Air suspension self-adaptive adjusting method and system based on road condition recognition and prediction

Abstract

The invention discloses an air suspension self-adaptive adjustment method and system based on road condition recognition and prediction, and belongs to the field of air suspension adjustment. S1, parameters of an air suspension are initialized; s2, predicting road condition information of a road to be driven in real time; meanwhile, identifying the road condition information of the current driving road; s3, adjusting the height, rigidity and damping of the air suspension in combination with the predicted road condition information of the road to be driven and the identified road condition information of the current driving road; and S4, circularly executing the steps S2 to S3. By the adoption of the air suspension self-adaptive adjusting method and system based on road condition recognition and prediction, the height, rigidity and damping of the air suspension can be automatically adjusted according to the current road condition and the road condition of the road to be driven, the driving comfort and the driving stability of the vehicle are improved, and therefore the vehicle and passengers are protected; the system has the advantages of being high in universality and flexibility, low in maintenance cost, high in response speed, high in driving comfort and stability and the like.

Description

Technical Field

[0001] The present invention relates to the field of air suspension adjustment, and in particular to an air suspension adaptive adjustment method and system based on road condition recognition and prediction. Background Art

[0002] With the rapid development of the automobile industry, the comfort and safety of vehicle driving have attracted more and more attention from consumers. As an important component to improve riding comfort and driving stability, the performance of air suspension is directly related to the driving experience. The air suspension mainly determines the height change of the vehicle body through the driving computer according to different road conditions and the signal of the distance sensor, and then controls the air compressor and exhaust valve to automatically compress or extend the spring, thereby reducing or increasing the ground clearance of the chassis to increase the stability of the high-speed vehicle body or the passability of complex road conditions. However, most traditional air suspensions are adjusted based on fixed control logic and cannot be adjusted in real time according to different road conditions, thus affecting riding comfort and driving safety. Summary of the invention

[0003] To solve the above problems, the present invention provides an air suspension adaptive adjustment method and system based on road condition recognition and prediction, which can automatically adjust the height, stiffness and damping of the air suspension according to the current road conditions and the road conditions of the road to be traveled, thereby improving the vehicle's driving comfort and driving stability, thereby protecting the vehicle and passengers. It has strong versatility and flexibility, low maintenance cost, fast response speed, high driving comfort and stability, etc.

[0004] To achieve the above object, the present invention provides an air suspension adaptive adjustment method based on road condition recognition and prediction, comprising the following steps:

[0005] S1, initializing air suspension parameters, the air suspension parameters including initial height, initial stiffness and initial damping of the air suspension;

[0006] S2, obtaining vehicle location information, driving route information, historical road condition data and real-time traffic information through the vehicle navigation module, and transmitting them to the vehicle terminal ECU, generating road condition prediction information, and predicting the road condition information of the road to be driven in real time;

[0007] At the same time, the vehicle-mounted camera and acceleration sensor collect the road surface information of the current road in real time, and transmit the road surface information of the current road to the vehicle terminal ECU, and use image processing and machine learning algorithms to identify the road condition information of the current road;

[0008] S3, combining the predicted road condition information of the road to be traveled and the identified road condition information of the current traveled road, generating a road information data string, and transmitting the generated road information data string to the air suspension adaptive adjustment module to adjust the height, stiffness and damping of the air suspension;

[0009] S4. Execute steps S2 to S3 in a loop to achieve real-time adaptive adjustment of the air suspension.

[0010] Preferably, the step of predicting the road condition information of the road to be traveled in step S2 is as follows:

[0011] The first step is to obtain the vehicle's location information through the starting point set in the vehicle navigation module, and obtain the vehicle's driving route information in combination with the end point set in the vehicle navigation module to determine the road to be driven;

[0012] Step 2: Obtain historical road condition data and real-time traffic information through the vehicle navigation module;

[0013] The third step is to combine the driving route information, historical road condition data and real-time traffic information to predict the road condition information of the road to be driven.

