Suspension control method and device

Abstract

The invention discloses a suspension control method and device, relates to the technical field of suspension control, and mainly aims to control a suspension by adopting suspension control parameters suitable for a driver in advance. According to the main technical scheme, the method comprises the steps of obtaining real-time road condition information of a road section in front of a vehicle and real-time driving behaviors of a vehicle driver; based on the real-time road condition information and the real-time driving behavior, predicting a target driving behavior of the driver on the road section ahead; based on the real-time road condition information and the target driving behavior, suspension control parameters and gain coefficients corresponding to the vehicle are determined, and the gain coefficients are used for adjusting the suspension control parameters to be suitable for a driver to drive the vehicle; adjusting suspension control parameters based on the gain coefficient; and on the basis of the adjusted suspension control parameters, the suspension of the vehicle is controlled.

Description

Technical Field

[0001] The present application relates to the technical field of suspension control, and in particular to a suspension control method and device. Background Art

[0002] The suspension is a force transmission connection device between the vehicle body and the wheels, used to buffer the impact force transmitted to the vehicle body by the road surface and attenuate the vibration caused by the impact force. The purpose of attenuating the vehicle body vibration and improving driving comfort can be achieved through suspension control.

[0003] At present, when controlling the suspension, the suspension control parameters are usually determined based on the current vibration frequency and amplitude of the vehicle, and then the suspension is controlled based on the suspension control parameters. This suspension control method has the following two defects: First, when the vibration frequency and amplitude of the vehicle are obtained, the vehicle has already been driving on the road surface that causes the vehicle to vibrate. Therefore, the suspension control based on the suspension control parameters obtained by the vibration frequency and amplitude has lag. Second, the suspension control parameters are usually obtained based on the empirical data pre-calibrated by the vehicle manufacturer for the vehicle. The empirical data supports the determination of the suspension control parameters based on the vibration frequency and amplitude. However, the driver has his own personalized driving behavior. Therefore, the suspension control based on the suspension control parameters obtained by the empirical data may not be suitable for attenuating the body vibration when the driver is driving the vehicle.

[0004] It can be seen that how to control the suspension by adopting suspension control parameters suitable for the driver in advance has become a problem that needs to be solved urgently. Summary of the invention

[0005] The present application proposes a suspension control method and device, the main purpose of which is to control the suspension in advance using suspension control parameters suitable for the driver.

[0006] In order to achieve the above objectives, this application mainly provides the following technical solutions:

[0007] In a first aspect, the present application provides a suspension control method, which includes: obtaining real-time road condition information of a road section ahead of a vehicle and the real-time driving behavior of the vehicle driver; predicting the driver's target driving behavior on the road section ahead based on the real-time road condition information and the real-time driving behavior; determining the suspension control parameters and gain coefficients corresponding to the vehicle based on the real-time road condition information and the target driving behavior, the gain coefficients being used to adjust the suspension control parameters to be suitable for the driver to drive the vehicle; adjusting the suspension control parameters based on the gain coefficients; and controlling the suspension of the vehicle based on the adjusted suspension control parameters.

[0008] In some embodiments of the present application, the suspension control method provided in the embodiments of the present application may further include: storing a first correspondence between a plurality of sets of road condition information, driving parameters and suspension control parameters corresponding to the vehicle;

[0009] Then, based on the real-time road condition information and the target driving behavior, the suspension control parameters corresponding to the vehicle are determined, including: determining the target driving parameters of the vehicle when the driver drives on the road section ahead with the target driving behavior; searching for the first stored correspondence, and determining the suspension control parameters corresponding to the real-time road condition information and the target driving parameters as the suspension control parameters corresponding to the vehicle.

[0010] In some embodiments of the present application, the suspension control method provided in the embodiments of the present application may further include: storing a second correspondence between the plurality of sets of road condition information, driving behavior and gain coefficient corresponding to the driver;

[0011] Then, based on the real-time road condition information and the target driving behavior, determining the gain coefficient of the vehicle includes: searching for a stored second correspondence relationship, and determining the gain coefficient corresponding to the vehicle by using the gain coefficient corresponding to the real-time road condition information and the target driving behavior.

[0012] In some embodiments of the present application, the suspension control method provided by the embodiments of the present application may further include: if the suspension control parameters corresponding to the real-time road condition information and the target driving parameters are not found from the stored first correspondence, then searching whether the real-time road condition information exists in the stored first correspondence; if so, then determining the first driving parameter with the highest similarity to the target driving parameter from the driving parameters corresponding to the real-time road condition information, and determining the suspension control parameters corresponding to the real-time road condition information and the first driving parameter as the suspension control parameters corresponding to the vehicle; if not, then determining the similar road condition information with the highest similarity to the real-time road condition information from the stored first correspondence, and determining the second driving parameter with the highest similarity to the target driving parameter from the driving parameters corresponding to the similar road condition information, and determining the suspension control parameters corresponding to the similar road condition information and the second driving parameter as the suspension control parameters corresponding to the vehicle.

[0013] In some embodiments of the present application, the suspension control method provided by the embodiments of the present application may further include: if the suspension control parameters corresponding to the real-time road condition information and the target driving parameters are not found from the stored first correspondence relationship, a prompt is issued that the first correspondence relationship corresponding to the real-time road condition information and the target driving parameters is missing.

[0014] In some embodiments of the present application, the suspension control method provided by the embodiments of the present application may further include: if the gain coefficient corresponding to the real-time road condition information and the target driving behavior is not found from the stored second correspondence, then searching whether the real-time road condition information exists in the stored second correspondence; if so, then determining the similar driving behavior with the highest similarity to the target driving behavior from the driving behaviors corresponding to the real-time road condition information, and determining the suspension control parameters corresponding to the real-time road condition information and the similar driving behavior as the gain coefficient corresponding to the vehicle; if not, then determining the preset coefficient as the gain coefficient corresponding to the vehicle.

[0015] In some embodiments of the present application, the suspension control method provided by the embodiments of the present application may further include: if the gain coefficient corresponding to the real-time road condition information and the target driving behavior is not found from the stored second correspondence relationship, a prompt is issued that the second correspondence relationship corresponding to the real-time road condition information and the target driving behavior is missing.

[0016] In some embodiments of the present application, the suspension control method provided in the embodiments of the present application may further include: collecting historical suspension control parameters adopted for the suspension of the vehicle when the vehicle is driving with the same driving behavior under the same road condition information at multiple historical time points; for each driving behavior under each road condition information, performing: determining the driving parameters that the vehicle has when driving with the driving behavior under the road condition information; selecting from the collected historical suspension control parameters the suspension control parameters that have the best suspension control effect on the vehicle when the vehicle is driven with the driving behavior under the road condition information; and setting a first corresponding relationship between the road condition information, the determined driving parameters, and the selected suspension control parameters.

[0017] In some embodiments of the present application, the suspension control method provided in the embodiments of the present application may further include: collecting the gain coefficients used for the suspension control parameters of the vehicle when the driver of the vehicle is driving with the same driving behavior under the same road condition information at multiple historical time points; for each driving behavior under each road condition information, executing: from the collected gain coefficients, selecting the suspension control parameters that have the best suspension control effect on the vehicle when the vehicle is driven with the driving behavior under the road condition information; determining the gain coefficient used to adjust the suspension control parameters to obtain the best effect; and setting a second corresponding relationship between the road condition information, the driving behavior, and the determined gain coefficient.

[0018] In some embodiments of the present application, based on the real-time road condition information and the real-time driving behavior, predicting the driver's target driving behavior on the road section ahead includes: using a driving behavior prediction model corresponding to the driver to process the real-time road condition information and the real-time driving behavior, the driving behavior prediction model is trained based on multiple groups of historical data corresponding to the driver, each group of historical data includes road condition information, the driving behavior of the vehicle driver when the vehicle is about to reach the road section corresponding to the road condition information, and the driving behavior of the vehicle driver when the vehicle reaches the road section corresponding to the road condition information; the driving behavior obtained by processing the driving behavior prediction model is determined as the driver's target driving behavior on the road section ahead.

