Road condition determination device, road condition determination method, and computer-readable storage medium

Abstract

The present invention relates to a road condition determination device, a road condition determination method, and a computer-readable storage medium. The road condition determination device includes an acquisition module configured to acquire the vertical acceleration signals of the wheel centers of each wheel of the vehicle; a preprocessing module arranged at the subsequent stage of the acquisition module and configured to preprocess the vertical acceleration signals of the wheel centers to obtain an initial determination signal capable of characterizing its fluctuation condition; a high-pass filter arranged at the subsequent stage of the preprocessing module and configured to perform high-pass filtering on the initial determination signal with a high-pass filtering factor; a low-pass filter arranged in parallel with the high-pass filter at the subsequent stage of the preprocessing module and configured to perform low-pass filtering on the initial determination signal with a low-pass filtering factor; and a determination module configured to determine a road condition coefficient based on the initial determination signal after low-pass filtering and the initial determination signal after high-pass filtering.

Description

Technical Field

[0001] The present invention relates to a road condition determination device for a vehicle, a road condition determination method, and a computer-readable storage medium that can be used to execute such a road condition determination method. Background Art

[0002] In today's intelligent suspension systems, semi-active suspensions or active suspensions are usually equipped, which puts relatively high requirements on the vehicle's environmental perception ability. It requires the vehicle to be able to accurately obtain the road conditions in real time and transmit them to the suspension control unit, so that the suspension control unit can adjust the suspension control strategy in real time based on the road condition information. Considering this, generally, multiple or as many high-precision environmental perception sensors as possible are installed on the vehicle, such as lidar sensors, optical cameras, etc. Both of these will undoubtedly increase the vehicle's hardware cost. In addition, the signal processing process for high-precision environmental perception sensors requires a large amount of computing resources of the vehicle. Especially for optical cameras, in order to ensure the calculation accuracy, it must perceive the unevenness of the ground based on an image recognition algorithm with a large computational load. Summary of the Invention

[0003] According to different aspects of the present invention, it aims to provide an improved road condition determination device, a road condition determination method, and a computer-readable storage medium for executing such a method. Among them, the road condition determination device or method can achieve relatively accurate perception of the vehicle's driving road conditions with less computing resources occupied by the vehicle.

[0004] In addition, the present invention also aims to solve or alleviate other technical problems existing in the prior art.

[0005] To solve the above technical problems, according to one aspect of the present invention, a road condition determination device is provided, which includes:

[0006] An acquisition module configured to acquire the vertical acceleration signals of the wheel centers of each wheel of the vehicle;

[0007] A preprocessing module arranged at the subsequent stage of the acquisition module and configured to preprocess the vertical acceleration signals of the wheel centers to obtain an initial determination signal that can characterize its fluctuation;

[0008] A high-pass filter arranged at the subsequent stage of the preprocessing module and configured to perform high-pass filtering on the initial determination signal with a high-pass filtering factor;

[0009] A low-pass filter arranged in parallel with the high-pass filter at the subsequent stage of the preprocessing module and configured to perform low-pass filtering on the initial determination signal with a low-pass filtering factor;

[0010] A determination module configured to determine a road condition coefficient based on an initially determined signal after low-pass filtering and an initially determined signal after high-pass filtering.

[0011] According to an aspect of the present invention, in the road condition determination device, the determination module includes an analysis sub-module and a coefficient determination sub-module, wherein the analysis sub-module is configured to obtain a first amplitude and a first frequency of the initially determined signal after high-pass filtering and a second amplitude and a second frequency of the initially determined signal after low-pass filtering through Fourier analysis; the coefficient determination sub-module is configured to determine the road condition coefficient based on the first amplitude and first frequency and the second amplitude and second frequency.

[0012] According to an aspect of the present invention, in the road condition determination device, the acquisition module is connected to the vehicle data bus and filters out relevant signals related to road conditions therefrom, and the acquisition module obtains a vertical acceleration signal of the wheel center of each wheel based on the relevant signals, and the relevant signals are selected from the following group: a vertical acceleration signal of the vehicle body, a shock absorber stroke signal, an inertial measurement unit signal, and a vertical acceleration signal of the wheel.

