CN114148137A

CN114148137A - Vehicle running stability control method, device, equipment and storage medium

Info

Publication number: CN114148137A
Application number: CN202111589268.XA
Authority: CN
Inventors: 王智; 谭日成
Original assignee: Beijing Baidu Netcom Science and Technology Co Ltd
Current assignee: Beijing Baidu Netcom Science and Technology Co Ltd
Priority date: 2020-03-13
Filing date: 2020-03-13
Publication date: 2022-03-08
Also published as: CN114148136A; CN111347831A; CN111347831B

Abstract

The application discloses a vehicle driving stability control method, device, equipment and storage medium, and relates to the field of automatic driving. The specific implementation scheme is as follows: acquiring the front road condition prediction information and the current driving road condition information of the vehicle in the driving process; determining strategy information for adjusting a suspension system of the vehicle according to the front road condition prediction information and the current driving road condition information; and adjusting the suspension system of the vehicle according to the strategy information. The embodiment of the application can timely make the strategy information for adjusting the suspension system of the vehicle, so that the suspension system of the vehicle can be timely adjusted to be suitable for the front road condition, the vehicle can stably run, and the riding comfort level is improved.

Description

Vehicle running stability control method, device, equipment and storage medium

The invention is a divisional application of an invention patent application with application number 202010175377.6 entitled "method, device, apparatus and storage medium for controlling vehicle driving stability", which is filed on 13.03.2020.

Technical Field

The application relates to the technical field of data processing, in particular to an automatic driving technology.

Background

During the running of the vehicle, the smooth running of the vehicle is an important factor for the comfort of the passengers in the vehicle.

Currently, in the aspect of vehicle smooth driving control, the overall height of a vehicle is adjusted by monitoring the driving speed of the vehicle, for example, if the current driving speed is too high, the overall height of the vehicle is adjusted to be low, so as to reduce the wind resistance. Or the vehicle-mounted inertia measurement unit monitors and analyzes the vehicle running bump degree to adjust the damping coefficient of the vehicle suspension system, for example, if the current road section is bumpy, the damping coefficient of the vehicle suspension system is reduced.

However, when the above prior art is adopted to perform the smooth driving control of the vehicle, the phenomenon of adjustment lag often occurs, so that the comfort level after adjustment often cannot meet the requirement of the passenger on the stability.

Disclosure of Invention

In a first aspect, an embodiment of the present application provides a method for controlling vehicle running stability, including: acquiring the front road condition prediction information and the current driving road condition information of the vehicle in the driving process; determining strategy information for adjusting a suspension system of the vehicle according to the front road condition prediction information and the current driving road condition information; and adjusting the suspension system of the vehicle according to the strategy information.

According to the embodiment of the application, the front road condition prediction information and the current driving road condition information of the vehicle in the driving process are obtained, the strategy information for adjusting the suspension system of the vehicle is determined according to the front road condition prediction information and the current driving road condition information, and the suspension system of the vehicle is adjusted according to the strategy information, so that the aim of enabling the vehicle to stably drive is fulfilled. The method has the advantages that the current driving road condition information of the vehicle in the driving process can be indicated, the prediction information of the front road condition in the driving process of the vehicle is also obtained, the front road condition can be sensed in advance according to the prediction information of the front road condition, and the strategy information for adjusting the suspension system of the vehicle is made in time by combining the current driving road condition information, so that the suspension system of the vehicle is adjusted to be suitable for the front road condition in time, the vehicle can stably drive, and the riding comfort level is improved.

Optionally, the obtaining of the predicted information of the road condition ahead of the vehicle in the driving process includes: acquiring an environment image of the surrounding environment of the vehicle, which is acquired by an image acquisition device on the vehicle; and inputting the environment image into a road condition prediction model obtained by pre-training to obtain the front road condition prediction information of the vehicle in the driving process.

Optionally, the road condition prediction model is obtained by training a neural network by using the following process: acquiring an environment sample image of the surrounding environment in the driving process of a vehicle and road condition prediction marking information corresponding to the environment sample image; and taking the environment sample image as the input of a neural network, taking the road condition prediction marking information as the output of the neural network, and performing iterative training on the neural network to obtain the road condition prediction model.

According to the embodiment of the application, the road condition prediction model is obtained by training the neural network, so that the environment image of the surrounding environment of the vehicle, which is acquired by the image acquisition equipment on the vehicle, is input into the road condition prediction model to predict the front road condition.

Optionally, the obtaining the current driving road condition information of the vehicle includes: acquiring attitude information of the vehicle, which is acquired by an inertial measurement unit on the vehicle; and determining the current driving road condition information of the vehicle according to the attitude information of the vehicle.

Optionally, the determining the current driving road condition information of the vehicle according to the posture information of the vehicle includes: determining the variation of the attitude information according to the attitude information of the vehicle within a preset time period; determining that the current driving road condition information is an unstable road section under the condition that the variation of the attitude information is larger than a preset variation; and determining that the current driving road condition information is a stable road section under the condition that the variation of the attitude information is less than or equal to the preset variation.

The embodiment of the application determines the current road condition of the vehicle according to the variable quantity of the attitude information of the vehicle in a period of time, wherein the attitude information is inclination angle information used for representing the vehicle, and if the current road condition of the vehicle is an unstable road section, the variable quantity of the inclination angle information is unstable, the current road condition information of the vehicle can be conveniently determined according to the principle, in addition, the attitude information can be obtained according to an existing inertia measurement unit on the vehicle, and an additional sensor is not required to be added.

Optionally, the determining the current driving road condition information of the vehicle according to the posture information of the vehicle includes: and inputting the attitude information of the vehicle into a road condition detection model obtained by pre-training to obtain the current driving road condition information of the vehicle.

Optionally, the road condition detection model is obtained by training a neural network by using the following process: acquiring attitude sample data in the vehicle driving process and current driving road condition marking information corresponding to the attitude sample data; and taking the attitude sample data as the input of a neural network, taking the current driving road condition marking information as the output of the neural network, and performing iterative training on the neural network to obtain the road condition detection model.

According to the embodiment of the application, the road condition detection model is obtained by training the neural network, so that the attitude information of the vehicle is input into the road condition detection model and is predicted on the front road condition, and the road condition detection model is obtained by training the neural network, so that compared with the situation that the current driving road condition is determined directly according to the variation of the attitude information, the road condition detection model can more accurately detect the current driving road condition.

Optionally, the determining policy information for adjusting the suspension system of the vehicle according to the predicted front road condition information and the current driving road condition information includes: and inputting the predicted information of the road condition ahead of the vehicle in the driving process and the current driving road condition information into a strategy determination model obtained by pre-training to obtain strategy information for adjusting a suspension system of the vehicle.

