Unmanned vehicle driving capability improvement control method and system

Abstract

The invention discloses a driving capability improvement control method and system for an unmanned vehicle. The method comprises the step of realizing road condition perception and tire pressure optimization through vehicle cloud cooperation. A front vehicle collects road surface data and uploads the road surface data to a cloud server after preprocessing, and the server carries out aggregation analysis and constructs and updates a road surface state map. The rear vehicle requests road condition data, verification is carried out, remote data and local data are fused after weight distribution, and road surface state estimation is generated. And calculating a road surface friction coefficient and a weather influence factor based on the fused data, processing tire temperature data, monitoring a load-carrying vehicle speed, formulating an adjusting strategy, and carrying out modeling solving to obtain the optimal tire pressure. And an adjusting strategy is generated according to the optimal tire pressure, differential tire pressure control is implemented, adjustment is executed through closed-loop and predictive control, the effect is monitored in real time, and data are uploaded to a cloud optimization system. The control stability and trafficability of the vehicle under complex road conditions are effectively improved, and driving safety and riding comfort are guaranteed.

Description

Technical Field

[0001] The present invention belongs to the field of intelligent tire pressure control, and more specifically, relates to a control method and system for improving the driving capability of an unmanned vehicle. Background Art

[0002] Amid the booming development of intelligent transportation technologies, driverless vehicles, leveraging their automation and intelligence, are becoming a key development direction in the transportation sector. Autonomous vehicles utilize onboard sensors to perceive environmental information and automatically navigate and drive according to pre-set programs or algorithms. This not only frees drivers and passengers from the need for manual operation but also allows them to independently complete transportation tasks without passengers, significantly improving transportation efficiency and flexibility.

[0003] However, autonomous vehicles face numerous challenges in practical application, not least the complex and ever-changing terrain that severely tests their driving capabilities. Autonomous vehicles often navigate diverse road surfaces, including gravel, muddy roads, snowy terrain, and steep slopes, each with significantly varying coefficients of friction. Furthermore, weather factors such as rain, snow, high temperatures, and low temperatures can cause the coefficient of friction on the same road surface to fluctuate dynamically over time, further increasing the complexity and uncertainty of vehicle operation.

[0004] Tire pressure, a core factor influencing vehicle driving performance, is closely related to traction, braking force, handling stability, and off-road capability. Traditional vehicles generally use a fixed tire pressure design, which cannot dynamically adjust to real-time road conditions and cannot meet the performance requirements of autonomous vehicles in complex environments. While some high-end vehicles are equipped with tire pressure monitoring systems, their function is often limited to alerting the driver of abnormal tire pressures. They lack the ability to automatically adjust and adapt to changing road conditions to optimize vehicle performance.

[0005] While existing vehicle tire inflation and deflation systems can adjust tire pressure based on road surface information, they suffer from significant practical drawbacks. These systems often rely on crude road surface information to implement tire pressure adjustment strategies, making it difficult to accurately adapt to the complex variations in road adhesion under varying weather conditions. During actual driving, the inability to accurately adjust tire pressure can easily lead to wheel slippage, severely impacting vehicle stability and significantly increasing the risk of traffic accidents. Furthermore, under complex road conditions, existing systems are unable to achieve an optimal match between tire pressure and road friction, limiting the vehicle's maneuverability and off-road performance, hindering the full realization of the technological advantages of autonomous vehicles.

[0006] Therefore, there is an urgent need to develop a technical solution that can achieve adaptive tire pressure adjustment based on actual road and weather conditions. This solution requires accurate identification of road friction parameters through external means, combined with key factors such as tire positive pressure, to precisely calculate the optimal tire pressure value, thereby achieving the optimal tire-ground contact area and maximizing friction, significantly improving vehicle maneuverability and off-road performance, and ensuring the safe and efficient operation of autonomous vehicles in complex environments. Summary of the Invention

[0007] The present invention aims to overcome the shortcomings of existing technologies by providing a control method for improving the driving performance of unmanned vehicles. By fusing multi-source data to analyze ground friction characteristics, a mapping relationship between tire pressure and ground friction is established, enabling adaptive adjustment of tire pressure under all operating conditions. This optimizes tire grip, improving vehicle traction, braking performance, and off-road capability, ensuring passability and handling stability in complex road conditions, while also extending tire life, reducing energy consumption, and enhancing endurance.

[0008] In response to the above-mentioned deficiencies or improvement needs of the prior art, as a first aspect of the present invention, the present invention provides a control method for improving the driving capability of an unmanned vehicle, comprising:

[0009] S1. The leading vehicle collects road condition data in real time and uploads it to a cloud server after preprocessing. The cloud server aggregates and analyzes the received data from multiple vehicles, synchronously building and updating a road condition map. The following vehicle requests road condition data from the server based on multi-dimensional information, including current location and driving direction. After verifying the road condition data, the following vehicle assigns a corresponding weight to it, and then fuses the remote data with the local data to generate a final road condition estimate.

[0010] S2. Based on the fused data, a baseline value is first determined based on the road surface type. Correction factors, including weather, road surface, tire, and vehicle status, are then combined to calculate the road friction coefficient, and its credibility is evaluated to adjust the control strategy. A weather and road surface condition mapping model is also constructed, and weather influencing factors are calculated for friction coefficient and tire pressure adjustment. The strategy is optimized in advance by predicting weather changes. A monitoring system is built to process tire temperature data and establish a temperature-tire pressure model. This model adaptively controls tire pressure within different temperature ranges and handles abnormal conditions. Multiple sensors are used to monitor load and vehicle speed, calculate load distribution and dynamic transfer, identify driving patterns, and establish a mapping model to design tire pressure adjustment strategies. Finally, a tire pressure optimization model is developed, combined with operating condition formulas and temperature compensation solutions, and multiple safety constraint checks are performed to determine the optimal tire pressure.

