Data processing method and device, storage medium, product, equipment and vehicle

Abstract

The invention relates to a data processing method and device, a storage medium, a product, equipment and a vehicle, and the data processing method comprises the steps: determining a target road adhesion coefficient of a driving road according to a road image and driving parameters of the vehicle; according to the method, when the road adhesion coefficient of the driving road is determined, the road image and the driving parameters of the vehicle are combined, so that more comprehensive road information in more dimensions can be extracted, and the accuracy of road adhesion coefficient estimation is improved; and mutual complementation of the road surface image and the driving parameters is facilitated, so that the road surface change is quickly responded, more complex road surface conditions are adapted, and the real-time performance and the accuracy of road surface adhesion coefficient estimation are improved.

Description

Technical Field

[0001] The present application relates to the field of vehicle technology, and in particular to a data processing method, device, storage medium, product, equipment and vehicle. Background Art

[0002] During vehicle driving, the road adhesion coefficient of the driving road is a key parameter required for vehicle control, which has an important impact on the stability and safety of the vehicle. In related technologies, the road adhesion coefficient is usually estimated based on data collected by on-board sensors, such as wheel speed sensors and acceleration sensors, combined with vehicle dynamics models. However, this estimation method uses relatively single data and cannot ensure the accuracy of the road adhesion coefficient estimation when facing complex road conditions. Summary of the invention

[0003] The embodiments of the present application provide a data processing method, device, storage medium, product, equipment and vehicle, which improve the accuracy of road adhesion coefficient estimation to at least partially solve the above-mentioned technical problems.

[0004] In order to achieve the above-mentioned purpose, according to a first aspect of the present application, a data processing method is provided, comprising: determining a target road surface adhesion coefficient of a driving road surface according to a road surface image and driving parameters of a vehicle.

[0005] Optionally, determining a target road adhesion coefficient of the driving road surface based on a road surface image and driving parameters of the vehicle includes: obtaining a first road adhesion coefficient of the driving road surface based on the road surface image; obtaining a second road adhesion coefficient of the driving road surface based on the driving parameters of the vehicle; and determining the target road adhesion coefficient of the driving road surface based on the first road adhesion coefficient and the second road adhesion coefficient.

[0006] Optionally, determining the target road adhesion coefficient of the driving road surface based on the first road adhesion coefficient and the second road adhesion coefficient includes: determining the target road adhesion coefficient of the driving road surface from the first road adhesion coefficient and the second road adhesion coefficient; wherein the target road adhesion coefficient is the first road adhesion coefficient or the second road adhesion coefficient.

[0007] Optionally, determining the target road adhesion coefficient of the driving road surface from the first road adhesion coefficient and the second road adhesion coefficient includes: if the driving road surface meets a first preset condition, determining the target road adhesion coefficient of the driving road surface from the first road adhesion coefficient and the second road adhesion coefficient; wherein the target road adhesion coefficient is the first road adhesion coefficient; if the driving road surface does not meet the first preset condition, determining the target road adhesion coefficient of the driving road surface from the first road adhesion coefficient and the second road adhesion coefficient; wherein the target road adhesion coefficient is the second road adhesion coefficient.

[0008] Optionally, the first preset condition includes a sudden change in the driving road surface and a deterioration in driving conditions corresponding to the driving road surface.

[0009] Optionally, the method also includes: determining a road surface adhesion coefficient difference based on the first road surface adhesion coefficient at the current moment and the first road surface adhesion coefficient at a historical moment; comparing the road surface adhesion coefficient difference with a preset adhesion coefficient threshold; wherein the comparison result between the road surface adhesion coefficient difference and the adhesion coefficient threshold is used to determine whether the driving road surface meets the first preset condition.

[0010] Optionally, the method also includes: if the absolute value of the road adhesion coefficient difference is less than the adhesion coefficient threshold, determining the target road adhesion coefficient of the driving road surface from the first road adhesion coefficient and the second road adhesion coefficient; wherein the target road adhesion coefficient is the second road adhesion coefficient; if the absolute value of the road adhesion coefficient difference is greater than the adhesion coefficient threshold, and the first road adhesion coefficient at the current moment is greater than the first road adhesion coefficient at the historical moment, determining the target road adhesion coefficient of the driving road surface from the first road adhesion coefficient and the second road adhesion coefficient; wherein the target road adhesion coefficient is the second road adhesion coefficient; if the absolute value of the road adhesion coefficient difference is greater than the adhesion coefficient threshold, and the first road adhesion coefficient at the current moment is less than the first road adhesion coefficient at the historical moment, determining the target road adhesion coefficient of the driving road surface from the first road adhesion coefficient and the second road adhesion coefficient; wherein the target road adhesion coefficient is the first road adhesion coefficient.

[0011] Optionally, determining the target road surface adhesion coefficient of the driving road surface based on the first road surface adhesion coefficient and the second road surface adhesion coefficient includes: performing weighted sum processing on the first road surface adhesion coefficient and the second road surface adhesion coefficient to obtain the target road surface adhesion coefficient of the driving road surface.

[0012] Optionally, acquiring a first road surface adhesion coefficient of the driving road surface according to the road surface image includes: acquiring a target road surface type of the driving road surface according to the road surface image; and acquiring the first road surface adhesion coefficient of the driving road surface according to the target road surface type.

[0013] Optionally, obtaining a target road surface type of the driving road surface based on a road surface image includes: performing road surface recognition on the road surface image through a recognition model to obtain a road surface area in the road surface image; and performing road surface classification on the road surface area through a classification model to obtain the target road surface type of the driving road surface.

[0014] Optionally, obtaining the first road surface adhesion coefficient of the driving road surface according to the target road surface type includes: determining the first road surface adhesion coefficient of the driving road surface according to the target road surface type and a preset mapping relationship table; wherein the mapping relationship table includes at least one set of mapping relationships between road surface types and road surface adhesion coefficients.

[0015] Optionally, obtaining the second road adhesion coefficient of the driving road surface based on the driving parameters of the vehicle includes: obtaining road adhesion coefficients corresponding to multiple tires of the vehicle respectively according to the driving parameters of the vehicle; and determining the second road adhesion coefficient of the driving road surface based on the road adhesion coefficients corresponding to the multiple tires respectively.

[0016] Optionally, obtaining the road adhesion coefficients respectively corresponding to the multiple tires of the vehicle based on the driving parameters of the vehicle includes: determining the state parameters of the vehicle based on the driving parameters of the vehicle; determining the normalized forces respectively corresponding to the multiple tires of the vehicle based on the driving parameters and the state parameters; determining the road adhesion coefficients respectively corresponding to the multiple tires based on the driving parameters, the state parameters and the normalized forces respectively corresponding to the multiple tires.

[0017] Optionally, the driving parameter includes at least one of the following: the longitudinal acceleration of the vehicle, the lateral acceleration of the vehicle, the front wheel turning angle of the vehicle, the longitudinal speed of the vehicle, and the wheel speed of the vehicle.

[0018] Optionally, the state parameter includes at least one of the following: a sideslip angle of the center of mass of the vehicle, and a yaw rate of the vehicle.

