A method and device for intelligently monitoring tire wear of an autonomous driving vehicle

Abstract

The present invention discloses an intelligent tire wear monitoring method and device for autonomous vehicles, comprising: collecting road environment data, driving behavior data, and tire data through vehicle sensors; constructing a tire wear profile based on the road environment data, driving behavior data, and tire data; determining whether the road environment data or driving behavior data has changed, and if so, obtaining the vehicle's operating time under the tire wear profile; and obtaining a tire wear value based on the tire wear profile and operating time using a preset wear model. When the road environment data or driving behavior data changes while the vehicle is driving, the wear model is used to determine a corresponding tire wear value based on the tire wear profile and the vehicle's operating time under the unchanged tire wear profile, thereby enabling accurate tire wear assessment after the vehicle has traveled through a road section.

Description

Technical Field

[0001] The present invention relates to the field of autonomous driving technology, and in particular to a method and device for intelligently monitoring tire wear of an autonomous driving vehicle. Background Art

[0002] Safe tire operation is a key component of vehicle safety. In traditional manual driving scenarios, tire condition is typically assessed manually. However, autonomous vehicles, without human operators, make it difficult to effectively check tire condition before driving. Furthermore, tire wear is closely related to road type, environmental conditions, and driving behavior data, factors that traditional tire pressure monitoring systems (TPMS) cannot comprehensively account for. Summary of the Invention

[0003] The technical problem to be solved by the present invention is to provide a method and device for intelligent monitoring of tire wear of autonomous driving vehicles, so as to realize real-time analysis and prediction of tire loss.

[0004] In order to solve the above technical problems, the technical solution adopted by the present invention is:

[0005] An intelligent tire wear monitoring method for an autonomous driving vehicle, comprising:

[0006] Collect road environment data, driving behavior data, and tire data through vehicle sensors;

[0007] constructing a tire wear feature based on the road environment data, driving behavior data, and tire data;

[0008] Determining whether the road environment data or driving behavior data has changed, and if so, obtaining the vehicle's operating time under the tire wear characteristics;

[0009] The tire wear value is obtained according to the tire wear characteristics and the operating time using a preset wear model.

[0010] In order to solve the above technical problems, another technical solution adopted by the present invention is:

[0011] An intelligent tire wear monitoring device for an autonomous driving vehicle includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, each step of the above-mentioned intelligent tire wear monitoring method for an autonomous driving vehicle is implemented.

[0012] The beneficial effects of the present invention are as follows: by collecting road environment data information, driving behavior data and tire data during vehicle driving, and constructing the collected data into tire wear characteristics; when the road environment data or driving behavior data of the vehicle changes during driving, it indicates that the driving process has changed significantly. At this time, the corresponding tire wear value is obtained through the wear model according to the tire wear characteristics and the running time of the vehicle in the unchanged tire wear characteristics, thereby realizing accurate evaluation of tire wear after the vehicle has traveled through the road section. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] Figure 1 This is a flowchart of the steps of an intelligent monitoring method for tire wear of an autonomous driving vehicle according to an embodiment of the present invention;

[0014] Figure 2 This is a flowchart of another step of a method for intelligently monitoring tire wear of an autonomous driving vehicle according to an embodiment of the present invention;

[0015] Figure 3 Schematic diagram of the structure of an intelligent monitoring device for tire wear of an autonomous driving vehicle in an embodiment of the present invention. DETAILED DESCRIPTION

[0016] To illustrate the technical content, achieved objectives and effects of the present invention in detail, the following description is given in conjunction with the embodiments and accompanying drawings.

[0017] An intelligent tire wear monitoring method for an autonomous driving vehicle, comprising:

[0018] Collect road environment data, driving behavior data, and tire data through vehicle sensors;

[0019] constructing a tire wear feature based on the road environment data, driving behavior data, and tire data;

[0020] Determining whether the road environment data or driving behavior data has changed, and if so, obtaining the vehicle's operating time under the tire wear characteristics;

[0021] The tire wear value is obtained according to the tire wear characteristics and the operating time using a preset wear model.