[0011] S3. First, determine the need for adjustment based on the optimal tire pressure. Combined with the road condition optimization strategy, the target tire pressure and adjustment parameters are clearly defined. Then, differentiated tire pressure plans are developed for each wheel based on different operating conditions, including acceleration and braking, and constraints are set. The adjustment execution process is controlled through closed-loop and predictive control methods. The tire pressure adjustment effect is continuously monitored, and the adjustment results and the latest road condition data are uploaded to the cloud server.

[0012] Furthermore, the specific method for determining the base value according to the road surface type in S2 and calculating the road friction coefficient by combining correction factors including weather, road surface, tire, and vehicle status is as follows:

[0013] First, according to the road type, the basic friction coefficient μ is determined by looking up the table. base , different road surface types correspond to different value ranges;

[0014] Calculate the correction factors for different influencing factors:

[0015] Weather correction factor f weather :According to the calculation of precipitation and temperature, different conditions of precipitation and temperature will make f weather Take values ​​within a certain range:

[0016] Road surface condition correction factor f surface : Considering the road surface slipperiness and roughness, let the road surface slipperiness be S, then:

[0017] f surface =1-k s ×ΔS

[0018] Among them, k s is the coefficient related to the road surface, and is the change in ΔS slipperiness

[0019] Tire condition correction factor f tire : Considering the tire temperature T and the degree of wear, the temperature change will make f tire Produce corresponding changes, assuming the temperature change is ΔT, then:

[0020] f tire =1+k t ×ΔT

[0021] Among them, k t is the coefficient related to the tire;

[0022] Vehicle state correction factor f vehicle : Considering the load W and speed v, the load change will make f vehicle Change, let the load change be ΔW

[0023] f vehicle =1-k w ×ΔW

[0024] where k w is a coefficient related to the tire.

[0025] Then the calculation formula for the road surface friction coefficient μ is as follows:

[0026] μ = μ base × f weather × f surface × f tire × f vehicle

[0027] In the formula, μ represents the road surface friction coefficient corrected by the correction factors including weather, road surface, tire, and vehicle state.

[0028] Furthermore, the method for calculating the weather influence factor in S2 for friction coefficient and tire pressure adjustment, and specifically optimizing the strategy in advance by predicting weather changes is:

[0029] Let the precipitation intensity be I, the temperature be T, the wind speed be V, the reference tire pressure be P ref , the actual tire pressure be P, and the tire pressure adjustment amount be ΔP;

[0030] Considering the influence of precipitation, temperature, and wind speed on the weather influence factor comprehensively, the fed weather calculation formula is:

[0031] f weather (I, T, V) = a - bI - c(T - T0) - d(V - V0)

[0032] Among them, a is the base coefficient, representing the initial value of the weather influence factor when there is no precipitation, the temperature is the reference temperature T0, and the wind speed is the reference wind speed V0;

[0033] b is the precipitation influence coefficient, b > 0, and the greater the precipitation intensity I, the more f weather decreases;

[0034] c is the temperature influence coefficient, used to quantify the influence degree of temperature on f weather . When T > T0, for every certain increase in temperature, f weather changes by a multiple of c; when T < T0, the same applies;

[0035] d is the wind speed influence coefficient. When the wind speed V deviates from the reference wind speed V0, f weather will change;

[0036] Assume that there is a linear relationship between the tire pressure adjustment amount ΔP and the weather influence factor f weather :

[0037] ΔP = e × f weather

[0038] Among them, e is the tire pressure adjustment coefficient, which reflects the influence of weather factors on the tire pressure adjustment amount;

[0039] The calculation formula for the actual tire pressure P is:

[0040] P=P ref +ΔP=P ref +e×f weather

[0041] Right now:

[0042] P=P ref +e×(a-bI-c(T-T0)-d(V-V0))

[0043] If the system predicts that the future precipitation intensity is I pred , temperature is T pred , wind speed is V pred , then the future weather impact factor can be calculated

[0044]

[0045] Then adjust the tire pressure adjustment amount ΔP in advance pred :

[0046]

[0047] And adjust the actual tire pressure to: P pred =P ref +ΔP pred .

[0048] Furthermore, the specific method of building a monitoring system to process tire temperature data in S2 is:

[0049] Assume the tire temperature series is T tire ={T tire1 ,T tire2 ,…,T tiren};

[0050] Data is filtered by median filtering method: Assume the filter window size m, for each measurement point i, the filtered value T′ tirei is the median of the m values ​​in the window, that is,

[0051]

[0052] By calculating the tire temperature change rate:

[0053]

[0054] To predict the warming trend, where Δt is the measurement time interval;

[0055] Assume that the temperature set of the local area of ​​the tire is:

[0056] T tire-local ={T tirej1 ,T tirej2 ,…,T tirejn}

[0057] Calculate local temperature difference

[0058] ΔT tire-local =max(T tire-local )-min(T tire-local )

[0059] When the local temperature difference exceeds the set threshold, it is considered abnormal;

[0060] Based on the temperature measurement values ​​at different positions of the tire, a mapping relationship T between tire temperature and position is constructed. tire (x,y) is used to generate a tire temperature distribution heat map to detect potential hot spots.