[0019] Optionally, determining the second road adhesion coefficient of the driving road surface based on the road adhesion coefficients respectively corresponding to the multiple tires includes: determining the second road adhesion coefficient of the driving road surface from the road adhesion coefficients respectively corresponding to the multiple tires; wherein the second road adhesion coefficient is the road adhesion coefficient that meets a second preset condition among the road adhesion coefficients respectively corresponding to the multiple tires.

[0020] Optionally, the second preset condition includes that the road adhesion coefficient has a minimum value.

[0021] Optionally, the method further comprises: determining a target transmission ratio of a steering system of the vehicle according to the target road adhesion coefficient and a preset transmission ratio function.

[0022] Optionally, determining the target transmission ratio of the steering system of the vehicle based on the target road adhesion coefficient and a preset transmission ratio function includes: determining the maximum transmission ratio and the minimum transmission ratio of the steering system of the vehicle based on the target road adhesion coefficient; determining the target transmission ratio of the steering system based on the maximum transmission ratio, the minimum transmission ratio, the driving speed of the vehicle and a preset transmission ratio function.

[0023] Optionally, the method further comprises: determining a target front wheel angle of the vehicle according to the target transmission ratio and the steering wheel angle; wherein the front wheel target angle is used to control the vehicle.

[0024] According to a second aspect of the present application, a data processing device is provided, comprising: a coefficient acquisition module for determining a target road adhesion coefficient of a driving road surface based on a road surface image and driving parameters of a vehicle.

[0025] According to a third aspect of the present application, a computer-readable storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, the above-mentioned data processing method is implemented.

[0026] According to a fourth aspect of the present application, a computer program product is provided, comprising a computer program, wherein the computer program implements the above-mentioned data processing method when executed by a processor.

[0027] According to a fifth aspect of the present application, an electronic device is provided, comprising: a memory on which a computer program is stored; and a processor for executing the computer program in the memory to implement the above-mentioned data processing method.

[0028] According to a sixth aspect of the present application, a vehicle is provided, comprising the above-mentioned data processing device, or comprising the above-mentioned electronic device.

[0029] The data processing method provided in the embodiment of the present application combines the road surface image and the driving parameters of the vehicle when determining the road surface adhesion coefficient of the driving road, which not only helps to extract more dimensional and more comprehensive road surface information and improve the accuracy of the road surface adhesion coefficient estimation; it also helps the road surface image and driving parameters to complement each other so as to quickly respond to road surface changes, adapt to more complex road conditions, and improve the real-time and accuracy of the road surface adhesion coefficient estimation.

[0030] Other features and advantages of the present application will be described in detail in the subsequent detailed description. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present application, and those skilled in the art can obtain other drawings based on these drawings without creative work.

[0032] In order to more completely understand the present application and its beneficial effects, the following description will be given in conjunction with the accompanying drawings, wherein the same figure numbers represent the same parts in the following description.

[0033] Figure 1 is a flow chart of a data processing method provided in an embodiment of the present application;

[0034] Figure 2 is a flow chart of another data processing method provided in an embodiment of the present application;

[0035] Figure 3 is a schematic diagram of a mapping relationship table provided in an embodiment of the present application;

[0036] Figure 4 is a schematic diagram of another mapping relationship table provided in an embodiment of the present application;

[0037] Figure 5 is a schematic diagram of obtaining a second road surface adhesion coefficient provided in an embodiment of the present application;

[0038] Figure 6 is a schematic diagram of obtaining a target road surface adhesion coefficient provided in an embodiment of the present application;

[0039] Figure 7 is a block diagram of a data processing device provided in an embodiment of the present application;

[0040] Figure 8 is a block diagram of another data processing device provided in an embodiment of the present application;

[0041] Fig. 9 It is a schematic diagram of a vehicle provided in an embodiment of the present application. DETAILED DESCRIPTION

[0042] The technical solutions in the embodiments of the present application will be described clearly and completely below 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 of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present application.

[0043] According to the first aspect of the present application, an embodiment of the present application provides a data processing method.

[0044] See also Figure 1 , Figure 1 1 is a flow chart of a data processing method provided in an embodiment of the present application. The data processing method may include the following step S100:

[0045] Step S100: determining a target road adhesion coefficient of the driving road according to the road image and the driving parameters of the vehicle.

[0046] The road surface image is an image of the road surface collected during the driving process of the vehicle. In some embodiments, the road surface image may be collected by a sensor mounted on the vehicle, such as a vehicle-mounted camera. In some embodiments, the road surface image may be an image of the road surface in front of the vehicle, or an image of the road surface to the side of the vehicle.

[0047] Driving parameters are parameters related to vehicle motion collected during vehicle driving. In some embodiments, the driving parameters may be collected by sensors carried by the vehicle, such as a speed sensor, an acceleration sensor, a wheel speed sensor, etc. In some embodiments, the driving parameters include at least one of the following: longitudinal acceleration of the vehicle, lateral acceleration of the vehicle, front wheel turning angle of the vehicle, longitudinal speed of the vehicle, and wheel speed of the vehicle.

[0048] The road adhesion coefficient is used to indicate the magnitude of the friction between the tires of the vehicle and the road surface. In step S100, the target road adhesion coefficient of the road surface can be determined based on the collected road image and the driving parameters of the vehicle. The specific method for determining the target road adhesion coefficient is not limited in the embodiment of the present application. In some embodiments, the road image and the driving parameters can be input into a deep learning model, etc., so that the deep learning model, etc. outputs the target road adhesion coefficient of the road surface; or, a road adhesion coefficient can be obtained based on the road image and the driving parameters respectively, and then the target road adhesion coefficient of the road surface can be determined based on these two road adhesion coefficients. In some embodiments, the road image and driving parameters based on step S100 can be data collected at the same time, or data collected within the same data collection cycle.

[0049] In summary, the data processing method provided in the embodiment of the present application combines the road surface image and the driving parameters of the vehicle when determining the road surface adhesion coefficient of the driving road, which not only helps to extract more dimensional and more comprehensive road surface information and improve the accuracy of the road surface adhesion coefficient estimation; it also helps the road surface image and driving parameters to complement each other so as to quickly respond to road surface changes, adapt to more complex road conditions, and improve the real-time and accuracy of the road surface adhesion coefficient estimation.

[0050] In some embodiments, see Figure 2 , the data processing method provided in the embodiment of the present application may further include the following steps S110 to S130. That is, the above step S100 may include the following steps S110 to S130:

[0051] Step S110: acquiring a first road adhesion coefficient of the driving road according to the road surface image;

[0052] Step S120: acquiring a second road surface adhesion coefficient of the driving road surface according to the driving parameters of the vehicle;

[0053] Step S130: Determine a target road surface adhesion coefficient of the driving road surface according to the first road surface adhesion coefficient and the second road surface adhesion coefficient.

[0054] In step S110, the first road adhesion coefficient of the driving road surface can be directly predicted based on the road surface image. For example, the road surface image is input into a deep learning model, etc., so that the deep learning model, etc. outputs the first road adhesion coefficient of the driving road surface; or, road surface information such as the road surface area and road surface type can be first obtained based on the road surface image, and then the first road adhesion coefficient of the driving road surface can be further obtained based on the road surface information.

