Cross-country driving mode self-adaptive adjusting method and system based on intelligent tire sensing

Abstract

The invention provides a cross-country driving mode self-adaptive adjusting method and system based on intelligent tire sensing, and belongs to the technical field of vehicle engineering. The tactile sensor is installed in the tire, three-axis acceleration data are collected in real time, and after preprocessing, feature extraction and road condition type recognition of the data processing unit, the driving mode controller maps the road condition type to a corresponding driving mode and generates a control instruction; a vehicle control system dynamically adjusts parameters such as engine / motor output, gearbox gears, a suspension system and tire pressure, and a user interface displays related information and supports manual intervention. By the adoption of the cross-country driving mode self-adaptive adjusting method and system based on intelligent tire sensing, the dynamic characteristics of the tires and the road condition can be monitored in real time, the complex road condition can be rapidly recognized, the driving mode is automatically switched, the driving safety is improved, and the driving performance is optimized.

Description

Technical Field

[0001] The present invention relates to the field of vehicle engineering technology, and in particular to a method and system for adaptively adjusting an off-road driving mode based on intelligent tire perception. Background Art

[0002] When navigating complex and ever-changing road conditions, off-road vehicles must adjust their driving modes based on varying road conditions (such as mud, sand, rocks, ice, and snow) to optimize the vehicle's maneuverability and handling. Traditional driving mode adjustments typically rely on manual switching based on the driver's experience, or on limited automatic adjustments by the vehicle's electronic control system based on preset sensor data.

[0003] However, these methods have the following shortcomings: (1) Limitations of manual switching: The driver may not be able to judge the current road conditions in a timely and accurate manner, resulting in untimely or inaccurate driving mode switching.

[0004] (2) Deficiencies of existing automatic adjustment systems: Traditional automatic adjustment systems usually rely on the vehicle's suspension sensors, steering angle sensors, etc., and cannot directly sense the contact status and dynamic characteristics between the tire and the road conditions. The adjustment accuracy and adaptability are limited.

[0005] (3) Lack of real-time feedback: The existing system cannot monitor the dynamic changes of tires (such as tire pressure, tread wear, tire slip, etc.) in real time, making it difficult to respond quickly to emergencies. Summary of the Invention

[0006] The purpose of the present invention is to provide an off-road driving mode adaptive adjustment method and system based on intelligent tire perception. By real-time monitoring of the tire acceleration signal and combining it with the vehicle's driving status, the current road conditions can be automatically identified and the driving mode can be automatically switched according to the road conditions to optimize the vehicle's driving performance and safety.

[0007] To achieve the above objectives, the present invention provides an off-road driving mode adaptive adjustment system based on intelligent tire perception, comprising: A data acquisition module, including a tactile sensor installed inside the tire, is used to collect the tire's three-axis acceleration data in real time, including longitudinal, lateral and vertical acceleration; A data processing unit, connected to the touch sensor, is used to pre-process acceleration data, extract features, and identify road conditions; A driving mode controller maps the road condition type output by the data processing unit to a corresponding driving mode and generates a control instruction; Vehicle control system, which executes control commands and dynamically adjusts engine / motor output, transmission gear, suspension system, and tire pressure; The user interface displays the current road condition type, driving mode and adjustment parameters, and supports manual intervention of the user in mode switching.

[0008] Preferably, the data processing unit includes: Data preprocessing module, which performs low-pass filtering, normalization and time synchronization on acceleration data; Feature extraction module, extracting the peak value, mean, variance, frequency distribution and correlation of acceleration in different directions of the acceleration signal; The road condition type recognition module classifies the extracted features based on the machine learning model and identifies different road condition types.

[0009] Preferably, the feature extraction module further calculates: tire slip rate, tire sideslip angle, road surface friction coefficient, and road surface roughness.

[0010] Preferably, the driving mode controller includes: Road condition type and driving mode mapping module, which defines the correspondence between road condition type and driving mode; The mode switching logic module ensures smooth transition of vehicle parameters when switching modes, and that the user's manual mode takes priority over automatic switching.