[0022] From the above description, it can be seen that the beneficial effects of the present invention are: by collecting road environment data information, driving behavior data and tire data during the vehicle's driving process, and constructing the collected data into tire wear characteristics; when the road environment data or driving behavior data of the vehicle changes during driving, it means that the driving process has changed significantly. At this time, the corresponding tire wear value is obtained through the wear model according to the tire wear characteristics and the vehicle's running time in the unchanged tire wear characteristics, thereby realizing accurate evaluation of tire wear after the vehicle has traveled through the road section.

[0023] Furthermore, it also includes:

[0024] Obtaining a target driving section of a preset distance after the current driving route through the navigation system;

[0025] Acquire target segment data corresponding to the target driving segment, and historical segment data related to the target driving segment;

[0026] The predicted tire loss corresponding to the target driving section is predicted based on the target section data and the historical section data.

[0027] From the above description, it can be seen that after the navigation system obtains the target driving section that the vehicle will travel, by obtaining the target section data corresponding to the target driving section and the related historical section data, the corresponding predicted tire loss can be accurately predicted based on the target section data and the historical section data.

[0028] Furthermore, after obtaining the predicted tire loss corresponding to the target driving section, the method further includes:

[0029] The driving behavior data is optimized based on the predicted tire wear.

[0030] From the above description, it can be seen that by obtaining the predicted tire loss corresponding to the target driving section that the vehicle will travel on, the driving behavior data is optimized. For example, when entering a low-loss section, the vehicle speed is appropriately increased to improve driving efficiency; and the vehicle speed is reduced in a high-loss section to reduce wear, thereby optimizing the vehicle's driving strategy using the loss prediction results.

[0031] Furthermore, it also includes:

[0032] Obtain at least two different sections of road to be traveled after the current travel route through the navigation system;

[0033] respectively predicting the predicted tire losses corresponding to all the road sections to be traveled;

[0034] The road section to be traveled with the lowest predicted tire loss is used as the target travel section.

[0035] From the above description, it can be seen that after the navigation system obtains multiple selectable road sections to be traveled, the predicted tire loss corresponding to each road section to be traveled is predicted respectively, and the road section to be traveled with the lowest predicted tire loss is selected as the target road section, so as to optimize the path selection by using the loss prediction results, thereby reducing the wear and tear on the tires during driving.

[0036] Furthermore, it also includes:

[0037] Obtain tire status inspection data after driving the vehicle;

[0038] Comparing the tire condition inspection data with the tire loss value to obtain a comparison result;

[0039] The loss model is optimized according to the comparison result.

[0040] From the above description, it can be seen that by obtaining tire status inspection data after the vehicle is driven and optimizing the loss model based on the comparison results of the tire status inspection data and the tire loss value, the accuracy of the loss model in subsequent prediction tasks can be improved.

[0041] Furthermore, the road environment data is collected by laser radar and camera, which is expressed as:

[0042] X road =[R(t),S(t)];

[0043] Among them, X road represents road environment data, R(t) represents road surface type, and S(t) represents road condition characteristics.

[0044] Furthermore, the acceleration, braking intensity, and turning angle are obtained by the vehicle electronic control unit as the driving behavior data, which is expressed as:

[0045] X drive =[A(t),B(t),θ(t)];

[0046] Among them, X drive Represents driving behavior data, acceleration is A(t), braking intensity is B(t), turning angle is .

[0047] Furthermore, tire pressure, temperature, and vibration characteristics are obtained from the wheel hub sensor and the tire pressure monitoring system as the tire data, which is expressed as:

[0048] X tire =[P(t),T(t),V(t)];

[0049] Among them, the tire data is X tire , tire pressure is P(t), temperature is T(t), and vibration characteristic is V(t).

[0050] Furthermore, obtaining the tire wear value according to the tire wear characteristics and the operating time using a preset wear model includes:

[0051] Predict tire wear during each driving session:

[0052]

[0053] in, Indicates the target section running time;

[0054] The total tire loss is expressed as:

[0055]

[0056] Where D(t) represents the tire wear characteristic, which is the degree of tire wear per unit time.

[0057] Another embodiment of the present invention provides an intelligent monitoring device for tire wear of an autonomous driving vehicle, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the processor implements the various steps in the above-mentioned intelligent monitoring method for tire wear of an autonomous driving vehicle.