[0019] In some embodiments of the present application, adjusting the suspension control parameter based on the gain coefficient includes: determining the product of the gain coefficient and the suspension control parameter as the adjusted suspension control parameter.

[0020] In some embodiments of the present application, obtaining real-time traffic condition information of a road section ahead of a vehicle includes: using a collection device provided in the vehicle to collect target data of the road section ahead of the vehicle in real time; performing traffic condition analysis on the target data to obtain real-time traffic condition information of the road section ahead of the vehicle; wherein, if the collection device includes a camera, the target data includes an image, and if the collection device includes a radar, the target data includes radar data.

[0021] In some embodiments of the present application, obtaining the real-time driving behavior of a vehicle driver includes: based on driving behavior collection points set in the vehicle, collecting the real-time driving behavior of the driver of the vehicle, wherein the collection points are set in vehicle components in the vehicle that are related to the driver's driving behavior.

[0022] In some embodiments of the present application, the real-time traffic condition information includes at least one of the following: traffic signs on the road section, driving guidance signs on the road section, the maximum speed allowed on the road section, the minimum speed allowed on the road section, obstacles on the road section, whether there is a ramp entrance on the road section and the distance between the vehicle and the ramp entrance when there is a ramp entrance, whether there is an intersection on the road section and the distance between the vehicle and the intersection when there is an intersection, the congestion level of the road section, and the road surface condition of the road section.

[0023] In some embodiments of the present application, the real-time driving behavior includes at least one of the following: vehicle speed, vehicle acceleration, vehicle driving mode, vehicle load status, vehicle load members, vehicle turn signal status, vehicle throttle opening, vehicle brake pedal opening, and vehicle gear position.

[0024] In a second aspect, the present application provides a suspension control device, the suspension control device comprising:

[0025] An acquisition module is used to acquire real-time traffic information of the road section ahead of the vehicle and the real-time driving behavior of the vehicle driver;

[0026] A prediction module, configured to predict the target driving behavior of the driver on the road ahead based on the real-time road condition information and the real-time driving behavior;

[0027] A determination module, configured to determine the suspension control parameters and gain coefficients corresponding to the vehicle based on the real-time road condition information and the target driving behavior, wherein the gain coefficients are used to adjust the suspension control parameters to be suitable for the driver to drive the vehicle;

[0028] An adjustment module, configured to adjust the suspension control parameter based on the gain coefficient;

[0029] A control module is used to control the suspension of the vehicle based on the adjusted suspension control parameters.

[0030] In a third aspect, the present application provides a computer-readable storage medium, wherein the storage medium includes a stored program, wherein when the program is executed, the device where the storage medium is located is controlled to execute the suspension control method described in the first aspect.

[0031] In a fourth aspect, the present application provides an electronic device, comprising: a memory for storing a program; and a processor, coupled to the memory, for running the program to execute the suspension control method described in the first aspect.

[0032] The suspension control method and device provided by the present application obtain the real-time road condition information of the road section ahead of the vehicle and the real-time driving behavior of the vehicle driver during the use of the vehicle, and predict the target driving behavior of the driver on the road section ahead based on the real-time road condition information and the real-time driving behavior. Then, based on the real-time road condition information and the target driving behavior, the suspension control parameters corresponding to the vehicle and the gain coefficient used to adjust the suspension control parameters suitable for the driver to drive the vehicle are determined. Finally, the suspension control parameters are adjusted based on the gain coefficient, and the suspension of the vehicle is controlled based on the adjusted suspension control parameters. It can be seen that the scheme provided by the present application can determine the suspension control parameters suitable for the driver to drive the vehicle on the road ahead in advance through the real-time road condition information of the road section ahead that the vehicle is about to drive to and the target driving behavior of the vehicle driver driving the vehicle on the road ahead predicted in advance, and control the suspension in advance based on the suspension control parameters. In this way, when the driver actually drives the vehicle on the road ahead, the suspension has been working based on the applicable suspension control parameters, so it can timely and better buffer the impact force transmitted to the vehicle body by the road surface, thereby attenuating the vibration caused by the impact force, thereby improving the driving experience.

[0033] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0035] Figure 1 A flow chart of a suspension control method provided by an embodiment of the present application is shown;

[0036] Figure 2 A schematic structural diagram of a suspension control device provided by an embodiment of the present application is shown;

[0037] Figure 3 A schematic structural diagram of a suspension control device provided in another embodiment of the present application is shown. DETAILED DESCRIPTION

[0038] The exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although the exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided in order to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art.

[0039] Suspension is one of the important devices that determine the vehicle's dynamic performance. Suspension control can improve vehicle vibration to maintain vehicle smoothness and handling stability and improve the driving experience.

[0040] The existing suspension control method has the following two defects. First, the suspension control is performed when the vehicle is already driving on a road surface that causes the vehicle to vibrate, and the suspension control has a lag. Second, the suspension control is performed based on the suspension control parameters obtained from the empirical data pre-calibrated by the vehicle manufacturer for the vehicle. However, the driver has his own personalized driving behavior. Therefore, the suspension control performed by the suspension control parameters obtained based on the empirical data may not be suitable for the driver to drive the vehicle, and thus cannot achieve a good vibration attenuation effect. Based on the above two defects, it can be seen that the existing suspension control method cannot control the suspension in advance using the suspension control parameters suitable for the driver, so the suspension control effect is poor and it is difficult to bring a better driving experience.

[0041] After research, it was found that if the road condition information of the road ahead that the vehicle is about to drive to is known in advance, and the driving behavior of the vehicle driver on the road ahead is predicted in advance, then the suspension control parameters suitable for the driver to drive the vehicle on the road ahead can be determined in advance based on the road condition information and driving behavior, and the suspension can be controlled in advance based on the suspension control parameters. In this way, when the driver actually drives the vehicle on the road ahead, the suspension is already working based on the applicable suspension control parameters, so it can better buffer the impact force transmitted to the vehicle body by the road surface and attenuate the vibration caused by the impact force, thereby improving the driving experience.

[0042] Based on the above findings, the embodiment of the present application specifically provides a technical solution for suspension control, specifically: obtaining real-time traffic information of the road section ahead of the vehicle and the real-time driving behavior of the vehicle driver. Based on the real-time traffic information and the real-time driving behavior, predict the driver's target driving behavior on the road section ahead. Based on the real-time traffic information and the target driving behavior, determine the suspension control parameters and gain coefficients corresponding to the vehicle, and the gain coefficients are used to adjust the suspension control parameters to be suitable for the driver to drive the vehicle. Based on the gain coefficients, adjust the suspension control parameters. Based on the adjusted suspension control parameters, control the suspension of the vehicle.

[0043] The suspension control technical solution provided in the embodiment of the present application can be applied to any vehicle to control the suspension in advance using suspension control parameters suitable for the driver, thereby improving the driving experience of the vehicle. This embodiment does not limit the type of vehicle. For example, the type of vehicle may include but is not limited to the following types: fuel vehicles, hybrid vehicles, pure electric vehicles, fuel cell vehicles, alcohol ether fuel vehicles, and natural gas vehicles.

[0044] Based on the above-mentioned technical solution of suspension control, the embodiment of the present application specifically provides a suspension control method and device. The suspension control method and device provided in this embodiment are specifically described below.

[0045] like Figure 1 As shown, the embodiment of the present application provides a suspension control method, which may include the following steps 101 to 104:

[0046] 101. Obtain real-time traffic information of the road section ahead of the vehicle and the real-time driving behavior of the vehicle driver.

