Road surface adhesion coefficient monitoring method and device, electronic equipment and storage medium

Abstract

The application provides a road adhesion coefficient monitoring method and device, electronic equipment and a storage medium. The method comprises the following steps: acquiring a wheel speed signal and a CAN signal of a vehicle; obtaining a first normalized traction and a first slip rate based on the wheel speed signal, the CAN signal and the working condition of the vehicle; acquiring a traction measurement value of the vehicle, determining a constraint condition and an error function based on the traction measurement value and the first normalized traction, and performing graph optimization processing on the first normalized traction to obtain a second normalized traction; screening the first slip rate based on an instantaneous standard deviation to obtain a second slip rate; inputting the second normalized traction and the second slip rate into a preset Kalman filter model to obtain a linear slope of the second normalized traction and the second slip rate, and obtaining the adhesion coefficient of the road based on the linear slope. The application can monitor the road adhesion coefficient without installing additional sensors on the vehicle and without acquiring prior data of the road.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of road surface monitoring, and in particular to a road surface adhesion coefficient monitoring method and device, an electronic device and a storage medium. BACKGROUND

[0002] With the development of sensor technology and automatic driving, the cost of sensors such as radars has been greatly reduced, and more and more newly launched vehicles are equipped with various types of optical cameras and radar sensors, so the development of road surface adhesion coefficient recognition technology has also begun to flourish. According to the technical route and the difference in recognition principle, the recognition method can be preliminarily divided into two categories: a road surface adhesion coefficient estimation method based on priori and a road surface adhesion coefficient estimation method based on a vehicle physical model.

[0003] The main shortcomings of the road surface adhesion coefficient estimation method based on priori are: the need to install additional sensors, high cost; poor recognition effect on unfamiliar complex roads that have not been trained; only road types can be identified, and specific road adhesion coefficient values cannot be obtained. The main shortcomings of the road surface adhesion coefficient estimation method based on a vehicle physical model are: when the vehicle driving condition is stable, the estimation reliability of the road adhesion coefficient is low.

[0004] Therefore, it is necessary to provide a method that can monitor the road adhesion coefficient without the need to install additional sensors on the vehicle and without the need to obtain priori data of the road. SUMMARY

[0005] Therefore, it is necessary to provide a method that can monitor the road adhesion coefficient without the need to install additional sensors on the vehicle and without the need to obtain priori data of the road.

[0006] In order to achieve the above-mentioned purpose, the present application provides a road surface adhesion coefficient monitoring method, comprising:

[0007] obtaining a wheel speed signal and a CAN signal of a vehicle driving on a road surface;

[0008] determining a vehicle operating condition, and based on the wheel speed signal, the CAN signal and the vehicle operating condition, obtaining a first normalized traction and a first slip ratio of the vehicle;

[0009] obtaining a traction measurement value of the vehicle, determining a constraint condition and an error function of graph optimization based on the traction measurement value and the first normalized traction, and performing graph optimization processing on the first normalized traction based on the constraint condition and the error function to obtain a second normalized traction;

[0010] determine a transient standard deviation of the first slip rate, and screen the first slip rate based on the transient standard deviation to obtain a second slip rate;

[0011] input the second normalized traction force and the second slip rate into a preset Kalman filter model to obtain a linear slope of the second normalized traction force and the second slip rate, and obtain the adhesion coefficient of the road surface based on the linear slope.

[0012] Further, the first normalized traction force and the first slip rate of the vehicle are obtained based on the wheel speed signal, the CAN signal and the vehicle operating condition, including:

[0013] In a case where the vehicle operating condition is a non-braking condition and a non-shifting condition, the first normalized traction force is obtained based on the wheel speed signal and the CAN signal.

[0014] In a case where the vehicle operating condition is a turning condition, an initial wheel speed of the vehicle is obtained based on the wheel speed signal, the initial wheel speed is corrected based on a yaw rate of the vehicle to obtain a corrected wheel speed, and the first slip rate is obtained based on the corrected wheel speed.

[0015] Further, the first normalized traction force is obtained based on the wheel speed signal and the CAN signal in a case where the vehicle operating condition is a non-braking condition and a non-shifting condition, including:

[0016] In a case where the vehicle operating condition is a non-braking condition and a non-shifting condition, an engine torque and an engine speed of the vehicle are obtained based on the CAN signal, and a transmission ratio is determined based on the wheel speed signal and the engine speed.

[0017] The first normalized traction force is determined based on the engine torque and the transmission ratio, and a preset wheel rolling radius and mechanical conversion efficiency.

[0018] Further, the vehicle operating condition is determined, including:

[0019] A braking condition is determined based on the CAN signal.

[0020] A shifting condition is determined based on the transmission ratio and a change rate corresponding to the transmission ratio.

[0021] Further, the first normalized traction force is determined based on the following formula:

[0022] F = Tor x T ratio x η / r

[0023] Wherein, F is the first normalized traction, Tor is the engine torque, T ratio is the transmission ratio, r is the wheel rolling radius, and η is the mechanical conversion efficiency.