[0055] In step S120, the road adhesion coefficient corresponding to each tire of the vehicle can be obtained based on a vehicle-related mathematical model such as a tire model, and then a second road adhesion coefficient of the driving road can be further obtained.

[0056] In step S130, a road adhesion coefficient may be selected from the first road adhesion coefficient and the second road adhesion coefficient as the target road adhesion coefficient of the driving road surface, that is, the first road adhesion coefficient is selected as the target road adhesion coefficient, or the second road adhesion coefficient is selected as the target road adhesion coefficient. Alternatively, in step S130, the first road adhesion coefficient and the second road adhesion coefficient may be weighted summed to obtain the target road adhesion coefficient of the driving road surface.

[0057] In some embodiments, the above step S110 may include the following steps S111 to S112:

[0058] Step S111: acquiring a target road surface type of the driving road according to the road surface image;

[0059] Step S112: Obtain a first road surface adhesion coefficient of the driving road surface according to the target road surface type.

[0060] In some embodiments, the above step S111 may include: performing road surface recognition on the road surface image through a recognition model to obtain a road surface area in the road surface image; performing road surface classification on the road surface area through a classification model to obtain a target road surface type of the driving road surface.

[0061] The recognition model may be a semantic recognition model, such as a U-Net semantic segmentation network, etc.; the classification model may be a ResNet network, etc. The recognition model may be used to segment the road surface image, thereby identifying and extracting the road surface area; the classification model may be used to classify the road surface area to determine the target road surface type of the driving road surface. In some embodiments, the road surface type of the driving road surface includes, but is not limited to: highway, mountain road, asphalt road, concrete road, gravel road, snow, rain, sand, ice, etc.

[0062] In some embodiments, the above step S112 may include: determining a first road surface adhesion coefficient of the driving road surface according to the target road surface type and a preset mapping relationship table.

[0063] The mapping relationship table includes at least one mapping relationship between a road surface type and a road surface adhesion coefficient. In some embodiments, the road surface adhesion coefficient corresponding to a road surface type in the mapping relationship table can be a determined coefficient value, such as Figure 3 As shown; it can also be a coefficient value range, such as Figure 4 shown.

[0064] In the case where the road adhesion coefficient corresponding to a road surface type in the mapping relationship table is a coefficient value range, the coefficient value range corresponding to the target road surface type of the driving road surface can be determined according to the mapping relationship table, and then a coefficient value can be arbitrarily selected from the coefficient value range, such as the minimum coefficient value, the maximum coefficient value or the intermediate coefficient value (that is, the average of the minimum coefficient value and the maximum coefficient value), etc., as the first road adhesion coefficient of the driving road surface. In the case where the intermediate coefficient value is used as the first road adhesion coefficient, when the road surface undergoes a sudden change, the vehicle lateral force and the tangential force have a certain margin from the maximum amount.

[0065] In some embodiments, the above step S120 may include the following steps S121 to S122:

[0066] Step S121: acquiring road adhesion coefficients corresponding to a plurality of tires of the vehicle respectively according to the driving parameters of the vehicle;

[0067] Step S122: Determine a second road surface adhesion coefficient of the driving road surface according to the road surface adhesion coefficients corresponding to the plurality of tires.

[0068] In some embodiments, the above step S121 may include: determining the state parameters of the vehicle according to the driving parameters of the vehicle; determining the normalized forces corresponding to the multiple tires of the vehicle according to the driving parameters and the state parameters; determining the road adhesion coefficients corresponding to the multiple tires according to the driving parameters, the state parameters and the normalized forces corresponding to the multiple tires. The driving parameters include at least one of the following: the longitudinal acceleration of the vehicle, the lateral acceleration of the vehicle, the front wheel turning angle of the vehicle, the longitudinal speed of the vehicle, and the wheel speed of the vehicle; the state parameters include at least one of the following: the sideslip angle of the center of mass of the vehicle and the yaw rate of the vehicle.

[0069] For example, Figure 5 As shown, in step S121, the collected driving parameters, such as the longitudinal acceleration a of the vehicle fed back by the sensor mounted on the vehicle, can be firstly x , lateral acceleration a y and the front wheel steering angle δ, etc., are input to the vehicle state estimator based on EKF (Extended Kalman Filter) to obtain the vehicle state parameters, such as the vehicle's center of mass offset angle θ and yaw rate γ, etc.; then the vehicle's driving parameters and state parameters, such as the vehicle's center of mass offset angle θ, yaw rate γ, longitudinal acceleration a x , lateral acceleration a y , front wheel angle δ and wheel speed ω i etc., are input into the Dugoff tire model to obtain the normalized forces corresponding to the multiple tires of the vehicle, that is, the longitudinal forces and lateral forces corresponding to the multiple tires; then the vehicle's driving parameters, state parameters and the normalized forces corresponding to the multiple tires are input into the road adhesion coefficient estimator based on UKF (Unscented Kalman Filter) to obtain the road adhesion coefficients corresponding to the multiple tires. For example, according to the following formulas 1, 2 and 3, the road adhesion coefficients corresponding to the multiple tires can be obtained: fl , μ fr , μ rl and μ rr :

[0070] Formula 1:

[0071] Formula 2:

[0072] Formula 3:

[0073] in, is the yaw angular acceleration of the vehicle, m is the mass of the vehicle, I z is the moment of inertia of the vehicle around the Z axis, t w is the wheelbase of the front and rear wheels of the vehicle, l f is the distance from the front axle to the center of mass of the vehicle, l r is the distance from the rear axle to the center of mass of the vehicle, is the longitudinal force of the vehicle's tire, is the lateral force of the vehicle.

[0074] In some embodiments, the above step S122 may include: determining a second road adhesion coefficient of the driving road from the road adhesion coefficients corresponding to the multiple tires; wherein the second road adhesion coefficient is a road adhesion coefficient that satisfies the second preset condition from the road adhesion coefficients corresponding to the multiple tires. That is, the above step S122 may include: selecting a road adhesion coefficient that satisfies the second preset condition from the road adhesion coefficients corresponding to the multiple tires as the second road adhesion coefficient of the driving road. The embodiment of the present application does not limit the specific content of the second preset condition. In some embodiments, the second preset condition includes the minimum value of the road adhesion coefficient; or the second preset condition includes the maximum value of the road adhesion coefficient.

[0075] In some embodiments, the above step S130 may include: determining a target road adhesion coefficient of the driving road from the first road adhesion coefficient and the second road adhesion coefficient; wherein the target road adhesion coefficient is the first road adhesion coefficient or the second road adhesion coefficient. That is, the above step S130 includes: selecting the first road adhesion coefficient from the first road adhesion coefficient and the second road adhesion coefficient as the target road adhesion coefficient of the driving road; or selecting the second road adhesion coefficient from the first road adhesion coefficient and the second road adhesion coefficient as the target road adhesion coefficient of the driving road.