[0013] According to an aspect of the present invention, in the road condition determination device, the preprocessing module includes an additional low-pass filter configured to perform low-pass filtering on the initially determined signal with an initial filtering factor before the initially determined signal is output to the low-pass filter and the high-pass filter.

[0014] According to an aspect of the present invention, in the road condition determination device, the road condition determination device further includes a filtering factor determination module configured to set an initial filtering factor, a high-pass filtering factor, and a low-pass filtering factor based on a vehicle state signal, wherein the vehicle state signal includes a vehicle speed signal and a vehicle start / stop signal.

[0015] According to an aspect of the present invention, in the road condition determination device, the filtering factor determination module is configured to determine the initial filtering factor, the high-pass filtering factor, and the low-pass filtering factor according to the following formula:

[0016]

[0017] wherein, FrqD is the initial filtering factor;

[0018] HFrqD is the high-pass filtering factor;

[0019] LFrqD is the low-pass filtering factor;

[0020] v is the vehicle speed;

[0021] k FrqD 、k HFrqD 、k LFrqD 、FrqD M, HFrqD M is a preset constant.

[0022] According to another aspect of the present invention, there is also provided a road condition determination method executable by the above-mentioned road condition determination device, which includes the following steps:

[0023] S100: Obtain the vertical acceleration signals of the wheel centers of each wheel and the initial determination signal that can characterize its fluctuation condition;

[0024] S200: Perform high-pass filtering on the initial determination signal with a preset high-pass filtering factor, and perform low-pass filtering on the initial determination signal with a preset low-pass filtering factor;

[0025] S300: Determine the road condition coefficient based on the initial determination signal after high-pass filtering and the initial determination signal after low-pass filtering.

[0026] For the road condition determination method proposed according to another aspect of the present invention, step S100 includes the following sub-steps:

[0027] S110: Obtain the deviation values of the vertical acceleration signals of each wheel center at each moment relative to the average vertical acceleration signal of the wheel centers within a preset time period;

[0028] S120: Perform low-pass filtering on the deviation values with a preset initial filtering factor to generate the initial determination signal.

[0029] For the road condition determination method proposed according to another aspect of the present invention, step S300 includes the following sub-steps:

[0030] S310: By means of Fourier analysis, respectively obtain the first amplitude and the first frequency of the initial determination signal after high-pass filtering, and obtain the second amplitude and the second frequency of the initial determination signal after low-pass filtering;

[0031] S320: Find the first amplitude, the first frequency, the second amplitude, and the second frequency of each pair of wheels on the same side of the vehicle and determine the lateral road condition coefficients of each side of the vehicle based on them respectively;

[0032] S330: Obtain the weighted average value of the lateral road condition coefficients of each side of the vehicle as the road condition coefficient.

[0033] For the road condition determination method proposed according to another aspect of the present invention, sub-step S320 includes the following steps:

[0034] S321: Obtain the amplitude average value of the first amplitude and the second amplitude of each pair of wheels on the same side respectively, and obtain the frequency average value of the first frequency and the second frequency of each pair of wheels on the same side respectively;

[0035] S322: Obtain the amplitude average value of each pair of same-side wheels and their weighted average value with each other as the amplitude weighted value; and obtain the frequency average value of each pair of same-side wheels and their weighted average value with each other as the frequency weighted value;

[0036] S323: Calculate the root mean square of the product of the amplitude weighted value and the frequency weighted value as the lateral road condition coefficient.

[0037] Finally, according to another aspect of the present invention, a computer-readable storage medium is also proposed, on which a computer program is stored, wherein when the computer program is executed by a processor, the road condition determination method of the type described above is implemented.