[0024] Further, the first normalized traction and the first slip ratio of the vehicle are obtained based on the wheel speed signal, the CAN signal and the vehicle operating condition, and the method further comprises:

[0025] In the case that the vehicle operating condition is a non-turning condition, the first slip ratio is obtained based on the wheel speed signal.

[0026] Further, the second slip ratio is obtained by screening the first slip ratio based on the instantaneous standard deviation, and the method comprises:

[0027] A preset tolerance value is added to the instantaneous standard deviation to obtain a screening range;

[0028] The second slip ratio is obtained by screening the first slip ratio based on the screening range.

[0029] The application also provides a road adhesion coefficient monitoring device, comprising:

[0030] An acquisition module is configured to acquire a wheel speed signal and a CAN signal of a vehicle driving on a road surface;

[0031] A first calculation module is configured to determine a vehicle operating condition, and obtain a first normalized traction and a first slip ratio of the vehicle based on the wheel speed signal, the CAN signal and the vehicle operating condition;

[0032] A second calculation module is configured to acquire a traction measurement value of the vehicle, determine a constraint condition and an error function of graph optimization based on the traction measurement value and the first normalized traction, and perform graph optimization processing on the first normalized traction based on the constraint condition and the error function to obtain a second normalized traction;

[0033] A third calculation module is configured to determine an instantaneous standard deviation of the first slip ratio, and screen the first slip ratio based on the instantaneous standard deviation to obtain a second slip ratio;

[0034] A fourth calculation module is configured to input the second normalized traction and the second slip ratio into a preset Kalman filter model to obtain a linear slope of the second normalized traction and the second slip ratio, and obtain the adhesion coefficient of the road surface based on the linear slope.

[0035] The application also provides an electronic device comprising a memory and a processor, wherein,

[0036] The memory is configured to store a program.

[0037] The processor is coupled to the memory and is configured to execute the program stored in the memory to implement the steps of the road adhesion coefficient monitoring method.

[0038] The application further provides a non-transitory computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the road adhesion coefficient monitoring method.

[0039] The road adhesion coefficient monitoring method, device, electronic equipment and storage medium provided by the application have the following beneficial effects: the first normalized traction and the first slip rate of the vehicle are determined based on the wheel speed signal, the CAN signal and the running condition of the vehicle, the constraint condition and the error function of graph optimization are determined based on the measured value of the traction and the first normalized traction, the first normalized traction is subjected to graph optimization based on the constraint condition and the error function, the second normalized traction is obtained, the instantaneous standard deviation of the first slip rate is determined, the first slip rate is screened based on the instantaneous standard deviation, and the second slip rate is obtained, the second normalized traction and the second slip rate are input into a preset Kalman filter model, the linear slope of the second normalized traction and the second slip rate is obtained, and the adhesion coefficient of the road is determined based on the linear slope.

[0040] The wheel speed signal and the CAN signal are basic signals of the vehicle body, and no additional sensor data is needed; the constraint condition and the error function of graph optimization are determined based on the measured value of the traction and the first normalized traction, the first normalized traction is subjected to graph optimization based on the constraint condition and the error function, the first slip rate is screened based on the instantaneous standard deviation of the first slip rate, the signals of the vehicle under special conditions such as turning, braking and gear shifting are screened and disabled, the reliability of the estimation of the road adhesion coefficient when the vehicle is in a stable driving condition is improved, and thus the road adhesion coefficient can be monitored without the need of installing additional sensors on the vehicle and obtaining prior data of the road. BRIEF DESCRIPTION OF DRAWINGS

[0041] In order to more clearly illustrate the technical solutions in the embodiments of the application, the drawings needed in the embodiment description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the application, and other drawings can be obtained by those skilled in the art without creative effort.

[0042] Figure 1 A flowchart of an embodiment of the road adhesion coefficient monitoring method provided by the application;

[0043] Figure 2 Data variation and graph optimization constraint condition schematic diagram provided by the present application;

[0044] Figure 3 Flowchart of vehicle gear shifting state recognition provided by the present application;

[0045] Figure 4 Flowchart of another embodiment of the road surface adhesion coefficient monitoring method provided by the present application;

[0046] Figure 5 Structure schematic diagram of the road surface adhesion coefficient monitoring device provided by the present application;

[0047] Figure 6 Structure schematic diagram of one embodiment of the electronic device provided by the present application. DETAILED DESCRIPTION

[0048] The technical solutions in the embodiments of the present application will be clearly and completely described in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by a person skilled in the art without creative labor fall within the protection scope of the present application.

[0049] In the description of the embodiments of the present application, the meaning of "a plurality of" is two or more, unless otherwise specified.

[0050] In the embodiments of the present application, the terms "comprising" and "having" and any variations thereof are intended to cover the non-exclusive inclusion, for example, the process, method, device, product or equipment comprising a series of steps or modules does not have to be limited to the clearly listed steps or modules, but can include other steps or modules that are not clearly listed or inherent to these processes, methods, products or equipment.