[0076] In some embodiments, for selecting one of the first road adhesion coefficient and the second road adhesion coefficient as the target road adhesion coefficient, a first preset condition may be set; thus, the above step S130 may include:

[0077] Step S1311: if the driving road surface meets the first preset condition, determining a target road surface adhesion coefficient of the driving road surface from the first road surface adhesion coefficient and the second road surface adhesion coefficient; wherein the target road surface adhesion coefficient is the first road surface adhesion coefficient;

[0078] Step S1312: If the driving road surface does not meet the first preset condition, a target road surface adhesion coefficient of the driving road surface is determined from the first road surface adhesion coefficient and the second road surface adhesion coefficient; wherein the target road surface adhesion coefficient is the second road surface adhesion coefficient.

[0079] That is, if the driving road surface meets the first preset condition, the first road surface adhesion coefficient is selected from the first road surface adhesion coefficient and the second road surface adhesion coefficient as the target road surface adhesion coefficient of the driving road surface; if the driving road surface does not meet the first preset condition, the second road surface adhesion coefficient is selected from the first road surface adhesion coefficient and the second road surface adhesion coefficient as the target road surface adhesion coefficient of the driving road surface.

[0080] The embodiments of the present application do not limit the specific content of the first preset condition. In some embodiments, the first preset condition includes a sudden change in the driving road surface and a deterioration in the driving conditions corresponding to the driving road surface; or, the first preset condition includes a sudden change in the driving road surface; or, the first preset condition includes a deterioration in the driving conditions corresponding to the driving road surface. The deterioration of the driving conditions corresponding to the driving road surface means that the driving road surface suddenly changes in the direction of the deterioration, for example, the driving road surface becomes slippery and icy, fog and strong winds occur around the driving road surface, and the number of bends on the driving road surface increases. The sudden change in the driving road surface includes but is not limited to: a sudden change in the shape of the driving road surface, a sudden change in the road surface material of the driving road surface, a sudden change in the road surface structure of the driving road surface, a sudden change in the surrounding environment of the driving road surface, etc.

[0081] In some embodiments, the data processing method provided in the embodiments of the present application may further include the following steps S001 to S002:

[0082] Step S001: determining a road surface adhesion coefficient difference according to a first road surface adhesion coefficient at a current moment and a first road surface adhesion coefficient at a historical moment;

[0083] Step S002: comparing the road surface adhesion coefficient difference with a preset adhesion coefficient threshold; wherein the comparison result between the road surface adhesion coefficient difference and the adhesion coefficient threshold is used to determine whether the driving road surface meets a first preset condition.

[0084] The first road adhesion coefficient at the current moment refers to the first road adhesion coefficient obtained when the target road adhesion coefficient is currently determined, that is, the first road adhesion coefficient obtained in the above step S110. The first road adhesion coefficient at the historical moment refers to the first road adhesion coefficient at the previous moment before the current moment, that is, the first road adhesion coefficient obtained when the target road adhesion coefficient was determined last time.

[0085] The embodiment of the present application determines whether the driving road surface meets the first preset condition based on the comparison result between the first road surface adhesion coefficient at the current moment and the first road surface adhesion coefficient at the historical moment. Based on this, in some embodiments, the above step S130 may include the following steps S1321 to S1323:

[0086] Step S1321: if the absolute value of the road surface adhesion coefficient difference is less than the adhesion coefficient threshold, determining a target road surface adhesion coefficient of the driving road surface from the first road surface adhesion coefficient and the second road surface adhesion coefficient; wherein the target road surface adhesion coefficient is the second road surface adhesion coefficient;

[0087] Step S1322: if the absolute value of the road surface adhesion coefficient difference is greater than the adhesion coefficient threshold, and the first road surface adhesion coefficient at the current moment is greater than the first road surface adhesion coefficient at the historical moment, then determine the target road surface adhesion coefficient of the driving road surface from the first road surface adhesion coefficient and the second road surface adhesion coefficient; wherein the target road surface adhesion coefficient is the second road surface adhesion coefficient;

[0088] Step S1323: If the absolute value of the road adhesion coefficient difference is greater than the adhesion coefficient threshold, and the first road adhesion coefficient at the current moment is less than the first road adhesion coefficient at the historical moment, then determine the target road adhesion coefficient of the driving road from the first road adhesion coefficient and the second road adhesion coefficient; wherein the target road adhesion coefficient is the first road adhesion coefficient.

[0089] The above step S1321 is that if the absolute value of the difference in road adhesion coefficient is less than the adhesion coefficient threshold, the second road adhesion coefficient is selected from the first road adhesion coefficient and the second road adhesion coefficient as the target road adhesion coefficient of the driving road. The above step S1322 is that if the absolute value of the difference in road adhesion coefficient is greater than the adhesion coefficient threshold, and the first road adhesion coefficient at the current moment is greater than the first road adhesion coefficient at the historical moment, the second road adhesion coefficient is selected from the first road adhesion coefficient and the second road adhesion coefficient as the target road adhesion coefficient of the driving road. The above step S1323 is that if the absolute value of the difference in road adhesion coefficient is greater than the adhesion coefficient threshold, and the first road adhesion coefficient at the current moment is less than the first road adhesion coefficient at the historical moment, the first road adhesion coefficient is selected from the first road adhesion coefficient and the second road adhesion coefficient as the target road adhesion coefficient of the driving road.

[0090] For example, Figure 6 As shown, the difference between the first road adhesion coefficient at the current moment and the first road adhesion coefficient at the historical moment, that is, the road adhesion coefficient difference Δμ, can be obtained. a ; The road adhesion coefficient difference Δμ a The absolute value of |Δμ a | is compared with the preset adhesion coefficient threshold, such as the preset adhesion coefficient threshold is 0.1, 0.2, 0.3, etc.; if |Δμ a | is less than or equal to the adhesion coefficient threshold, then the driving road surface has not undergone a sudden change, and the second road surface adhesion coefficient is selected as the target road surface adhesion coefficient; if |Δμ a| is greater than the adhesion coefficient threshold, the driving road surface changes suddenly. At this time, if the first road surface adhesion coefficient at the current moment is greater than the first road surface adhesion coefficient at the historical moment, the driving condition corresponding to the driving road surface becomes better, and the second road surface adhesion coefficient is selected as the target road surface adhesion coefficient. If the first road surface adhesion coefficient at the current moment is less than or equal to the first road surface adhesion coefficient at the historical moment, the driving condition corresponding to the driving road surface becomes worse, and the first road surface adhesion coefficient is selected as the target road surface adhesion coefficient.

[0091] It should be understood that the first road adhesion coefficient is obtained based on the road image, and the road image is predictive for the road ahead; the second road adhesion coefficient is obtained based on the vehicle's driving parameters. When the vehicle is in a stable working condition, the calculation of the second road adhesion coefficient is more accurate, and the stability and maneuverability of the vehicle can be better exerted. Therefore, in the embodiment of the present application, when the driving road surface undergoes a sudden change and the driving conditions corresponding to the driving road surface deteriorate, the first road adhesion coefficient is selected as the target road adhesion coefficient, which can give play to the predictiveness of the road surface image, so as to control the vehicle to decelerate or brake in advance; and when the driving road surface does not undergo a sudden change, or the driving road surface undergoes a sudden change but the driving conditions corresponding to the driving road surface improve, the second road adhesion coefficient is selected as the target road adhesion coefficient, which can give play to the stability and maneuverability of the vehicle, and avoid the vehicle control system from constantly changing the control strategy, which puts a burden on the vehicle system.