[0038] Based on the sensor signals, the road condition determination method according to the present disclosure can obtain the road condition coefficient with a relatively low computational load, which can reduce the vehicle software and hardware costs to a certain extent. On the other hand, by retaining both the high-frequency characteristics and low-frequency characteristics of the vertical acceleration signals of each wheel center, it can be closer to the actual driving conditions of the vehicle. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Next, the present invention will be described in more detail with reference to the drawings, where:

[0040] Figure 1 Show a schematic block diagram of a road condition determination device according to the present invention;

[0041] Figure 2 Show the main steps of a road condition determination method according to the present invention;

[0042] Figure 3 and Figure 4 Respectively show the main sub-steps of the road condition determination method in block diagrams. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0043] It is easy to understand that according to the technical solution of the present invention, under the condition of not changing the essential spirit of the present invention, those of ordinary skill in the art can propose various interchangeable structural forms and implementation manners. Therefore, the following detailed embodiments and the drawings are only exemplary descriptions of the technical solution of the present invention, and should not be regarded as all of the present invention or as a limitation or restriction on the technical solution of the present invention.

[0044] The orientation terms such as up, down, left, right, front, back, front side, back side, top, bottom, etc. mentioned or possibly mentioned in this specification are defined relative to the structures shown in the respective drawings, and they are relative concepts. Therefore, they may change accordingly according to their different positions and different usage states. Therefore, these or other orientation terms should not be construed as restrictive terms.

[0045] Refer to Figure 1 and Figure 2, which respectively show the main steps of a road condition determination device for a vehicle and a road condition determination method executable thereby according to an embodiment. The road condition determination device 100 includes an acquisition module 110, a preprocessing module 120, a high-pass filter 130, a low-pass filter 140, and a determination module 150. The preprocessing module 120 is located at the subsequent stage of the acquisition module 110 for preprocessing the signal for road condition determination acquired by the acquisition module 110. Between the preprocessing module 120 and the determination module 150, the low-pass filter 140 and the high-pass filter 130 are connected in parallel to respectively retain the high-frequency characteristics and low-frequency characteristics of the signal, which is closer to the real driving environment of the vehicle compared to the series connection method. For example, the vertical acceleration signal of the wheel center on a gravel road has more obvious high-frequency characteristics compared to that on a relatively gentle large-arc uneven road surface. The determination module 150 is located at the subsequent stage of these two filters and is configured to jointly determine a road condition coefficient according to the high-pass filtered and low-pass filtered signals. The road condition coefficient can represent the severity of the vehicle driving road condition, and the relationship (such as positive correlation, negative correlation) between the road condition coefficient and the road condition severity can be set according to requirements or according to the suspension control logic.

[0046] Correspondingly, the road condition determination method according to the present invention can be implemented as follows by means of such a road condition determination device:

[0047] First, acquire the vertical acceleration signal of the wheel center of each wheel and an initial determination signal that can characterize its fluctuation condition, that is, step S100;

[0048] Secondly, perform high-pass filtering on the initial determination signal by means of a high-pass filter with a preset high-pass filtering factor, and perform low-pass filtering on the initial determination signal by means of a low-pass filter with a preset low-pass filtering factor, that is, step S200;

[0049] Then, based on the high-pass filtered initial determination signal and the low-pass filtered initial determination signal, determine the road condition coefficient, that is, step S300.

[0050] Here, instead of an additional high-precision environment perception sensor, determining the road condition based on the original vehicle equipment or the detected sensor signals can reduce the vehicle hardware cost to a certain extent. In addition, compared with the complex image processing algorithms in the prior art, the road condition determination device and the road condition determination method are based on the vertical acceleration signal of the wheel center and the involved calculation process requires not too much computing load, thereby being able to release the vehicle-end computing resources.

[0051] The acquisition module 110 is configured to acquire the vertical acceleration signals of the wheel centers of each wheel of the vehicle, that is, the acceleration signals of the wheel centers in the vertical direction. The unevenness and roughness of the road surface on which the vehicle travels can be intuitively reflected in the bouncing motion of the unsprung mass. Among them, the vertical acceleration of the wheel center of the unsprung mass "wheel" can be and is relatively suitable as a reference for determining the driving road conditions of the vehicle. The acquisition module 110 can be communicatively connected to the vehicle data bus to read and filter out relevant signals associated with the road conditions therefrom; or it can be communicatively connected directly to the vehicle sensors and obtain the vertical acceleration signals of the wheel centers based on the received relevant signals; or on a vehicle equipped with a vertical acceleration sensor of the wheel center, the acquisition module 110 can be communicatively connected directly thereto and call the detected vertical acceleration signals of the wheel centers of each wheel.