[0061] Furthermore, the specific method of establishing the temperature-tire pressure model in S2 and adaptively controlling the tire pressure according to different temperature ranges and handling abnormal situations is as follows:

[0062] Build an ideal gas model:

[0063]

[0064] Among them, P1 and T tire1 are the initial tire pressure and initial tire temperature, in the same unit, the temperature is in Kelvin, P2 and T tire2 The current tire pressure and current tire temperature;

[0065] Then the modified model of the temperature-tire pressure relationship model is:

[0066]

[0067] Where k is the correction factor, f(T tire2 ) is the high temperature compensation function, which is about the current tire temperature T tire2 function;

[0068] When the tire temperature T tire When in the standard range, tire pressure P=P base , P base is the base tire pressure;

[0069] If T tire If the tire pressure is lower than the lower limit of the standard range, adjust the tire pressure to P=P base (1-α low ), α lowis the low temperature compensation coefficient, used to compensate for the rubber hardening effect;

[0070] If T tire When the tire pressure is higher than the upper limit of the standard range but not reaching the dangerous value, the tire pressure is adjusted to P = P base (1+α high ), α high It is a high temperature compensation coefficient to prevent excessive expansion of the tire;

[0071] If T tire If the dangerous threshold is exceeded, the high temperature alarm is triggered and the tire pressure is adjusted to P=P base (1-α danger ), α danger A larger reduction factor.

[0072] Furthermore, the specific method for solving the optimal tire pressure in S2 is:

[0073] The tire pressure optimization problem is modeled as a constrained optimization problem:

[0074] The goal is to maximize the ground friction function F(P,T tire ):

[0075]

[0076] Among them, F z is the ground friction, P is the tire pressure, T tire is the tire temperature, P min and P max are the minimum and maximum tire pressures allowed, T tire_min and T tire_max are the safe minimum and maximum tire temperatures, respectively;

[0077] Establish a model relationship between tire pressure P and ground contact area A, considering temperature T tire Impact:

[0078]

[0079] Where k is the proportional constant, F z is the ground friction, α is the temperature compensation coefficient, T tire_0 is the reference temperature.

[0080] Furthermore, the specific method of differentiating tire pressure solutions and setting constraints in S3 is:

[0081] Calculate the tire pressure correction value under each working condition, assuming that under acceleration condition Under braking condition Under turning conditions Off-road conditions

[0082] Then, the differentiated tire pressure control can be expressed by the following formula:

[0083] For the target tire pressure Pi of the i-th wheel:

[0084]

[0085] The differentiated constraint scheme is specifically as follows:

[0086] Safety limit: For adjacent wheels i and j, |P i -P j |≤0.5bar;

[0087] Performance optimization: Assume that the adhesion of each wheel is According to the adhesion distribution, the optimization goal is: Among them, η i is the weight coefficient of each wheel adhesion, and P i The relationship between adhesion and tire pressure must be met;

[0088] Comfort considerations: By establishing a comfort evaluation function C(P1, P2, P3, P4), the effect of tire pressure differences on comfort is limited. That is, setting C min is the minimum acceptable value of comfort, then there is a constraint: C(P1,P2,P3,P4)≥C min .

[0089] Furthermore, the specific calculation method of the tire pressure correction value under each working condition in S2 is:

[0090] Acceleration conditions:

[0091] On a good road surface, let the driving wheel be j, then

[0092]

[0093] Among them, β1>0 is the proportional coefficient;

[0094] On wet roads,

[0095]

[0096] Among them, β2>0 is the proportionality coefficient;

[0097] Braking conditions:

[0098] Assume the coordinates of the vehicle's center of mass are (x c ,y c ), the position coordinates of each wheel are (x i ,y i ),i=(1,2,3,4), where i represents four wheels; the load transfer coefficient is k load, calculate the tire pressure correction value caused by the positive pressure change of each wheel based on the center of mass position and load transfer

[0099] First calculate the distance of each wheel relative to the center of mass:

[0100] di=(xi-xc)²+(yi-yc)²

[0101] The positive pressure change of each wheel is obtained based on the load transfer relationship, and the tire pressure correction value is obtained based on the tire characteristics:

[0102]

[0103] Among them, γ i is the proportionality coefficient related to the tire;

[0104] Turning condition: Let the outer wheel of the vehicle be m and the inner wheel be n when turning.

[0105]

[0106]

[0107] Among them, β3>0 and the inner and outer tire pressure difference is satisfied

[0108] Then, for the i-th tire, the calculation formula for the corrected tire pressure under cornering conditions is:

[0109]

[0110] Off-road conditions: For all wheels i, where β4∈[0.3,0.4].

[0111] Furthermore, the specific method of controlling the adjustment execution process by the closed-loop and predictive control method in S2 is:

[0112] Assume the target tire pressure is P target , the control voltage is V or the duty cycle of the pulse width modulation PWM signal is D, there is a functional relationship V=f1(P target ) or D=f2(P target ), used to convert the target tire pressure into a corresponding control signal to control the operation of the air pressure regulating valve and the air pump;

[0113] Closed-loop control algorithm: Let the real-time feedback tire pressure be P real , the adjustment amount of the control signal is ΔS, and the closed-loop control algorithm can be expressed as ΔS=f3(P target -P real ), by continuously adjusting the control signal according to the difference between the target tire pressure and the real-time tire pressure, so that P real Approaching Ptarget ,Right now in Indicates the real-time tire pressure after the nth adjustment;

[0114] Predictive control: Assume that the natural change in tire pressure estimated based on temperature change is ΔP T , which is a function of the temperature change ΔT, namely ΔP T =f4(ΔT); When adjusting the control signal, consider this estimated change, and the adjusted control signal S adjusted For S adjusted =S initial +f5(ΔP T ), where S initial is the initial control signal without considering the temperature change, and f5 is the function for adjusting the control signal according to ΔPT.

[0115] As a second aspect of the present invention, there is also provided a control system for improving the driving capability of an unmanned vehicle, comprising:

[0116] The data processing unit collects road condition data in real time from the leading vehicle and uploads it to the cloud server after data preprocessing. The cloud server aggregates and analyzes the received data from multiple vehicles, synchronously building and updating a road condition map. The following vehicle requests road condition data from the server based on multi-dimensional information, including current location and driving direction. After verifying the road condition data, the following vehicle assigns a corresponding weight to it and then fuses the remote data with the local data to generate a final road condition estimate.