[0051] The naming or numbering of the steps appearing in the embodiments of the present application does not mean that the steps in the method flow must be executed in the time / logical order indicated by the naming or numbering. The flow steps that have been named or numbered can change the execution order according to the technical purpose to be achieved, as long as the same or similar technical effects can be achieved.

[0052] In this document, the reference to "embodiments" means that the specific features, structures or characteristics described in connection with the embodiments can be included in at least one embodiment of the present application. The phrase appears at various places in the specification does not necessarily all refer to the same embodiment, nor is it necessarily independent or alternative embodiments to other embodiments. It is explicitly and implicitly understood by a person skilled in the art that the embodiments described herein can be combined with other embodiments.

[0053] The application provides a road adhesion coefficient monitoring method and device, electronic equipment and a storage medium.

[0054] As shown in the accompanying drawings, Figure 1 The application provides a road adhesion coefficient monitoring method, comprising:

[0055] Step 110, acquiring wheel speed signals and CAN signals of a vehicle driving on a road.

[0056] It can be understood that the wheel speed signals of the vehicle can be acquired based on a vehicle body sensor of the vehicle, and the CAN (Controller Area Network) signals can be acquired through a CAN bus of the vehicle, without the need to install additional sensors on the vehicle.

[0057] Step 120, determining a vehicle operating condition, and based on the wheel speed signals, the CAN signals and the vehicle operating condition, obtaining a first normalized traction and a first slip ratio of the vehicle.

[0058] It can be understood that the vehicle operating condition can include turning, braking and gear shifting conditions. The signals acquired from the vehicle body sensor under special conditions such as turning, braking and gear shifting have errors. For the two cases of braking and gear shifting, since the size of the normalized traction cannot be accurately acquired, such conditions need to be accurately screened out and disabled, and only other conditions other than braking and gear shifting are selected to calculate the first normalized traction.

[0059] Step 130, acquiring a traction measurement value of the vehicle, determining a constraint condition and an error function of graph optimization based on the traction measurement value and the first normalized traction, and performing graph optimization processing on the first normalized traction based on the constraint condition and the error function to obtain a second normalized traction.

[0060] It can be understood that the current general vehicle model is not equipped with a sensor that can directly provide a wheel end traction value, so the specific value of the wheel end traction, i.e. the traction calculation value, needs to be indirectly calculated based on the CAN signal. The traction calculation value is the first normalized traction.

[0061] The main source of traction signal error is the error e(t) between the wheel end traction measurement value TF m (t) calculated based on the engine torque signal and the traction calculation value TF c (t) calculated based on the wheel speed signal to accelerate the vehicle in each system cycle.

[0062] TFc(t) = TF m (t) + e(t)

[0063] Since the engine torque signal corresponds to the CAN bus on the preservation of the update cycle is 20 ms, that is, the corresponding two periods of the algorithm, the measured value of the traction force every two system cycle will be updated, and the calculated value of the traction force is updated every system. The calculated value of the traction force is updated every cycle as a standard to constrain the measured value of the traction force, and the data change and graph optimization constraint condition diagram is shown in Figure 2 .

[0064] Because of the hysteresis of the measured value of the traction force, there will be a large residual between the updated values of the two measured values of the traction force. According to the sum of squares of the state residuals of adjacent time and taking the state of the earliest time as the initial value, the constraint condition can be obtained. Considering the reliability and real-time of optimization, the adjacent 3 time points are selected as an optimization sample group, that is, each sample group contains a hysteresis value of the measured value of the traction force. According to the constraint condition between TF m (t) and TFc(t), the optimization of TF m (t) can be made. Let the traction force value at time i be TF i , the true value of the traction force at this time be z i , then the error function between the node TF i at time i and the node j(e i ) can be recorded as the following formula:

[0065] e i =(z i -TF i )

[0066] The change of the true value of the traction force from the current time i to the time j corresponding to the last cycle is Trans i,j , then the error function between the current node i and the node j(e i,j ) can be recorded as:

[0067] e i,j =(Trans i,j z i -TF i )

[0068] The mean square sum of the error function between the vertices is taken as the objective function of the optimization, as follows:

[0069]

[0070] Assuming that the initial value of the calculated value of the traction force to be optimized is TF k , and the initial value is given an increment ΔTF, then the estimated value of the edge becomes F k (TF k +ΔTF), and the error term can be e k(TF k ) is written as e k (TF k + ΔTF), the error term can be expanded to the first order to obtain the following equation:

[0071]

[0072] In the formula, J k is e k Regarding the inverse of TF k , the Jacobian matrix obtained after writing it in matrix form, the algorithm makes a linear assumption near the estimation point, that is, the function value here can be approximated by the first-order inverse, and for this, the k edges corresponding to the objective function can be further expanded to obtain the following equation:

[0073]

[0074] Finally, a simplified and arranged formula is obtained, in which all terms unrelated to ΔTF are combined into a constant term C k , all the coefficients of the first-order terms are written as 2b k , and the coefficients of the second-order terms are H k . It is easy to see that C k here is actually the initial value of the edge, so after the increment occurs, the change value of the objective function F k can be written as the following equation.