[0047] In this embodiment, real-time traffic condition information of the road section ahead of the vehicle and the real-time driving behavior of the vehicle driver are obtained to follow the movement of the vehicle. Based on the real-time traffic condition information and the real-time driving behavior, the suspension is controlled in advance using suspension control parameters suitable for the driver to improve the driving experience during vehicle travel.

[0048] The specific process of obtaining real-time traffic information of the road section ahead of the vehicle may include the following steps: using a collection device installed in the vehicle to collect target data of the road section ahead of the vehicle in real time; performing traffic analysis on the target data to obtain real-time traffic information of the road section ahead of the vehicle.

[0049] The road section ahead is the road section that the vehicle has not yet traveled to and will travel to. Real-time road condition information is used to describe the road conditions in the road section ahead that affect the vibration of the vehicle. Real-time road condition information is obtained based on the target data of the road section ahead of the vehicle collected in real time by the acquisition device. The type of target data is related to the type of the acquisition device. Specifically, the acquisition device may include at least one of the following: a camera and a radar. Therefore, when the acquisition device includes a camera, the target data includes an image. When the acquisition device includes a radar, the target data includes radar data.

[0050] In this embodiment, a preset road condition analysis method can be used to perform road condition analysis on the target data to obtain real-time road condition information of the road section ahead of the vehicle. When the target data includes an image, the road condition analysis method includes a road condition analysis method based on image recognition. When the target data includes radar data, the road condition analysis method includes a road condition analysis method based on radar data recognition.

[0051] The acquired real-time traffic information can be set based on business needs, which is not limited in this embodiment. For example, the real-time traffic information may include but is not limited to at least one of the following: traffic signs on the road section, driving guidance signs on the road section, the maximum speed allowed on the road section, the minimum speed allowed on the road section, obstacles on the road section, whether there is a ramp entrance on the road section and the distance between the vehicle and the ramp entrance when there is a ramp entrance, whether there is an intersection on the road section and the distance between the vehicle and the intersection when there is an intersection, the congestion level of the road section, and the road surface condition of the road section.

[0052] The specific process of obtaining the real-time driving behavior of the vehicle driver may include the following steps: based on the driving behavior collection points set in the vehicle, collecting the real-time driving behavior of the vehicle driver, the collection points are set in the vehicle components related to the driver's driving behavior.

[0053] Real-time driving behavior is the driving behavior of the driver on the current road section. Real-time driving behavior can be set based on business needs, and this embodiment does not limit this. Exemplarily, real-time driving behavior may include but is not limited to at least one of the following: vehicle speed, vehicle acceleration, vehicle driving mode, vehicle load status, vehicle turn signal status, vehicle throttle opening, vehicle brake pedal opening, and vehicle gear position. Among them, the vehicle load status reflects the displacement of the wheel in the vertical direction and the displacement of the vehicle body in the vertical direction.

[0054] 102. Based on real-time traffic information and real-time driving behavior, predict the driver's target driving behavior on the road ahead.

[0055] Each driver has his own personalized driving behavior when driving a vehicle, and the driver's personalized driving behavior, together with the road condition information, is an important factor causing the vehicle vibration. Therefore, in order to realize the use of suspension control parameters suitable for the driver in advance to control the suspension so as to more effectively attenuate the vehicle vibration after the vehicle drives to the road ahead, this embodiment predicts the driver's target driving behavior on the road ahead based on the real-time road condition information and the real-time driving behavior. The target driving behavior predicts the driving behavior that the driver may have when driving the vehicle to the road ahead.

[0056] Based on real-time traffic information and real-time driving behavior, the specific process of predicting the driver's target driving behavior on the road ahead may include the following steps: using a driving behavior prediction model corresponding to the driver to process the real-time traffic information and real-time driving behavior; and determining the driving behavior obtained by the driving behavior prediction model as the driver's target driving behavior on the road ahead.

[0057] Considering that different drivers have different personalized driving behaviors, in order to accurately predict the driver's target driving behavior on the road section ahead, the driving behavior prediction model corresponding to the driver is used to achieve the prediction of the target driving behavior. The driving behavior prediction model is trained based on multiple sets of historical data corresponding to the driver, and each set of historical data includes road condition information, the driving behavior of the vehicle driver when the vehicle is about to reach the road section corresponding to the road condition information, and the driving behavior of the vehicle driver when the vehicle reaches the road section corresponding to the road condition information. It should be noted that in order to continuously improve the prediction effect of the driving behavior prediction model, the road condition information, the driving behavior of the vehicle driver when the vehicle is about to reach the road section corresponding to the road condition information, and the driving behavior of the vehicle driver when the vehicle reaches the road section corresponding to the road condition information can be continuously collected, and update data for iteratively updating the driving behavior prediction model is formed, so as to continuously adjust the driving behavior prediction model using the updated data.

[0058] Furthermore, this embodiment takes into account that the owner of the vehicle (i.e., the default driver) may lend the vehicle to another person (i.e., a new driver) for use. When the vehicle is lent to the new driver for use, there may not be a driving behavior prediction model corresponding to the new driver. In this case, in order to achieve the purpose of controlling the suspension in advance using suspension control parameters suitable for the driver, the attribute information of the new driver may be obtained (for example, the attribute information may include but is not limited to at least one of the following: gender, age, driving experience, etc.), and then the driving behavior prediction model corresponding to the attribute information is obtained from a general driving behavior prediction model library, and the target driving behavior is predicted by the obtained driving behavior prediction model.

[0059] 103. Based on the real-time road condition information and the target driving behavior, the suspension control parameters and gain coefficients corresponding to the vehicle are determined. The gain coefficients are used to adjust the suspension control parameters to be suitable for the driver to drive the vehicle.

[0060] The present embodiment takes into account the driver's personalized driving behavior, which is an important factor causing vehicle vibration together with road condition information. Therefore, in order to more effectively control the suspension in advance using suspension control parameters suitable for the driver, the present embodiment determines the suspension control parameters and gain coefficients corresponding to the vehicle based on real-time road condition information and target driving behavior.

[0061] In order to accurately and quickly determine the suspension control parameters corresponding to the vehicle, the suspension control method provided in this embodiment may also include the following steps: storing a first correspondence between multiple sets of road condition information, driving parameters and suspension control parameters corresponding to the vehicle, so as to more accurately and quickly determine the suspension control parameters corresponding to the vehicle.

[0062] On the premise of the stored first correspondence, the specific process of determining the suspension control parameters corresponding to the vehicle based on the real-time road condition information and the target driving behavior may include the following steps: determining the target driving parameters of the vehicle when the driver drives on the road ahead with the target driving behavior; searching the stored first correspondence, and determining the suspension control parameters corresponding to the real-time road condition information and the target driving parameters as the suspension control parameters corresponding to the vehicle.

[0063] When the driver drives the vehicle with a certain driving behavior, the vehicle will inevitably drive with the driving parameters corresponding to the driving behavior. Therefore, when determining the suspension control parameters corresponding to the vehicle, it is first necessary to determine the target driving parameters of the vehicle when the driver drives on the road ahead with the target driving behavior. The target driving parameters can be determined using a preset driving parameter determination model, which is trained based on a large number of driving behaviors and driving parameters corresponding to the driving behaviors. Driving parameters may include, but are not limited to: vehicle speed, acceleration, displacement of the wheels in the vertical direction, and displacement of the vehicle body in the vertical direction.

[0064] After the target driving parameters are determined, the stored first corresponding relationship is searched, and the suspension control parameters corresponding to the real-time road condition information and the target driving parameters in the first corresponding relationship are determined as the suspension control parameters corresponding to the vehicle.

[0065] For example, the real-time traffic information includes that the road surface state of the road section is that there is a speed bump on the road surface 50 meters away from the vehicle. The target driving parameter includes that the vehicle is driving at a speed of 40 km / h in a sports driving mode. After searching the stored corresponding relationship, the suspension control parameters corresponding to the real-time traffic information and the target driving parameter are determined as the suspension control parameters corresponding to the vehicle.