[0117] The parameter calculation unit is used to determine the base value based on the fused data according to the road surface type, and then calculate the road friction coefficient by combining correction factors including weather, road surface, tire, and vehicle status, and evaluate its credibility to adjust the control strategy. At the same time, a weather and road surface status mapping model is constructed to calculate the weather influencing factor for friction coefficient and tire pressure adjustment, and optimize the strategy in advance by predicting weather changes. A monitoring system is set up to process tire temperature data and establish a temperature-tire pressure model to adaptively control tire pressure according to different temperature ranges and handle abnormal situations. Multiple sensors are used to monitor load and vehicle speed, calculate load distribution and dynamic transfer, identify driving modes, and establish a mapping model to design tire pressure adjustment strategies. Finally, the tire pressure optimization model is built, combined with the working condition formula and temperature compensation solution, and multiple safety constraint checks are performed to obtain the optimal tire pressure.

[0118] The execution control unit is used to first determine the adjustment needs based on the optimal tire pressure, and combine it with the road condition optimization strategy to clarify the target tire pressure and adjustment parameters; then formulate differentiated tire pressure plans for each wheel and set constraints for different working conditions including acceleration and braking; control the adjustment execution process through closed-loop and predictive control methods; continuously monitor the tire pressure adjustment effect, and upload the adjustment effect and the latest road condition data to the cloud server.

[0119] In general, the above technical solutions conceived by the present invention can achieve the following beneficial effects compared with the prior art:

[0120] 1. The present invention provides a control method for improving the driving capabilities of unmanned vehicles. By establishing a data sharing and cloud-based collaborative analysis mechanism between vehicles, it achieves efficient interaction and in-depth mining of road condition information. The leading vehicle performs noise filtering, feature extraction, and other operations on the collected raw data before uploading it to the cloud server. Multiple strategies are used for data uploading, including regular, burst, hierarchical, and bandwidth adaptive. After receiving data from multiple vehicles, the cloud server performs aggregate analysis and constructs a dynamic road condition map that includes a layer of recommended tire pressure values. The following vehicle requests road condition information ahead from the server on demand, and verifies and integrates it with local sensor data. This technical feature breaks down the information barriers between vehicles, allowing the following vehicle to obtain accurate road conditions in advance, adjust tire pressure strategies in a timely manner, and reduce driving risks caused by sudden changes in road conditions. At the same time, the adjustment experience and road condition data of each vehicle are uploaded to the cloud, enabling collective learning for the entire fleet, continuously optimizing the system's ability to predict and respond to complex road conditions, and improving the driving safety and traffic efficiency of the vehicle group.

[0121] 2. The present invention provides a control method for improving the driving ability of an unmanned vehicle, which realizes adaptive adjustment of tire pressure by establishing a mapping relationship model between tire pressure and ground friction. Based on tire dynamics and ground friction characteristics, this method models tire pressure optimization as a constrained optimization problem, comprehensively considering parameters such as vehicle driving state, road friction coefficient, and tire temperature, and solves the optimal tire pressure value that optimizes the tire-ground contact area and maximizes friction. At the same time, special tire pressure calculation formulas are designed for different working conditions, and temperature compensation is performed to ensure that the tire's grip is always in the optimal state under various conditions. This not only improves the vehicle's driving efficiency, reduces energy consumption, and extends endurance, but also allows the tire to operate within a safe temperature range while ensuring maximum ground adhesion, thereby extending the tire's service life.

[0122] 3. The present invention provides a control method for improving the driving ability of unmanned vehicles, which enhances the vehicle's passability in complex road conditions by implementing differentiated tire pressure control and intelligent execution strategies. The system calculates the optimal tire pressure for each wheel individually and formulates a differentiated control plan based on different driving conditions such as vehicle acceleration, braking, turning, off-roading, and special situations such as single-wheel slippage and rollover risks. During the execution process, a closed-loop control algorithm and predictive control are used to accurately adjust the tire pressure, and the adjustment rate, accuracy, smoothness and noise are controlled. In addition, through inter-vehicle communication to share road information, the following vehicle can adjust the tire pressure in advance, achieving collaborative optimization of the vehicle group. These technical means effectively improve the vehicle's handling stability and passability in complex road conditions, ensuring driving safety and ride comfort. BRIEF DESCRIPTION OF THE DRAWINGS

[0123] Figure 1 This is a flow chart of a method for controlling the improvement of the driving capability of an unmanned vehicle according to an embodiment of the present invention;

[0124] Figure 2 A diagram of a multi-vehicle cooperative system according to an embodiment of the present invention;

[0125] Figure 3 This is a multi-vehicle collaboration logic diagram of an embodiment of the present invention;

[0126] Figure 4 This is a parameter adjustment logic diagram of an embodiment of the present invention;

[0127] Figure 5 2 is a diagram of system units according to an embodiment of the present invention. DETAILED DESCRIPTION

[0128] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only intended to illustrate the present invention and are not intended to limit the present invention. In addition, the technical features involved in the various embodiments of the present invention described below may be combined with each other as long as they do not conflict with each other.

[0129] Example 1

[0130] Please refer to Figure 1 This embodiment 1 provides a method for improving the driving capability of an unmanned vehicle, including:

[0131] (1) Data processing

[0132] Please refer to Figure 2 as well as Figure 3 , this embodiment will expand and explain the data processing process:

[0133] 1.1 Preprocessing and uploading of preceding vehicle data

[0134] The preceding vehicle collects road condition data in real time, preprocesses the data, and uploads it to the cloud server. Data preprocessing involves multiple steps. The first involves noise filtering and outlier detection, using statistical methods to identify and eliminate abnormal readings. Feature extraction then occurs, using convolutional neural networks to extract road surface type features from the image and spectral analysis to extract road surface roughness features from the radar point cloud. To reduce transmission bandwidth requirements, a lossless compression algorithm is used to compress the data.