[0075] ΔF k = 2b k ΔTF + ΔTF T H k ΔTF

[0076] The final goal of this optimization problem is to find the increment ΔTF so that the increment of F k is the minimum value, so the derivative of the equation can be directly solved, and the derivative value is 0, to obtain the following equation:

[0077]

[0078] Finally, the entire optimization problem is transformed into a linear equation problem, and solving the equation can obtain the optimized traction, that is, the second normalized traction.

[0079] Step 140, determine the instantaneous standard deviation of the first slip rate, and filter the first slip rate based on the instantaneous standard deviation to obtain a second slip rate.

[0080] It can be understood that the optimization method for the slip rate is based on the slip rate anomaly point screening of the data standard deviation, and the isolated points far away from the dense area of the slip rate-normalized traction force scatter point distribution are screened out, so that the result accuracy is further improved, that is, the first slip rate is screened to obtain the second slip rate. Since the vehicle motion state changes in real time, the method for screening the slip rate should change the screening range of the slip rate as the slip rate distribution changes. The slip rate fluctuates around a certain value, and the points distributed outside this range need to be screened out. The normal fluctuation range can be represented by the standard deviation of the data in statistics, so the instantaneous standard deviation of the slip rate data can be calculated to represent the normal fluctuation range of the slip rate.

[0081] Step 150, input the second normalized traction force and the second slip rate into a preset Kalman filter model to obtain a linear slope of the second normalized traction force and the second slip rate, and based on the linear slope, obtain the adhesion coefficient of the road surface.

[0082] It can be understood that the second normalized traction force and the second slip rate are input into the Kalman filter to estimate the time-varying slope k and mainly eliminate the influence of observation noise on the system. The state equation of the Kalman filter is as follows:

[0083] x k =F k x k-1 +u k +w k

[0084] In the formula, x k represents the state at time k, x k-1 represents the state at time k-1, u k represents the input at current time k, w k is the estimation noise. For the linear Gaussian system y=kx estimated by the algorithm, F k =1, u k =0, the state equation of the Kalman filter corresponding to the system can be written as follows:

[0085] x k =x k-1 +w k

[0086] The observation equation of the Kalman filter is as follows:

[0087] z k =H k x k +ν k

[0088] In the formula, x k represents the state at time k, Hk is the state transition equation of the real state of the system to the observation space, v k is the observation noise. The corresponding state transition equation in the present algorithm can be expressed as:

[0089]

[0090] Combining the above equations, we can get:

[0091] z k = kx k + v k

[0092] After determining the state equation and the observation equation of the Kalman filter of the system, the prediction and update process of the Kalman filter can be started. First, the estimation noise w k and the observation noise v k satisfy the zero-mean Gaussian distribution, and the variances of the two are R and Q respectively, then the prediction process of the Kalman filter of the present system can be expressed as:

[0093]

[0094]

[0095] The update of the Kalman gain K can be expressed as:

[0096]

[0097] Finally, the result is calculated by the following formula:

[0098]

[0099] The algorithm takes the slope of the normalized traction force and slip rate output by the Kalman filter as the estimation result of the road adhesion coefficient, and the change of the slope k of the fitted straight line can represent the change of the road adhesion coefficient.

[0100] In some embodiments, the first normalized traction force and the first slip rate of the vehicle are obtained based on the wheel speed signal, the CAN signal and the vehicle operating condition, including:

[0101] In the case that the vehicle operating condition is a non-braking condition and a non-shifting condition, the first normalized traction force is obtained based on the wheel speed signal and the CAN signal;

[0102] In the case that the vehicle operating condition is a turning condition, the initial wheel speed of the vehicle is obtained based on the wheel speed signal, the initial wheel speed is corrected based on the yaw rate of the vehicle to obtain a corrected wheel speed, and the first slip rate is obtained based on the corrected wheel speed.

[0103] It can be understood that the slip ratio of the vehicle is calculated separately for the left side and the right side, and in normal cases, it is considered that the rolling radii of the four wheels of the vehicle are the same, so in the present application, the slip ratio is calculated as follows, taking the left side slip ratio of the front-wheel drive vehicle as an example.

[0104]

[0105] where ω FL is the angular velocity of the left wheel of the front-wheel drive vehicle, ω FR is the angular velocity of the right wheel of the front-wheel drive vehicle, and s is the left side slip ratio (i.e., the first slip ratio) of the front-wheel drive vehicle.

[0106] The signals obtained from the vehicle body under special working conditions such as turning, braking and gear shifting have errors. For the two cases of braking and gear shifting, since the size of the normalized traction force cannot be accurately obtained, such working conditions need to be accurately screened out and disabled, and then under the condition that the working condition of the vehicle is a non-braking working condition and a non-gear shifting working condition, the first normalized traction force is obtained based on the wheel speed signal and the CAN signal.