[0014] Preferably, the prediction of the road condition information of the road to be traveled further includes the following steps: during the vehicle travel, real-time acquisition of vehicle position sampling points preset by the vehicle navigation module, and calculation of the angle between the straight lines where two adjacent vehicle position sampling points are located:

[0015] Assume that the current coordinates of the vehicle are (x0, y0) and the coordinates of the adjacent sampling points are (x1, y1), then the angle α = arcsin[(x1-x0) / (y1-y0)];

[0016] If the calculated angle is greater than the preset angle γ, it is determined that the vehicle will enter a turning condition within a set time in the future.

[0017] Preferably, the step of identifying the road condition information of the current driving road in step S2 is as follows:

[0018] The first step is to collect road images through the vehicle camera and transmit them to the vehicle terminal ECU, and use MATLAB software to process the images to determine the current road surface flatness, road potholes, and road slipperiness;

[0019] The second step is to collect the current road slope value through the on-board longitudinal acceleration sensor, and collect the current body roll angle through the on-board lateral acceleration sensor, and transmit the collected current road slope value and current body roll angle to the on-board terminal ECU, and use the convolutional neural network learning algorithm to determine the preliminary type of the current road;

[0020] The third step is to transmit the current road surface flatness, road pothole degree, road surface slipperiness and current driving road type to the air suspension adaptive adjustment module for call.

[0021] Preferably, if the collected current driving road slope value is greater than a preset road slope value α, the current driving road is determined to be a bumpy road section;

[0022] If the collected current vehicle body roll angle is greater than the preset roll angle β, it is determined that the current driving road is an inclined winding road section.

[0023] Preferably, in step S3, the type of the road to be traveled is determined according to the road information data string, and the air suspension adjustment strategy is determined according to the type of the road to be traveled, and the type of the road to be traveled includes urban roads, highways and mountain roads;

[0024] When the road to be driven is an urban road, the model is adjusted through built-in parameters to reduce the height of the air suspension and increase the stiffness;

[0025] When the road to be driven is a highway, the model is adjusted through built-in parameters to increase the height of the air suspension and the damping;

[0026] When the road to be driven is a mountainous road, the model is adjusted through built-in parameters to increase the height of the air suspension and reduce the stiffness.

[0027] Preferably, the parameter adjustment model adopts a convolutional neural network adaptive learning algorithm, and uses the simulink module in the matlab software to simulate a parameter simulation model under each road type, and the parameter simulation model includes an optimal height, an optimal stiffness, and an optimal damping;

[0028] During vehicle driving, the parameter adjustment model adaptively learns the road characteristics of the current road and the road to be driven on based on the road information data string, matches the parameter simulation model according to the identified road type, and adjusts the air suspension according to the optimal height, optimal stiffness and optimal damping in the parameter simulation model.

[0029] A system for an air suspension adaptive adjustment method based on road condition recognition and prediction, comprising a road condition recognition module, a road condition prediction module, an on-board navigation module, an on-board terminal ECU and an air suspension adaptive adjustment module, wherein the on-board navigation module is connected to the road condition prediction module, the road condition prediction module and the road condition recognition module are both connected to the on-board terminal ECU, and the on-board terminal ECU is connected to the air suspension adaptive adjustment module;

[0030] The road condition recognition module is used to recognize the current road condition information of the vehicle;

[0031] A road condition prediction module is used to predict the road condition information of the road on which the vehicle is to travel;

[0032] The vehicle terminal ECU is used to generate a road information data string based on the road condition information of the road to be traveled and generate an air suspension adjustment strategy based on the road information data string;

[0033] The air suspension adaptive adjustment module is used to adjust the height, stiffness and damping of the air suspension according to the air suspension adjustment strategy.

[0034] The present invention has the following beneficial effects:

[0035] The height, stiffness and damping of the air suspension can be automatically adjusted according to the current road conditions and the road conditions to be traveled, preventing the vehicle from experiencing severe bumps and body roll, thereby improving the vehicle's driving stability and driving comfort.

[0036] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 The present invention is a flow chart of the air suspension adaptive adjustment method based on road condition recognition and prediction. DETAILED DESCRIPTION

[0038] In order to make the purpose, technical scheme and advantages disclosed in the embodiments of the present invention clearer, the embodiments of the present invention are further described in detail in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the embodiments of the present invention and are not intended to limit the embodiments of the present invention. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application. Examples of the embodiments are shown in the accompanying drawings, where the same or similar reference numerals throughout represent the same or similar elements or elements with the same or similar functions.