Optionally, the strategy determination model is obtained by training a neural network by using the following process: acquiring training sample data and adjustment strategy marking information corresponding to the training sample data, wherein the training sample data comprises front road condition sample data and current driving road condition sample data; and taking the training sample data as the input of a neural network, taking the adjustment strategy marking information as the output of the neural network, and performing iterative training on the neural network to obtain the strategy determination model.

Optionally, the determining policy information for adjusting the suspension system of the vehicle according to the predicted front road condition information and the current driving road condition information includes: and if the front road condition prediction information indicates that the road surface is slippery and the current driving road condition information indicates that the road section is a stable road section, determining to reduce the suspension height of the suspension system of the vehicle.

Optionally, the determining policy information for adjusting the suspension system of the vehicle according to the predicted front road condition information and the current driving road condition information includes: and if the front road condition prediction information indicates that the road section jounces and the current driving road condition information indicates that the degree of the current road section jounces is larger and larger, determining to reduce the damping coefficient of the suspension system and increase the suspension height of the suspension system of the vehicle.

According to the embodiment of the application, the strategy determination model is obtained by training the neural network, so that the obtained front road condition prediction information and the current driving road condition information are input into the strategy determination model, and the strategy information for adjusting the suspension system of the vehicle is determined.

Optionally, the method further includes: acquiring wind resistance information of the vehicle; and determining strategy information for adjusting the suspension system of the vehicle according to the front road condition prediction information, the current driving road condition information and the wind resistance information.

Optionally, the acquiring the wind resistance information of the vehicle includes: acquiring speed information and shape structure information of the vehicle; and inputting the speed information and the shape structure information of the vehicle into a preset wind resistance model to obtain the wind resistance information of the vehicle.

Optionally, the determining policy information for adjusting the suspension system of the vehicle according to the predicted information of the road condition ahead, the current driving road condition information, and the wind resistance information includes: and inputting the front road condition prediction information, the current driving road condition information and the wind resistance information into a strategy determination model obtained by pre-training to obtain strategy information for adjusting a suspension system of the vehicle.

Optionally, the strategy determination model is obtained by training a neural network by using the following process: acquiring training sample data and adjustment strategy marking information corresponding to the training sample data, wherein the training sample data comprises front road condition sample data, current driving road condition sample data, speed sample data and vehicle shape structure information; and taking the training sample data as the input of a neural network, taking the adjustment strategy marking information as the output of the neural network, and performing iterative training on the neural network to obtain the strategy determination model.

Optionally, the determining policy information for adjusting the suspension system of the vehicle according to the predicted information of the road condition ahead, the current driving road condition information, and the wind resistance information includes: and if the front road condition prediction information indicates that the road section is stable, the current driving road condition information indicates that the road section is stable and the wind resistance information indicates that the wind resistance is continuously increased, determining to increase the damping coefficient of the suspension system of the vehicle and decrease the suspension height.

According to the embodiment of the application, the current state of the vehicle can be determined from different angles by combining wind resistance information on the basis of attitude information, and then the adjustment strategy information more suitable for the current scene can be made for the vehicle by combining the front road condition prediction information, so that the vehicle can be adjusted, and the comfort level after adjustment can better meet the requirement of passengers on stability.

In a second aspect, an embodiment of the present application provides a vehicle running stability control apparatus, including: the acquisition module is used for acquiring the front road condition prediction information and the current driving road condition information of the vehicle in the driving process; the strategy determining module is used for determining strategy information for adjusting a suspension system of the vehicle according to the front road condition prediction information and the current driving road condition information; and the adjusting module is used for adjusting the suspension system of the vehicle according to the strategy information.

In a third aspect, an embodiment of the present application provides a vehicle running stability control apparatus, including:

at least one processor; and

a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,

the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of the first aspect.

In a fourth aspect, embodiments of the present application provide a vehicle including the vehicle running stability control apparatus of the third aspect.

Optionally, the vehicle further comprises: the image acquisition unit is used for acquiring an environment image of the surrounding environment of the vehicle in the running process of the vehicle; and the inertia measurement unit is used for acquiring the attitude information of the vehicle in the running process of the vehicle.

Optionally, the vehicle further comprises: the speed sensor is used for collecting the speed information of the vehicle in the running process of the vehicle.

In a fifth aspect, embodiments of the present application provide a non-transitory computer-readable storage medium having stored thereon computer instructions for causing a computer to perform the method of the first aspect.

One embodiment in the above application has the following advantages or benefits: the method comprises the steps of obtaining the front road condition prediction information and the current driving road condition information of the vehicle in the driving process, determining strategy information for adjusting a suspension system of the vehicle according to the front road condition prediction information and the current driving road condition information, and adjusting the suspension system of the vehicle according to the strategy information so as to achieve the purpose of enabling the vehicle to stably drive. The method has the advantages that the current driving road condition information of the vehicle in the driving process can be indicated, the prediction information of the front road condition in the driving process of the vehicle is also obtained, the front road condition can be sensed in advance according to the prediction information of the front road condition, and the strategy information for adjusting the suspension system of the vehicle is made in time by combining the current driving road condition information, so that the suspension system of the vehicle is adjusted to be suitable for the front road condition in time, the vehicle can stably drive, and the riding comfort level is improved. Because the technical means of obtaining the current driving road condition information capable of indicating the driving process of the vehicle, the prediction information of the front road condition in the driving process of the vehicle, determining the strategy information for adjusting the suspension system of the vehicle according to the prediction information of the front road condition and the current driving road condition information, and adjusting the suspension system of the vehicle according to the strategy information are adopted, the technical problems that the adjustment lags frequently occurring when the vehicle is stably driven and controlled in the prior art, and the comfort level after adjustment cannot meet the requirement of passengers on stability frequently are solved, and the strategy information for adjusting the suspension system of the vehicle is timely made, so that the suspension system of the vehicle is timely adjusted to be suitable for the front road condition, the vehicle is stably driven, and the riding comfort level is improved are achieved.

Other effects of the above-described alternative will be described below with reference to specific embodiments.

Drawings

The drawings are included to provide a better understanding of the present solution and are not intended to limit the present application. Wherein:

FIG. 1 is a block diagram of a vehicle suspension system provided by an embodiment of the present application;

FIG. 2 is a flowchart of a smooth driving control method for a vehicle according to an embodiment of the present application;

FIG. 3 is a schematic diagram of control logic for a vehicle provided by an embodiment of the present application;

FIG. 4 is a schematic diagram of a smooth-running control method for a vehicle according to an example of the present application;

FIG. 5 is a schematic diagram of a smooth-running control method for a vehicle according to an example of the present application;

FIG. 6 is a flowchart of a smooth-running control method for a vehicle according to another embodiment of the present application;

FIG. 7 is a schematic diagram of a smooth-running control method for a vehicle according to an example of the present application;

FIG. 8 is a schematic structural diagram of a smooth running control device for a vehicle according to an embodiment of the present application;

fig. 9 is a block diagram of a vehicle smooth running control device provided in an embodiment of the present application.