[0011] Preferably, the correspondence between road condition type and driving mode includes: Muddy road conditions are mapped to Muddy mode, and the following adjustment parameters are included: engine / motor output is reduced to 60% of the maximum torque, the transmission is switched to 1st gear, the suspension travel is increased to 80% of the maximum travel, and the tire pressure is reduced to 80% of the standard pressure; Sandy road conditions are mapped to Sand Mode, with the following parameters adjusted: engine / motor output maintained at 70% of maximum torque, the transmission shifted to first gear, suspension travel increased to 70% of maximum travel, and tire pressure reduced to 60% of standard pressure; Rocky road conditions are mapped to Rock Mode, with the following parameters adjusted: engine / motor output increased to 90% of maximum torque, the transmission shifted to first gear, suspension travel increased to 90% of maximum travel, and tire pressure maintained at 70% of standard pressure; Ice and snow road conditions are mapped to Ice and Snow mode, which adjusts parameters including: engine / motor output is reduced to 50% of maximum torque, the transmission shifts to first gear, suspension travel is increased to 80% of maximum travel, and the electronic stability system is activated; Normal road conditions are mapped to normal mode, and the adjustment parameters include: engine / motor output is maintained at 80% of the maximum torque, the transmission is switched to automatic mode, the suspension system is adjusted to comfort mode, and the tire pressure is maintained at the standard pressure.

[0012] The present invention also provides an off-road driving mode adaptive adjustment method based on intelligent tire perception, comprising the following steps: The data acquisition module collects three-axis acceleration data in real time through the tire's internal tactile sensor; The data preprocessing module filters, normalizes and time-synchronizes the acceleration data; The feature extraction module extracts acceleration features and further calculates tire slip rate, tire side slip angle, road friction coefficient, and road roughness. The road condition type recognition module identifies the road condition type based on a machine learning model. The driving mode controller switches to the corresponding driving mode according to the mapping relationship based on the road conditions, and dynamically adjusts the engine output, transmission gear, suspension system and tire pressure through the vehicle control system; The user interface provides feedback on the current road condition type, driving mode and adjustment parameters, and allows users to intervene manually.

[0013] Preferably, the slip ratio is calculated as follows: ; in, Indicates the actual speed of the vehicle, Indicates the linear speed of the tire.

[0014] Preferably, the calculation formula of the sideslip angle is as follows: ; in, represents the sideslip angle, Indicates the lateral acceleration of the tire.

[0015] Preferably, the sampling frequency of the tactile sensor of the data acquisition module is at least 1000 Hz, and the acquired acceleration signal is sent to the data processing unit of the vehicle via wireless transmission technology.

[0016] Preferably, the user interface is also used to receive user feedback on the suitability of the current driving mode. The system optimizes the mode switching logic based on user feedback, provides a system optimization and upgrade interface, and regularly updates the machine learning model.

[0017] Therefore, the present invention adopts the above-mentioned off-road driving mode adaptive adjustment method and system based on intelligent tire perception, and the beneficial technical effects are as follows: (1) Improving driving safety: By monitoring the dynamic characteristics of tires and road conditions in real time, the present invention can quickly identify potentially dangerous road conditions and automatically switch to the most suitable driving mode, significantly improving the driving safety of the vehicle under complex road conditions.

[0018] (2) Optimize driving performance: Automatically adjust the vehicle's engine / motor output, suspension system, tire pressure and other parameters according to different road conditions to optimize the vehicle's passability and controllability, and enhance the driving experience.

[0019] (3) Reduce the burden on the driver: Automated driving mode switching reduces the driver's operating burden, especially in complex and changeable off-road conditions, and improves driving convenience and comfort.

[0020] (4) Enhanced system adaptability: Through machine learning algorithms and multi-sensor data fusion technology, the present invention can adapt to a variety of complex road conditions and driving scenarios, and has high robustness and adaptability. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 This is a structural diagram of the off-road driving mode adaptive adjustment system based on intelligent tire perception of the present invention; Figure 2 This is a flow chart of the off-road driving mode adaptive adjustment method based on intelligent tire perception of the present invention. DETAILED DESCRIPTION

[0022] The technical solution of the present invention is further described below with reference to the accompanying drawings and embodiments.