[0066] In some embodiments, further considering that in actual applications, there may be a situation where the suspension control parameters corresponding to the real-time road condition information and the target driving parameters cannot be found from the stored first correspondence relationship, in order to handle this situation, the suspension control method provided in this embodiment may also include two handling methods:

[0067] One is that in order to be able to determine the suspension control parameters corresponding to the vehicle in this case, the suspension control method provided in this embodiment may also include the following steps: if the suspension control parameters corresponding to the real-time road condition information and the target driving parameters are not found from the stored first correspondence, then search whether the real-time road condition information exists in the stored first correspondence; if so, then determine the first driving parameter with the highest similarity to the target driving parameter from the driving parameters corresponding to the real-time road condition information, and determine the suspension control parameters corresponding to the real-time road condition information and the first driving parameter as the suspension control parameters corresponding to the vehicle; if not, then determine the similar road condition information with the highest similarity to the real-time road condition information from the stored first correspondence, and determine the second driving parameter with the highest similarity to the target driving parameter from the driving parameters corresponding to the similar road condition information, and determine the suspension control parameters corresponding to the similar road condition information and the second driving parameter as the suspension control parameters corresponding to the vehicle.

[0068] Suspension control is usually performed during vehicle use. If suspension control is interrupted because suspension control parameters corresponding to real-time road condition information and target driving parameters are not found in the stored first correspondence, it is bound to bring a bad driving experience to the occupants. Therefore, in order to avoid suspension control interruption during vehicle use, if suspension control parameters corresponding to real-time road condition information and target driving parameters are not found in the stored first correspondence, it is checked whether there is real-time road condition information in the stored first correspondence.

[0069] If it is found that real-time road condition information exists in the stored first corresponding relationship, it means that the first corresponding relationship contains suspension control parameters suitable for the road section with real-time road condition information, but does not contain suspension control parameters suitable for the target driving parameters. Therefore, in order to ensure that the suspension can be controlled in advance with suspension control parameters suitable for the driver, the first driving parameter with the highest similarity to the target driving parameter is determined from the driving parameters corresponding to the real-time road condition information, and the suspension control parameters corresponding to the real-time road condition information and the first driving parameter are determined as the suspension control parameters corresponding to the vehicle, so as to use suspension control parameters that are as close as possible to the driver's personalized driving behavior for suspension control.

[0070] If it is found that there is no real-time road condition information in the stored first correspondence, it means that there are no suspension control parameters suitable for the real-time road condition information and the target driving parameters in the first correspondence. Therefore, in order to smoothly perform suspension control, similar road condition information with the highest similarity to the real-time road condition information is determined from the stored first correspondence, and the second driving parameter with the highest similarity to the target driving parameter is determined from the driving parameters corresponding to the similar road condition information. The suspension control parameters corresponding to the similar road condition information and the second driving parameters are determined as the suspension control parameters corresponding to the vehicle, so as to use suspension control parameters that are as close to the road section ahead and the driver's personalized driving behavior as possible for suspension control.

[0071] Another is to issue a prompt that the first corresponding relationship between the real-time road condition information and the target driving parameters is missing, so as to inform the user through the prompt that the first corresponding relationship between the real-time road condition information and the target driving parameters needs to be added for subsequent suspension control.

[0072] The above two disposal methods can be used alone or in combination, which is not limited in this embodiment and can be used flexibly based on business needs.

[0073] In some embodiments, the suspension control method provided by the embodiments of the present application may further include a setting process of a first corresponding relationship, and the setting process may specifically include the following steps: collecting historical suspension control parameters adopted for the vehicle's suspension when the vehicle is driving with the same driving behavior under the same road condition information at multiple historical time points; for each driving behavior under each road condition information, executing: determining the driving parameters that the vehicle has when driving with the driving behavior under the road condition information; selecting, from the collected historical suspension control parameters, the suspension control parameters that have the best suspension control effect on the vehicle when the vehicle is driven with the driving behavior under the road condition information; and setting a first corresponding relationship between the road condition information, the determined driving parameters, and the selected suspension control parameters.

[0074] In order to make the first correspondence more applicable to the vehicle, after the vehicle leaves the factory or is purchased, multiple road condition information and multiple driving behaviors are determined, and historical suspension control parameters used for the vehicle's suspension when the vehicle is driving with the same driving behavior under the same road condition information at multiple historical time points are collected. Then, for each driving behavior of each road condition information, the following steps are performed: the driving parameters of the vehicle when driving with the driving behavior under the road condition information are determined, and the suspension control parameters with the best suspension control effect on the vehicle when driving the vehicle with the driving behavior under the road condition information are selected from the collected historical suspension control parameters. The suspension control parameters with the best effect can best attenuate vehicle vibration, ensure smooth operation of the vehicle, and improve the driving experience, and then set the first correspondence between the road condition information, the determined driving parameters, and the selected suspension control parameters, so as to complete the setting of the first correspondence between the driving parameters corresponding to the road condition information and the driving behavior.

[0075] It should be noted that, considering that the performance of the vehicle may change as the vehicle is used, a preset period may be used to periodically update the first corresponding relationship to ensure that the determined suspension control parameters are more suitable for the vehicle.

[0076] In order to obtain suspension control parameters that are more suitable for the driver's personalized driving, this embodiment also needs to determine the gain coefficient corresponding to the vehicle based on real-time road condition information and target driving behavior. The gain coefficient is used to adjust the suspension control parameters to be suitable for the driver to drive the vehicle.

[0077] In order to accurately and quickly determine the gain coefficient corresponding to the vehicle, the suspension control method provided in this embodiment may also include the following steps: storing a second correspondence between multiple sets of road condition information, driving behavior and gain coefficient corresponding to the driver, so as to more accurately and quickly determine the gain coefficient corresponding to the vehicle, and adjust the determined suspension control parameters to be suitable for the driver to drive the vehicle.

[0078] On the premise of the stored second correspondence, the specific process of determining the gain coefficient corresponding to the vehicle based on the real-time road condition information and the target driving behavior may include the following steps: searching the stored second correspondence, and determining the gain coefficient corresponding to the vehicle using the gain coefficient corresponding to the real-time road condition information and the target driving behavior.

[0079] The stored second correspondence is searched, and the gain coefficient corresponding to the real-time road condition information and the target driving behavior in the second correspondence is determined as the gain coefficient corresponding to the vehicle. The determined gain coefficient can adjust the suspension control parameters determined based on the real-time road condition information and the target driving behavior to be more suitable for the driver of the vehicle to drive the vehicle.

[0080] For example, the real-time traffic information of the road ahead of the vehicle includes "the road surface state of the road is that there is a speed bump on the road ahead" and the real-time driving behavior of the vehicle driver includes "the current vehicle speed is 60 km / h". Based on the real-time traffic information and the real-time driving behavior, it is predicted that the driver's target driving behavior on the road ahead includes "decelerating by 20% on the basis of 60 km / h to a speed of 48 km / h". After searching the stored corresponding relationship, the gain coefficient corresponding to "there is a speed bump on the road ahead" and "decelerating by 20% on the basis of 60 km / h to a speed of 48 km / h" is determined as the gain coefficient corresponding to the vehicle.

[0081] Exemplarily, the real-time traffic condition information of the road section ahead of the vehicle is obtained as "the road surface state of the road section is that there is a speed bump on the road section ahead" and the real-time driving behavior of the vehicle driver includes "the number of passengers carried by the vehicle is multiple, and there are family passengers who are not the driver, and the current vehicle speed is 60km / h". Based on the real-time traffic condition information and real-time driving behavior, it is predicted that the driver's target driving behavior on the road section ahead includes "decelerating 50% on the basis of 60km / h to a speed of 39km / h". After searching the stored corresponding relationship, the gain coefficient corresponding to "there is a speed bump on the road section ahead" and "decelerating 50% on the basis of 60km / h to a speed of 39km / h" is determined as the gain coefficient corresponding to the vehicle. The determined gain coefficient can reduce the suspension control parameters to achieve a softer attenuation vibration based on the adjusted suspension control parameters, so that the driver and passengers are more comfortable. It should be noted that the number of passengers on the vehicle can be predicted by the suspension height sensor.