[0039] It should be noted that the terms "include", "have" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or server comprising a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to these processes, methods, products or devices.

[0040] Like reference numerals and letters denote similar items in the following drawings, and thus, once an item is defined in one drawing, further definition and explanation thereof is not required in subsequent drawings.

[0041] In the description of the present invention, it should be noted that the terms "upper", "lower", "inside", "outside", etc. indicate directions or positional relationships based on the directions or positional relationships shown in the accompanying drawings, or are directions or positional relationships in which the product of the invention is usually placed when in use. They are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific direction, be constructed and operated in a specific direction, and therefore should not be understood as a limitation on the present invention.

[0042] In the description of the present invention, it is also necessary to explain that, unless otherwise clearly specified and limited, the terms "setting", "installation" and "connection" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be an indirect connection through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0043] like Figure 1 As shown, the air suspension adaptive adjustment method based on road condition recognition and prediction includes the following steps:

[0044] S1, initializing air suspension parameters, the air suspension parameters including initial height, initial stiffness and initial damping of the air suspension;

[0045] S2, obtaining vehicle location information, driving route information, historical road condition data and real-time traffic information through the vehicle navigation module, and transmitting them to the vehicle terminal ECU, generating road condition prediction information, and predicting the road condition information of the road to be driven in real time;

[0046] The steps of predicting the road condition information of the road to be traveled in step S2 are as follows:

[0047] The first step is to obtain the vehicle's location information through the starting point set in the vehicle navigation module, and obtain the vehicle's driving route information in combination with the end point set in the vehicle navigation module to determine the road to be driven;

[0048] Step 2: Obtain historical road condition data and real-time traffic information through the vehicle navigation module;

[0049] The third step is to combine the driving route information, historical road condition data and real-time traffic information to predict the road condition information of the road to be driven, such as congestion, construction, rain and snow, etc.

[0050] The prediction of the road condition information of the road to be traveled also includes the following steps: during the vehicle driving process, the vehicle position sampling points preset by the vehicle navigation module are obtained in real time, and the angle between the straight lines where two adjacent vehicle position sampling points are located is calculated:

[0051] Assume that the current coordinates of the vehicle are (x0, y0) and the coordinates of the adjacent sampling points are (x1, y1), then the angle α = arcsin[(x1-x0) / (y1-y0)];

[0052] If the calculated angle is greater than the preset angle γ, it is determined that the vehicle will enter a turning condition within a set time in the future, and the predicted data is transmitted to the on-board terminal ECU in real time for integration.

[0053] At the same time, the vehicle-mounted camera and acceleration sensor collect the road surface information of the current road in real time, and transmit the road surface information of the current road to the vehicle terminal ECU, and use image processing and machine learning algorithms to identify the road condition information of the current road;

[0054] The steps of identifying the road condition information of the current driving road in step S2 are as follows:

[0055] The first step is to collect road images through the on-board camera and transmit them to the on-board terminal ECU, and use MATLAB software to perform image processing to determine the current road surface flatness, road potholes, and road slipperiness;

[0056] The second step is to collect the current road slope value through the on-board longitudinal acceleration sensor, and collect the current body roll angle through the on-board lateral acceleration sensor, and transmit the collected current road slope value and current body roll angle to the on-board terminal ECU, and use the convolutional neural network learning algorithm to determine the preliminary type of the current road, such as urban road, highway, mountain road;

[0057] If the current road slope value collected is greater than the preset road slope value α, the current road is determined to be a bumpy section; if the current vehicle body roll angle collected is greater than the preset roll angle β, the current road is determined to be an inclined winding section.

[0058] The third step is to transmit the current road surface flatness, road pothole degree, road surface slipperiness and current driving road type to the air suspension adaptive adjustment module for call.