Detailed Description

Fig. 1 is a structural diagram of a vehicle suspension system according to an embodiment of the present application. As shown in fig. 1, a vehicle 10 is provided with a suspension system 11, which includes corresponding control parameters, and the comfort of the ride can be improved by adjusting the control parameters of the suspension system. The control parameters of the suspension system include the damping coefficient and the suspension height.

Wherein the adjustment of the damping coefficient can be realized by adjusting the spring constant of the spring 12 in the suspension system 11. For example, the spring of the suspension system can be hardened, i.e. the damping coefficient can be adjusted high, to improve the stability of the vehicle body when the vehicle is running at high speed, and the control unit of the vehicle can think that the vehicle is passing through a bumpy road surface and can soften the suspension, i.e. the damping coefficient can be adjusted low, to absorb shock and improve the comfort when the vehicle is running at low speed for a long time.

In addition, the suspension height of the suspension system is understood to be the distance between the vehicle chassis and the ground, and the vehicle chassis can be raised or lowered by adjusting the suspension height of the suspension system. In different scenarios, different suspension heights may lead to different riding experiences, for example, when driving on a rough mountain, the suspension height may be adjusted higher so that the distance between the vehicle chassis and the ground is increased, thereby making the riding experience for the passengers less jerky. When the vehicle runs on a highway, the suspension height can be adjusted to reduce the distance between the chassis of the vehicle and the ground, the ground gripping capability of tires can be increased, the wind resistance is reduced, the safety and the stability of the vehicle running are facilitated, and the oil consumption is reduced along with the reduction of the wind resistance.

For the adjustment of the suspension height, the adjustment modes are different for different types of suspension systems, taking an air suspension system as an example, the air suspension system generally adopts an air spring, and the suspension height of the suspension system can be adjusted by inflating or deflating the air spring. For the adjustment of the suspension height of other types of suspension systems, reference is made to the description of the prior art, which is not described here.

In the prior art, the vehicle is adjusted only by depending on the data collected by the speed or inertia measurement unit, but the data collected by the speed and inertia measurement unit can only represent the historical state or the current state of the vehicle, when the vehicle is adjusted according to the historical state and the current state of the vehicle, if the road conditions of the current driving road section and the front road section of the vehicle are consistent, the adjusting mode has no problem, and once the road conditions of the front road section and the current driving road section are inconsistent, the phenomenon of adjustment lag occurs, so that the vehicle stability is low, the passenger riding experience is not good, and the comfort level is not high. This application is at the vehicle in-process of traveling, the in-process of controlling vehicle stationarity, through perception the place ahead road conditions in advance, increase the prediction information to the place ahead road conditions, the road conditions of traveling at present is reunited, determine the strategic information who adjusts the suspension system of vehicle in the state of future constantly, so that the vehicle is according to this strategic information, the suspension system to the vehicle adjusts, thereby in time adjust the vehicle, so that the suspension system's of vehicle setting parameter can adapt to the place ahead road conditions, reach the effect that makes the vehicle steadily travel.

The following detailed description of exemplary embodiments of the present application, taken in conjunction with the accompanying drawings, include various details of the embodiments of the application for the purpose of understanding, which are to be regarded as merely exemplary. Accordingly, those of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the present application. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.

FIG. 2 is a flowchart of a method for controlling vehicle driving smoothness according to an embodiment of the present application.

The embodiment of the application provides a vehicle running stability control method aiming at the above technical problems in the prior art, and the method comprises the following specific steps:

step 201, obtaining the road condition prediction information ahead of the vehicle in the driving process and the current driving road condition information.

As shown in fig. 3, the vehicle of the present embodiment includes a central control system 31, and further includes various types of sensors: an image acquisition device 32, an Inertial Measurement Unit (IMU) 33, a velocity sensor 34, and the like. The vehicle may be an unmanned vehicle, may be a vehicle equipped with an Advanced Driving Assistance System (ADAS), and may be applied to the configuration shown in fig. 3 regardless of whether the vehicle is an unmanned vehicle or a vehicle equipped with an ADAS.

The image acquisition equipment can be a vehicle-mounted camera and is used for shooting the surrounding environment in the running process of the vehicle to obtain an environment image, and the prediction information of the road condition in front of the vehicle can be obtained by analyzing and processing the environment image.

The inertial measurement unit is used for acquiring attitude information of the vehicle in the running process, wherein the attitude information of the vehicle can be understood as an attitude angle of the vehicle, including a roll angle, a pitch angle and a course angle of the vehicle. The attitude angle of the vehicle can represent the inclination angle between the vehicle and the road surface, and the current road condition of the vehicle can be determined according to the current attitude angle and the historical attitude angle of the vehicle. For example, if the vehicle continues to travel on a smooth road, the amount of change in the inclination angle between the vehicle and the road surface should be a constant value, whereas if the vehicle continues to travel on a bumpy road, the amount of change in the inclination angle between the vehicle and the road surface changes. According to the principle, the current road condition information can be determined according to the attitude angle. For specific definitions of the roll angle, the pitch angle and the heading angle, reference may be made to the description of the prior art, and details thereof are not repeated here.

The execution main body of the embodiment may be a central control system of the vehicle, and in an optional implementation, the central control system of the vehicle may respectively obtain the environment image and the attitude angle from the image acquisition device and the IMU, and respectively determine the ahead road condition prediction information and the current road condition information during the driving process of the vehicle according to the environment image and the attitude angle.

Step 202, determining strategy information for adjusting a suspension system of the vehicle according to the road condition prediction information in front and the current driving road condition information.

In this embodiment, after acquiring the predicted information of the front road condition and the current driving road condition, the central control system may determine the policy information for adjusting the damping coefficient and/or the suspension height of the suspension system of the vehicle according to the predicted information of the front road condition and the current driving road condition. Determining strategy information for adjusting the damping coefficient and/or the suspension height of a suspension system of the vehicle according to the predicted information of the road condition ahead and the current driving road condition information, wherein the strategy information at least comprises the following optional scenes:

in a first optional scenario, if the front road condition prediction information indicates that the road surface is slippery and the current driving road condition information indicates a smooth road section, determining to lower the suspension height of the suspension system of the vehicle. Illustratively, if the prediction result obtained according to the environment image acquired by the vehicle-mounted camera indicates that the road surface is slippery, and the detection result obtained according to the posture information acquired by the IMU indicates that the current road section is a smooth road section, the central control system determines the strategy information for lowering the suspension height of the suspension system of the vehicle.