[0135] There are multiple mechanisms for data upload. Regular upload aggregates data every 30 seconds and uploads it in batches. If a sudden change in road conditions is detected, such as waterlogging, ice, or potholes, a burst upload is immediately triggered, with a transmission delay of less than 0.5 seconds. Tiered upload prioritizes data based on its importance, prioritizing critical road condition data. A bandwidth adaptation mechanism automatically adjusts the level of detail of uploaded data based on network conditions. Uploaded data uses a standardized JSON structure and contains metadata such as vehicle ID, timestamp, and location information, as well as payload data such as road surface characteristics and status information.

[0136] 1.2 Server Data Aggregation and Analysis

[0137] After receiving data from multiple vehicles, the cloud server aggregates and analyzes it. During data aggregation, data integrity and timeliness are verified, and outliers are removed. Next, spatiotemporal clustering is performed, integrating data describing the same road section. Clustering radius is limited to 5 meters, and time windows are between 25 and 35 minutes. Multi-source fusion utilizes a weighted averaging algorithm, with weights dynamically adjusted based on data freshness and sensor reliability. Finally, a sliding window averaging method is used to eliminate transient data fluctuations. The window size is adaptively adjusted between 5 and 20 data points based on data density.

[0138] Data analysis covers multiple aspects. It classifies road surfaces based on machine learning models. It assesses road surface parameters such as slipperiness and roughness and quantifies them into standardized values ​​on a scale of 0 to 100. It calculates the theoretical coefficient of friction based on road surface type and condition. It also identifies areas of abnormal road conditions. The server utilizes a distributed computing architecture, with a single data processing latency of less than 100ms, capable of processing data uploads from over 1,000 vehicles per second.

[0139] 1.3 Road State Map Construction

[0140] There are specific rules for constructing road condition maps. High-frequency slippery state attributes are updated in real time, medium-frequency road temperature attributes are updated every 10 to 30 minutes, and low-frequency road material attributes are updated weekly or monthly. The map includes a time dimension, recording trends in road conditions over time. The system specifically marks dangerous areas and provides historical accident data and recommended passing strategies. The map also includes a tire pressure recommendation layer, which, based on historical vehicle traffic records and an optimal tire pressure calculation model, provides optimal tire pressure setting reference values ​​with an accuracy of 0.05 bar for each section of road.

[0141] 1.4 Requesting and receiving data from following vehicles

[0142] The following vehicle sends a request to the server via the vehicle communication unit to obtain information about the road ahead. The request includes the current location with GPS accuracy of less than 3 meters, the direction of travel in 16 directions, the destination and expected route based on the navigation system's plan, and the vehicle type and load status for a personalized road condition assessment. The desired level of data detail is dynamically adjusted based on vehicle speed and computing power.

[0143] The request range strategy prioritizes high-precision data with a resolution of 0.1 to 1 meter within the 5-10 km range ahead, while simultaneously obtaining summary data with a resolution of 5 to 10 meters within the 10-50 km range. Requests are automatically updated every 5 minutes or every 3 kilometers of driving. If significant changes in road conditions are detected, such as entering a rainy area, additional requests will be triggered.

[0144] After receiving the data, it undergoes decryption and integrity verification and is stored in a local cache to construct a local road condition model. The receiving mechanism utilizes breakpoint-resume technology to ensure that data transmission can be resumed after a communication interruption. The system performs a timestamp check on received data, discarding data that is more than one hour out of date. For important but potentially outdated data, it proactively initiates update requests.

[0145] 1.5 Local Data Verification

[0146] The following vehicle uses data collected by its own sensors to verify the remote data received from the server. This verification process includes feature matching, requiring that the road surface features detected by the local sensors be at least 80% similar to those sent by the server. A consistency assessment is then performed to calculate consistency indicators between the local and remote data in terms of road surface type, wetness, and roughness. Based on the consistency assessment results, the remote data's credibility is calculated and categorized as high, medium, and low. If significant discrepancies are found, the system analyzes the causes.

[0147] Verification algorithms include image feature matching using the SIFT / SURF algorithm, point cloud registration using the ICP algorithm, and feature space distance calculation using the Mahalanobis distance. Verification results influence the weighting of remote data in subsequent fusion processes. If server data consistency falls below 30%, the system automatically switches to a mode that relies entirely on local data and uploads updated data to the server.

[0148] 1.6 Multi-source data fusion

[0149] Finally, remote data and local data are fused to generate the final road condition estimate. The fusion uses a Bayesian probability model to calculate the credibility weight of data from different sources. The specific fusion algorithm is:

[0150] μ=ω1·μ server +ω1·μ local

[0151] Among them, μ server The friction coefficient provided by the server, μ local The friction coefficient is estimated by the local sensor. The fusion process is layered. First, raw sensor data is fused at the feature level. Then, intermediate results such as road surface type and slipperiness are fused at the state level. Finally, the friction coefficient estimate is fused at the decision level. When conflicting remote and local data occurs, the system dynamically adjusts the weight based on data reliability and timeliness. In extreme cases, the data from the other source may be completely adopted. The fusion result includes the friction coefficient estimate and the estimated uncertainty. The higher the uncertainty, the more conservative the tire pressure control strategy.

