit 111 outputs the data received from the mobile terminal 1206 to the sensor processing unit 207. Note that this application may be configured to allow the position and weight of luggage to be input.

[0053] FIG. 16 is a schematic diagram showing input and output of the sensor processing unit 207. The sensor processing unit 207 receives the position data of the loaded object input by the driver, whether the seat belt is fastened, and the image captured by the camera 106. The driver's input and whether the seat belt is fastened can estimate the number of passengers with high accuracy. If the number of passengers can be estimated, the sensor processing unit 207 can calculate the weight by multiplying it by the standard weight of an adult, for example. In addition, the input of the loading position by the driver may be made to be not only the number of passengers but also the weight. The sensor processing unit 207 may estimate the number of passengers in the vehicle 9 by applying known face recognition processing or person detection processing to the image captured by the camera 106. The sensor processing unit 207 may further recognize the physique of the passengers, i.e., the body size, from the captured image and estimate the weight based on the physique.

[0054] According to the above-described second embodiment, the following advantageous effects can be obtained.

[0055] (10) Weight-related data is data related to the weight of a person riding in the vehicle 9. The acquisition unit 206 acquires the output of the camera 106 that captures the interior of the vehicle 9. The suspension ECU 107A includes a sensor processing unit 207 that detects a person from an image captured by the camera 106, estimates the weight of the person, and outputs the weight to the acquisition unit 206.

[0056] (11) The acquisition unit 206 acquires weight-related data based on the output of a sensor mounted on the vehicle 9 or an input by a vehicle occupant.

[0057] --Third embodiment-- A third embodiment of a suspension system will be described with reference to Fig. 17. In the following description, the same components as those in the first embodiment are given the same reference numerals, and differences will be mainly described. Points that are not specifically described are the same as those in the first embodiment. This embodiment differs from the first embodiment mainly in that the neural network is updated.

[0058] 1 is a configuration diagram of a neural network updating system S. The neural network updating system S includes a server 1301 and a vehicle 9B. The server 1301 and the vehicle 9B are connected via an external network 1401. The external network 1401 is a network including wireless communication. The external network 1401 may include the Internet, or may include a base station managed by a communication carrier.

[0059] The configuration of the vehicle 9B is substantially the same as that of the vehicle 9 in the first embodiment. The difference is that the vehicle 9B further includes an in-vehicle communication unit 112. The in-vehicle communication unit 112 writes data transmitted from the server 1301 to the neural network storage unit 204.

[0060] The server 1301 includes a server communication unit 1304, a control unit 1305, a teacher data storage unit 1306, a learning unit 1307, and a learning result storage unit 1308. The server communication unit 1304 communicates with the vehicle 9B via an external network 1401. The server communication unit 1304 is, for example, a network interface card.

[0061] The teacher data storage unit 1306 and the learning result storage unit 1308 are non-volatile storage devices, such as a hard disk drive. The teacher data storage unit 1306 stores teacher data for learning that has been collected in advance. This teacher data is a plurality of sets of values ​​of the input layer N-IN and the output layer N-OUT of the neural network N shown in FIG. 3. The control unit 1305 issues an instruction to the learning unit 1307 to execute learning. When the learning process by the learning unit 1307 is completed, the control unit 1305 transmits the data stored in the learning result storage unit 1308 to the vehicle 9B via the server communication unit 1304.

[0062] The learning unit 1307 uses the teacher data stored in the teacher data storage unit 1306 to update the parameters of the neural network N stored in the learning result storage unit 1308. However, in this embodiment, the configuration of the neural network N is determined in advance. For example, when the number of epochs reaches a predetermined threshold, the learning unit 1307 ends the learning process and stores the parameters in the learning result storage unit 1308.

[0063] 17, weighting parameters learned from a data set acquired in one learning vehicle can be reflected in multiple vehicles 9B, so long as the vehicles are of the same model. In addition, by continuously acquiring data sets from learning vehicles and having the server 1301 learn or perform additional learning, the weighting parameters can be updated, and as a result, the accuracy of physical value estimation by the vehicle state estimation unit 202 is improved, thereby achieving a better ride comfort.

[0064] According to the above-described third embodiment, the neural network stored in the neural network storage unit 204 can be updated as appropriate.