[0023] Unless otherwise defined, technical or scientific terms used in the present invention shall have the same meaning as commonly understood by one of ordinary skill in the art to which the present invention belongs.

[0024] Example 1 like Figure 1 As shown, the off-road driving mode adaptive adjustment system based on intelligent tire perception includes: (1) The data acquisition module includes a tactile sensor installed inside the tire (a three-axis acceleration sensor, installed in the center of the tire inner liner, one for each tire). Its core function is to collect the tire's three-axis acceleration data in real time, covering longitudinal, lateral, and vertical acceleration information. To accurately capture the subtle dynamic changes of the tire under different road conditions, the sampling frequency is set to at least 1000Hz. The collected acceleration signal is sent to the vehicle's data processing unit in real time and stably via wireless transmission technology, ensuring the timeliness and accuracy of the data.

[0025] (2) Data processing unit, connected to the touch sensor, used for preprocessing acceleration data, feature extraction and road condition type identification.

[0026] Specifically, the data processing unit includes: Data preprocessing module, which performs low-pass filtering, normalization and time synchronization on acceleration data; Among them, filtering processing: low-pass filtering is performed on the collected acceleration signal, and the filtering frequency is set to 400 Hz to remove high-frequency noise and retain useful low-frequency signals.

[0027] Normalization: Normalize the acceleration signal to the range of -1 to 1.

[0028] Time synchronization: Ensure that the tactile sensor data is synchronized with other vehicle sensors (such as speed sensors and steering angle sensors), with the error controlled within 1ms.

[0029] The feature extraction module extracts the peak value, mean value, variance, frequency distribution and correlation of acceleration in different directions of the acceleration signal; the feature extraction module further calculates: Tire slip rate, calculated as: ; in, Indicates the actual speed of the vehicle, Indicates the linear speed of the tire.

[0030] Tire slip angle, calculated as: ; in, represents the sideslip angle, Indicates the lateral acceleration of the tire.

[0031] Road friction coefficient: By analyzing the longitudinal and lateral acceleration signals of the tire and combining them with tire dynamics models (such as the magic formula, brush model, UniTire model, SWIFT model, etc.), the road friction coefficient is estimated using the least squares method or Kalman filter algorithm.

[0032] Road roughness: By analyzing the frequency distribution and amplitude changes of the tire's vertical acceleration signal, the Fast Fourier Transform (FFT) algorithm is used to extract road roughness characteristics. The power spectral density (PSD) and root mean square (RMS) of the road roughness are calculated.

[0033] The road condition type recognition module classifies the extracted features based on machine learning models (convolutional neural networks such as LSTM, support vector machines and other machine learning models) to identify different road condition types.

[0034] These parameters can also be derived using triaxial acceleration signals collected by tire-mounted sensors (such as triaxial accelerometers) as input data. By constructing a labeled dataset (i.e., data pairs containing sensor signals and corresponding true parameter values), machine learning models (such as spatiotemporal convolutional neural networks or neural networks embedded with physical constraints) can be trained to directly estimate tire slip, sideslip angle, road friction coefficient, and road roughness. This data-driven approach aims to leverage the powerful feature extraction and nonlinear fitting capabilities of machine learning to complement or replace traditional physical modeling methods, improving the robustness and generalization of parameter estimation.

[0035] The road condition classification is shown in Table 1: Table 1 Road condition classification ;

[0036] Supplementary Note: The typical value ranges listed in Table 1 are based on ISO 8608 and GB / T 7031 standards, and are preliminarily delineated by integrating large-scale road test data and manual annotation results. They can be used as a reference at the current stage; they still require continuous verification and dynamic revision.

[0037] (3) The driving mode controller maps the road condition type output by the data processing unit to the corresponding driving mode and generates control instructions.