[0052] Optionally, the relevant signals associated with the road conditions can involve wheel vertical acceleration signals, body vertical acceleration signals, suspension compression displacement signals, shock absorber stroke signals, and inertial measurement unit signals. Correspondingly, it can involve combinations of the following sensors: a vertical acceleration sensor at the sprung body (for example, three vertical acceleration sensors are arranged to be able to acquire body attitude parameters, such as body roll characteristics, body pitch characteristics, etc.) and a shock absorber stroke sensor at the unsprung shock absorber (for example, each shock absorber is respectively assigned with this shock absorber stroke sensor), an inertial measurement unit sensor at the sprung body (for example, one) and a shock absorber stroke sensor at the unsprung shock absorber (for example, each shock absorber is respectively assigned with this shock absorber stroke sensor), a vertical acceleration sensor at the sprung body (for example, three) and a vertical acceleration sensor at the wheel center of the unsprung front wheels (for example, located at the left front wheel and the right front wheel respectively). It should be noted here that the selection of the above-mentioned relevant signals or their combination methods and the selection of the sensors involved or their combination methods can be designed according to the existing vehicle configuration scheme.

[0053] The preprocessing module 120 is configured to process the received vertical wheel center acceleration signal into an initial determination signal that can characterize its fluctuation condition. Optionally, the initial determination signal represents the deviation value of the vertical wheel center acceleration at each moment or each sampling point from the average vertical wheel center acceleration within a preset time period. This deviation value reflects the fluctuation condition of the vertical wheel center acceleration and thereby reflects the change condition of the uneven parts of the driving road surface. Here, on the one hand, the preset time period can relate to historical sampling data. In this case, the arithmetic mean value of the vertical wheel center accelerations at multiple sampling points is first obtained as the average vertical wheel center acceleration within a continuously accumulated historical road surface length or historical driving time period, and the difference is taken between this value and the vertically wheel center acceleration signals collected in real time to obtain this deviation value. Here, vehicle motion state signals can also be applied. For example, according to a Boolean signal (where "0" represents the vehicle is stationary and "1" represents the vehicle is in motion). When the Boolean signal changes, it means that the sampling points based on which the arithmetic mean value is calculated are reset. In this case, it should be noted that the length of the preset time period should be set and adjusted according to the required calculation accuracy. On the other hand, the preset time period can also relate to the sampling data of the ongoing road condition determination process. In this case, the difference can be taken between the arithmetic mean value of the sampling data within a partial sampling data period or the entire sampling period and the vertical wheel center acceleration at each moment to obtain this deviation value.

[0054] More specifically, before the initial determination signal is respectively input into the subsequent high-pass filter 130 and low-pass filter 140, it is necessary to pre-filter the initial determination signal to filter the signal and improve the calculation accuracy. Based on this, the preprocessing module 120 further includes an additional low-pass filter (not shown), which is configured to perform low-pass filtering on the initial determination signal with an initial filtering factor, especially with a preset initial filtering factor.

[0055] Generally speaking, in the preprocessing module 120, the vertical wheel center acceleration signals of each wheel obtained based on the relevant signals or directly read from the sensors respectively undergo the following processes: obtaining the deviation value of the vertical wheel center acceleration signal at each moment from the average vertical wheel center acceleration signal within a preset time period (i.e., sub-step S110); performing low-pass filtering on the deviation value with a preset initial filtering factor to generate the initial determination signal (i.e., sub-step S120).

[0056] Optionally, the initial filtering factor of the additional low-pass filter, the high-pass filtering factor of the high-pass filter 130, and the low-pass filtering factor of the low-pass filter 140 can be preset and stored in the corresponding computing unit; or they can also be set and adjusted according to the vehicle state signal. The concept of "filtering factor" refers to the critical frequency for filtering processing, which can also be referred to as the cut-off frequency. For example, in high-pass filtering, the part of the initial determination signal below the high-pass filtering factor is removed. Specifically, the road condition determination device according to the present invention further has a filtering factor determination module 160, which can obtain the above three filtering factors according to the vehicle state signal, such as the vehicle speed signal and the vehicle start / stop signal. Among them, the vehicle start / stop signal can be used to correct the vehicle speed signal. Since there is a close relationship between the vehicle speed and the wheel bounce and the vertical acceleration of the wheel center, for example, on the same uneven road surface, the wheel bounce at a higher vehicle speed is more intense than that at a lower vehicle speed. Therefore, it is possible to advantageously determine at least one of the above three filtering factors according to the vehicle speed, and particularly advantageously determine the above three filtering factors according to the vehicle speed.