[0059] S3, combining the predicted road condition information of the road to be traveled and the identified road condition information of the current traveled road, generating a road information data string, and transmitting the generated road information data string to the air suspension adaptive adjustment module to adjust the height, stiffness and damping of the air suspension;

[0060] In step S3, the type of the road to be traveled is determined according to the road information data string, and the air suspension adjustment strategy is determined according to the type of the road to be traveled, and the type of the road to be traveled includes urban roads, highways and mountain roads;

[0061] When the road to be driven is an urban road, the model is adjusted through built-in parameters to reduce the height of the air suspension and increase the rigidity to reduce the body roll and improve driving stability;

[0062] When the road to be driven is a highway, the model is adjusted through built-in parameters to increase the height of the air suspension and the damping to provide better riding comfort;

[0063] When the road to be driven is a mountain road, the model is adjusted through built-in parameters to increase the height of the air suspension and reduce the stiffness to better absorb road vibrations and protect the vehicle and passengers.

[0064] The parameter adjustment model adopts the convolutional neural network adaptive learning algorithm, and uses the Simulink module in the Matlab software to simulate the parameter simulation model under each road type. The parameter simulation model includes the optimal height, optimal stiffness and optimal damping.

[0065] During vehicle driving, the parameter adjustment model adaptively learns the road characteristics of the current road and the road to be driven on based on the road information data string, and matches the parameter simulation model according to the identified road type, and adjusts the air suspension according to the optimal height, optimal stiffness and optimal damping in the parameter simulation model to make the vehicle reach the optimal state.

[0066] S4. Execute steps S2 to S3 in a loop to achieve real-time adaptive adjustment of the air suspension.

[0067] A system for an air suspension adaptive adjustment method based on road condition recognition and prediction, comprising a road condition recognition module, a road condition prediction module, an on-board navigation module, an on-board terminal ECU and an air suspension adaptive adjustment module, wherein the on-board navigation module is connected to the road condition prediction module, the road condition prediction module and the road condition recognition module are both connected to the on-board terminal ECU, and the on-board terminal ECU is connected to the air suspension adaptive adjustment module;

[0068] Among them, the road condition recognition module is used to identify the current road condition information of the vehicle; the road condition prediction module is used to predict the road condition information of the road to be traveled by the vehicle; the on-board terminal ECU is used to generate a road information data string in combination with the road condition information of the road to be traveled, and generate an air suspension adjustment strategy based on the road information data string; the air suspension adaptive adjustment module is used to adjust the height, stiffness and damping of the air suspension according to the air suspension adjustment strategy.

[0069] Therefore, the present invention adopts the above-mentioned air suspension adaptive adjustment method and system based on road condition recognition and prediction, which can automatically adjust the height, stiffness and damping of the air suspension according to the current road conditions and the road conditions of the road to be traveled, thereby improving the vehicle's driving comfort and driving stability, thereby protecting the vehicle and passengers. It has strong versatility and flexibility, low maintenance cost, fast response speed, high driving comfort and stability, etc.

[0070] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that they can still modify or replace the technical solution of the present invention with equivalents, and these modifications or equivalent replacements cannot cause the modified technical solution to deviate from the spirit and scope of the technical solution of the present invention.

Claims

1. An air suspension adaptive adjustment method based on road condition recognition and prediction, characterized in that: The following steps are involved: S1, initializing air suspension parameters, the air suspension parameters including initial height, initial stiffness and initial damping of the air suspension; S2, obtaining vehicle location information, driving route information, historical road condition data and real-time traffic information through the vehicle navigation module, and transmitting them to the vehicle terminal ECU, generating road condition prediction information, and predicting the road condition information of the road to be driven in real time; At the same time, the vehicle-mounted camera and acceleration sensor collect the road surface information of the current road in real time, and transmit the road surface information of the current road to the vehicle terminal ECU, and use image processing and machine learning algorithms to identify the road condition information of the current road; S3, combining the predicted road condition information of the road to be traveled and the identified road condition information of the current traveled road, generating a road information data string, and transmitting the generated road information data string to the air suspension adaptive adjustment module to adjust the height, stiffness and damping of the air suspension; S4. Execute steps S2 to S3 in a loop to achieve real-time adaptive adjustment of the air suspension.

2. The method for adaptively adjusting air suspension based on road condition recognition and prediction according to claim 1, characterized in that: The steps of predicting the road condition information of the road to be traveled in step S2 are as follows: The first step is to obtain the vehicle's location information through the starting point set in the vehicle navigation module, and obtain the vehicle's driving route information in combination with the end point set in the vehicle navigation module to determine the road to be driven; Step 2: Obtain historical road condition data and real-time traffic information through the vehicle navigation module; The third step is to combine the driving route information, historical road condition data and real-time traffic information to predict the road condition information of the road to be driven.