[0152] (2) Parameter calculation

[0153] Please refer to Figure 4 , this embodiment will expand and explain the specific process of parameter calculation:

[0154] 2.1 Estimation of road friction coefficient

[0155] First, according to the road type, the basic friction coefficient μ is determined by looking up the table. base , different road surface types correspond to different value ranges;

[0156] Calculate the correction factors for different influencing factors:

[0157] Weather correction factor f weather :According to the calculation of precipitation and temperature, different conditions of precipitation and temperature will make f weather Take values ​​within a certain range:

[0158] Road surface condition correction factor f surface : Considering the road surface slipperiness and roughness, let the road surface slipperiness be S, then:

[0159] f surface =1-k s ×ΔS

[0160] Among them, k s is the coefficient related to the road surface, and is the change in ΔS slipperiness

[0161] Tire condition correction factor f tire : Considering the tire temperature T and the degree of wear, the temperature change will make f tire Produce corresponding changes, assuming the temperature change is ΔT, then:

[0162] f tire =1+k t ×ΔT

[0163] Among them, k t is the coefficient related to the tire;

[0164] Vehicle state correction factor f vehicle : Considering the load W and speed v, the load change will make f vehicle Change, let the load change be ΔW

[0165] f vehicle =1-k w ×ΔW

[0166] Among them, k w is the coefficient related to the tire.

[0167] The calculation formula of the road friction coefficient μ is as follows:

[0168] μ=μ base ×f weather ×f surface ×f tire ×f vehicle

[0169] Where μ represents the road friction coefficient after correction factors including weather, road surface, tire, and vehicle status.

[0170] In a preferred embodiment, the basic friction coefficient μ is determined by looking up the table for the road type. base For example, the dry asphalt pavement is 0.8-0.9, the wet asphalt pavement is 0.5-0.7, etc.; the weather correction factor f weather Considering the influence of precipitation and temperature, light rain reduces the friction coefficient by 10-15%; the road surface condition correction factor f surface Considering the wetness and roughness of the road surface, the friction coefficient decreases by about 3-5% for every 10% increase in wetness; the tire condition correction factor f tireConsidering the tire temperature and wear degree, within the normal range, for every 10°C increase in temperature, the friction coefficient changes by ±2%; vehicle state correction factor f vehicle Considering the load and speed, for every 10% increase in load, the effective friction coefficient decreases by approximately 1 - 2%. The system divides the confidence level of the friction coefficient estimation into three levels. When the high confidence level (>80%) is reached, it completely relies on the estimated value. When the medium confidence level (50 - 80%) is reached, it combines with the empirical value. When the low confidence level (<50%) is reached, it adopts a conservative strategy.

[0171] 2.2 Calculation of weather influence factor

[0172] Let the precipitation intensity be I, the temperature be T, the wind speed be V, and the reference tire pressure be P ref , the actual tire pressure be P, and the tire pressure adjustment amount be ΔP;

[0173] Comprehensively considering the influence of precipitation, temperature, and wind speed on the weather influence factor, the described f weather The calculation formula is:

[0174] f weather (I,T,V)=a - bI - c(T - T0) - d(V - V0)

[0175] Among them, a is the basic coefficient, representing the initial value of the weather influence factor when there is no precipitation, the temperature is the reference temperature T0, and the wind speed is the reference wind speed V0;

[0176] b is the precipitation influence coefficient, b>0, the greater the precipitation intensity I, the more f weather decreases;

[0177] c is the temperature influence coefficient, used to quantify the influence degree of temperature on f weather When T>T0, for every certain increase in temperature, f weather changes in multiples of c; when T<T0, the same principle applies;

[0178] d is the wind speed influence coefficient, when the wind speed V deviates from the reference wind speed V0, f weather will change;

[0179] [[ID=4�]]Let there be a linear relationship between the tire pressure adjustment amount ΔP and the weather influence factor f weather :

[0180] ΔP=e×f weather

[0181] Among them, e is the tire pressure adjustment coefficient, which reflects the influence degree of the weather influence factor on the tire pressure adjustment amount; <°

[0182] The calculation formula for the actual tire pressure P is:

[0183] P=P ref +ΔP=Pref +e×f weather

[0184] Right now:

[0185] P=P ref +e×(a-bI-c(T-T0)-d(V-V0))

[0186] If the system predicts that the future precipitation intensity is I pred , temperature is T pred , wind speed is V pred , then the future weather impact factor can be calculated

[0187]

[0188] Then adjust the tire pressure adjustment amount ΔP in advance pred :

[0189]

[0190] And adjust the actual tire pressure to: P pred =P ref +ΔP pred .

[0191] In a preferred embodiment, the system analyzes the impact of weather conditions on road friction coefficient and tire pressure settings, establishing a mapping model between various weather conditions and road conditions. In the precipitation impact model, light rain (<2mm / h) reduces the friction coefficient by 10-15%. In the temperature impact model, high temperatures (>35°C) cause tire rubber to soften, requiring a 10-15% reduction in tire pressure. The combined precipitation-temperature model considers the combined effects of both, adopting a conservative tire pressure strategy during rainy and low-temperature conditions. In the wind speed impact model, strong crosswinds (>10m / s) require a 5-10% increase in tire pressure on the windward side.

[0192] 2.3 Tire Temperature Impact Assessment

[0193] Build a monitoring system to process tire temperature data: Assume the tire temperature sequence is

[0194] T tire ={T tire1 ,T tire2 ,…,T tiren}

[0195] Data is filtered by median filtering method: Assume the filter window size m, for each measurement point i, the filtered value T′ tirei is the median of the m values ​​in the window, that is,

[0196]

[0197] By calculating the tire temperature change rate:

[0198]

[0199] To predict the warming trend, where Δt is the measurement time interval;

[0200] Assume that the temperature set of the local area of ​​the tire is:

[0201] T tire-local ={T tirej1 ,T tirej2 ,…,T tirejn}

[0202] Calculate local temperature difference

[0203] ΔT tire-local =max(T tire-local )-min(T tire-local )

[0204] When the local temperature difference exceeds the set threshold, it is considered abnormal;

[0205] Based on the temperature measurement values ​​at different positions of the tire, a mapping relationship T between tire temperature and position is constructed. tire (x,y) is used to generate a tire temperature distribution heat map to detect potential hot spots.