Under a second optional scene, if the front road condition prediction information indicates that the road section jolts and the current driving road condition information indicates that the degree of jolt of the current road section is larger and larger, determining to reduce the damping coefficient of the suspension system and increase the suspension height of the suspension system of the vehicle. Illustratively, if the prediction result obtained from the environmental image acquired by the vehicle-mounted camera indicates that the road section jolts, and the detection result obtained from the attitude information acquired by the IMU indicates that the current road section jolts more and more, the central control system determines to reduce the damping coefficient of the suspension system and increase the strategy information of the suspension height of the suspension system of the vehicle.

In a third optional scenario, if the predicted front road condition information indicates that a deceleration strip exists in the front road section and the current driving road condition information indicates that the current driving road condition information indicates a stable road section, determining to lower the suspension height of the suspension system of the vehicle. For example, if the predicted result obtained from the environment image acquired by the vehicle-mounted camera indicates that a deceleration strip exists in the road section ahead, and the detection result obtained from the attitude information acquired by the IMU indicates that the current road section is a stable road section, the central control system may determine the strategy information for lowering the suspension height of the suspension system of the vehicle.

And step 203, adjusting the suspension system of the vehicle according to the strategy information.

After determining strategy information for adjusting the damping coefficient and/or the suspension height of the suspension system of the vehicle, the central control system sends the strategy adjustment information to the adjustment system. After receiving strategy information for adjusting the suspension system of the vehicle, an adjusting system of the vehicle, such as a control system of the suspension system, adjusts a damping coefficient and/or a suspension height of the suspension system of the vehicle according to the strategy information, so that parameters of the suspension system adapt to a road condition ahead, the vehicle runs stably, and the riding comfort of passengers is improved. The following respectively illustrates how the adjustment system adjusts the suspension system by taking the above three scenarios as examples:

corresponding to the first alternative scenario, the adjustment system may lower the suspension height of the suspension system of the vehicle, so that the center of gravity of the vehicle is lowered, and the vehicle is more closely attached to the ground, thereby smoothly passing through the wet road section.

Corresponding to the second optional scenario, the adjustment system may lower the damping coefficient of the suspension system and increase the suspension height of the suspension system of the vehicle, so that the spring elastic coefficient of the suspension system is lower, the spring is softer, and the height of the vehicle chassis from the ground is greater, so that the rider does not feel that the vehicle is jolting when passing through a jolting road section.

Corresponding to the third optional scene, the suspension height of the suspension system of the vehicle can be reduced by the adjusting system, the shaking degree of the vehicle cannot be too large, the stability of the vehicle is higher, a rider cannot feel violent shaking, and the comfort level is higher.

Since the present embodiment is a strategy for adjusting the suspension system of the vehicle according to the predicted information of the road condition ahead and the current driving road condition information, and the predicted information of the road condition ahead is a prediction of the road condition ahead, the suspension system of the vehicle can be adjusted at a preset time interval after the strategy information is received in order to avoid the adjustment in advance. The preset time can be set according to the distance between the vehicle and the road section ahead, the data acquisition, the strategy determination and the like.

In the above embodiment, an implementation manner is described in which a central control system of a vehicle respectively acquires an environment image and an attitude angle from an image acquisition device and an IMU, and respectively acquires ahead road condition prediction information and current road condition information during a vehicle driving process according to the environment image and the attitude angle.

In order to reduce the calculation pressure of the central control system and make the vehicle adjustment more timely, in another optional implementation, the other module units outside the central control system may also be used to acquire the environment image from the image acquisition device, determine the road condition prediction information ahead of the vehicle in the driving process according to the environment image, acquire the attitude information from the IMU, and determine the current road condition information of the vehicle in the driving process according to the attitude information. For a specific implementation process, please refer to the following description:

the information for predicting the road condition ahead of the vehicle in the driving process can be acquired in the following way: acquiring an environment image of the surrounding environment of the vehicle, which is acquired by an image acquisition device on the vehicle; and inputting the environmental image into a road condition prediction model obtained by pre-training to obtain the front road condition prediction information of the vehicle in the driving process. The road condition prediction model is obtained by training the neural network in the process of obtaining an environment sample image of the surrounding environment in the driving process of the vehicle and road condition prediction marking information corresponding to the environment sample image, taking the environment sample image as the input of the neural network, taking the road condition prediction marking information as the output of the neural network and performing iterative training on the neural network. In this embodiment, the iterative training process of the neural network may refer to the description of the prior art, and is not described here again.

Taking the image acquisition device as an example of a vehicle-mounted camera, as shown in fig. 4, the vehicle-mounted camera continuously acquires an environment image of the surrounding environment of the vehicle, the environment image includes the road in front of the vehicle, and inputs the road condition prediction model, the road condition prediction model extracts a feature map in the environment image according to the environment image, and determines the road condition prediction information in front of the vehicle according to the extracted feature map and outputs the information to the central control system.

For the current road condition information of the vehicle in the driving process, the following method can be adopted to obtain the current road condition information: determining the variable quantity of the attitude information according to the attitude information of the vehicle in a preset time period; determining that the current driving road condition information is an unstable road section under the condition that the variation of the attitude information is larger than the preset variation; and under the condition that the variation of the attitude information is less than or equal to the preset variation, determining that the current driving road condition information is a stable road section.

In this embodiment, the preset variation may be set to be 0, and considering that some errors may exist in the actual application process, therefore, the preset variation may be set to be a value greater than 0, and a specific value may be set by a person skilled in the art according to an actual situation.

Taking the preset variation as 0 as an example, in the T time period, the T time period sequentially includes a time T1, a time T2, a time T3, a time T4 and a time T5 according to time sequence, the attitude angles at the time T1, the time T2, the time T3, the time T4 and the time T5 can be obtained through the IMU, the attitude angles at the time T1, the time T2, the time T3, the time T4 and the time T5 are analyzed, whether the variation of the attitude angles at the adjacent times is 0 or not is determined, if the attitude angles at the adjacent times are all 0 or most of 0, the current driving road condition of the vehicle is represented as a stable road section, otherwise, the current driving road condition of the vehicle is represented as an unstable road section.

The time t1, the time t2, the time t3, the time t4, and the time t5 may be in a continuous time series or a discrete time series, which is not specifically limited in this embodiment. In addition, the current driving road condition information in the embodiment may be obtained by analyzing 1 attitude angle, or may be obtained by analyzing 2 or 3 attitude angles. Under the condition that the current driving road condition information is analyzed according to 2 or 3 attitude angles, if the variable quantity of one attitude angle is larger than the preset variable quantity, the current driving road condition is considered to be an unstable road section.

