Air suspension adaptive adjustment method and system based on road condition recognition and prediction

Abstract

The present invention discloses an air suspension adaptive adjustment method and system based on road condition identification and prediction, belonging to the field of air suspension adjustment, S1, initializing air suspension parameters; S2, real-time prediction of road condition information of the road to be traveled; and simultaneously identifying road condition information of the current road to be traveled; S3, adjusting the height, stiffness, and damping of the air suspension based on the predicted road condition information of the road to be traveled and the identified road condition information of the current road to be traveled; S4, looping through steps S2 to S3. The present invention adopts the above-mentioned air suspension adaptive adjustment method and system based on road condition identification and prediction, which can automatically adjust the height, stiffness, and damping of the air suspension according to the current road condition and the road condition of the road to be traveled, thereby improving the vehicle's driving comfort and driving stability, thereby protecting the vehicle and passengers. The method has the advantages of strong versatility and flexibility, low maintenance cost, fast response speed, high driving comfort and stability, etc.

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 automotive industry, vehicle comfort and safety are gaining increasing attention from consumers. As a key component for improving ride comfort and driving stability, air suspension performance is directly impacted by the driving experience. Air suspension primarily uses road conditions and signals from distance sensors to determine vehicle height changes via the onboard computer. This information then controls the air compressor and exhaust valves, automatically compressing or extending the springs to lower or raise the chassis ground clearance, enhancing vehicle stability at high speeds or maneuverability in complex road conditions. However, traditional air suspension systems are mostly based on fixed control logic and lack real-time adjustments to varying road conditions, compromising ride 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 objectives, the present invention provides an air suspension adaptive adjustment method based on road condition recognition and prediction, comprising the following steps:

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

[0006] S2. Obtain vehicle location information, driving route information, historical road condition data, and real-time traffic information through the vehicle navigation module, and transmit it to the vehicle terminal ECU to generate road condition prediction information and predict the road condition information of the road to be traveled in real time;

[0007] At the same time, the vehicle's onboard camera and acceleration sensor collect real-time road surface information of the current road, and transmit the road surface information of the current road to the onboard terminal ECU, using 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 steps of predicting the road condition information of the road to be traveled in step S2 are 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 combine it with the end point set in the vehicle navigation module to obtain the vehicle's driving route information and 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 traveled.

[0014] Preferably, the prediction of the road condition information of the road to be traveled further includes the following steps: during the vehicle travel, obtaining the vehicle position sampling points preset by the vehicle navigation module in real time, and calculating 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 point 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 steps of identifying the road condition information of the current driving road in step S2 are as follows:

[0018] The first step is to collect road images through the on-board camera and transmit them to the on-board terminal ECU. Matlab software is used to process the images to determine the current road surface flatness, potholes, and slipperiness.

[0019] The second step is to collect the current road slope value through the on-board longitudinal acceleration sensor and the current body roll angle through the on-board lateral acceleration sensor. The collected current road slope value and current body roll angle are transmitted to the on-board terminal ECU, and the convolutional neural network learning algorithm is used 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, it is determined that the current driving road is 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 lower the air suspension height 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 air suspension height and damping;

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

[0028] During vehicle driving, the parameter adjustment model adaptively learns the road characteristics of the current and future roads 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 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, and the on-board terminal ECU is connected to the air suspension adaptive adjustment module.

[0030] Among them, the road condition recognition module is used to identify 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 This is a flow chart of the air suspension adaptive adjustment method based on road condition recognition and prediction of the present invention. DETAILED DESCRIPTION

[0038] In order to make the purpose, technical solutions and advantages disclosed in the embodiments of the present invention clearer, the embodiments of the present invention are further described in detail with reference to 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 numbers throughout represent the same or similar elements or elements with the same or similar functions.

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

[0040] Like reference numerals and letters denote like items in the following drawings, and thus, once an item is defined in one drawing, it does not require further definition or explanation in subsequent drawings.

[0041] In the description of the present invention, it should be noted that the terms "upper", "lower", "inside", "outside", etc. indicate orientations or positional relationships based on the orientations or positional relationships shown in the accompanying drawings, or are the orientations or positional relationships in which the inventive product 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 orientation, be constructed and operated in a specific orientation. Therefore, they should not be understood as limiting the present invention.