[0206] The specific methods for establishing a temperature-tire pressure model, adaptively controlling tire pressure according to different temperature ranges, and handling abnormal situations are as follows:

[0207] Build an ideal gas model:

[0208]

[0209] Among them, P1 and T tire1 are the initial tire pressure and initial tire temperature, in the same unit, the temperature is in Kelvin, P2 and T tire2 The current tire pressure and current tire temperature;

[0210] Then the modified model of the temperature-tire pressure relationship model is:

[0211]

[0212] Where k is the correction coefficient, which is between 0.8-0.95; f(T tire2 ) is the high temperature compensation function, which is about the current tire temperature T tire2 function;

[0213] When the tire temperature T tire When in the standard range, tire pressure P=P base , P base is the base tire pressure;

[0214] If T tire If the tire pressure is lower than the lower limit of the standard range, adjust the tire pressure to P=P base (1-α low ), α low is the low temperature compensation coefficient, used to compensate for the rubber hardening effect;

[0215] If T tire When the tire pressure is higher than the upper limit of the standard range but not reaching the dangerous value, the tire pressure is adjusted to P = P base (1+α high ), α high It is a high temperature compensation coefficient to prevent excessive expansion of the tire;

[0216] If T tire If the dangerous threshold is exceeded, the high temperature alarm is triggered and the tire pressure is adjusted to P=P base (1-α danger ), α danger A larger reduction factor.

[0217] In a preferred embodiment, data filtering utilizes a median filter algorithm with a filter window size of 5-9 samples. A normal temperature rise rate is considered <2°C / minute, and a local temperature difference >15°C is considered abnormal. Model-based temperature-adaptive tire pressure control is implemented, with different tire pressure adjustment strategies corresponding to different temperature ranges. For example, baseline tire pressure is maintained in the standard temperature range (20-50°C), while an alarm is triggered and tire pressure is reduced if necessary in the dangerously high temperature range (>80°C). Specific measures are also implemented for abnormal temperatures. Rapid temperature increases (>3°C / minute for more than three minutes) result in a 5-10% reduction in tire pressure. Uneven tire temperatures (a temperature difference >15°C) require a check for pressure uniformity.

[0218] 2.4 Load and speed parameter adjustment

[0219] The vehicle adjusts tire pressure calculation parameters according to load and speed conditions; load monitoring measures vehicle load through various methods such as suspension displacement sensors, pneumatic suspension sensors, and strain gauge sensors, with an accuracy of ±50kg or ±2% of full load;

[0220] The system filters load data to eliminate fluctuations caused by vibration and jolts. It also calculates load distribution, specifically the ratio of load on the front and rear axles and between the left and right wheels, which is crucial for differentiated tire pressure control. It also identifies load variations, such as dynamic load transfer caused by uphill and downhill driving, acceleration, and braking. It then monitors vehicle speed, integrating data sources such as wheel speed sensors, GPS speed, and inertial navigation to obtain precise vehicle speed. It also calculates acceleration to identify acceleration / deceleration states and identifies driving modes, such as city driving, high-speed cruising, and off-road driving.

[0221] Establish a load-speed-tire pressure mapping model:

[0222] P optimal =P base ·f(W)·g(v),

[0223] Among them, P base is the reference tire pressure, and f(W) is the load correction function:

[0224]

[0225] g(v) is the velocity correction function:

[0226]

[0227] Develop tire pressure adjustment strategies based on different combinations of vehicle load and speed. When the vehicle is unladen and traveling at low speeds, reduce tire pressure by 5-10%. When unladen and traveling at high speeds, increase tire pressure by 5-10%. For fully laden vehicles, increase tire pressure by 15-25% at low speeds and 30-40% at high speeds.

[0228] Under special operating conditions, the strategy is further refined. When driving uphill and fully loaded, the front tire pressure is increased by 10-15%; when driving downhill and fully loaded, the rear tire pressure is increased by 10-15%, thereby optimizing vehicle driving performance and safety.

[0229] 2.5 Optimal Tire Pressure Solution

[0230] The tire pressure optimization problem is modeled as a constrained optimization problem:

[0231] The goal is to maximize the ground friction function F(P,T tire ):

[0232]

[0233] Among them, F z is the ground friction, P is the tire pressure, T tire is the tire temperature, P min and P max are the minimum and maximum tire pressures allowed, T tire_min and T tire_max The minimum and maximum security

[0234] Tire temperature;

[0235] Establish a model relationship between tire pressure P and ground contact area A, considering temperature T tire Impact:

[0236]

[0237] Where k is the proportional constant, F zis the ground friction, α is the temperature compensation coefficient, T tire_0 is the reference temperature.

[0238] In a preferred embodiment, different tire pressure calculation formulas are used for different working conditions:

[0239]

[0240] Among them, P base is the base tire pressure, which is a basic reference value; k μ 、k v 、 are the adjustment coefficients for road friction coefficient, vehicle speed, and tire vertical load respectively;

[0241] For standard road driving: k μ ∈[0.4,0.6],k v ∈[0.15,0.25],

[0242] For off-road conditions: k μ ∈[1.4,1.6],k v ∈[0.05,0.15],

[0243] Finally, perform temperature compensation:

[0244]

[0245] Among them, T reference The reference temperature is 293K. After the calculation is completed, the preferred embodiment will also perform multiple safety constraint checks, such as: setting a minimum tire pressure limit to avoid tire unbearing; specifying a maximum tire pressure threshold to prevent tire bursts; executing a temperature constraint strategy to prioritize safety at high temperatures; and implementing a difference constraint to ensure that the tire pressure difference between adjacent vehicles does not exceed 30%, thereby ensuring that the tire pressure adjustment results are safe and reliable.