For the current road condition information of the vehicle in the driving process, besides the determination according to the preset variable quantity, the attitude angle can be obtained from the IMU through other module units except the central control system, and the current road condition information of the vehicle in the driving process can be determined according to the attitude angle. For a specific implementation process, please refer to the following description:

referring to fig. 4, the posture information of the vehicle may be input into the road condition detection model obtained by pre-training, so as to obtain the current driving road condition information of the vehicle. Acquiring attitude sample data in the driving process of the vehicle and current driving road condition marking information corresponding to the attitude sample data; and taking the posture sample data as the input of a neural network, taking the current driving road condition marking information as the output of the neural network, and performing iterative training on the neural network to obtain the posture sample data. The iterative training process for the neural network can be referred to the description of the prior art, and is not described herein again.

The inertial measurement unit IMU continuously collects attitude angles of the vehicle and inputs a road condition detection model, and the road condition detection model outputs current driving road condition prediction information of the vehicle to the central control system according to the attitude angles of the vehicle in a period of time. In the driving process of the vehicle, the central control system of the vehicle can continuously acquire the road condition prediction information in front of the vehicle from the road condition prediction model, determine the current road condition of the vehicle according to the attitude angle of the vehicle acquired by the IMU within a period of time, or continuously acquire the current driving road condition information of the vehicle from the road condition detection model. Compared with the implementation mode of directly enabling the central control system to determine the current driving road condition information according to the attitude angle, the mode of adopting the road condition detection model can reduce the calculation pressure of the central control system, so that the vehicle can be adjusted more timely.

For automatic driving, in the determination process of the strategy information, the accuracy of the strategy is of the utmost importance, and the driving safety of the vehicle is determined by the accuracy of the strategy, so that in order to improve the accuracy of the strategy, the predicted information of the road condition ahead of the vehicle in the driving process and the current driving road condition information can be input into a strategy determination model obtained by pre-training, and the strategy information for adjusting the suspension system of the vehicle is obtained. The strategy determination model is characterized by acquiring training sample data and adjusting strategy marking information corresponding to the training sample data, wherein the training sample data comprises front road condition sample data and current driving road condition sample data; and taking the training sample data as the input of a neural network, taking the adjustment strategy marking information as the output of the neural network, and performing iterative training on the neural network to obtain the adjustment strategy marking information.

For example, as shown in fig. 4, after acquiring the predicted information of the front road condition and the current driving road condition, the central control system inputs the predicted information of the front road condition and the current driving road condition into the policy determination model, and the policy determination model outputs corresponding policy information for adjusting the suspension system of the vehicle. Of course, the policy determination model may also be independent of the central control system, and those skilled in the art may perform corresponding settings according to actual situations, which is not specifically limited in this embodiment.

For example, as shown in fig. 5, if the road condition prediction model indicates that the road surface is slippery according to the prediction result obtained from the environment image acquired by the vehicle-mounted camera, and the road condition detection model indicates that the current road section is a stable road section according to the detection result obtained from the posture information acquired by the IMU, the central control system determines the strategy information for lowering the suspension height of the suspension system of the vehicle, and the adjustment system lowers the strategy information for lowering the suspension height of the suspension system of the vehicle according to the strategy information.

On the basis of the above embodiment, more sensor data, for example, vehicle speed data collected by a speed sensor on the vehicle, may be fused to determine strategy information for adjusting the suspension system of the vehicle. For details, reference is made to the following description of the embodiments:

FIG. 6 is a flowchart of a method for controlling vehicle smoothness during driving according to another embodiment of the present application. On the basis of the above embodiment, the method for controlling the running stability of the vehicle provided by the embodiment specifically includes the following steps:

step 601, obtaining the front road condition prediction information, the current driving road condition information and the wind resistance information of the vehicle in the driving process.

For obtaining the predicted information of the road condition ahead of the vehicle during the driving process and the current driving road condition information, reference may be made to the description of the foregoing embodiments, which are not described herein again.

Compared with the previous embodiment, the wind resistance information is added, the wind resistance information refers to the resistance from air in the running process of the vehicle, and the wind resistance information can be determined according to the speed information and the shape structure information of the vehicle. The shape structure information of the vehicle refers to the outer shape structure of the vehicle, such as the size (including length, width, and height) and streamline structure of the vehicle. The shape structure information of the vehicle may be stored in advance on the vehicle, and the speed of the vehicle may be acquired by a speed sensor mounted on the vehicle.

Generally speaking, the greater the vehicle speed, the greater the wind resistance, for the same vehicle shape configuration; at the same vehicle speed, the larger the size of the vehicle-shaped structure, the greater the wind resistance.

In order to conveniently and quickly determine the current wind resistance information of the vehicle, the speed information and the shape structure information of the vehicle can be input into a preset wind resistance model to obtain the wind resistance information of the vehicle. The preset wind resistance model is obtained by modeling according to the speed information, the shape structure information and the corresponding wind resistance information of the vehicle. The central control system can acquire the current wind resistance information of the vehicle from the wind resistance model.

Step 602, determining strategy information for adjusting a suspension system of a vehicle according to the road condition prediction information in front, the current driving road condition information and the wind resistance information.

In an optional scenario, if the front road condition prediction information indicates that the road section is stable, the current driving road condition information indicates that the road section is stable, and the wind resistance information indicates that the wind resistance is continuously increased, determining to increase the damping coefficient and the suspension height of the suspension system of the vehicle.

On the basis of the mode that the model is combined with the front road condition prediction information and the current driving road condition information to determine the strategy information, the embodiment can also input the front road condition prediction information, the current driving road condition information and the wind resistance information into the strategy determination model obtained by pre-training to obtain the strategy information for adjusting the suspension system of the vehicle. Compared with the strategy determination model in the foregoing embodiment, the strategy determination model in this embodiment adopts the method of obtaining training sample data and the adjustment strategy marking information corresponding to the training sample data, where the training sample data includes front road condition sample data, current driving road condition sample data, speed sample data, and vehicle shape structure information; and obtaining the training sample data as the input of the neural network, the adjustment strategy marking information as the output of the neural network, and performing iterative training on the neural network. For a specific training process of the neural network, reference may be made to description of the prior art, and this embodiment will not be described in detail.

The trained strategy determination model can be stored in the central control system, and after the central control system acquires the front road condition prediction information, the current driving road condition information and the wind resistance information, the front road condition prediction information, the current driving road condition information and the wind resistance information are input into the trained strategy determination model, and the strategy determination model can output strategy information for adjusting a suspension system of the vehicle. The strategy information may be sent to an adjustment system, such as a control system of the suspension system, to control the suspension system to make corresponding adjustments.

Step 603, adjusting the suspension system of the vehicle according to the strategy information.

After receiving strategy information for adjusting the suspension system of the vehicle, an adjusting system of the vehicle, such as a control system of the suspension system, adjusts a damping coefficient and/or a suspension height of the suspension system of the vehicle according to the strategy information, so that parameters of the suspension system adapt to a road condition ahead, the vehicle runs stably, and the riding comfort of passengers is improved.