[0057] More specifically, the initial filtering factor FrqD, the high-pass filtering factor HFrqD, and the low-pass filtering factor LFrqD can be determined by the following formula (1):

[0058]

[0059] where v is the vehicle speed;

[0060] k FrqD 、k HFrqD 、k LFrqD 、FrqD M 、HFrqD M are preset constants. The low-pass filtering factor LFrqD is proportional to the vehicle speed v; the high-pass filtering factor HFrqD is set by finding the maximum value; the initial filtering factor FrqD is obtained by finding the minimum value.

[0061] Optionally, the determination module 150 includes an analysis sub-module 151 and a coefficient determination sub-module 152. The analysis sub-module 151 is configured to obtain the amplitude and frequency of the initially determined signal after high-pass filtering and low-pass filtering respectively through Fourier analysis, for example, through fast Fourier transform. Specifically, the analysis sub-module 151 obtains a first amplitude and a first frequency of the initially determined signal after high-pass filtering by means of a high-pass filter through fast Fourier transform, and obtains a second amplitude and a second frequency of the initially determined signal after low-pass filtering by means of a low-pass filter through fast Fourier transform. The coefficient determination sub-module 152 is used to obtain a road condition coefficient based on these amplitudes and frequencies. The analysis sub-module 151 respectively obtains the amplitude and frequency assigned to each wheel center vertical acceleration of each wheel, and then in the coefficient determination sub-module 152, the lateral road condition coefficients of each side of the vehicle are respectively determined based on this, and thus the road condition coefficient of the vehicle as a whole is obtained.

[0062] More specifically, the above-mentioned step S300 executed by the determination module 150 includes the following sub-steps:

[0063] S310: By means of Fourier analysis, obtain a first amplitude and a first frequency of the initially determined signal after high-pass filtering respectively, and obtain a second amplitude and a second frequency of the initially determined signal after low-pass filtering;

[0064] S320: Find the first amplitude, first frequency, second amplitude, and second frequency of each same-side wheel located on the same side of the vehicle and respectively determine the lateral road condition coefficients of each side of the vehicle based on them;

[0065] S330: Obtain the weighted average of the lateral road condition coefficients of each side of the vehicle as the road condition coefficient.

[0066] Here, sub-step S310 is performed in the analysis sub-module 151, and steps S320 and S330 are performed in the coefficient determination sub-module 152. In sub-steps S320 and S330, the amplitudes and frequencies assigned to each wheel on the same side are integrated into a lateral road condition coefficient that can reflect the road condition of this side through secondary calculations (such as adding a custom function, weighted average), and then through secondary calculations (such as adding a custom function, weighted average), all lateral road condition coefficients are integrated into an overall road condition coefficient, which can be used for the suspension control system. Here, for a four-wheel vehicle, it can be divided into the left side and the right side, and the wheels can be divided into left-side wheels (including the left front wheel and the left rear wheel) and right-side wheels (including the right front wheel and the right rear wheel), which will be elaborated in more detail below.

[0067] More specifically, the acquisition of the above-mentioned lateral road condition coefficients can be achieved through the following sub-steps:

[0068] S321: Obtaining the average amplitude of the first amplitude and the second amplitude of each wheel on the same side, and obtaining the average frequency of the first frequency and the second frequency of each wheel on the same side;

[0069] S322: Calculate the average amplitude value of each wheel on the same side and the weighted average value of each other as the amplitude weighted value; and calculate the average frequency value of each wheel on the same side and the weighted average value of each other as the frequency weighted value;

[0070] S323: Calculate the root mean square of the product of the amplitude weighted value and the frequency weighted value as the lateral road condition coefficient.