[0082] Exemplarily, the real-time traffic condition information of the road section ahead of the vehicle is obtained as "the road surface state of the road section is that there is a speed bump on the road section ahead" and the real-time driving behavior of the vehicle driver includes "the vehicle driving mode is the sports mode, and the current vehicle speed is 60km / h". Based on the real-time traffic condition information and the real-time driving behavior, it is predicted that the driver's target driving behavior on the road section ahead includes "no deceleration, the vehicle speed is maintained at 60km / h". After searching the stored corresponding relationship, the gain coefficient corresponding to "there is a speed bump on the road section ahead" and "no deceleration, the vehicle speed is maintained at 60km / h" is determined as the gain coefficient corresponding to the vehicle. The determined gain coefficient can increase the suspension control parameter to attenuate the vibration more quickly based on the adjusted suspension control parameter.

[0083] For example, the real-time traffic condition information of the road section ahead of the vehicle is obtained as "the road surface state of the road section is that the road section ahead is flat" and the real-time driving behavior of the vehicle driver includes "the current vehicle speed is 60 km / h". Based on the real-time traffic condition information and the real-time driving behavior, it is predicted that the driver's target driving behavior on the road section ahead includes "rapid acceleration to 80 km / h". After searching the stored corresponding relationship, the gain coefficient corresponding to "the road section ahead is flat" and "rapid acceleration to 80 km / h" is determined as the gain coefficient corresponding to the vehicle. The determined gain coefficient can increase the suspension control parameter to attenuate the vibration more quickly based on the adjusted suspension control parameter.

[0084] Exemplarily, the real-time traffic condition information of the road section ahead of the vehicle is obtained as "the road surface state of the road section is that the road section ahead is flat" and the real-time driving behavior of the vehicle driver includes "the current vehicle speed is 60km / h". Based on the real-time traffic condition information and the real-time driving behavior, it is predicted that the driver's target driving behavior on the road section ahead includes "gentle acceleration to 80km / h". After searching the stored corresponding relationship, the gain coefficient corresponding to "the road section ahead is flat" and "gentle acceleration to 80km / h" is determined as the gain coefficient corresponding to the vehicle. The determined gain coefficient can reduce the suspension control parameter to achieve a softer attenuation vibration based on the adjusted suspension control parameter, so that the driver and passengers are more comfortable.

[0085] For example, the real-time traffic information of the road ahead of the vehicle is obtained as "the road surface state of the road is that there is an obstacle in the road ahead" and the real-time driving behavior of the vehicle driver includes "the current vehicle speed is 60km / h". Based on the real-time traffic information and the real-time driving behavior, it is predicted that the driver's target driving behavior in the road ahead includes "sudden deceleration to 30km / h". After searching the stored corresponding relationship, the gain coefficient corresponding to "there is an obstacle in the road ahead" and "sudden deceleration to 30km / h" is determined as the gain coefficient corresponding to the vehicle. The determined gain coefficient can increase the suspension control parameter to attenuate the vibration more quickly based on the adjusted suspension control parameter.

[0086] For example, the real-time traffic information of the road ahead of the vehicle is obtained as "the traffic sign of the road is a red light" and the real-time driving behavior of the vehicle driver includes "the vehicle speed is 0 km / h and the vehicle is in a stopped state". Based on the real-time traffic information and the real-time driving behavior, it is predicted that the driver's target driving behavior on the road ahead includes "starting at a speed of 30 km / h". After searching the stored corresponding relationship, the gain coefficient corresponding to "there is an obstacle on the road ahead" and "suddenly decelerate to 30 km / h" is determined as the gain coefficient corresponding to the vehicle.

[0087] For example, the real-time traffic information of the road section ahead of the vehicle is obtained as "there is a ramp confluence on the road section and the distance between the vehicle and the ramp confluence is 20 meters" and the real-time driving behavior of the vehicle driver includes "the vehicle turn signal is on for right turn, the vehicle speed is 40 km / h, and the vehicle is in a parked state". Based on the real-time traffic information and the real-time driving behavior, it is predicted that the driver's target driving behavior on the road section ahead includes "accelerating to 50 km / h in the right direction". After searching the stored corresponding relationship, the gain coefficients corresponding to "there is a ramp confluence on the road section and the distance between the vehicle and the ramp confluence is 20 meters" and "accelerating to 50 km / h in the right direction" are determined as the gain coefficients corresponding to the vehicle.

[0088] In some embodiments, further considering that in actual applications, there may be a situation where the gain coefficient corresponding to the real-time road condition information and the target driving behavior cannot be found from the stored second correspondence relationship. In order to handle this situation, the suspension control method provided in this embodiment may also include two handling methods:

[0089] First, in order to be able to determine the gain coefficient corresponding to the vehicle in this case, the suspension control method provided in this embodiment may also include the following steps: if the gain coefficient corresponding to the real-time road condition information and the target driving behavior is not found from the stored second corresponding relationship, then search whether there is real-time road condition information in the stored second corresponding relationship; if so, determine the similar driving behavior with the highest similarity to the target driving behavior from the driving behaviors corresponding to the real-time road condition information, and determine the suspension control parameters corresponding to the real-time road condition information and the similar driving behavior as the gain coefficient corresponding to the vehicle; if not, determine the preset coefficient as the gain coefficient corresponding to the vehicle.

[0090] Suspension control is usually performed during vehicle use. If the gain coefficient corresponding to the real-time road condition information and the target driving behavior is not found in the stored second correspondence, the suspension control parameters of the vehicle cannot be adjusted to be suitable for the driver to drive the vehicle, which may bring a bad driving experience to the occupants. Therefore, if the gain coefficient corresponding to the real-time road condition information and the target driving behavior is not found in the stored second correspondence, it is checked whether there is real-time road condition information in the stored second correspondence.

[0091] If real-time road condition information is stored in the stored second correspondence, it means that the second correspondence contains a gain coefficient applicable to the road section with real-time road condition information, but does not contain a gain coefficient applicable to the target driving behavior. Therefore, a similar driving behavior with the highest similarity to the target driving behavior is determined from the driving behaviors corresponding to the real-time road condition information, and the suspension control parameters corresponding to the real-time road condition information and the similar driving behavior are determined as the gain coefficients corresponding to the vehicle, so as to use gain coefficients that are as close to the driver's personalized driving behavior as possible to adjust the suspension control parameters.

[0092] If it is found that there is no real-time road condition information in the stored second corresponding relationship, it means that there is no suspension control parameter suitable for the real-time road condition information and the target driving behavior in the second corresponding relationship, so the preset coefficient is determined as the gain coefficient corresponding to the vehicle, and the preset coefficient is a coefficient calibrated based on the driving behavior of different drivers, which can adjust the suspension control parameter to be as suitable as possible for the driver to drive the vehicle. The size of the preset coefficient can be set based on business needs, and this embodiment does not make specific limitations on this.

[0093] The second method is to issue a prompt that the second corresponding relationship between the real-time traffic information and the target driving behavior is missing, so as to inform the user through the prompt that the second corresponding relationship between the real-time traffic information and the target driving behavior needs to be added for subsequent suspension control.

[0094] The above two disposal methods can be used alone or in combination, which is not limited in this embodiment and can be used flexibly based on business needs.