[0092] In some embodiments, the above step S130 may include the following steps: performing weighted sum processing on the first road adhesion coefficient and the second road adhesion coefficient to obtain a target road adhesion coefficient of the driving road surface. In some embodiments, the weights of the first road adhesion coefficient and the second road adhesion coefficient in the weighted sum processing may be preset, or may be dynamically changed according to the driving road surface, for example, different mapping relationships between driving roads and weights are preset. In some embodiments, when the weight of the first road adhesion coefficient is equal to the weight of the second road adhesion coefficient, it means that the first road adhesion coefficient and the second road adhesion coefficient are averaged to obtain the target road adhesion coefficient of the driving road surface.

[0093] In summary, the data processing method provided in the embodiment of the present application obtains the first road adhesion coefficient and the second road adhesion coefficient of the driving road surface according to the road surface image and the driving parameters of the vehicle respectively; and then determines the target road adhesion coefficient of the driving road surface according to the two road adhesion coefficients. Among them, the target road adhesion coefficient can be obtained by selecting one of the first road adhesion coefficient and the second road adhesion coefficient. When the driving road surface undergoes a sudden change and the driving conditions corresponding to the driving road surface deteriorate, the first road adhesion coefficient is selected as the target road adhesion coefficient, which can give play to the predictiveness of the road surface image so as to control the vehicle to decelerate or brake in advance. When the driving road surface does not undergo a sudden change, or the driving road surface undergoes a sudden change but the driving conditions corresponding to the driving road surface improve, the second road adhesion coefficient is selected as the target road adhesion coefficient, which can give play to the stability and maneuverability of the vehicle, and avoid the vehicle control system from constantly changing the control strategy, which puts a burden on the vehicle system.

[0094] In some embodiments, the data processing method provided in the embodiments of the present application may further include: determining a target transmission ratio of the vehicle's steering system based on a target road adhesion coefficient and a preset transmission ratio function.

[0095] The transmission ratio of the steering system refers to the ratio between the steering wheel angle and the front wheel angle. In the embodiment of the present application, a transmission ratio function is preset, and the independent variable in the transmission ratio function can be determined based on the target road adhesion coefficient, thereby outputting the target transmission ratio of the steering system.

[0096] In some embodiments, determining the target transmission ratio of the vehicle's steering system based on the target road adhesion coefficient and a preset transmission ratio function may include: determining the maximum transmission ratio and the minimum transmission ratio of the vehicle's steering system based on the target road adhesion coefficient; determining the target transmission ratio of the steering system based on the maximum transmission ratio, the minimum transmission ratio, the vehicle's driving speed and a preset transmission ratio function.

[0097] That is, the independent variables in the transmission ratio function include the maximum transmission ratio, the minimum transmission ratio and the driving speed of the vehicle. After determining the values ​​of the independent variables, the transmission ratio function can output the target transmission ratio of the steering system. The driving speed of the vehicle can be obtained by a sensor carried by the vehicle, and the maximum transmission ratio and the minimum transmission ratio of the steering system can be corrected based on the target road adhesion coefficient. For an introduction to the transmission ratio function, the calculation method of the maximum transmission ratio and the minimum transmission ratio, please refer to the following embodiments, which will not be elaborated here.

[0098] In some embodiments, the data processing method provided by the embodiments of the present application may further include: determining the target front wheel angle of the vehicle based on the target transmission ratio and the collected steering wheel angle; wherein the front wheel target angle is used to control the vehicle.

[0099] Next, the principle of the transmission ratio function is introduced and explained.

[0100] First, a two-degree-of-freedom vehicle steering model can be constructed, and its motion differential equation can be expressed as:

[0101] Formula 4:

[0102] Among them, I z is the moment of inertia of the vehicle around the z axis; C f and C r are the front and rear wheel cornering stiffness of the vehicle respectively; β is the center of mass side slip angle; L a and L b are the distances from the front and rear axles of the vehicle to the center of mass of the vehicle; v x and v y are the longitudinal velocity and lateral velocity of the vehicle respectively; m is the mass of the vehicle; ω is the yaw angular velocity of the vehicle.

[0103] When the vehicle is in steady-state steering, the yaw rate of the vehicle is a constant value, and the lateral speed is a constant value. At this time, the ideal yaw rate gain can be calculated based on the two-degree-of-freedom model:

[0104] Formula 5:

[0105] Among them, G f is the ideal yaw rate gain of the vehicle, ω d is the yaw rate of the vehicle, δ f is the front wheel turning angle of the vehicle.

[0106] According to the definition of the transmission ratio of the steering system It can be deduced that G f With G sw The relationship between , the ideal transmission ratio is obtained:

[0107] Formula 6:

[0108] Among them, G sw is the yaw angular velocity ω d and steering wheel angle δ sw The ratio of G f With the change of vehicle speed, if the angular transmission ratio i is designed reasonably so that G swIf it is kept constant, the steering characteristics expected by the driver can be achieved. Usually, the desired steering characteristics are that when the vehicle is at low speed, a larger front wheel angle can be achieved with less force and less steering wheel angle. Therefore, a smaller angular transmission ratio and a larger steering assist are expected at low speeds; conversely, a larger angular transmission ratio and a smaller steering assist are expected at high speeds. At the same time, when designing the angular transmission ratio i, it is necessary to consider the steering stability of the vehicle when it is driving at high speeds to prevent the system from being sluggish due to excessive transmission ratios, resulting in poor accuracy and real-time performance of lane changing and obstacle avoidance. Therefore, it is necessary to adjust the maximum transmission ratio i max When the vehicle is at low speed, in order to ensure the ease of steering and prevent the system from being too sensitive and the front wheels from reaching the maximum value due to a small transmission ratio, a minimum transmission ratio i should be set. min , the transmission ratio can be expressed as:

[0109] Formula 7:

[0110] The above formula 7 can meet the basic requirements of the steering system transmission ratio design, but the transmission ratio curve is not smooth at points v1 and v2. When the vehicle decelerates or accelerates and the vehicle speed passes through points v1 and v2, it will cause the angular acceleration of the steering motor to fluctuate, thereby affecting the performance and life of the system and generating noise. In order to solve this problem, formula 7 is further fitted into a smooth curve, and the expression of the fitting curve is:

[0111] Formula 8:

[0112] Among them, i max is the maximum transmission ratio, i min is the maximum transmission ratio, i max with i min The difference is a constant value.

[0113] To ensure the lateral stability of the vehicle, the road adhesion limit usually needs to be considered.

[0114] Formula 9:

[0115] Among them, μg is the maximum friction force.

[0116] According to Formula 6, Formula 9 can be further expressed as Formula 10:

[0117] Formula 10:

[0118] Further unification max The value range of is:

[0119] Formula 11:

[0120] The transmission ratio curve, vehicle speed, and road adhesion coefficient have the following rules:

[0121] (1) The changing trends of the transmission ratio curves of different road adhesion coefficients are basically consistent;

[0122] (2) At the same speed, the transmission ratio increases with the increase of the road adhesion coefficient, which is basically a linear relationship.