[0107] The signal optimization for the turning working condition of the vehicle is based on the yaw rate, and the initial wheel speed obtained from the corresponding wheel speed signal is corrected to obtain a more accurate slip ratio, which can improve the accuracy of the output result and does not affect the real-time performance of the algorithm. The yaw rate expression of the vehicle in the turning working condition is as follows, where Y is the yaw rate, Δt is the time length of the turning process, is the arc length of the wheel in the turning process, and the arc length can be calculated by the following formula:

[0108]

[0109]

[0110] The wheel speeds of other wheels are corrected based on the right rear wheel of the vehicle, and the turning radius calculation formula of the right rear wheel can be obtained according to the above formula as follows:

[0111]

[0112] In combination with the above formula, the expression of the angular velocity of each wheel is obtained.

[0113]

[0114] When the tire pressure condition of the vehicle is normal, it is considered that the rolling radii of the four wheels of the vehicle are the same r rl = r rr = r fl = r fr = r0, then the above formula can be changed to the following formula:

[0115]

[0116] By analyzing the model of vehicle slip rate and road adhesion coefficient, the estimation of road adhesion coefficient is converted into the estimation of the slope of a straight line. When the tire works in the linear region, the data distribution on the graph is a straight line passing through the origin. However, due to the error of the vehicle roll radius, the calculation error of the slip rate will cause the straight line to have an intercept on the horizontal axis. The algorithm selects the appropriate number of data cluster sample points n to accurately and efficiently estimate the radius error, and eliminates the intercept error caused by the wheel radius.

[0117] In some embodiments, when the vehicle operating condition is a non-braking condition and a non-shifting condition, the first normalized traction force is obtained based on the wheel speed signal and the CAN signal, including:

[0118] When the vehicle operating condition is a non-braking condition and a non-shifting condition, the engine torque and the engine speed of the vehicle are obtained based on the CAN signal, and the transmission ratio is determined based on the wheel speed signal and the engine speed.

[0119] Based on the engine torque and the transmission ratio, as well as the preset wheel rolling radius and mechanical conversion efficiency, the first normalized traction force is determined.

[0120] It can be understood that the common vehicle models on the market are not equipped with sensors that can directly provide the wheel end traction force value, so it is necessary to indirectly calculate the specific value of the wheel end traction force based on the existing signals on the vehicle CAN network.

[0121] In some embodiments, the determination of the vehicle operating condition includes:

[0122] The braking condition is determined based on the CAN signal.

[0123] The shifting condition is determined based on the transmission ratio and the corresponding change rate of the transmission ratio.

[0124] It can be understood that for the two cases of braking and shifting, since the size of the normalized traction force cannot be accurately obtained, such conditions need to be accurately screened out and disabled. The braking condition can be directly screened out through the brake signal in the CAN signal.

[0125] In order to adapt to the shifting condition screening of all vehicle models, the shifting state can be determined from the two aspects of the transmission ratio value and the transmission ratio change rate. A transmission ratio value is calculated every period. First, the newly obtained transmission ratio value is subtracted from the value of the last period at the beginning of each period to obtain a transmission ratio change rate.

[0126] The transmission ratio and its rate of change are processed using first-order low-pass filters to reduce the impact of outliers, resulting in filtered transmission ratios and their rates of change. This preprocessing is performed at the beginning of each cycle. Next, the transmission ratio value is evaluated. If it exceeds a certain maximum or falls below a certain minimum, it indicates a transmission ratio outside the normal driving range, and a shift is identified. Then, the rate of change of the filtered transmission ratio is evaluated. If it exceeds a certain threshold, a shift is identified. Finally, the difference between the transmission ratio and the filtered transmission ratio is evaluated. If the difference exceeds a certain percentage, a shift is identified. Based on extensive data testing, this system sets this threshold to + / -4%. The above describes the shift identification process. The conditions for switching back to normal mode from a shift state are slightly stricter to ensure the shift process is completely complete. The logic flowchart for the entire shift identification process is as follows: Figure 3 As shown.

[0127] In some embodiments, the first normalized traction force is determined based on the following formula:

[0128] F = Tor × T ratio ×η / r

[0129] Where F is the first normalized traction force, Tor is the engine torque, and T ratio Let r be the gear ratio of the gearbox, r be the rolling radius of the wheel, and η be the mechanical conversion efficiency.

[0130] Understandably, this embodiment uses the following formula to calculate the wheel-end traction force (i.e., the first normalized traction force):

[0131] F = Tor × T ratio ×η / r

[0132] In the formula, F is the required wheel-end traction force, Tor is the engine torque which can be directly obtained from the vehicle's CAN network, r is the wheel rolling radius, which can be obtained from the vehicle parameter manual, η is the mechanical conversion efficiency, which can be regarded as a constant in the calculation, and T... ratio The gearbox ratio can be calculated using the following formula based on the wheel angular velocity and engine speed (RPM) collected from the CAN network. Here, we take a front-wheel drive vehicle as an example:

[0133] T ratio =[2π / 0.5(ω) FL +ω FR )] / (RPM / 60) / 2

[0134] In some embodiments, the obtaining the first normalized traction force and the first slip ratio of the vehicle based on the wheel speed signal, the CAN signal and the vehicle operating condition further comprises:

[0135] In the case that the vehicle operating condition is the non-turning condition, the first slip ratio is obtained based on the wheel speed signal.