[0038] The driving mode controller includes: The road condition type and driving mode mapping module defines the corresponding relationship between road condition type and driving mode, where: Muddy road conditions are mapped to Muddy mode, and the following adjustment parameters are included: engine / motor output is reduced to 60% of the maximum torque, the transmission is switched to 1st gear, the suspension travel is increased to 80% of the maximum travel, and the tire pressure is reduced to 80% of the standard pressure; Sandy road conditions are mapped to Sand Mode, with the following parameters adjusted: engine / motor output maintained at 70% of maximum torque, the transmission shifted to first gear, suspension travel increased to 70% of maximum travel, and tire pressure reduced to 60% of standard pressure; Rocky road conditions are mapped to Rock Mode, with the following parameters adjusted: engine / motor output increased to 90% of maximum torque, the transmission shifted to first gear, suspension travel increased to 90% of maximum travel, and tire pressure maintained at 70% of standard pressure; Ice and snow road conditions are mapped to Ice and Snow mode, which adjusts parameters including: engine / motor output is reduced to 50% of maximum torque, the transmission shifts to first gear, suspension travel is increased to 80% of maximum travel, and the electronic stability system is activated; Normal road conditions are mapped to Normal mode, and the adjustment parameters include: engine / motor output is maintained at 80% of the maximum torque, the transmission is switched to automatic mode, the suspension system is adjusted to Comfort mode, and the tire pressure is maintained at standard pressure; The mode switching logic module ensures smooth transition of vehicle parameters when switching modes, and that the user's manual mode takes priority over automatic switching.

[0039] Smooth Transition: During mode switching, the driving mode controller ensures smooth transitions in vehicle parameters to avoid discomfort to the vehicle and passengers. For example, engine / motor torque is adjusted gradually to avoid sudden changes.

[0040] (4) Vehicle control system, which executes control instructions and dynamically adjusts engine / motor output, transmission gear, suspension system and tire pressure.

[0041] (5) User interface, which displays the current road condition type, driving mode, and vehicle parameter adjustment information, allowing users to understand the vehicle status at any time. At the same time, users can provide feedback on the suitability of the current driving mode through the interface, and the system optimizes the mode switching logic based on user feedback. In addition, a system optimization and upgrade interface is provided to regularly update the machine learning model and optimize the road condition recognition algorithm and driving mode switching strategy based on new driving data.

[0042] Example 2 like Figure 2 As shown, a method for adaptively adjusting off-road driving mode based on intelligent tire tactile perception includes the following steps: The data acquisition module collects three-axis acceleration data in real time through the tire's internal tactile sensor; The data preprocessing module filters, normalizes and time-synchronizes the acceleration data; The feature extraction module extracts acceleration features and further calculates tire slip rate, tire side slip angle, road friction coefficient, and road roughness. The road condition type recognition module identifies the road condition type based on a machine learning model. The driving mode controller switches to the corresponding driving mode according to the mapping relationship based on the road conditions, and dynamically adjusts the engine output, transmission gear, suspension system and tire pressure through the vehicle control system; The user interface displays the current road condition type, driving mode, and vehicle parameter adjustment information, allowing users to keep informed of vehicle status. Users can also provide feedback on the suitability of the current driving mode through the interface, and the system optimizes mode switching logic based on this feedback. Furthermore, a system optimization and upgrade interface is provided to regularly update machine learning models and optimize road condition recognition algorithms and driving mode switching strategies based on new driving data.

[0043] It is worth noting that the contents not elaborated in detail in the present invention are all prior art and are well known to those skilled in the art.

[0044] Therefore, the present invention adopts the above-mentioned off-road driving mode adaptive adjustment method and system based on intelligent tire perception, which automatically identifies the current road conditions by real-time monitoring of the tire acceleration signal and combining it with the vehicle's driving status, and automatically switches the driving mode according to the road conditions to optimize the vehicle's driving performance and safety.

[0045] 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. Off-road driving mode adaptive adjustment system based on intelligent tire perception, characterized by: include: A data acquisition module, including a tactile sensor installed inside the tire, is used to collect the tire's three-axis acceleration data in real time, including longitudinal, lateral and vertical acceleration; A data processing unit, connected to the touch sensor, is used to pre-process acceleration data, extract features, and identify road conditions; A driving mode controller maps the road condition type output by the data processing unit to a corresponding driving mode and generates a control instruction; Vehicle control system, which executes control commands and dynamically adjusts engine / motor output, transmission gear, suspension system, and tire pressure; The user interface displays the current road condition type, driving mode and adjustment parameters, and supports manual intervention of the user in mode switching.