[0123] Combining formula 8, the transmission ratio function can be transformed into a nonlinear function related to the road adhesion coefficient and vehicle speed:

[0124] Formula 12:

[0125] Formula 13:

[0126] Among them, k1 and k2 are functions of μ. k1 and k2 determine the maximum and minimum values ​​of the transmission ratio curve, that is, the maximum transmission ratio and the minimum transmission ratio, and the difference between k1 and k2 is a constant. From the perspective of ensuring vehicle safety and stability, the smaller the road adhesion coefficient, the larger the values ​​of k1 and k2. Therefore, k1 and k2 are determined to be linearly related to μ, and further, a, b, and c can be assigned values ​​according to the vehicle's overall performance, as shown in Formula 13.

[0127] In order to verify the accuracy of the assigned formulas 12 and 13, simulation models were built in Carsim and MATLAB / Simulink. The road adhesion coefficients can be set to 9 working conditions such as 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, and 0.9 in Carsim, and simulations can be performed at different vehicle speeds to obtain the yaw rate at different road adhesion coefficients. The transmission ratio curves at different road adhesion coefficients were calculated according to formulas 8 and 11, and compared with the results of formulas 12 and 13, the error control was within 10%, so the a, b, and c values ​​assigned by formula 13 were accurate. Formulas 12 and 13 can be used to calculate the transmission ratio of the steering system at different vehicle speeds and different road adhesion coefficients.

[0128] It should be understood that the transmission ratio function described in the embodiment of the present application can be the above formula 12; the maximum transmission ratio can be k1 in the above formulas 12 and 13, and the minimum transmission ratio can be k2 in the above formulas 12 and 13; the target road adhesion coefficient can be μ in the above formula 13; and the vehicle's driving speed can be v in the above formula 12.

[0129] In summary, the data processing method provided in the embodiment of the present application takes into account the influence of the road adhesion coefficient on the yaw angular velocity of the vehicle, corrects the transmission ratio of the steering system based on the target road adhesion coefficient of the driving road, and improves the high-speed steering stability and low-speed steering portability of the vehicle under different road conditions.

[0130] According to a second aspect of the present application, an embodiment of the present application provides a data processing device.

[0131] See also Figure 7 , Figure 7 is a block diagram of a data processing device provided in an embodiment of the present application. The data processing device may be an electronic device, or may be arranged in an electronic device. Figure 7 As shown, the data processing device 700 includes: a coefficient acquisition module 710.

[0132] The coefficient acquisition module 710 is used to determine a target road adhesion coefficient of the driving road according to the road image and the driving parameters of the vehicle.

[0133] In some embodiments, Figure 8 As shown, the coefficient acquisition module 710 includes:

[0134] A first acquisition module 711 is used to acquire a first road adhesion coefficient of a driving road according to a road image;

[0135] A second acquisition module 712, configured to acquire a second road adhesion coefficient of the driving road according to the driving parameters of the vehicle;

[0136] The target acquisition module 713 is used to determine a target road surface adhesion coefficient of the driving road surface according to the first road surface adhesion coefficient and the second road surface adhesion coefficient.

[0137] In some embodiments, Figure 8 As shown, the target acquisition module 713 is also used for:

[0138] determining a target road surface adhesion coefficient of the driving road surface from the first road surface adhesion coefficient and the second road surface adhesion coefficient;

[0139] The target road surface adhesion coefficient is the first road surface adhesion coefficient or the second road surface adhesion coefficient.

[0140] In some embodiments, Figure 8 As shown, the target acquisition module 713 is also used for:

[0141] If the driving road surface satisfies a first preset condition, determining a target road surface adhesion coefficient of the driving road surface from the first road surface adhesion coefficient and the second road surface adhesion coefficient; wherein the target road surface adhesion coefficient is the first road surface adhesion coefficient;

[0142] If the driving road surface does not meet the first preset condition, a target road surface adhesion coefficient of the driving road surface is determined from the first road surface adhesion coefficient and the second road surface adhesion coefficient; wherein the target road surface adhesion coefficient is the second road surface adhesion coefficient.

[0143] In some embodiments, the first preset condition includes a sudden change in the driving road surface and a deterioration in driving conditions corresponding to the driving road surface.

[0144] In some embodiments, Figure 8 As shown, the target acquisition module 713 is also used for:

[0145] Determine a road surface adhesion coefficient difference according to the first road surface adhesion coefficient at a current moment and the first road surface adhesion coefficient at a historical moment;

[0146] The road surface adhesion coefficient difference is compared with a preset adhesion coefficient threshold; wherein the comparison result between the road surface adhesion coefficient difference and the adhesion coefficient threshold is used to determine whether the driving road surface meets the first preset condition.

[0147] In some embodiments, Figure 8 As shown, the target acquisition module 713 is also used for:

[0148] If the absolute value of the road adhesion coefficient difference is less than the adhesion coefficient threshold, determining a target road adhesion coefficient of the driving road surface from the first road adhesion coefficient and the second road adhesion coefficient; wherein the target road adhesion coefficient is the second road adhesion coefficient;

[0149] If the absolute value of the road adhesion coefficient difference is greater than the adhesion coefficient threshold, and the first road adhesion coefficient at the current moment is greater than the first road adhesion coefficient at the historical moment, then determining the target road adhesion coefficient of the driving road from the first road adhesion coefficient and the second road adhesion coefficient; wherein the target road adhesion coefficient is the second road adhesion coefficient;

[0150] If the absolute value of the road adhesion coefficient difference is greater than the adhesion coefficient threshold, and the first road adhesion coefficient at the current moment is less than the first road adhesion coefficient at the historical moment, then the target road adhesion coefficient of the driving road is determined from the first road adhesion coefficient and the second road adhesion coefficient; wherein the target road adhesion coefficient is the first road adhesion coefficient.

[0151] In some embodiments, Figure 8 As shown, the target acquisition module 713 is also used for:

[0152] A weighted summation process is performed on the first road surface adhesion coefficient and the second road surface adhesion coefficient to obtain a target road surface adhesion coefficient of the driving road surface.

[0153] In some embodiments, Figure 8 As shown, the first acquisition module 711 is further used for:

[0154] According to the road surface image, a target road surface type of the driving road surface is obtained;

[0155] A first road surface adhesion coefficient of the driving road surface is obtained according to the target road surface type.

[0156] In some embodiments, Figure 8 As shown, the first acquisition module 711 is further used for:

[0157] Performing road surface recognition on the road surface image by using a recognition model to obtain a road surface area in the road surface image;

[0158] The road surface area is classified by a classification model to obtain a target road surface type of the driving road surface.

[0159] In some embodiments, Figure 8 As shown, the first acquisition module 711 is further used for:

[0160] A first road surface adhesion coefficient of the driving road surface is determined according to the target road surface type and a preset mapping relationship table; wherein the mapping relationship table includes at least one set of mapping relationships between road surface types and road surface adhesion coefficients.