3. The method for adaptively adjusting air suspension based on road condition recognition and prediction according to claim 2, characterized in that: The prediction of the road condition information of the road to be traveled also includes the following steps: during the vehicle driving process, the vehicle position sampling points preset by the vehicle navigation module are obtained in real time, and the angle between the straight lines where two adjacent vehicle position sampling points are located is calculated: Assume that the current coordinates of the vehicle are (x0, y0) and the coordinates of the adjacent sampling points are (x1, y1), then the angle α = arcsin[(x1-x0) / (y1-y0)]; If the calculated angle is greater than the preset angle γ, it is determined that the vehicle will enter a turning condition within a set time in the future.

4. The method for adaptively adjusting air suspension based on road condition recognition and prediction according to claim 3 is characterized in that: The steps of identifying the road condition information of the current driving road in step S2 are as follows: The first step is to collect road images through the vehicle camera and transmit them to the vehicle terminal ECU, and use MATLAB software to process the images to determine the current road surface flatness, road potholes, and road slipperiness; The second step is to collect the current road slope value through the on-board longitudinal acceleration sensor, and collect the current body roll angle through the on-board lateral acceleration sensor, and transmit the collected current road slope value and current body roll angle to the on-board terminal ECU, and use the convolutional neural network learning algorithm to determine the preliminary type of the current road; The third step is to transmit the current road surface flatness, road pothole degree, road surface slipperiness and current driving road type to the air suspension adaptive adjustment module for call.

5. The method for adaptively adjusting air suspension based on road condition recognition and prediction according to claim 4, characterized in that: If the collected current driving road slope value is greater than the preset road slope value α, the current driving road is determined to be a bumpy road section; If the collected current vehicle body roll angle is greater than the preset roll angle β, it is determined that the current driving road is an inclined winding road section.

6. The method for adaptively adjusting air suspension based on road condition recognition and prediction according to claim 5, characterized in that: In step S3, the type of the road to be traveled is determined according to the road information data string, and the air suspension adjustment strategy is determined according to the type of the road to be traveled, and the type of the road to be traveled includes urban roads, highways and mountain roads; When the road to be driven is an urban road, the model is adjusted through built-in parameters to reduce the height of the air suspension and increase the stiffness; When the road to be driven is a highway, the model is adjusted through built-in parameters to increase the height of the air suspension and the damping; When the road to be driven is a mountainous road, the model is adjusted through built-in parameters to increase the height of the air suspension and reduce the stiffness.

7. The method for adaptively adjusting air suspension based on road condition recognition and prediction according to claim 6, characterized in that: The parameter adjustment model adopts the convolutional neural network adaptive learning algorithm, and uses the Simulink module in the Matlab software to simulate the parameter simulation model under each road type. The parameter simulation model includes the optimal height, optimal stiffness and optimal damping. During vehicle driving, the parameter adjustment model adaptively learns the road characteristics of the current road and the road to be driven on based on the road information data string, matches the parameter simulation model according to the identified road type, and adjusts the air suspension according to the optimal height, optimal stiffness and optimal damping in the parameter simulation model.

8. The system of the air suspension adaptive adjustment method based on road condition recognition and prediction according to any one of claims 1 to 7, characterized in that: It includes a road condition recognition module, a road condition prediction module, an on-board navigation module, an on-board terminal ECU and an air suspension adaptive adjustment module. The on-board navigation module is connected to the road condition prediction module. The road condition prediction module and the road condition recognition module are both connected to the on-board terminal ECU. The on-board terminal ECU is connected to the air suspension adaptive adjustment module. The road condition recognition module is used to recognize the current road condition information of the vehicle; A road condition prediction module is used to predict the road condition information of the road on which the vehicle is to travel; The vehicle terminal ECU is used to generate a road information data string in combination with the road condition information of the road to be traveled, and generate an air suspension adjustment strategy based on the road information data string; The air suspension adaptive adjustment module is used to adjust the height, stiffness and damping of the air suspension according to the air suspension adjustment strategy.

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