[0095] In some embodiments, the suspension control method provided by the embodiments of the present application may further include a setting process of a second corresponding relationship, which may specifically include the following steps: collecting the gain coefficients used for the suspension control parameters of the vehicle when the driver of the vehicle is driving with the same driving behavior under the same road condition information at multiple historical time points; for each driving behavior under each road condition information, executing: from the collected gain coefficients, selecting the suspension control parameters that have the best suspension control effect on the vehicle when the vehicle is driven with the driving behavior under the road condition information; determining the gain coefficient used for the suspension control parameter that is adjusted to obtain the best effect; setting a second corresponding relationship between the road condition information, the driving behavior, and the determined gain coefficient.

[0096] In order to make the second correspondence more suitable for the driver of the vehicle, after the vehicle is purchased, different road condition information and different driving behaviors related to the driver are collected, and the gain coefficients of the suspension control parameters of the vehicle adopted by the driver of the vehicle when driving with the same driving behavior under the same road condition information at multiple historical time points are collected. For each driving behavior under each road condition information, the following steps are performed: from the collected gain coefficients, the suspension control parameter with the best suspension control effect on the vehicle when driving the vehicle with the driving behavior under the road condition information is selected; the gain coefficient used for the suspension control parameter with the best effect is determined, and the gain coefficient with the best effect can adjust the suspension control parameter to be suitable for the driver to drive the vehicle, thereby better attenuating the vibration of the vehicle, ensuring the smooth operation of the vehicle, and improving the driving experience, and then setting the second correspondence between the road condition information, the driving behavior and the determined gain coefficient, so as to complete the setting of the second correspondence between the gain coefficient corresponding to the road condition information and the driving behavior.

[0097] It should be noted that, considering that the driving behavior of the vehicle driver will change with the use of the vehicle, a preset period can be used to periodically update the second corresponding relationship to ensure that the determined gain coefficient is more suitable for adjusting the suspension control parameters for the driver to drive the vehicle.

[0098] 104. Based on the gain coefficient, adjust the suspension control parameters.

[0099] After determining the gain coefficient and suspension control parameters corresponding to the vehicle, the suspension control parameters can be adjusted based on the gain coefficient to obtain a setting that is more suitable for the driver to drive the vehicle in the road section ahead corresponding to the real-time road condition information, thereby realizing the use of the adjusted suspension control parameters to control the suspension in advance.

[0100] Based on the gain coefficient, the specific process of adjusting the suspension control parameter may include the following steps: determining the product between the gain coefficient and the suspension control parameter as the adjusted suspension control parameter. That is, the suspension control parameter is appropriately amplified or reduced by the gain coefficient so that the suspension control parameter is more suitable for the driver to drive the vehicle in the front section corresponding to the real-time road condition information.

[0101] The suspension control parameters may be based on business needs, and are not specifically limited in this embodiment. For example, if the suspension is a shock absorber or an air spring, the suspension control parameters may include but are not limited to damping force, stiffness, and the like.

[0102] 105. Based on the adjusted suspension control parameters, the suspension of the vehicle is controlled.

[0103] The adjusted suspension control parameters are the suspension control parameters that are suitable for the driver to drive the vehicle on the road section ahead corresponding to the real-time road condition information. Therefore, the suspension control parameters can be used in advance to control the suspension, so that when the vehicle drives to the road ahead corresponding to the real-time road condition information, the impact force of the road on the vehicle can be timely attenuated to improve the driving experience.

[0104] Based on the adjusted suspension control parameters, the specific process of controlling the vehicle's suspension may include the following steps: transmitting execution data such as current and level corresponding to the adjusted suspension control parameters to the actuator of the suspension, so that the actuator adjusts the suspension based on the execution data to attenuate the impact of the road on the vehicle.

[0105] The suspension control method provided by the embodiment of the present application obtains the real-time traffic information of the road section ahead of the vehicle and the real-time driving behavior of the vehicle driver during the use of the vehicle, and predicts the target driving behavior of the driver on the road section ahead based on the real-time traffic information and the real-time driving behavior. Then, based on the real-time traffic information and the target driving behavior, the suspension control parameters corresponding to the vehicle and the gain coefficient used to adjust the suspension control parameters suitable for the driver to drive the vehicle are determined. Finally, the suspension control parameters are adjusted based on the gain coefficient, and the suspension of the vehicle is controlled based on the adjusted suspension control parameters. It can be seen that the scheme provided by the embodiment of the present application can determine the suspension control parameters suitable for the driver to drive the vehicle on the road ahead in advance through the real-time traffic information of the road section ahead that the vehicle is about to drive to and the target driving behavior of the vehicle driver driving the vehicle on the road ahead predicted in advance, and control the suspension in advance based on the suspension control parameters. In this way, when the driver actually drives the vehicle on the road ahead, the suspension has been working based on the applicable suspension control parameters, so it can timely and better buffer the impact force transmitted to the vehicle body by the road surface, thereby attenuating the vibration caused by the impact force, thereby improving the driving experience.

[0106] Furthermore, an embodiment of the present application also provides a suspension control device, such as Figure 2 As shown, the suspension control device includes:

[0107] An acquisition module 21 is used to acquire real-time traffic information of the road section ahead of the vehicle and the real-time driving behavior of the vehicle driver;

[0108] A prediction module 22, configured to predict a target driving behavior of the driver on the road ahead based on the real-time road condition information and the real-time driving behavior;

[0109] A determination module 23, configured to determine the suspension control parameters and gain coefficients corresponding to the vehicle based on the real-time road condition information and the target driving behavior, wherein the gain coefficients are used to adjust the suspension control parameters to be suitable for the driver to drive the vehicle;

[0110] An adjustment module 24, configured to adjust the suspension control parameter based on the gain coefficient;

[0111] The control module 25 is used to control the suspension of the vehicle based on the adjusted suspension control parameters.

[0112] The suspension control device provided by the embodiment of the present application obtains the real-time traffic information of the road section ahead of the vehicle and the real-time driving behavior of the vehicle driver during the use of the vehicle, and predicts the target driving behavior of the driver on the road section ahead based on the real-time traffic information and the real-time driving behavior. Then, based on the real-time traffic information and the target driving behavior, the suspension control parameters corresponding to the vehicle and the gain coefficient for adjusting the suspension control parameters applicable to the driver driving the vehicle are determined. Finally, the suspension control parameters are adjusted based on the gain coefficient, and the suspension of the vehicle is controlled based on the adjusted suspension control parameters. It can be seen that the scheme provided by the embodiment of the present application can determine the suspension control parameters applicable to the driver driving the vehicle on the road ahead in advance through the real-time traffic information of the road section ahead that the vehicle is about to drive to and the target driving behavior of the vehicle driver driving the vehicle on the road ahead predicted in advance, and control the suspension in advance based on the suspension control parameters. In this way, when the driver actually drives the vehicle on the road ahead, the suspension has been working based on the applicable suspension control parameters, so it can timely and better buffer the impact force transmitted to the vehicle body by the road surface, thereby attenuating the vibration caused by the impact force, thereby improving the driving experience.

[0113] In some embodiments of the present application, Figure 3 As shown, the suspension control device provided in this embodiment may further include: a first storage module 26, used to store a first correspondence between a plurality of sets of road condition information, driving parameters and suspension control parameters corresponding to the vehicle;

[0114] Then, the determination module 23 includes: a first determination unit 231, used to determine the target driving parameters of the vehicle when the driver drives on the front road section with the target driving behavior; searching the first corresponding relationship stored in the first storage module 26, and determining the suspension control parameters corresponding to the real-time road condition information and the target driving parameters as the suspension control parameters corresponding to the vehicle.

[0115] In some embodiments of the present application, Figure 3 As shown, the suspension control device provided in this embodiment may further include: a second storage module 27, used to store a second corresponding relationship between the plurality of sets of road condition information, driving behavior and gain coefficient corresponding to the driver;

[0116] Then, the determination module 23 includes: a second determination unit 232, which is used to search the second corresponding relationship stored in the second storage module 27, and determine the gain coefficient corresponding to the vehicle by the gain coefficient corresponding to the real-time road condition information and the target driving behavior.