[0136] It can be understood that when the vehicle operating condition is the turning condition, the wheel speed of the vehicle obtained based on the wheel speed signal needs to be corrected based on the yaw rate of the vehicle, and when the vehicle operating condition is the non-turning condition, the wheel speed of the vehicle can be directly obtained based on the wheel speed signal without correction.

[0137] In some embodiments, the screening the first slip ratio based on the instantaneous standard deviation to obtain the second slip ratio comprises:

[0138] A preset tolerance value is added to the instantaneous standard deviation to obtain a screening range;

[0139] The first slip ratio is screened based on the screening range to obtain the second slip ratio.

[0140] It can be understood that in order to not make the screening condition too strict to greatly reduce the online rate of the system, a tolerance value is further added in this embodiment, which can be set according to the quality of the vehicle wheel speed signal, so as to ensure that the effective data points are retained as much as possible while the abnormal points are screened out.

[0141] In other embodiments, the road adhesion coefficient monitoring method is as shown in Figure 4 In this embodiment, a model based on the slip ratio and the road adhesion coefficient is adopted, without the need of using additional sensors, thereby reducing the cost, and without the need of prior data, which is applicable to any road.

[0142] Compared with the existing road adhesion coefficient identification method based on the vehicle physical model, only the wheel speed signal and the CAN signal are needed in this embodiment, and these two signals exist in all vehicle models, so this embodiment can be compatible with all existing vehicle models. The requirement for the number of core signals is greatly reduced for the existing road adhesion force estimation method based on vehicle dynamics, and through the research and analysis of the error sources of the vehicle body signals in this embodiment, a traction force signal optimization method based on graph optimization, a slip ratio abnormal point screening method based on data standard deviation, a shift state identification method based on the transmission ratio and a wheel speed signal correction method based on the yaw rate in the turning condition are proposed, which greatly improves the quality of the input signals. Here, the comparison will be made from the aspects of identification accuracy, real-time performance, the number of core signals required, and portability on real vehicles, and the comparison results are shown in Table 1.

[0143] Table 1

[0144]

[0145] From the comparison result, it can be seen that the three methods can realize the estimation of the road adhesion coefficient with high accuracy and real-time performance. However, the method provided in the embodiment has less demand for the number of core signals, and the basic signals of the vehicle body are obtained on low-configured vehicles, which has strong portability between different vehicle models and high practical application prospect.

[0146] In summary, in the road adhesion coefficient monitoring method provided by the application, the first normalized traction and the first slip rate of the vehicle are determined through the wheel speed signal, the CAN signal and the vehicle operating condition, the constraint condition and the error function of graph optimization are determined based on the traction measurement value and the first normalized traction, the first normalized traction is graph optimized based on the constraint condition and the error function, and the second normalized traction is obtained; the instantaneous standard deviation of the first slip rate is determined, and the first slip rate is screened based on the instantaneous standard deviation to obtain the second slip rate; the second normalized traction and the second slip rate are input into the preset Kalman filter model to obtain the linear slope of the second normalized traction and the second slip rate, and the adhesion coefficient of the road surface is determined based on the linear slope.

[0147] The wheel speed signal and the CAN signal are basic signals of the vehicle body, and no additional sensor data is needed; the constraint condition and the error function of graph optimization are determined based on the traction measurement value and the first normalized traction, the first normalized traction is graph optimized based on the constraint condition and the error function, and the first slip rate is screened based on the instantaneous standard deviation of the first slip rate, so that the signals of the vehicle in special conditions such as turning, braking and gear shifting are screened and disabled, the reliability of the estimation of the road adhesion coefficient when the vehicle driving condition is stable is improved, and thus the road adhesion coefficient can be monitored without installing additional sensors on the vehicle and without obtaining prior data of the road surface.

[0148] As shown in Figure 5 The application further provides a road adhesion coefficient monitoring device 500, which comprises:

[0149] An acquisition module 510 is configured to acquire the wheel speed signal and the CAN signal of a vehicle driving on a road surface;

[0150] A first calculation module 520 is configured to determine the vehicle operating condition, and obtain the first normalized traction and the first slip rate of the vehicle based on the wheel speed signal, the CAN signal and the vehicle operating condition;

[0151] The second calculation module 530 is used to obtain the traction force measurement value of the vehicle, determine the constraint conditions and error function of graph optimization based on the traction force measurement value and the first normalized traction force, and perform graph optimization processing on the first normalized traction force based on the constraint conditions and the error function to obtain the second normalized traction force.

[0152] The third calculation module 540 is used to determine the instantaneous standard deviation of the first slip ratio, and to filter the first slip ratio based on the instantaneous standard deviation to obtain the second slip ratio;

[0153] The fourth calculation module 550 is used to input the second normalized traction force and the second slip ratio into a preset Kalman filter model to obtain the linear slope of the second normalized traction force and the second slip ratio, and to obtain the adhesion coefficient of the road surface based on the linear slope.