[0042] In the description of the present invention, it should also be noted that, unless otherwise expressly specified or limited, the terms "disposed," "installed," and "connected" should be understood broadly. For example, they may refer to fixed, detachable, or integral connections; mechanical or electrical connections; direct or indirect connections through an intermediate medium; and internal communication between two components. Those skilled in the art will understand the specific meanings of the above terms in the present invention based on the 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. Initialize air suspension parameters, including initial height, initial stiffness, and initial damping of the air suspension;

[0045] S2. Obtain vehicle location information, driving route information, historical road condition data, and real-time traffic information through the vehicle navigation module, and transmit it to the vehicle terminal ECU to generate road condition prediction information and predict the road condition information of the road to be traveled 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 combine it with the end point set in the vehicle navigation module to obtain the vehicle's driving route information and 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 traveled, 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, obtaining the vehicle position sampling points preset by the vehicle navigation module in real time, and calculating the angle between the straight lines where two adjacent vehicle position sampling points are located:

[0051] Assume that the current coordinates of the vehicle are (x0, y0) and the coordinates of the adjacent sampling point 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's onboard camera and acceleration sensor collect real-time road surface information of the current road, and transmit the road surface information of the current road to the onboard terminal ECU, using image processing and machine learning algorithms to identify the road condition information of the current road;

[0054] The steps for 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. Matlab software is used to process the images to determine the current road surface flatness, potholes, and slipperiness.

[0056] The second step is to collect the current road slope value through the on-board longitudinal acceleration sensor and the current body roll angle through the on-board lateral acceleration sensor. The collected current road slope value and current body roll angle are transmitted to the on-board terminal ECU, and a convolutional neural network learning algorithm is used to determine the preliminary type of the current road, such as urban road, highway, or 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. The types of the road to be traveled include 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 lower the air suspension height and increase the stiffness to reduce 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 air suspension height and damping to provide better ride comfort;

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

[0064] The parameter adjustment model uses a convolutional neural network adaptive learning algorithm and uses the Simulink module in Matlab software to simulate the parameter simulation model for 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 and future roads 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 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, 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 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; 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 solutions of the present invention rather than to limit the same. 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 solutions of the present invention with equivalents, and these modifications or equivalent replacements cannot cause the modified technical solutions to deviate from the spirit and scope of the technical solutions of the present invention.

Claims

1. An air suspension adaptive adjustment method based on road condition recognition and prediction, characterized by: The following steps are involved: S1. Initialize air suspension parameters, including initial height, initial stiffness, and initial damping of the air suspension; S2. Obtain vehicle location information, driving route information, historical road condition data, and real-time traffic information through the vehicle navigation module, and transmit it to the vehicle terminal ECU to generate road condition prediction information and predict the road condition information of the road to be traveled in real time; At the same time, the vehicle's onboard camera and acceleration sensor collect real-time road surface information of the current road, and transmit the road surface information of the current road to the onboard terminal ECU, using image processing and machine learning algorithms to identify the road condition information of the current road; The steps for predicting the road condition information of the road to be traveled 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 combine it with the end point set in the vehicle navigation module to obtain the vehicle's driving route information and determine the road to be driven; Step 2: Obtain historical road condition data and real-time traffic information through the vehicle navigation module; Step 3: Combine the driving route information, historical traffic data and real-time traffic information to predict the road condition information of the road to be traveled; The prediction of the road condition information of the road to be traveled also includes the following steps: during the vehicle driving process, obtaining the vehicle position sampling points preset by the vehicle navigation module in real time, and calculating the angle between the straight lines where two adjacent vehicle position sampling points are located: Assume the current coordinates of the vehicle are , the coordinates of adjacent sampling points are , then the angle ; If the calculated angle is greater than the preset angle , it is determined that the vehicle will enter a turning condition within the set time in the future; 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 an air suspension based on road condition recognition and prediction according to claim 1, characterized in that: The steps for 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 on-board camera and transmit them to the on-board terminal ECU. Matlab software is used to process the images to determine the current road surface flatness, potholes, and slipperiness. The second step is to collect the current road slope value through the on-board longitudinal acceleration sensor and the current body roll angle through the on-board lateral acceleration sensor. The collected current road slope value and current body roll angle are transmitted to the on-board terminal ECU, and the convolutional neural network learning algorithm is used 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.

3. The air suspension adaptive adjustment method based on road condition recognition and prediction according to claim 2, characterized in that: If the current road slope value collected is greater than the preset road slope value When , it is determined that the current driving road is a bumpy road section; If the current vehicle roll angle collected is greater than the preset roll angle , it is determined that the current driving road is an inclined winding road section.

4. The air suspension adaptive adjustment method based on road condition recognition and prediction according to claim 3, 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. The types of the road to be traveled include 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 lower the air suspension height and increase the stiffness; When the road to be driven is a highway, the model is adjusted through built-in parameters to increase the air suspension height and damping; When the road to be driven is a mountainous road, the model is adjusted through built-in parameters to increase the air suspension height and reduce the stiffness.

5. The air suspension adaptive adjustment method based on road condition recognition and prediction according to claim 4, characterized in that: The parameter adjustment model uses a convolutional neural network adaptive learning algorithm and uses the Simulink module in Matlab software to simulate the parameter simulation model for 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 and future roads 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.

6. A system using the air suspension adaptive adjustment method based on road condition recognition and prediction according to any one of claims 1 to 5, 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, and the on-board terminal ECU is connected to the air suspension adaptive adjustment module. Among them, the road condition recognition module is used to identify 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 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; 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.

Images