Exemplarily, as shown in fig. 7, the environmental image acquired by the image acquisition device is input into the road condition prediction model, the attitude information acquired by the IMU is input into the road condition detection model, and the vehicle speed is input into the wind resistance model, if the road condition prediction information in front predicted by the road condition prediction model indicates that the road section is stable, the current driving road condition information output by the road condition detection model indicates that the road section is stable, and the wind resistance information output by the wind resistance model indicates that the wind resistance is continuously increased, the strategy determination model determines the damping coefficient of the suspension system of the vehicle to be increased and the strategy information of the suspension height to be decreased according to the outputs of the road condition prediction model, the road condition detection model and the wind resistance model, and sends the strategy information to the adjustment system, so that the adjustment system increases the damping coefficient of the suspension system of the vehicle and decreases the suspension height, the center of gravity of the vehicle is decreased, and the wind resistance to the vehicle is decreased more closely, the vehicle is driven more smoothly.

According to the embodiment of the application, the front road condition prediction information, the current driving road condition information and the current wind resistance information of the vehicle in the driving process are obtained, the strategy information for adjusting the suspension system of the vehicle is determined according to the front road condition prediction information, the current driving road condition information and the current wind resistance information, and the suspension system of the vehicle is adjusted according to the strategy information, so that the purpose of enabling the vehicle to stably drive is achieved. The method has the advantages that the current driving road condition information of the vehicle in the driving process can be indicated, the prediction information of the front road condition in the driving process of the vehicle is also obtained, the front road condition can be sensed in advance according to the prediction information of the front road condition, and the strategy information for adjusting the suspension system of the vehicle is made in time by combining the current driving road condition information, so that the suspension system of the vehicle is adjusted to be suitable for the front road condition in time, the vehicle can stably drive, and the riding comfort level is improved. In addition, the current state of the vehicle can be determined from different angles according to the attitude information and the wind resistance information, and then the adjustment strategy information more suitable for the current scene can be made for the vehicle by combining the front road condition prediction information so as to adjust the vehicle, and the comfort degree after adjustment can better meet the requirement of passengers on stability.

FIG. 8 is a flowchart of a vehicle driving stability control apparatus according to an embodiment of the present application. The vehicle running stability control device of the present embodiment may be a module in the central control system of the above embodiment, and on the basis of the above embodiment, the vehicle running stability control device provided by the present embodiment includes: an acquisition module 81, a policy determination module 82, and an adjustment module 83; the acquiring module 81 is configured to acquire the road condition prediction information ahead of the vehicle in the driving process and the current driving road condition information; a strategy determination module 82, configured to determine strategy information for adjusting a suspension system of the vehicle according to the predicted front road condition information and the current driving road condition information; and an adjusting module 83, configured to adjust a suspension system of the vehicle according to the strategy information.

Optionally, the obtaining module 81 obtains the front road condition prediction information of the vehicle in the driving process, including: acquiring an environment image of the surrounding environment of the vehicle, which is acquired by an image acquisition device on the vehicle; and inputting the environment image into a road condition prediction model obtained by pre-training to obtain the front road condition prediction information of the vehicle in the driving process.

Optionally, the apparatus further includes a first training module 84, where the first training module 84 obtains the road condition prediction model by training the neural network through the following process: acquiring an environment sample image of the surrounding environment in the driving process of a vehicle and road condition prediction marking information corresponding to the environment sample image; and taking the environment sample image as the input of a neural network, taking the road condition prediction marking information as the output of the neural network, and performing iterative training on the neural network to obtain the road condition prediction model.

Optionally, the obtaining module 81 obtains the current driving road condition information of the vehicle, and specifically includes: acquiring attitude information of the vehicle, which is acquired by an inertial measurement unit on the vehicle; and determining the current driving road condition information of the vehicle according to the attitude information of the vehicle.

Optionally, the obtaining module 81 determines the current driving road condition information of the vehicle according to the posture information of the vehicle, including: determining the variation of the attitude information according to the attitude information of the vehicle within a preset time period; determining that the current driving road condition information is an unstable road section under the condition that the variation of the attitude information is larger than a preset variation; and determining that the current driving road condition information is a stable road section under the condition that the variation of the attitude information is less than or equal to the preset variation.

Optionally, the obtaining module 81 determines the current driving road condition information of the vehicle according to the posture information of the vehicle, including: and inputting the attitude information of the vehicle into a road condition detection model obtained by pre-training to obtain the current driving road condition information of the vehicle.

Optionally, the apparatus further includes a second training module 85, where the second training module 85 obtains the road condition detection model by training the neural network through the following process: acquiring attitude sample data in the vehicle driving process and current driving road condition marking information corresponding to the attitude sample data; and taking the attitude sample data as the input of a neural network, taking the current driving road condition marking information as the output of the neural network, and performing iterative training on the neural network to obtain the road condition detection model.

Optionally, the policy determining module 82 determines policy information for adjusting a suspension system of the vehicle according to the predicted front road condition information and the current driving road condition information, and specifically includes: and inputting the predicted information of the road condition ahead of the vehicle in the driving process and the current driving road condition information into a strategy determination model obtained by pre-training to obtain strategy information for adjusting a suspension system of the vehicle.

Optionally, the apparatus further comprises: a third training module 86, wherein the third training module 86 trains the neural network to obtain the strategy determination model by using the following processes: acquiring training sample data and adjustment strategy marking information corresponding to the training sample data, wherein the training sample data comprises front road condition sample data and current driving road condition sample data; and taking the training sample data as the input of a neural network, taking the adjustment strategy marking information as the output of the neural network, and performing iterative training on the neural network to obtain the strategy determination model.

Optionally, the policy determining module 82 determines policy information for adjusting a suspension system of the vehicle according to the predicted front road condition information and the current driving road condition information, and specifically includes: and if the front road condition prediction information indicates that the road surface is slippery and the current driving road condition information indicates that the road section is a stable road section, determining to reduce the suspension height of the suspension system of the vehicle.

Optionally, the policy determining module 82 determines policy information for adjusting a suspension system of the vehicle according to the predicted front road condition information and the current driving road condition information, and specifically includes: and if the front road condition prediction information indicates that the road section jounces and the current driving road condition information indicates that the degree of the current road section jounces is larger and larger, determining to reduce the damping coefficient of the suspension system and increase the suspension height of the suspension system of the vehicle.

Optionally, the obtaining module 81 is further configured to obtain wind resistance information of the vehicle; the policy determining module 82 is further configured to determine policy information for adjusting a suspension system of the vehicle according to the predicted front road condition information, the current driving road condition information, and the wind resistance information.

Optionally, the obtaining module 81 obtains the wind resistance information of the vehicle, and specifically includes: acquiring speed information and shape structure information of the vehicle; and inputting the speed information and the shape structure information of the vehicle into a preset wind resistance model to obtain the wind resistance information of the vehicle.