[0071] It should be noted that the road condition determination device, road condition determination method and computer readable storage medium according to the present invention can be applied to different types of vehicles, and the differences may involve different hardware configurations, including different numbers of axles, different numbers of wheels, etc. Figure 3 and Figure 4 A specific implementation of the road condition determination method is described in detail based on a four-wheel vehicle.

[0072] First, the acquisition module 110 of the road condition determination device reads the following signals from the vehicle data bus: the vertical acceleration signal of the vehicle body directly above the four wheels, denoted as TMAZ * (including TMAZFL, TMAZFR, TMAZRL, TMAZRR), and the 4 suspension compression signals installed on the suspension, recorded as WhlZ * (WhlZFL, WhlZFR, WhlZRL, WhlZRR). * Take the second derivative and compare it with the vehicle body vertical acceleration signal TMAZ * By making a difference, we can get the vertical acceleration signal of the wheel center of each wheel, which is recorded as RoadStSig * (It includes RoadStSigFL, RoadStSigFR, RoadStSigRL, RoadStSigRR.) It should be noted that the suffix "FL" in the marking symbols used here or below represents the left front wheel of the vehicle, "FR" represents the right front wheel of the vehicle, "RL" represents the left rear wheel of the vehicle, and "RR" represents the right rear wheel of the vehicle.

[0073] Next, in the preprocessing module 120 of the road condition determination device, the wheel center vertical acceleration signal RoadStSig * and the average wheel center vertical acceleration signal RoadStSig * 'Make the difference and get the deviation value ΔRoadStSig *; Subsequently, in the additional low-pass filter, the ΔRoadStSig is low-pass filtered with the initial filtering factor FrqD * to obtain the initial determination signal RoadStRaw * (which includes RoadStRawFL, RoadStRawFR, RoadStRawRL, RoadStRawRR).

[0074] Immediately afterwards, according to Figure 3 Based on the initial determination signal RoadStRaw * the relevant amplitudes and frequencies are obtained. On the one hand, after the initial determination signal RoadStRaw is high-pass filtered in the high-pass filter 130 with the high-pass filtering factor HFrqD * the first amplitude RoadStMagHP after high-pass filtering is obtained through fast Fourier transform (abbreviated as FFT, Fast Fourier Fransform) * (which includes RoadStMagHPFL, RoadStMagHPFR, RoadStMagHPRL, RoadStMagHPRR); and the first frequency RoadStFreqHP after high-pass filtering is obtained through fast Fourier transform, peak detection and peak period calculation * (which includes RoadStFreqHPFL, RoadStFreqHPFR, RoadStFreqHPRL, RoadStFreqHPRR). On the other hand, at the same time, the initial determination signal RoadStRaw is low-pass filtered in the low-pass filter 140 with the low-pass filtering factor LFrqD * After that, the second amplitude RoadStMagLP after low-pass filtering is obtained through fast Fourier transform * (which includes RoadStMagLPFL, RoadStMagLPFR, RoadStMagLPRL, RoadStMagLPRR); and the second frequency RoadStFreqLP after high-pass filtering is obtained through fast Fourier transform, peak detection and peak period calculation * (RoadStFreqLPFL, RoadStFreqLPFR, RoadStFreqLPRL, RoadStFreqLPRR). In this process, the above calculations need to be performed separately on the initial determination signals RoadStRawFL, RoadStRawFR, RoadStRawRL, RoadStRawRR of each wheel.

[0075] Subsequently, in the coefficient determination sub-module 152 of this road condition determination device, according to Figure 4Based on the received RoadStMagHP * 、RoadStFreqHP * 、RoadStMagLP * and RoadStFreqLP * to obtain the road condition coefficient. First, divide the above parameters, that is, divide the above parameters of the left front wheel and the left rear wheel into the first group, divide the above parameters of the right front wheel and the right rear wheel into the second group, and calculate the lateral road condition coefficients on the left side and the right side of the vehicle respectively based on this. Now, take the acquisition of the lateral road condition coefficient on the left side as an example for elaboration.