[0058] The method and device for intelligent tire wear monitoring of autonomous driving vehicles provided by the present invention can be applied to autonomous driving vehicle control scenarios, and are described below through specific implementation methods:

[0059] Currently, autonomous vehicles are equipped with a rich array of sensors. These sensors enable the vehicle to fully understand the state of road environmental data, making it possible to accurately analyze tire wear. By collecting multimodal environmental information and dynamic response data during driving, tire wear can be accurately estimated. After each drive, the tire health model is updated and synchronized with the cloud. Furthermore, during driving, the tire wear prediction can be further refined based on the vehicle's next planned driving path, combined with historical data and real-time feedback, providing a basis for decision-making in optimizing driving strategies and vehicle maintenance. The specific implementation is as follows:

[0060] Example 1

[0061] Please refer to Figure 1 as well as Figure 2 , an intelligent monitoring method for tire wear of an autonomous driving vehicle, comprising:

[0062] S1. Collect road environment data, driving behavior data, and tire data through vehicle sensors. For example, for road environment data, use lidar and cameras to identify road surface types such as asphalt, sand, gravel, etc., as well as road condition characteristics such as wetness and potholes, expressed as:

[0063] X road =[R(t),S(t)];

[0064] Among them, R(t) represents the road surface type and S(t) represents the road condition characteristics.

[0065] For driving behavior data, the vehicle ECU (Electronic Control Unit) obtains acceleration A(t), braking intensity B(t), turning angle The information is taken as driving behavior data and expressed as:

[0066] X drive =[A(t),B(t),θ(t)].

[0067] For tire data, tire pressure P(t), temperature T(t), and vibration characteristics V(t) are obtained from the wheel hub sensor and tire pressure monitoring system as tire data, which can be expressed as:

[0068] X tire =[P(t),T(t),V(t)].

[0069] S2. Construct a tire wear feature based on the road environment data, driving behavior data, and tire data. For example, the tire wear feature is represented by a vector, and the following is obtained:

[0070] X total =[X road , X drive , X tire ];

[0071] And use the deep learning based loss model f loss Predict tire loss, expressed as:

[0072] D(t)=f loss (X total );

[0073] Where D(t) represents the degree of tire wear per unit time.

[0074] S3. Determine whether the road environment data or driving behavior data has changed. If so, obtain the vehicle's operating time under the tire wear characteristic. That is, during vehicle driving, the system dynamically updates the wear prediction model based on real-time data. When a road surface change is detected, such as a change from asphalt to sand or gravel, or a change in driving behavior, such as a change from straight driving to turning, or the vehicle speed input exceeds a change threshold, such as a vehicle speed change of more than ±10 km / h in a short period of time during stable driving, indicating that the vehicle is currently experiencing acceleration or deceleration changes, the duration T of the tire wear characteristic is obtained. drive The system uses the duration of the vehicle's previous road environment data and driving behavior to assess tire wear during that time. Simultaneously, it generates new tire wear characteristics based on changing road environment data or driving behavior data, which are used to predict and calculate tire wear for the next time period.

[0075] S4. Determine the tire wear value based on the tire wear characteristics and the operating time using a preset wear model. For example, the tire wear value during each driving period is predicted by combining the current driving behavior and road type with historical data.

[0076]

[0077] in, Indicates the target section running time;

[0078] The total tire loss is expressed as:

[0079]

[0080] Here, D(t) represents the tire wear characteristic, which is the degree of tire wear per unit time. In actual applications, when a new tire is replaced, the initial state parameters of the tire are recorded and the system is adaptively calibrated to ensure the accuracy of the basic assessment model. For example, this includes recording core data such as tire pressure, temperature, tread thickness, and wear. The initial calibration also involves measuring vehicle dynamic parameters such as vehicle load and steering sensitivity. The adaptive calibration method enables the assessment system to have high-precision state monitoring capabilities. At the same time, a tire state acquisition and dynamic loss assessment system is constructed for autonomous vehicles. That is, the tire loss during each driving process and the total tire loss are predicted using the above-mentioned loss model. During the model prediction process, the normal operation of the tire loss estimation system is determined. When the tire loss estimation system is abnormal, a maintenance alarm is triggered, and the abnormal source is located through multi-dimensional log analysis and dynamic adjustments are implemented.