[0117] In some embodiments of the present application, Figure 3 As shown, the determination module 23 also includes:

[0118] The third determination unit 233 is used for searching whether the real-time road condition information and the target driving parameter exist in the stored first correspondence if the first determination unit 231 fails to find the suspension control parameter corresponding to the real-time road condition information and the target driving parameter from the stored first correspondence; if so, determining the first driving parameter with the highest similarity to the target driving parameter from the driving parameters corresponding to the real-time road condition information, and determining the suspension control parameter corresponding to the real-time road condition information and the first driving parameter as the suspension control parameter corresponding to the vehicle; if not, determining similar road condition information with the highest similarity to the real-time road condition information from the stored first correspondence, and determining the second driving parameter with the highest similarity to the target driving parameter from the driving parameters corresponding to the similar road condition information, and determining the suspension control parameter corresponding to the similar road condition information and the second driving parameter as the suspension control parameter corresponding to the vehicle.

[0119] In some embodiments of the present application, Figure 3 As shown, the determination module 23 also includes:

[0120] The first prompt unit 234 is used to issue a prompt of the lack of the first corresponding relationship between the real-time road condition information and the target driving parameter if the first determination unit 231 fails to find the suspension control parameters corresponding to the real-time road condition information and the target driving parameter from the stored first corresponding relationship.

[0121] In some embodiments of the present application, Figure 3 As shown, the determination module 23 also includes:

[0122] The fourth determination unit 235 is used to search whether the real-time road condition information and the target driving behavior exist in the stored second correspondence if the second determination unit 232 fails to find the gain coefficient corresponding to the real-time road condition information and the target driving behavior from the stored second correspondence; if so, determine the similar driving behavior with the highest similarity to the target driving behavior from the driving behaviors corresponding to the real-time road condition information; determine the suspension control parameters corresponding to the real-time road condition information and the similar driving behavior as the gain coefficient corresponding to the vehicle; if not, determine the preset coefficient as the gain coefficient corresponding to the vehicle.

[0123] In some embodiments of the present application, Figure 3 As shown, the determination module 23 also includes:

[0124] The second prompt unit 236 is used to issue a prompt of the lack of the second corresponding relationship between the real-time road condition information and the target driving behavior if the second determination unit 232 fails to find the gain coefficient corresponding to the real-time road condition information and the target driving behavior from the stored second corresponding relationship.

[0125] In some embodiments of the present application, Figure 3 As shown, the suspension control device provided in this embodiment may also include:

[0126] The first setting module 28 is used to collect historical suspension control parameters used for the suspension of the vehicle when the vehicle is driving with the same driving behavior under the same road condition information at multiple historical time points; for each driving behavior under each road condition information, the following are performed: determining the driving parameters of the vehicle when it is driving with the driving behavior under the road condition information; selecting from the collected historical suspension control parameters the suspension control parameters that have the best suspension control effect on the vehicle when the vehicle is driven with the driving behavior under the road condition information; and setting a first corresponding relationship among the road condition information, the determined driving parameters and the selected suspension control parameters.

[0127] In some embodiments of the present application, Figure 3 As shown, the suspension control device provided in this embodiment may also include:

[0128] The second setting module 29 is used to collect the gain coefficients of the suspension control parameters of the vehicle when the driver of the vehicle drives the vehicle with the same driving behavior under the same road condition information at multiple historical time points; for each driving behavior under each road condition information, the following are performed: from the collected gain coefficients, the suspension control parameters that have the best suspension control effect on the vehicle when the vehicle is driven with the driving behavior under the road condition information; the gain coefficients used to adjust the suspension control parameters to obtain the best effect are determined; and a second corresponding relationship between the road condition information, the driving behavior and the determined gain coefficients is set.

[0129] In some embodiments of the present application, Figure 3As shown, the prediction module 22 is specifically used to use the driving behavior prediction model corresponding to the driver to process the real-time road condition information and the real-time driving behavior, the driving behavior prediction model is trained based on multiple groups of historical data corresponding to the driver, each group of historical data includes road condition information, the driving behavior of the vehicle driver when the vehicle is about to reach the section corresponding to the road condition information, and the driving behavior of the vehicle driver when the vehicle reaches the section corresponding to the road condition information; the driving behavior obtained by processing the driving behavior prediction model is determined as the target driving behavior of the driver in the section ahead.

[0130] In some embodiments of the present application, Figure 3 As shown, the adjustment module 24 is specifically used to determine the product of the gain coefficient and the suspension control parameter as the adjusted suspension control parameter.

[0131] In some embodiments of the present application, Figure 3 As shown, the acquisition module 21 includes:

[0132] The first acquisition unit 211 is used to use the acquisition device set in the vehicle to collect target data of the road section ahead of the vehicle in real time; perform road condition analysis on the target data to obtain real-time road condition information of the road section ahead of the vehicle; wherein, if the acquisition device includes a camera, the target data includes an image; and if the acquisition device includes a radar, the target data includes radar data.

[0133] In some embodiments of the present application, Figure 3 As shown, the acquisition module 21 includes:

[0134] The second acquisition unit 212 is used to collect the real-time driving behavior of the driver of the vehicle based on the driving behavior collection points set in the vehicle, and the collection points are set in the vehicle components related to the driver's driving behavior in the vehicle.

[0135] In some embodiments of the present application, Figure 3 As shown, the real-time traffic condition information acquired by the acquisition module 21 includes at least one of the following: traffic signs on the road section, driving guidance signs on the road section, the maximum speed allowed on the road section, the minimum speed allowed on the road section, obstacles on the road section, whether there is a ramp entrance on the road section and the distance between the vehicle and the ramp entrance when there is a ramp entrance, whether there is an intersection on the road section and the distance between the vehicle and the intersection when there is an intersection, the congestion level of the road section, and the road surface condition of the road section.

[0136] In some embodiments of the present application, Figure 3As shown, the real-time driving behavior acquired by the acquisition module 21 includes at least one of the following: vehicle speed, vehicle acceleration, vehicle driving mode, vehicle load status, vehicle load members, vehicle turn signal status, vehicle throttle opening, vehicle brake pedal opening, and vehicle gear position.

[0137] In the suspension control device provided in the embodiment of the present application, the detailed explanations used in the operation of each functional module can be found in the corresponding detailed explanations of the above-mentioned suspension control method embodiment, which will not be repeated here.

[0138] Furthermore, an embodiment of the present application also provides a computer-readable storage medium, wherein the storage medium includes a stored program, wherein when the program is executed, the device where the storage medium is located is controlled to execute the above-mentioned suspension control method.

[0139] Furthermore, an embodiment of the present application also provides an electronic device, which includes: a memory for storing a program; and a processor, coupled to the memory, for running the program to execute the above-mentioned suspension control method.

[0140] 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.

[0141] It is understandable that the related features in the above methods and devices can be referenced to each other. In addition, the "first", "second" and the like in the above embodiments are used to distinguish the embodiments, but do not represent the advantages and disadvantages of the embodiments.

[0142] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0143] The algorithm and display provided herein are not inherently related to any particular computer, virtual system or other device. Various general purpose systems can also be used together with the teachings based on this. According to the above description, it is obvious to construct the structure required for this type of system. In addition, the application is not directed to any specific programming language either. It should be understood that various programming languages ​​can be utilized to realize the content of the application described herein, and the description of the above specific language is to disclose the preferred embodiment of the application.

[0144] In addition, the memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM, and the memory includes at least one memory chip.

[0145] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0146] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of the processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data cutover device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data cutover device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0147] These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data switching device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce a manufactured product including an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0148] These computer program instructions can also be loaded onto a computer or other programmable data transfer device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for executing on the computer or other programmable device to implement the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0149] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0150] The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0151] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0152] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0153] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0154] The above are only embodiments of the present application and are not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included within the scope of the claims of the present application.