[0154] The road surface adhesion coefficient monitoring device provided in the above embodiments can realize the technical solutions described in the above road surface adhesion coefficient monitoring method embodiments. The specific implementation principles of each module or unit can be found in the corresponding content in the above road surface adhesion coefficient monitoring method embodiments, which will not be repeated here.

[0155] like Figure 6 As shown, the present invention also provides an electronic device 600. The electronic device 600 includes a processor 601, a memory 602, and a display 603. Figure 6 Only some components of the electronic device 600 are shown, but it should be understood that it is not required to implement all the components shown, and more or fewer components may be implemented instead.

[0156] In some embodiments, memory 602 may be an internal storage unit of electronic device 600, such as a hard disk or memory of electronic device 600. In other embodiments, memory 602 may also be an external storage device of electronic device 600, such as a plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, etc. equipped on electronic device 600.

[0157] Furthermore, the memory 602 may include both internal storage units of the electronic device 600 and external storage devices. The memory 602 is used to store application software and various types of data installed on the electronic device 600.

[0158] The processor 601 may, in some embodiments, be a central processing unit (CPU), a microprocessor, or other data processing chip, for running program codes stored in the memory 602 or processing data, such as the road adhesion coefficient monitoring method in the present application.

[0159] The display 603 may, in some embodiments, be an LED display, a liquid crystal display, a touch liquid crystal display, an OLED (Organic Light-Emitting Diode) touch, or the like. The display 603 is used to display information of the electronic device 600 and to display a visualized user interface. The components 601-603 of the electronic device 600 communicate with each other through a system bus.

[0160] In some embodiments of the present application, when the processor 601 executes the road adhesion coefficient monitoring program in the memory 602, the following steps can be implemented:

[0161] Obtaining wheel speed signals and CAN signals of a vehicle running on a road;

[0162] Determining a vehicle operating condition, and obtaining a first normalized traction and a first slip ratio of the vehicle based on the wheel speed signals, the CAN signals, and the vehicle operating condition;

[0163] Obtaining a traction measurement value of the vehicle, determining a constraint condition and an error function of graph optimization based on the traction measurement value and the first normalized traction, and performing graph optimization processing on the first normalized traction based on the constraint condition and the error function to obtain a second normalized traction;

[0164] Determining an instantaneous standard deviation of the first slip ratio, and screening the first slip ratio based on the instantaneous standard deviation to obtain a second slip ratio;

[0165] Inputting the second normalized traction and the second slip ratio into a preset Kalman filter model to obtain a linear slope of the second normalized traction and the second slip ratio, and obtaining the adhesion coefficient of the road based on the linear slope.

[0166] It should be understood that, in addition to the above functions, the processor 601 may, when executing the road adhesion coefficient monitoring program in the memory 602, also implement other functions, which can be referred to the description of the corresponding method embodiments.

[0167] Further, the embodiments of the present application do not make specific limitation on the type of the electronic device 600 mentioned above, and the electronic device 600 can be a mobile phone, a tablet computer, a personal digital assistant (PDA), a wearable device, a laptop computer, or the like. Exemplary embodiments of the portable electronic device include, but are not limited to, a portable electronic device running an IOS, an android, a microsoft, or other operating system. The portable electronic device described above can also be other portable electronic devices, such as a laptop computer having a touch-sensitive surface (e.g., a touch panel), and the like. It should also be understood that in some other embodiments of the present application, the electronic device 600 can also not be a portable electronic device, but a desktop computer having a touch-sensitive surface (e.g., a touch panel).

[0168] In yet another aspect, the present application also provides a non-transitory computer readable storage medium having stored thereon a computer program, which, when executed by a processor, implements the XXXX method provided by the above method, and the method comprises:

[0169] obtaining a wheel speed signal and a CAN signal of a vehicle running on a road surface;

[0170] determining a vehicle operating condition, and obtaining a first normalized traction and a first slip ratio of the vehicle based on the wheel speed signal, the CAN signal, and the vehicle operating condition;

[0171] obtaining a traction measurement value of the vehicle, determining a constraint condition and an error function of graph optimization based on the traction measurement value and the first normalized traction, and performing graph optimization processing on the first normalized traction based on the constraint condition and the error function to obtain a second normalized traction;

[0172] determining an instantaneous standard deviation of the first slip ratio, and screening the first slip ratio based on the instantaneous standard deviation to obtain a second slip ratio;

[0173] inputting the second normalized traction and the second slip ratio into a preset Kalman filter model to obtain a linear slope of the second normalized traction and the second slip ratio, and obtaining a road surface adhesion coefficient based on the linear slope.

[0174] Those skilled in the art can understand that all or part of the processes of the above-mentioned embodiments can be completed by a computer program instructing related hardware, and the program can be stored in a computer readable storage medium. The computer readable storage medium includes a magnetic disk, an optical disk, a read-only memory, or a random access memory, etc.

[0175] The road surface adhesion coefficient monitoring method, device, electronic equipment and storage medium provided by the present application are described in detail above, and the principles and implementation manners of the present application are described by applying specific examples. The above description of the embodiments is only used to help understand the method of the present application and its core idea; at the same time, for those skilled in the art, according to the idea of the present application, the specific implementation manners and application ranges will be changed, and the above description should not be understood as a limitation of the present application.