[0081] When the tire wear estimation system is operating normally, the system's robustness is verified using a combination of model scenarios and field testing. For example, simulation scenario testing includes complex road conditions and obstacles such as potholes and slippery roads, while field testing focuses on the authenticity of dynamic data and sensor accuracy to assess system reliability. Furthermore, when the tire wear data is normal, the model is combined with driving paths and historical data to achieve real-time tire wear assessment. When the tire wear data is abnormal, a comprehensive diagnosis of the system's operation log is performed, checking the sensor status and the data it collects, and analyzing the data for abnormal deviations. Precise corrections and optimizations are then implemented for any detected abnormal data.

[0082] After the vehicle has completed driving, tire status inspection data after driving is obtained, such as wear, pressure change, and other data; the tire status inspection data is then compared with the tire loss value to obtain a comparison result, and the loss model is optimized based on the comparison result:

[0083]

[0084] in, is the correction factor. Simultaneously, during driving, real-time tire status data is collected and the wear model is dynamically adjusted to improve real-time prediction capabilities. Furthermore, the wear model is regularly trained using accumulated driving history data to enhance its generalization capabilities.

[0085] In an optional embodiment, during the vehicle's driving process, the navigation system also obtains a target driving section at a preset distance after the current driving route, and obtains target section data corresponding to the target driving section, as well as historical section data related to the target driving section; then, based on the target section data and historical section data, the predicted tire loss corresponding to the target driving section is predicted; that is, the system predicts the tire loss D of the future section based on the real-time navigation data. next For example, the target road segment data includes road segment type R such as asphalt, sand, gravel, etc., road condition characteristics V such as slope, congestion, traffic lights, and driving behavior B such as turning angle, etc., then we can get:

[0086]

[0087] in, is the predicted path loss of the i-th path, is the type and state of the road surface in section i, is the road condition characteristic of the i-th road section, B i is the driving behavior in the i-th segment.

[0088] Combining the predictions of each segment, we get the total prediction loss:

[0089]

[0090] In another optional embodiment, the method further includes obtaining at least two different sections to be traveled after the current travel route through the navigation system, and respectively predicting the predicted tire losses corresponding to all the sections to be traveled, and then selecting the section to be traveled with the lowest predicted tire loss as the target travel section. The specific formula is expressed as follows:

[0091]

[0092] in, is the optimized path, is the set of all feasible paths, R j , V j , B j The road surface type, road condition characteristics, and driving behavior of each section on the path.

[0093] At the same time, after obtaining the predicted tire loss corresponding to the target driving section, the driving behavior data is further optimized according to the predicted tire loss. The specific driving strategy optimization is as follows:

[0094] (1) Acceleration optimization: For example, when it is predicted that a low-loss road section is about to be entered, such as a flat asphalt road, the vehicle speed V can be appropriately increased to improve driving efficiency; on the contrary, when entering a high-loss road section, such as sand or gravel, the vehicle speed can be reduced to reduce wear:

[0095]

[0096] in, The optimized vehicle speed.

[0097] (2) Braking optimization: When predicting congestion or traffic lights, the system reduces tire wear by applying light brakes in advance instead of sudden brakes, and adjusts the braking intensity. To optimize losses:

[0098]

[0099] in, For optimized braking strength.

[0100] (3) Turning optimization: On turning sections, by reducing the speed when turning and optimize steering angle , reduce the loss of lateral force on the tire:

[0101]

[0102] in, Optimized steering angle, Optimized speed when turning, Driving behavior when turning.

[0103] Example 2

[0104] Please refer to Figure 3 , an intelligent tire wear monitoring device for an autonomous driving vehicle, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the computer program, implements the following steps:

[0105] S1, collecting road environment data, driving behavior data and tire data through vehicle sensors;

[0106] S2. Constructing a tire wear feature based on the road environment data, driving behavior data, and tire data;

[0107] S3. Determine whether the road environment data or driving behavior data has changed, and if so, obtain the vehicle's operating time under the tire wear characteristics;

[0108] S4. Determine a tire wear value based on the tire wear characteristics and the operating time using a preset wear model. After the vehicle completes driving, obtain tire condition inspection data after driving, compare the tire condition inspection data with the tire wear value, and optimize the wear model based on the comparison result.

[0109] At the same time, during vehicle travel, the navigation system also acquires a target driving section at a preset distance beyond the current driving route, obtains target section data corresponding to the target driving section, and acquires historical section data related to the target driving section. Predicted tire wear corresponding to the target driving section is then predicted based on the target section data and historical section data. Furthermore, the driving behavior data can be optimized based on the predicted tire wear.