Optionally, the policy determining module 82 determines policy information for adjusting a suspension system of the vehicle according to the predicted front road condition information, the current driving road condition information, and the wind resistance information, and specifically includes: and inputting the front road condition prediction information, the current driving road condition information and the wind resistance information into a strategy determination model obtained by pre-training to obtain strategy information for adjusting a suspension system of the vehicle.

Optionally, the third training module 86 is further configured to train the neural network to obtain the policy determination model by using the following process: acquiring training sample data and adjustment strategy marking information corresponding to the training sample data, wherein the training sample data comprises front road condition sample data, current driving road condition sample data, speed sample data and vehicle shape structure information; and taking the training sample data as the input of a neural network, taking the adjustment strategy marking information as the output of the neural network, and performing iterative training on the neural network to obtain the strategy determination model.

Optionally, the policy determining module 82 determines policy information for adjusting a suspension system of the vehicle according to the predicted front road condition information, the current driving road condition information, and the wind resistance information, and specifically includes: and if the front road condition prediction information indicates that the road section is stable, the current driving road condition information indicates that the road section is stable and the wind resistance information indicates that the wind resistance is continuously increased, determining to increase the damping coefficient of the suspension system of the vehicle and decrease the suspension height.

The vehicle smooth-running control device of the embodiment shown in fig. 8 can be used for implementing the technical solution of the above method embodiment, and the implementation principle and technical effect are similar, and are not described herein again.

According to an embodiment of the present application, there is also provided a vehicle running stability control apparatus and a readable storage medium. Wherein the vehicle running stability control apparatus may be the center control system of the above embodiment, the center control system including a readable storage medium.

As shown in fig. 9, is a block diagram of a vehicle running stability control apparatus according to an embodiment of the present application. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be examples only, and are not meant to limit implementations of the present application that are described and/or claimed herein.

As shown in fig. 9, the vehicle running stability control apparatus includes: one or more processors 901, memory 902, and interfaces for connecting the various components, including a high-speed interface and a low-speed interface. The various components are interconnected using different buses and may be mounted on a common motherboard or in other manners as desired. The processor may process instructions for execution within the electronic device, including instructions stored in or on the memory to display graphical information of a GUI on an external input/output apparatus (such as a display device coupled to the interface). In other embodiments, multiple processors and/or multiple buses may be used, along with multiple memories and multiple memories, as desired. Also, multiple electronic devices may be connected, with each device providing portions of the necessary operations (e.g., as a server array, a group of blade servers, or a multi-processor system). Fig. 9 illustrates an example of a processor 901.

Memory 902 is a non-transitory computer readable storage medium as provided herein. The memory stores instructions executable by at least one processor to cause the at least one processor to execute the vehicle smooth driving control method provided by the application. The non-transitory computer-readable storage medium of the present application stores computer instructions for causing a computer to execute the vehicle smooth running control method provided by the present application.

The memory 902, which is a non-transitory computer-readable storage medium, may be used to store non-transitory software programs, non-transitory computer-executable programs, and modules, such as program instructions/modules corresponding to the method for vehicle smooth-running control in the embodiment of the present application (for example, the obtaining module 81, the policy determining module 82, and the adjusting module 83 shown in fig. 8). The processor 901 executes various functional applications of the server and data processing by running non-transitory software programs, instructions, and modules stored in the memory 902, that is, implements the vehicle smooth-running control method in the above-described method embodiment.

The memory 902 may include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application program required for at least one function; the storage data area may store data created according to use of the electronic device for vehicle smooth running control, and the like. Further, the memory 902 may include high speed random access memory, and may also include non-transitory memory, such as at least one magnetic disk storage device, flash memory device, or other non-transitory solid state storage device. In some embodiments, the memory 902 may optionally include a memory remotely located from the processor 901, and these remote memories may be connected to the vehicle ride stability control device via a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.

The vehicle running stability control apparatus may further include: an input device 903 and an output device 904. The processor 901, the memory 902, the input device 903 and the output device 904 may be connected by a bus or other means, and fig. 9 illustrates the connection by a bus as an example.

The input device 903 may receive input numeric or character information and generate key signal inputs related to user settings and function control of the electronic equipment for vehicle smooth driving control, such as an input device of a touch screen, a keypad, a mouse, a track pad, a touch pad, a pointing stick, one or more mouse buttons, a track ball, a joystick, etc. The output devices 904 may include a display device, auxiliary lighting devices (e.g., LEDs), tactile feedback devices (e.g., vibrating motors), and the like. The display device may include, but is not limited to, a Liquid Crystal Display (LCD), a Light Emitting Diode (LED) display, and a plasma display. In some implementations, the display device can be a touch screen.

Various implementations of the systems and techniques described here can be realized in digital electronic circuitry, integrated circuitry, application specific ASICs (application specific integrated circuits), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs that are executable and/or interpretable on a programmable system including at least one programmable processor, which may be special or general purpose, receiving data and instructions from, and transmitting data and instructions to, a storage system, at least one input device, and at least one output device.

These computer programs (also known as programs, software applications, or code) include machine instructions for a programmable processor, and may be implemented using high-level procedural and/or object-oriented programming languages, and/or assembly/machine languages. As used herein, the terms "machine-readable medium" and "computer-readable medium" refer to any computer program product, apparatus, and/or device (e.g., magnetic discs, optical disks, memory, Programmable Logic Devices (PLDs)) used to provide machine instructions and/or data to a programmable processor, including a machine-readable medium that receives machine instructions as a machine-readable signal. The term "machine-readable signal" refers to any signal used to provide machine instructions and/or data to a programmable processor.

To provide for interaction with a user, the systems and techniques described here can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and a pointing device (e.g., a mouse or a trackball) by which a user can provide input to the computer. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic, speech, or tactile input.

The systems and techniques described here can be implemented in a computing system that includes a back-end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such back-end, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), Wide Area Networks (WANs), and the Internet.

The computer system may include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other.

According to the technical scheme of the embodiment of the application, the strategy information for adjusting the suspension system of the vehicle is determined by acquiring the front road condition prediction information and the current driving road condition information of the vehicle in the driving process, and according to the front road condition prediction information and the current driving road condition information, the suspension system of the vehicle is adjusted according to the strategy information, so that the aim of enabling the vehicle to stably drive is fulfilled. The method has the advantages that the current driving road condition information of the vehicle in the driving process can be indicated, the prediction information of the front road condition in the driving process of the vehicle is also obtained, the front road condition can be sensed in advance according to the prediction information of the front road condition, and the strategy information for adjusting the suspension system of the vehicle is made in time by combining the current driving road condition information, so that the suspension system of the vehicle is adjusted to be suitable for the front road condition in time, the vehicle can stably drive, and the riding comfort level is improved.