Claims

1. A road surface adhesion coefficient monitoring method characterized by, The method comprises the following steps: obtaining wheel speed signals and CAN signals of a vehicle running on a road surface; determining a vehicle operating condition, obtaining a first normalized traction and a first slip ratio of the vehicle based on the wheel speed signals, the CAN signals and the vehicle operating condition; obtaining a traction measurement value of the vehicle, determining a constraint condition and an error function of graph optimization based on the traction measurement value and the first normalized traction, and performing graph optimization processing on the first normalized traction based on the constraint condition and the error function to obtain a second normalized traction; determining a transient standard deviation of the first slip ratio, and screening the first slip ratio based on the transient standard deviation to obtain a second slip ratio; inputting the second normalized traction and the second slip ratio into a preset Kalman filter model to obtain a linear slope of the second normalized traction and the second slip ratio, and obtaining a road surface adhesion coefficient based on the linear slope.

2. The road adhesion coefficient monitoring method according to claim 1, characterized by, The step of obtaining the first normalized traction and the first slip ratio of the vehicle based on the wheel speed signals, the CAN signals and the vehicle operating condition comprises the following steps: in the case that the vehicle operating condition is a non-braking condition and a non-shifting condition, obtaining the first normalized traction based on the wheel speed signals and the CAN signals; in the case that the vehicle operating condition is a turning condition, obtaining an initial wheel speed of the vehicle based on the wheel speed signals, correcting the initial wheel speed based on a yaw rate of the vehicle to obtain a corrected wheel speed, and obtaining the first slip ratio based on the corrected wheel speed.

3. The road adhesion coefficient monitoring method according to claim 2, characterized in that, The step of obtaining the first normalized traction based on the wheel speed signals and the CAN signals in the case that the vehicle operating condition is a non-braking condition and a non-shifting condition comprises the following steps: in the case that the vehicle operating condition is a non-braking condition and a non-shifting condition, obtaining an engine torque and an engine speed of the vehicle based on the CAN signals, and determining a transmission ratio based on the wheel speed signals and the engine speed; determining the first normalized traction based on the engine torque, the transmission ratio, a preset wheel rolling radius and a mechanical conversion efficiency.

4. The road adhesion coefficient monitoring method according to claim 3, characterized in that, The step of determining the vehicle operating condition comprises the following steps: determining a braking condition based on the CAN signals; determining a shifting condition based on the transmission ratio and a change rate corresponding to the transmission ratio.

5. The road adhesion coefficient monitoring method according to claim 3, characterized by, The first normalized traction is determined based on the following formula: F = Tor x T ratio x η / r where F is the first normalized traction, Tor is the engine torque, T ratio is the transmission ratio, r is the wheel rolling radius, and η is the mechanical transfer efficiency.

6. The road adhesion coefficient monitoring method according to claim 5, characterized in that, The step of obtaining the first normalized traction and the first slip ratio of the vehicle based on the wheel speed signals, the CAN signals and the vehicle operating condition further comprises the following step: in the case that the vehicle operating condition is a non-turning condition, obtaining the first slip ratio based on the wheel speed signals.

7. The road adhesion coefficient monitoring method according to any one of claims 1 to 6, characterized in that, The step of screening the first slip ratio based on the transient standard deviation to obtain a second slip ratio comprises the following steps: adding a preset tolerance value to the transient standard deviation to obtain a screening range; screening the first slip ratio based on the screening range to obtain the second slip ratio.

8. A road adhesion coefficient monitoring device, characterised in that The method comprises the following steps: an obtaining module, configured to obtain wheel speed signals and CAN signals of a vehicle running on a road surface; The first calculation module is configured to determine a vehicle operating condition, and obtain a first normalized traction and a first slip ratio of the vehicle based on the wheel speed signal, the CAN signal and the vehicle operating condition; The second calculation module is configured to obtain a traction measurement value of the vehicle, determine a constraint condition and an error function of graph optimization based on the traction measurement value and the first normalized traction, and perform graph optimization processing on the first normalized traction based on the constraint condition and the error function to obtain a second normalized traction; The third calculation module is configured to determine an instantaneous standard deviation of the first slip ratio, and perform screening on the first slip ratio based on the instantaneous standard deviation to obtain a second slip ratio; The fourth calculation module is configured to input the second normalized traction and the second slip ratio into a preset Kalman filter model to obtain a linear slope of the second normalized traction and the second slip ratio, and obtain the adhesion coefficient of the road surface based on the linear slope.

9. An electronic device, comprising: comprising a memory and a processor, wherein The memory is configured to store a program. The processor, coupled with the memory, is configured to execute the program stored in the memory to implement the steps of the road adhesion coefficient monitoring method in any one of claims 1 to 7. 10.A non-transitory computer-readable storage medium having stored thereon a computer program, characterized in that, The computer program, when executed by the processor, implements the road adhesion coefficient monitoring method in any one of claims 1 to 7.

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