Claims

1. A suspension control method, characterized in that: The method comprises: Obtain real-time traffic information of the road ahead of the vehicle and the real-time driving behavior of the vehicle driver; Based on the real-time traffic information and the real-time driving behavior, predicting the target driving behavior of the driver on the road ahead; Based on the real-time road condition information and the target driving behavior, determining a suspension control parameter and a gain coefficient corresponding to the vehicle, wherein the gain coefficient is used to adjust the suspension control parameter to be suitable for the driver to drive the vehicle; adjusting the suspension control parameter based on the gain coefficient; Based on the adjusted suspension control parameters, the suspension of the vehicle is controlled.

2. The method according to claim 1, characterized in that The method further includes: storing a first correspondence between a plurality of sets of road condition information, driving parameters and suspension control parameters corresponding to the vehicle; Then, based on the real-time road condition information and the target driving behavior, determining the suspension control parameters corresponding to the vehicle includes: determining the target driving parameters of the vehicle when the driver drives on the road section ahead with the target driving behavior; searching for a stored first corresponding relationship, and determining the suspension control parameters corresponding to the real-time road condition information and the target driving parameters as the suspension control parameters corresponding to the vehicle; and / or, The method further includes: storing a second correspondence between the plurality of sets of road condition information, driving behavior and gain coefficient corresponding to the driver; Then, based on the real-time road condition information and the target driving behavior, determining the gain coefficient of the vehicle includes: searching for a stored second correspondence, and determining the gain coefficient corresponding to the vehicle by using the gain coefficient corresponding to the real-time road condition information and the target driving behavior.

3. The method according to claim 2, characterized in that The method further comprises: If the suspension control parameters corresponding to the real-time road condition information and the target driving parameters are not found from the stored first corresponding relationship, then search whether the real-time road condition information exists in the stored first corresponding relationship; if so, determine the first driving parameter with the highest similarity to the target driving parameter from the driving parameters corresponding to the real-time road condition information, and determine the suspension control parameters corresponding to the real-time road condition information and the first driving parameter as the suspension control parameters corresponding to the vehicle; if not, determine similar road condition information with the highest similarity to the real-time road condition information from the stored first corresponding relationship, and determine the second driving parameter with the highest similarity to the target driving parameter from the driving parameters corresponding to the similar road condition information, and determine the suspension control parameters corresponding to the similar road condition information and the second driving parameter as the suspension control parameters corresponding to the vehicle; and / or, If the suspension control parameters corresponding to the real-time road condition information and the target driving parameters are not found from the stored first correspondence, a prompt is issued that the first correspondence between the real-time road condition information and the target driving parameters is missing.

4. The method according to claim 2, characterized in that: The method further comprises: If the gain coefficient corresponding to the real-time road condition information and the target driving behavior is not found in the stored second corresponding relationship, then the stored second corresponding relationship is searched for whether the real-time road condition information exists; if so, a similar driving behavior with the highest similarity to the target driving behavior is determined from the driving behaviors corresponding to the real-time road condition information, and the suspension control parameters corresponding to the real-time road condition information and the similar driving behaviors are determined as the gain coefficient corresponding to the vehicle; if not, a preset coefficient is determined as the gain coefficient corresponding to the vehicle; and / or, If the gain coefficient corresponding to the real-time traffic information and the target driving behavior is not found from the stored second correspondence, a prompt is issued indicating that the second correspondence between the real-time traffic information and the target driving behavior is missing.

5. The method according to claim 2, characterized in that: The method further includes: collecting historical suspension control parameters used for the suspension of the vehicle when the vehicle is driving with the same driving behavior under the same road condition information at multiple historical time points; performing, for each driving behavior under each road condition information: determining the driving parameters of the vehicle when the vehicle is driving with the driving behavior under the road condition information; selecting, from the collected historical suspension control parameters, the suspension control parameters that have the best suspension control effect on the vehicle when the vehicle is driven with the driving behavior under the road condition information; and setting a first corresponding relationship between the road condition information, the determined driving parameters, and the selected suspension control parameters; and / or, The method also includes: collecting gain coefficients of suspension control parameters of the vehicle when the driver of the vehicle drives the vehicle with the same driving behavior under the same road condition information at multiple historical time points; for each driving behavior under each road condition information, performing: selecting from the collected gain coefficients the suspension control parameters that have the best suspension control effect on the vehicle when the vehicle is driven with the driving behavior under the road condition information; determining the gain coefficient used to adjust the suspension control parameters to obtain the best effect; and setting a second corresponding relationship among the road condition information, the driving behavior, and the determined gain coefficient.

6. The method according to any one of claims 1 to 5, characterized in that Predicting the target driving behavior of the driver on the road ahead based on the real-time road condition information and the real-time driving behavior includes: The real-time road condition information and the real-time driving behavior are processed by using a driving behavior prediction model corresponding to the driver, wherein the driving behavior prediction model is trained based on multiple sets of historical data corresponding to the driver, each set of historical data including road condition information, the driving behavior of the driver of the vehicle when the vehicle is about to reach a road section corresponding to the road condition information, and the driving behavior of the driver of the vehicle when the vehicle reaches the road section corresponding to the road condition information; The driving behavior obtained by processing the driving behavior prediction model is determined as the target driving behavior of the driver on the road section ahead.

7. The method according to any one of claims 1 to 5, characterized in that Adjusting the suspension control parameter based on the gain coefficient includes: determining the product of the gain coefficient and the suspension control parameter as the adjusted suspension control parameter; and / or, Acquiring real-time traffic information of a road section ahead of a vehicle, comprising: using a collection device provided in the vehicle to collect target data of the road section ahead of the vehicle in real time; performing road condition analysis processing on the target data to obtain real-time traffic information of the road section ahead of the vehicle; wherein, if the collection device comprises a camera, the target data comprises an image, and if the collection device comprises a radar, the target data comprises radar data; and / or, Acquiring the real-time driving behavior of the vehicle driver, comprising: based on a driving behavior collection point set in the vehicle, collecting the real-time driving behavior of the driver of the vehicle, wherein the collection point is set in a vehicle component in the vehicle related to the driver's driving behavior; and / or, The real-time traffic information includes at least one of the following: traffic signs on the road section, driving guidance signs on the road section, the maximum speed allowed on the road section, the minimum speed allowed on the road section, obstacles on the road section, whether there is a ramp entrance on the road section and the distance between the vehicle and the ramp entrance when there is a ramp entrance, whether there is an intersection on the road section and the distance between the vehicle and the intersection when there is an intersection, the congestion level of the road section, and the road surface condition of the road section; and / or, The real-time driving behavior includes at least one of the following: vehicle speed, vehicle acceleration, vehicle driving mode, vehicle load status, members carried by the vehicle, vehicle turn signal status, vehicle throttle opening, vehicle brake pedal opening, and vehicle gear position.

8. A suspension control device, characterized in that: The device comprises: An acquisition module is used to acquire real-time traffic information of the road section ahead of the vehicle and the real-time driving behavior of the vehicle driver; A prediction module, configured to predict the target driving behavior of the driver on the road ahead based on the real-time road condition information and the real-time driving behavior; A determination module, configured to determine the suspension control parameters and gain coefficients corresponding to the vehicle based on the real-time road condition information and the target driving behavior, wherein the gain coefficients are used to adjust the suspension control parameters to be suitable for the driver to drive the vehicle; An adjustment module, configured to adjust the suspension control parameter based on the gain coefficient; A control module is used to control the suspension of the vehicle based on the adjusted suspension control parameters.

9. A computer-readable storage medium, characterized in that: The storage medium includes a stored program, wherein when the program is executed, the device where the storage medium is located is controlled to execute the suspension control method according to any one of claims 1 to 7.

10. An electronic device, characterized in that: The electronic device comprises: Memory, used to store programs; A processor, coupled to the memory, is used to run the program to execute the suspension control method according to any one of claims 1 to 7.

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