[0076] Perform a weighted average of the mathematical average of the first amplitude RoadStMagHPFL of the left front wheel and the second amplitude RoadStMagLPFL and the mathematical average of the first amplitude RoadStMagHPRL of the left rear wheel and the second amplitude RoadStMagLPRL, and denote it as the amplitude weighted value on the left side of the vehicle. In addition, perform a weighted average of the mathematical average of the first frequency RoadStFreqHPFL of the left front wheel and the second frequency RoadStFreqLPFL and the mathematical average of the first frequency RoadStFreqHPRL of the left rear wheel and the second frequency RoadStFreqLPRL, and denote it as the frequency weighted value on the left side of the vehicle. Calculate the RMS (Root Mean Square) effective value of the product of the amplitude weighted value and the frequency weighted value, especially perform the RMS effective value calculation in a preset sampling time period (for example, in the case of 100 sampling points), and obtain the lateral road condition coefficient RoadStL on the left side of the vehicle based on the obtained root mean square. In addition, obtain the lateral road condition coefficient RoadStR on the right side of the vehicle in the same way.

[0077] Finally, perform a weighted average calculation on these two lateral road condition coefficients RoadStL and RoadStR to obtain the road condition coefficient RoadSt * which can be output to the vehicle suspension control system. Here, the weight coefficients assigned to RoadStL and RoadStR respectively can be preset according to tests, or can also be adaptively adjusted according to the running mode of the vehicle, such as steering. In addition, these two lateral road condition coefficients RoadStL and RoadStR can also be directly transmitted to the suspension control system, which is especially beneficial when the vehicle is steering or when the road conditions on the left and right sides differ greatly.

[0078] In summary, the road condition determination method according to the present invention obtains the road condition coefficient based on sensor signals in a manner with a relatively low computational load, which can reduce the software and hardware costs of the vehicle to a certain extent. On the other hand, by simultaneously retaining the high-frequency and low-frequency characteristics of the vertical acceleration signals of each wheel center, it can be closer to the actual driving condition of the vehicle. In an embodiment of the present invention, by setting and adjusting the critical frequencies in each filtering process according to the vehicle speed, the interference of the vehicle speed on road condition determination can be excluded. In another embodiment of the present invention, by using the weighted average of the lateral road condition coefficients on the left and right sides of the vehicle as the road condition coefficient, a possibility of matching the vehicle driving state is provided and a more targeted reference can be given to the vehicle suspension control system.

[0079] Finally, the present invention also relates to a computer-readable storage medium for implementing the road condition determination method for a vehicle according to one or more embodiments of the present invention. The computer-readable storage medium mentioned herein includes various types of computer storage media and can be any available medium accessible by a general-purpose or special-purpose computer. For example, the computer-readable storage medium may include RAM, ROM, EPROM, E2PROM, registers, hard disks, removable disks, CD-ROMs or other optical disk memories, magnetic disk memories or other magnetic storage devices, or any other transient or non-transient medium capable of carrying or storing desired program code units in the form of instructions or data structures and accessible by a general-purpose or special-purpose computer, or a general-purpose or special-purpose processor. The description of the computer-readable storage medium according to the present invention can refer to the explanation of the road condition determination device or road condition determination method according to the present invention, which will not be elaborated herein.

Claims

1. A road condition determination device, characterized in that, comprising: an acquisition module configured to acquire the vertical acceleration signals of the wheel centers of each wheel of the vehicle; a preprocessing module arranged at the subsequent stage of the acquisition module and configured to preprocess the vertical acceleration signals of the wheel centers to obtain an initial determination signal that can characterize its fluctuation condition, wherein the initial determination signal includes the deviation value of the vertical acceleration signal of the wheel center relative to the average vertical acceleration signal of the wheel center within a preset time period; a high-pass filter arranged at the subsequent stage of the preprocessing module and configured to perform high-pass filtering on the initial determination signal with a high-pass filtering factor; a low-pass filter arranged in parallel with the high-pass filter at the subsequent stage of the preprocessing module and configured to perform low-pass filtering on the initial determination signal with a low-pass filtering factor; a determination module configured to determine a road condition coefficient based on the initial determination signal after low-pass filtering and the initial determination signal after high-pass filtering.