2. The off-road driving mode adaptive adjustment system based on intelligent tire perception according to claim 1 is characterized in that: The data processing unit includes: Data preprocessing module, which performs low-pass filtering, normalization and time synchronization on acceleration data; Feature extraction module, extracting the peak value, mean, variance, frequency distribution and correlation of acceleration in different directions of the acceleration signal; The road condition type recognition module classifies the extracted features based on the machine learning model and identifies different road condition types.

3. The off-road driving mode adaptive adjustment system based on intelligent tire perception according to claim 2 is characterized in that: The feature extraction module further calculates: tire slip rate, tire sideslip angle, road friction coefficient, and road roughness.

4. The off-road driving mode adaptive adjustment system based on intelligent tire perception according to claim 1 is characterized in that: The driving mode controller includes: Road condition type and driving mode mapping module, which defines the correspondence between road condition type and driving mode; The mode switching logic module ensures smooth transition of vehicle parameters when switching modes, and that the user's manual mode takes priority over automatic switching.

5. The off-road driving mode adaptive adjustment system based on intelligent tire perception according to claim 1 is characterized in that: The correspondence between road condition types and driving modes includes: Muddy road conditions are mapped to Muddy mode, and the following adjustment parameters are included: engine / motor output is reduced to 60% of the maximum torque, the transmission is switched to 1st gear, the suspension travel is increased to 80% of the maximum travel, and the tire pressure is reduced to 80% of the standard pressure; Sandy road conditions are mapped to Sand Mode, with the following parameters adjusted: engine / motor output maintained at 70% of maximum torque, the transmission shifted to first gear, suspension travel increased to 70% of maximum travel, and tire pressure reduced to 60% of standard pressure; Rocky road conditions are mapped to Rock Mode, with the following parameters adjusted: engine / motor output increased to 90% of maximum torque, the transmission shifted to first gear, suspension travel increased to 90% of maximum travel, and tire pressure maintained at 70% of standard pressure; Ice and snow road conditions are mapped to Ice and Snow mode, which adjusts parameters including: engine / motor output is reduced to 50% of maximum torque, the transmission shifts to first gear, suspension travel is increased to 80% of maximum travel, and the electronic stability system is activated; Normal road conditions are mapped to normal mode, and the adjustment parameters include: engine / motor output is maintained at 80% of the maximum torque, the transmission is switched to automatic mode, the suspension system is adjusted to comfort mode, and the tire pressure is maintained at the standard pressure.

6. The off-road driving mode adaptive adjustment method based on intelligent tire perception is characterized by: The following steps are involved: The data acquisition module collects three-axis acceleration data in real time through the tire's internal tactile sensor; The data preprocessing module filters, normalizes and time-synchronizes the acceleration data; The feature extraction module extracts acceleration features and further calculates tire slip rate, tire side slip angle, road friction coefficient, and road roughness. The road condition type recognition module identifies the road condition type based on a machine learning model. The driving mode controller switches to the corresponding driving mode according to the mapping relationship based on the road conditions, and dynamically adjusts the engine output, transmission gear, suspension system and tire pressure through the vehicle control system; The user interface provides feedback on the current road condition type, driving mode and adjustment parameters, and allows users to intervene manually.

7. The off-road driving mode adaptive adjustment method based on intelligent tire perception according to claim 6, characterized in that: The slip ratio is calculated as follows: ； in, Indicates the actual speed of the vehicle, Indicates the linear speed of the tire.

8. The off-road driving mode adaptive adjustment method based on intelligent tire perception according to claim 6, characterized in that: The formula for calculating the sideslip angle is as follows: ； in, represents the sideslip angle, Indicates the lateral acceleration of the tire.

9. The off-road driving mode adaptive adjustment method based on intelligent tire perception according to claim 6, characterized in that: The sampling frequency of the tactile sensor of the data acquisition module is at least 1000 Hz, and the collected acceleration signal is sent to the vehicle's data processing unit via wireless transmission technology.

10. The off-road driving mode adaptive adjustment method based on intelligent tire perception according to claim 6, characterized in that: The user interface is also used to receive user feedback on the suitability of the current driving mode. The system optimizes the mode switching logic based on user feedback, provides a system optimization and upgrade interface, and regularly updates the machine learning model.

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