[0110] Among them, after obtaining at least two different sections to be traveled after the current driving path through the navigation system, the predicted tire losses corresponding to all the sections to be traveled are predicted respectively, and the section to be traveled with the lowest predicted tire loss is used as the target driving section.

[0111] In summary, the intelligent tire wear monitoring method and device for autonomous vehicles provided by the present invention evaluates tire wear by combining road environment, driving behavior, and tire status data, and updates the wear model through post-driving feedback, thereby improving the accuracy of tire wear assessment and prediction. Furthermore, autonomous vehicles can dynamically evaluate the operating status of their tires, combining this with information such as planned road conditions to accurately assess whether the tires are suitable for the trip. During driving, wear prediction results are used to optimize route selection and driving strategies, reducing tire wear and maintenance costs. This comprehensively improves the autonomous vehicle's ability to perceive and manage tire status, enhancing driving safety and extending tire service life.

[0112] The above descriptions are merely embodiments of the present invention and are not intended to limit the patent scope of the present invention. Any equivalent transformations made using the contents of the present invention's description and drawings, or directly or indirectly applied in related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. An intelligent monitoring method for tire wear of an autonomous driving vehicle, characterized in that: include: Collect road environment data, driving behavior data, and tire data through vehicle sensors; constructing a tire wear feature based on the road environment data, driving behavior data, and tire data; Determining whether the road environment data or driving behavior data has changed, and if so, obtaining the vehicle's operating time under the tire wear characteristics; Obtaining a tire wear value according to the tire wear characteristics and the operating time using a preset wear model; Obtaining the tire wear value according to the tire wear characteristics and the operating time using a preset wear model includes: Predict tire wear during each driving session: in, Indicates the target road section running time; The total tire loss is expressed as: Where D(t) represents the tire wear characteristic, which is the degree of tire wear per unit time; Also includes: Obtaining a target driving section of a preset distance after the current driving route through the navigation system; Acquire target segment data corresponding to the target driving segment, and historical segment data related to the target driving segment; Predicting tire wear corresponding to the target driving section based on the target section data and historical section data; After obtaining the predicted tire loss corresponding to the target driving section, the method further includes: optimizing the driving behavior data according to the predicted tire wear; Also includes: Obtain at least two different sections of road to be traveled after the current travel route through the navigation system; respectively predicting the predicted tire losses corresponding to all the road sections to be traveled; The road section to be traveled with the lowest predicted tire loss is used as the target travel section.

2. The intelligent tire wear monitoring method for an autonomous driving vehicle according to claim 1, characterized in that: Also includes: Obtain tire status inspection data after driving the vehicle; Comparing the tire condition inspection data with the tire loss value to obtain a comparison result; The loss model is optimized according to the comparison result.

3. The intelligent tire wear monitoring method for an autonomous driving vehicle according to claim 1, characterized in that: The collecting of road environment data by vehicle sensors includes: The road environment data is collected by laser radar and camera and is expressed as: X road =[R(t),S(t)]; Among them, X road Represents road environment data and is stored in array form; R(t) represents the road surface type, and S(t) represents the road condition characteristics.

4. The intelligent tire wear monitoring method for an autonomous driving vehicle according to claim 1, characterized in that: The collecting of road environment data by vehicle sensors includes: The acceleration, braking intensity, and turning angle are obtained by the vehicle electronic control unit as the driving behavior data, which is expressed as: X drive =[A(t),B(t),θ(t)]; Among them, X drive Represents driving behavior data and is stored in array form; acceleration is A(t), braking intensity is B(t), turning angle is .

5. The intelligent tire wear monitoring method for an autonomous driving vehicle according to claim 1, characterized in that: The collecting of road environment data by vehicle sensors includes: The tire pressure, temperature, and vibration characteristics are obtained from the wheel hub sensor and the tire pressure monitoring system as the tire data, which is expressed as: X tire =[P(t),T(t),V(t)]; Among them, X tire is tire data and is stored in array form; tire pressure is P(t), temperature is T(t), and vibration characteristics is V(t).

6. An intelligent tire wear monitoring device for an autonomous vehicle, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, each step of the method for intelligent monitoring of tire wear of an autonomous driving vehicle as described in any one of claims 1-5 is implemented.

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