[0161] In some embodiments, Figure 8 As shown, the second acquisition module 712 is further used for:

[0162] According to the driving parameters of the vehicle, obtaining the road adhesion coefficients respectively corresponding to the multiple tires of the vehicle;

[0163] A second road surface adhesion coefficient of the driving road surface is determined according to the road surface adhesion coefficients respectively corresponding to the plurality of tires.

[0164] In some embodiments, Figure 8 As shown, the second acquisition module 712 is further used for:

[0165] Determining a state parameter of the vehicle according to a driving parameter of the vehicle;

[0166] Determining normalized forces corresponding to a plurality of tires of the vehicle respectively according to the driving parameter and the state parameter;

[0167] The road adhesion coefficients respectively corresponding to the plurality of tires are determined according to the driving parameters, the state parameters and the normalized forces respectively corresponding to the plurality of tires.

[0168] In some embodiments, the driving parameter includes at least one of the following: a longitudinal acceleration of the vehicle, a lateral acceleration of the vehicle, a front wheel turning angle of the vehicle, a longitudinal speed of the vehicle, and a wheel speed of the vehicle.

[0169] In some embodiments, the state parameter includes at least one of the following: a sideslip angle of the center of mass of the vehicle, and a yaw rate of the vehicle.

[0170] In some embodiments, Figure 8 As shown, the second acquisition module 712 is further used for:

[0171] determining a second road surface adhesion coefficient of the driving road surface from the road surface adhesion coefficients respectively corresponding to the plurality of tires;

[0172] The second road adhesion coefficient is the road adhesion coefficient that meets a second preset condition among the road adhesion coefficients corresponding to the plurality of tires.

[0173] In some embodiments, the second preset condition includes the road adhesion coefficient being minimum.

[0174] In some embodiments, the data processing device 700 further includes a transmission ratio determination module (not shown in the figure) for:

[0175] A target transmission ratio of the steering system of the vehicle is determined according to the target road adhesion coefficient and a preset transmission ratio function.

[0176] In some embodiments, the transmission ratio determination module is further used to:

[0177] Determining a maximum transmission ratio and a minimum transmission ratio of a steering system of the vehicle according to the target road adhesion coefficient;

[0178] The target transmission ratio of the steering system is determined according to the maximum transmission ratio, the minimum transmission ratio, the driving speed of the vehicle and a preset transmission ratio function.

[0179] In some embodiments, the data processing device 700 further includes a rotation angle determination module (not shown in the figure) for:

[0180] The target front wheel angle of the vehicle is determined according to the target transmission ratio and the steering wheel angle; wherein the target front wheel angle is used to control the vehicle.

[0181] It should be understood that, in specific implementation, the above modules can be implemented as independent entities, or can be arbitrarily combined and implemented as the same or several entities. In addition, the specific implementation methods and corresponding beneficial effects of the above modules can be referred to the above method embodiments, which will not be described in detail here.

[0182] According to the third aspect of the present application, the embodiment of the present application further provides a non-transitory computer-readable storage medium, on which a computer program is stored, and when the program is executed by a processor, the steps of the above-mentioned data processing method are implemented. The non-transitory computer-readable storage medium has all the beneficial effects of the above-mentioned data processing method, and the present application will not repeat them here.

[0183] According to the fourth aspect of the present application, an embodiment of the present application further provides an electronic device, comprising: a memory and a processor, wherein a computer program is stored in the memory; and the processor is used to execute the computer program in the memory to implement the steps of the above data processing method. The electronic device has all the beneficial effects of the above data processing method, which will not be described in detail in this application.

[0184] The computer-readable storage medium may be, for example, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or component, or any combination thereof, which is not specifically limited in the present application. More specific examples of computer-readable storage media may include, but are not limited to, an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof.

[0185] In some embodiments of the present application, computer-readable storage media may be any tangible media that contains or stores a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0186] The computer-readable storage medium may be included in the electronic device or may exist independently without being installed in the electronic device. The computer-readable storage medium carries one or more programs. When the one or more programs are executed by the electronic device, the electronic device:

[0187] According to the road surface image and the driving parameters of the vehicle, the target road adhesion coefficient of the driving road is determined.

[0188] Computer program code for performing the operations of some embodiments of the present application can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as an independent software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer can be connected to the user's computer through any type of network (including a local area network (LAN) or a wide area network (WAN)), or it can be connected to an external computer (for example, using an Internet service provider to connect through the Internet).

[0189] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present application. In this regard, each box in the flowchart or block diagram may represent a module, a program segment, or a portion of a code, which contains one or more executable instructions for implementing a specified logical function.

[0190] It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures.

[0191] For example, two boxes shown in succession may actually be executed substantially in parallel, and they may sometimes be executed in the reverse order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flow chart, and the combination of boxes in the block diagram and / or flow chart, can be implemented by a dedicated hardware-based system that performs the specified function or operation, or can be implemented by a combination of dedicated hardware and computer instructions.

[0192] The units described in some embodiments of the present application may be implemented by software or hardware. The units described may also be set in a processor, for example, it may be described as: a processor includes a coefficient acquisition module. The names of these units do not constitute a limitation on the units themselves in some cases, for example, the coefficient acquisition module may also be described as "a unit for determining a target road surface adhesion coefficient of a driving road surface based on a road surface image and driving parameters of a vehicle".

[0193] The functions described above herein may be performed at least in part by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), complex programmable logic devices (CPLDs), and the like.

[0194] According to a fifth aspect of the present application, Fig. 9 As shown, the embodiment of the present application further provides a vehicle 10, which includes the above data processing device, or includes the above electronic device. The vehicle has all the beneficial effects of the above data processing device or electronic device, etc., which will not be described in detail in this application.

[0195] The vehicle may be a fuel vehicle, a plug-in hybrid vehicle or a new energy vehicle, etc., and this application does not make any specific limitation on this.

[0196] In the description of this application, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more features. In the description of this application, the meaning of "plurality" is two or more, unless otherwise clearly and specifically defined.

[0197] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0198] The embodiments, implementation methods and related technical features of the present application can be combined and replaced with each other without conflict.

[0199] The above are only preferred embodiments of the present application and do not constitute any form of limitation to the present application. Although the descriptions of various embodiments in the embodiments of the present application have different focuses, for parts that are not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments. However, any simple modifications, equivalent changes and modifications made to the above embodiments based on the technical essence of the present application without departing from the content of the technical solution of the present application are still within the scope of the technical solution of the present application.

Claims

1. A data processing method, characterized in that: include: According to the road surface image and the driving parameters of the vehicle, the target road adhesion coefficient of the driving road is determined.

2. The method according to claim 1, characterized in that Determining a target road adhesion coefficient of the driving road according to the road image and the driving parameters of the vehicle includes: Acquiring a first road adhesion coefficient of the driving road surface according to the road surface image; Acquiring a second road adhesion coefficient of the driving road according to a driving parameter of the vehicle; A target road surface adhesion coefficient of the driving road surface is determined according to the first road surface adhesion coefficient and the second road surface adhesion coefficient.

3. The method according to claim 2, characterized in that The step of determining a target road surface adhesion coefficient of the driving road surface according to the first road surface adhesion coefficient and the second road surface adhesion coefficient comprises: determining a target road surface adhesion coefficient of the driving road surface from the first road surface adhesion coefficient and the second road surface adhesion coefficient; The target road surface adhesion coefficient is the first road surface adhesion coefficient or the second road surface adhesion coefficient.