[0065] In each of the above-mentioned embodiments and modifications, the configuration of the functional blocks is merely an example. Some functional configurations shown as separate functional blocks may be integrated, or a configuration shown in one functional block diagram may be divided into two or more functions. In addition, some of the functions of each functional block may be provided by other functional blocks.

[0066] In the above-described embodiments and modifications, the programs are stored in the ROM 42, but the programs may be stored in the storage device 44. The suspension ECU 107 may have an input / output interface (not shown), and the programs may be read from another device via the input / output interface and a medium available to the suspension ECU 107 when necessary. The medium here refers to, for example, a storage medium that is detachable from the input / output interface, or a communication medium, that is, a network such as a wired, wireless, or optical network, or a carrier wave or digital signal that propagates through the network. Some or all of the functions realized by the programs may be realized by a hardware circuit or an FPGA.

[0067] The above-mentioned embodiments and modifications may be combined with each other. Although various embodiments and modifications have been described above, the present invention is not limited to these. Other aspects that are conceivable within the scope of the technical idea of ​​the present invention are also included in the scope of the present invention. [Explanation of symbols]

[0068] 9: Vehicle 103: Weight measurement section 105:Instrument panel 106: Camera 107: Suspension ECU 201: Semi-active suspension 202: Vehicle state estimation unit 204: Neural network memory section 205:Reading part 206: Acquisition Department 207: Sensor processing unit 208: Output section 2051: Correspondence table

Claims

1. A suspension system that is mounted on a vehicle and estimates a state of a suspension mounted on the vehicle, comprising: A storage unit in which a plurality of neural networks are stored; An acquisition unit that acquires weight-related data, which is data related to a weight of a load of the vehicle; a vehicle state estimation unit that estimates a state of the suspension by inputting data related to the vehicle's traveling to any one of the neural networks stored in the memory unit that corresponds to the weight-related data.

2. 2. The suspension system of claim 1, The vehicle state estimation unit loads the plurality of neural networks, and the vehicle state estimation unit selects the neural network corresponding to the weight-related data from the plurality of loaded neural networks and estimates the state of the suspension.

3. 2. The suspension system of claim 1, a reading unit that reads out the neural network corresponding to the weight-related data from the storage unit and outputs the neural network to the vehicle state estimation unit, A suspension system, wherein the vehicle state estimation unit estimates a state of the suspension using the neural network output by the reading unit.

4. 2. The suspension system of claim 1, A suspension system, wherein the weight-related data is data on the weight of the load.

5. 2. The suspension system of claim 1, A suspension system, wherein the weight-related data is data on the weight of the load and the position of the load.

6. 2. The suspension system of claim 1, The weight-related data is data related to the weight of a person riding in the vehicle, The acquisition unit acquires an output from a camera that captures an image of the interior of the vehicle, The suspension system further includes a sensor processing unit that detects the person from an image captured by the camera, estimates the weight of the person, and outputs the weight to the acquisition unit.

7. 4. The suspension system of claim 3, A suspension system, wherein the reading unit reads out one of the neural networks from the memory unit based on the weight-related data each time it detects any one of the following: a door of the vehicle is closed, a seat belt is fastened in the vehicle, and an instruction from an occupant of the vehicle.

8. 3. The suspension system of claim 2, A suspension system, wherein the vehicle state estimation unit reselects the neural network corresponding to the weight-related data each time it detects any one of a door of the vehicle being closed, a seat belt being fastened in the vehicle, and an instruction from an occupant of the vehicle.

9. 2. The suspension system of claim 1, A suspension system, wherein the vehicle state estimating unit estimates a state of the suspension for each wheel of the vehicle.

10. 3. The suspension system of claim 2, A suspension system, wherein the acquisition unit acquires the weight-related data based on an output of a sensor mounted on the vehicle or an input by an occupant of the vehicle.

11. 2. The suspension system of claim 1, The suspension system further comprises an output unit that outputs the weight-related data acquired by the acquisition unit to a display device provided in the vehicle.

12. 1. An estimation method for estimating a state of a suspension mounted on a vehicle, the method comprising: acquiring weight-related data relating to a weight of a load on the vehicle based on an output of a sensor mounted on the vehicle; and estimating a state of the suspension by inputting data related to the vehicle's traveling to any one of the neural networks stored in the memory unit that corresponds to the weight-related data.

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