2. The road condition determination device according to claim 1, characterized in that, the determination module includes an analysis sub-module and a coefficient determination sub-module, wherein the analysis sub-module is configured to obtain the first amplitude and the first frequency of the initial determination signal after high-pass filtering and the second amplitude and the second frequency of the initial determination signal after low-pass filtering through Fourier analysis; the coefficient determination sub-module is used to determine the road condition coefficient based on the first amplitude and the first frequency and the second amplitude and the second frequency.

3. The road condition determination device according to claim 1, characterized in that, the acquisition module is connected to the vehicle data bus and filters out relevant signals related to road conditions therefrom, and the acquisition module obtains the vertical acceleration signals of the wheel centers of each wheel based on the relevant signals, and the relevant signals are selected from the following group: body vertical acceleration signal, shock absorber stroke signal, inertial measurement unit signal, and wheel vertical acceleration signal.

4. The road condition determination device according to any one of claims 1 to 3, characterized in that, the preprocessing module includes an additional low-pass filter configured to perform low-pass filtering on the initial determination signal with an initial filtering factor before the initial determination signal is output to the low-pass filter and the high-pass filter.

5. The road condition determination device according to claim 4, characterized in that, the road condition determination device further includes a filtering factor determination module configured to set the initial filtering factor, the high-pass filtering factor, and the low-pass filtering factor based on the vehicle state signal, wherein the vehicle state signal includes a vehicle speed signal and a vehicle start / stop signal.

6. The road condition determination device according to claim 5, characterized in that, the filtering factor determination module is configured to determine the initial filtering factor, the high-pass filtering factor, and the low-pass filtering factor according to the following formula: wherein, FrqD is the initial filtering factor; HFrqD is the high-pass filtering factor; LFrqD is the low-pass filtering factor; v is the vehicle speed; k FrqD , k HFrqD , k LFrqD , FrqD M , HFrqD M are preset constants.

7. A road condition determination method, which can be executed by the road condition determination device according to any one of claims 1 to 6, characterized in that, comprising the following steps: S100: Obtain the vertical acceleration signals of the wheel centers of each wheel and the initial determination signals that can characterize their fluctuation conditions; S200: Perform high-pass filtering on the initial determination signals with a preset high-pass filtering factor, and perform low-pass filtering on the initial determination signals with a preset low-pass filtering factor; S300: Determine the road condition coefficient based on the initial determination signals after high-pass filtering and the initial determination signals after low-pass filtering.

8. The road condition determination method according to claim 7, wherein, Step S100 includes the following sub-steps: S110: Obtain the deviation values of the vertical acceleration signals of each wheel center at each moment relative to the average vertical acceleration signal of the wheel centers within a preset time period; S120: Perform low-pass filtering on the deviation values with a preset initial filtering factor to generate the initial determination signals.

9. The road condition determination method according to claim 7 or 8, wherein, Step S300 includes the following sub-steps: S310: By means of Fourier analysis, respectively obtain the first amplitude and the first frequency of the initial determination signals after high-pass filtering, and obtain the second amplitude and the second frequency of the initial determination signals after low-pass filtering; S320: Find the first amplitude, the first frequency, the second amplitude and the second frequency of each set of same-side wheels on the same side of the vehicle and determine the lateral road condition coefficients of each side of the vehicle based on them respectively; S330: Obtain the weighted average value of the lateral road condition coefficients of each side of the vehicle as the road condition coefficient.

10. The road condition determination method according to claim 9, wherein, Sub-step S320 includes the following steps: S321: Obtain the average amplitude value of the first amplitude and the second amplitude of each set of same-side wheels respectively, and obtain the average frequency value of the first frequency and the second frequency of each set of same-side wheels respectively; S322: Obtain the weighted average value of the average amplitude values of each set of same-side wheels as the amplitude weighted value; and obtain the weighted average value of the average frequency values of each set of same-side wheels as the frequency weighted value; S323: Calculate the root mean square of the product of the amplitude weighted value and the frequency weighted value as the lateral road condition coefficient.

11. A computer-readable storage medium, on which a computer program is stored, wherein, when the computer program is executed by a processor, it implements the road condition determination method according to any one of claims 7 to 10.

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