4. The method according to claim 3, characterized in that Determining a target road surface adhesion coefficient of the driving road surface from the first road surface adhesion coefficient and the second road surface adhesion coefficient comprises: If the driving road surface satisfies a first preset condition, determining a target road surface adhesion coefficient of the driving road surface from the first road surface adhesion coefficient and the second road surface adhesion coefficient; wherein the target road surface adhesion coefficient is the first road surface adhesion coefficient; If the driving road surface does not meet the first preset condition, a target road surface adhesion coefficient of the driving road surface is determined from the first road surface adhesion coefficient and the second road surface adhesion coefficient; wherein the target road surface adhesion coefficient is the second road surface adhesion coefficient.

5. The method according to claim 4, characterized in that The first preset condition includes that the driving road surface undergoes a sudden change and the driving condition corresponding to the driving road surface deteriorates.

6. The method according to claim 5, characterized in that The method further comprises: Determine a road surface adhesion coefficient difference according to the first road surface adhesion coefficient at a current moment and the first road surface adhesion coefficient at a historical moment; The road surface adhesion coefficient difference is compared with a preset adhesion coefficient threshold; wherein the comparison result between the road surface adhesion coefficient difference and the adhesion coefficient threshold is used to determine whether the driving road surface meets the first preset condition.

7. The method according to claim 6, characterized in that The method further comprises: If the absolute value of the road adhesion coefficient difference is less than the adhesion coefficient threshold, determining a target road adhesion coefficient of the driving road surface from the first road adhesion coefficient and the second road adhesion coefficient; wherein the target road adhesion coefficient is the second road adhesion coefficient; If the absolute value of the road adhesion coefficient difference is greater than the adhesion coefficient threshold, and the first road adhesion coefficient at the current moment is greater than the first road adhesion coefficient at the historical moment, then determining the target road adhesion coefficient of the driving road from the first road adhesion coefficient and the second road adhesion coefficient; wherein the target road adhesion coefficient is the second road adhesion coefficient; If the absolute value of the road adhesion coefficient difference is greater than the adhesion coefficient threshold, and the first road adhesion coefficient at the current moment is less than the first road adhesion coefficient at the historical moment, then the target road adhesion coefficient of the driving road is determined from the first road adhesion coefficient and the second road adhesion coefficient; wherein the target road adhesion coefficient is the first road adhesion coefficient.

8. The method according to claim 2, characterized in that: The step of determining a target road surface adhesion coefficient of the driving road surface according to the first road surface adhesion coefficient and the second road surface adhesion coefficient comprises: A weighted summation process is performed on the first road surface adhesion coefficient and the second road surface adhesion coefficient to obtain a target road surface adhesion coefficient of the driving road surface.

9. The method according to claim 2, characterized in that: The step of obtaining a first road surface adhesion coefficient of the driving road surface according to the road surface image includes: According to the road surface image, a target road surface type of the driving road surface is obtained; A first road surface adhesion coefficient of the driving road surface is obtained according to the target road surface type.

10. The method according to claim 9, characterized in that The step of obtaining a target road surface type of the driving road surface according to the road surface image includes: Performing road surface recognition on the road surface image by using a recognition model to obtain a road surface area in the road surface image; The road surface area is classified by a classification model to obtain a target road surface type of the driving road surface.

11. The method according to claim 9, characterized in that The step of obtaining a first road surface adhesion coefficient of the driving road surface according to the target road surface type includes: A first road surface adhesion coefficient of the driving road surface is determined according to the target road surface type and a preset mapping relationship table; wherein the mapping relationship table includes at least one set of mapping relationships between road surface types and road surface adhesion coefficients.

12. The method according to claim 2, characterized in that: The step of obtaining a second road adhesion coefficient of the driving road according to the driving parameters of the vehicle includes: According to the driving parameters of the vehicle, obtaining the road adhesion coefficients respectively corresponding to the multiple tires of the vehicle; A second road surface adhesion coefficient of the driving road surface is determined according to the road surface adhesion coefficients respectively corresponding to the plurality of tires.

13. The method according to claim 12, characterized in that The step of obtaining the road adhesion coefficients respectively corresponding to the plurality of tires of the vehicle according to the driving parameters of the vehicle includes: Determining a state parameter of the vehicle according to a driving parameter of the vehicle; Determining normalized forces corresponding to a plurality of tires of the vehicle respectively according to the driving parameter and the state parameter; The road adhesion coefficients respectively corresponding to the plurality of tires are determined according to the driving parameters, the state parameters and the normalized forces respectively corresponding to the plurality of tires.

14. The method according to claim 13, characterized in that The driving parameter includes at least one of the following: the longitudinal acceleration of the vehicle, the lateral acceleration of the vehicle, the front wheel turning angle of the vehicle, the longitudinal speed of the vehicle, and the wheel speed of the vehicle.

15. The method according to claim 13, characterized in that The state parameter includes at least one of the following: a sideslip angle of the center of mass of the vehicle, and a yaw rate of the vehicle.

16. The method according to claim 12, characterized in that The determining of a second road surface adhesion coefficient of the driving road surface according to the road surface adhesion coefficients respectively corresponding to the plurality of tires comprises: determining a second road surface adhesion coefficient of the driving road surface from the road surface adhesion coefficients respectively corresponding to the plurality of tires; The second road adhesion coefficient is the road adhesion coefficient that meets a second preset condition among the road adhesion coefficients corresponding to the plurality of tires.

17. The method according to claim 16, characterized in that The second preset condition includes that the road adhesion coefficient has a minimum value.

18. The method according to any one of claims 1 to 17, characterized in that: The method further comprises: A target transmission ratio of the steering system of the vehicle is determined according to the target road adhesion coefficient and a preset transmission ratio function.

19. The method according to claim 18, characterized in that Determining a target transmission ratio of the steering system of the vehicle according to the target road adhesion coefficient and a preset transmission ratio function includes: Determining a maximum transmission ratio and a minimum transmission ratio of a steering system of the vehicle according to the target road adhesion coefficient; The target transmission ratio of the steering system is determined according to the maximum transmission ratio, the minimum transmission ratio, the driving speed of the vehicle and a preset transmission ratio function.

20. The method according to claim 18, characterized in that The method further comprises: The target front wheel angle of the vehicle is determined according to the target transmission ratio and the steering wheel angle; wherein the target front wheel angle is used to control the vehicle.

21. A data processing device, characterized in that: include: The coefficient acquisition module is used to determine the target road adhesion coefficient of the driving road according to the road image and the driving parameters of the vehicle.

22. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the data processing method according to any one of claims 1 to 20 is implemented.

23. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the data processing method according to any one of claims 1 to 20 is implemented.

24. An electronic device, characterized in that: include: a memory having a computer program stored thereon; A processor, configured to execute the computer program in the memory to implement the data processing method according to any one of claims 1 to 20.

25. A vehicle, characterized in that: Includes the data processing device as claimed in claim 21, or includes the electronic device as claimed in claim 24.

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