It should be understood that various forms of the flows shown above may be used, with steps reordered, added, or deleted. For example, the steps described in the present application may be executed in parallel, sequentially, or in different orders, and the present invention is not limited thereto as long as the desired results of the technical solutions disclosed in the present application can be achieved.

The above-described embodiments should not be construed as limiting the scope of the present application. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions may be made in accordance with design requirements and other factors. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A vehicle running stability control method characterized by comprising:

acquiring the front road condition prediction information and the current driving road condition information of the vehicle in the driving process;

determining strategy information for adjusting a suspension system of the vehicle according to the front road condition prediction information and the current driving road condition information;

adjusting a suspension system of the vehicle according to the strategy information;

the method further comprises the following steps:

acquiring wind resistance information of the vehicle;

inputting the front road condition prediction information, the current driving road condition information and the wind resistance information into a strategy determination model obtained by pre-training to obtain strategy information for adjusting a suspension system of the vehicle; the strategy determination model is obtained by training a neural network by adopting the following process: acquiring training sample data and adjustment strategy marking information corresponding to the training sample data, wherein the training sample data comprises front road condition sample data, current driving road condition sample data, speed sample data and vehicle shape structure information; and taking the training sample data as the input of a neural network, taking the adjustment strategy marking information as the output of the neural network, and performing iterative training on the neural network to obtain the strategy determination model.

2. The method according to claim 1, wherein the obtaining of the predicted information of the road condition ahead of the vehicle during driving comprises:

acquiring an environment image of the surrounding environment of the vehicle, which is acquired by an image acquisition device on the vehicle;

and inputting the environment image into a road condition prediction model obtained by pre-training to obtain the front road condition prediction information of the vehicle in the driving process.

3. The method of claim 2, wherein the road condition prediction model is obtained by training a neural network using the following process:

acquiring an environment sample image of the surrounding environment in the driving process of a vehicle and road condition prediction marking information corresponding to the environment sample image;

and taking the environment sample image as the input of a neural network, taking the road condition prediction marking information as the output of the neural network, and performing iterative training on the neural network to obtain the road condition prediction model.

4. The method according to any one of claims 1 to 3, wherein the obtaining the current driving road condition information of the vehicle comprises:

acquiring attitude information of the vehicle, which is acquired by an inertial measurement unit on the vehicle;

and determining the current driving road condition information of the vehicle according to the attitude information of the vehicle.

5. The method according to claim 4, wherein the determining the current driving road condition information of the vehicle according to the attitude information of the vehicle comprises:

determining the variation of the attitude information according to the attitude information of the vehicle within a preset time period;

determining that the current driving road condition information is an unstable road section under the condition that the variation of the attitude information is larger than a preset variation;

and determining that the current driving road condition information is a stable road section under the condition that the variation of the attitude information is less than or equal to the preset variation.

6. The method according to claim 4, wherein the determining the current driving road condition information of the vehicle according to the attitude information of the vehicle comprises:

and inputting the attitude information of the vehicle into a road condition detection model obtained by pre-training to obtain the current driving road condition information of the vehicle.

7. The method of claim 6, wherein the road condition detection model is obtained by training a neural network by the following process:

acquiring attitude sample data in the vehicle driving process and current driving road condition marking information corresponding to the attitude sample data;

and taking the attitude sample data as the input of a neural network, taking the current driving road condition marking information as the output of the neural network, and performing iterative training on the neural network to obtain the road condition detection model.

8. The method according to any one of claims 1 to 7, wherein determining strategy information for adjusting a suspension system of the vehicle according to the predicted traffic information ahead and the current driving traffic information comprises:

and inputting the predicted information of the road condition ahead of the vehicle in the driving process and the current driving road condition information into a strategy determination model obtained by pre-training to obtain strategy information for adjusting a suspension system of the vehicle.

9. The method of claim 8, wherein the strategy determination model is obtained by training a neural network using the following process:

acquiring training sample data and adjustment strategy marking information corresponding to the training sample data, wherein the training sample data comprises front road condition sample data and current driving road condition sample data;

and taking the training sample data as the input of a neural network, taking the adjustment strategy marking information as the output of the neural network, and performing iterative training on the neural network to obtain the strategy determination model.

10. The method according to any one of claims 1 to 9, wherein determining policy information for adjusting a suspension system of the vehicle according to the predicted traffic information ahead and the current driving traffic information comprises:

and if the front road condition prediction information indicates that the road surface is slippery and the current driving road condition information indicates that the road section is a stable road section, determining to reduce the suspension height of the suspension system of the vehicle.

11. The method according to any one of claims 1 to 9, wherein determining policy information for adjusting a suspension system of the vehicle according to the predicted traffic information ahead and the current driving traffic information comprises:

and if the front road condition prediction information indicates that the road section jounces and the current driving road condition information indicates that the degree of the current road section jounces is larger and larger, determining to reduce the damping coefficient of the suspension system and increase the suspension height of the suspension system of the vehicle.

12. The method of claim 1, wherein the obtaining of the wind resistance information of the vehicle comprises:

acquiring speed information and shape structure information of the vehicle;

and inputting the speed information and the shape structure information of the vehicle into a preset wind resistance model to obtain the wind resistance information of the vehicle.

13. The method according to any one of claims 1 to 12, wherein determining policy information for adjusting a suspension system of the vehicle according to the predicted road condition ahead, the current driving road condition information, and the wind resistance information comprises:

and if the front road condition prediction information indicates that the road section is stable, the current driving road condition information indicates that the road section is stable and the wind resistance information indicates that the wind resistance is continuously increased, determining to increase the damping coefficient of the suspension system of the vehicle and decrease the suspension height.

14. A vehicle running stability control apparatus characterized by comprising:

the acquisition module is used for acquiring the front road condition prediction information and the current driving road condition information of the vehicle in the driving process;

the strategy determining module is used for determining strategy information for adjusting a suspension system of the vehicle according to the front road condition prediction information and the current driving road condition information;

the adjusting module is used for adjusting a suspension system of the vehicle according to the strategy information;

the policy determination module is further to: acquiring wind resistance information of the vehicle;

15. A vehicle running stability control apparatus, characterized by comprising:

at least one processor; and

the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-13.

16. A vehicle characterized by comprising the vehicle running stability control apparatus according to claim 15.

17. The vehicle of claim 16, further comprising:

the image acquisition unit is used for acquiring an environment image of the surrounding environment of the vehicle in the running process of the vehicle;

and the inertia measurement unit is used for acquiring the attitude information of the vehicle in the running process of the vehicle.

18. The vehicle of claim 17, further comprising:

the speed sensor is used for collecting the speed information of the vehicle in the running process of the vehicle.

19. A non-transitory computer readable storage medium having stored thereon computer instructions for causing the computer to perform the method of any one of claims 1-13.