[0246] (3) Execution control

[0247] In a preferred embodiment, a specific tire pressure adjustment strategy is generated based on the calculated optimal tire pressure. The strategy first determines the need for adjustment, triggering it when the difference between the target tire pressure and the current tire pressure exceeds 0.1 bar. If environmental conditions change rapidly, the threshold can be lowered to 0.05 bar. The strategy is then optimized, considering the costs and benefits of adjustment to avoid frequent, small adjustments. Recent road condition trends are predicted to prevent repeated adjustments within a short period of time. Furthermore, specialized tire pressure plans are prepared in advance for upcoming special road conditions, such as off-road sections.

[0248] The strategy covers the target tire pressure (accurate to 0.01 bar), the adjustment rate (0.1 bar / minute for normal use and 0.3 bar / minute for emergency use), and the adjustment priority (categorized as safety, performance, or efficiency). The system designs specialized strategies for different road conditions: on highways, tire pressure is 0.2-0.3 bar higher than the standard to reduce rolling resistance; on slippery roads, tire pressure is 0.1-0.2 bar lower than the standard to increase contact area; on gravel roads, tire pressure is 0.3-0.5 bar lower than the standard to improve driving performance; on snow / ice, tire pressure is 0.4-0.6 bar lower than the standard to maximize contact area; when negotiating steep slopes, front tire pressure is reduced by 0.2-0.4 bar on uphill slopes and rear tire pressure is reduced by 0.2-0.4 bar on downhill slopes; and in emergency braking, all tire pressures are rapidly reduced by 0.2-0.3 bar to shorten braking distance.

[0249] In a preferred embodiment, differentiated tire pressure control schemes are formulated for different wheels, and the optimal tire pressure is calculated for each wheel based on the vehicle's driving state and road conditions:

[0250] Calculate the tire pressure correction value under each working condition, assuming that under acceleration condition Under braking condition Under turning conditions Off-road conditions

[0251] Then, the differentiated tire pressure control can be expressed by the following formula:

[0252] For the target tire pressure Pi of the i-th wheel:

[0253]

[0254] The differentiated constraint scheme is specifically as follows:

[0255] Safety limit: For adjacent wheels i and j, |P i -P j |≤0.5bar;

[0256] Performance optimization: Assume that the adhesion of each wheel is According to the adhesion distribution, the optimization goal is: Among them, η i is the weight coefficient of each wheel adhesion, and P i The relationship between adhesion and tire pressure must be met;

[0257] Comfort considerations: By establishing a comfort evaluation function C(P1, P2, P3, P4), the effect of tire pressure differences on comfort is limited. That is, setting C min is the minimum acceptable value of comfort, then there are constraints:

[0258] C(P1,P2,P3,P4)≥C min .

[0259] In a preferred embodiment, the specific calculation method of the tire pressure correction value under each working condition is:

[0260] Acceleration conditions:

[0261] On a good road surface, let the driving wheel be j, then

[0262]

[0263] Among them, β1>0 is the proportional coefficient;

[0264] On wet roads,

[0265]

[0266] Among them, β2>0 is the proportionality coefficient;

[0267] Braking conditions:

[0268] Assume the coordinates of the vehicle's center of mass are (x c ,y c ), the position coordinates of each wheel are (x i ,y i ),i=(1,2,3,4), where i represents four wheels; the load transfer coefficient is k load , calculate the tire pressure correction value caused by the positive pressure change of each wheel based on the center of mass position and load transfer

[0269] First calculate the distance of each wheel relative to the center of mass:

[0270] di=(xi-xc)²+(yi-yc)²

[0271] The positive pressure change of each wheel is obtained based on the load transfer relationship, and the tire pressure correction value is obtained based on the tire characteristics:

[0272]

[0273] Among them, γ i is the proportionality coefficient related to the tire;

[0274] Turning condition: Let the outer wheel of the vehicle be m and the inner wheel be n when turning.

[0275]

[0276]

[0277] Among them, β3>0 and the inner and outer tire pressure difference is satisfied

[0278] Then, for the i-th tire, the calculation formula for the corrected tire pressure under cornering conditions is:

[0279]

[0280] Off-road conditions: For all wheels i, where β4∈[0.3,0.4].

[0281] In a preferred embodiment, during acceleration, the drive tire pressure is increased on smooth roads to reduce drag, while on slippery roads, it is reduced to increase grip. During braking, the positive pressure change at each wheel is calculated based on the center of mass position and load transfer, and tire pressure is adjusted accordingly to optimize braking force distribution. During cornering, where the outer wheel bears greater lateral force, the outer tire pressure is appropriately reduced to increase contact area, with the inner and outer tire pressure differential typically being 0.1-0.3 bar. During off-road conditions, all tire pressures are significantly reduced, down to 60-70% of the standard pressure, to improve maneuverability. For safety, the tire pressure difference between adjacent tires should not exceed 0.5 bar to prevent unstable handling. For performance optimization, the four-wheel adhesion utilization is optimized based on the adhesion distribution. Comfort considerations are also taken to avoid a decrease in ride comfort due to excessive tire pressure differences. Specialized solutions are available for special conditions: intentionally increasing the front and rear tire pressure differential during drift control, increasing the outer tire pressure to prevent rollover when a rollover risk is detected, and increasing the tire pressure when a single wheel is slipping.

[0282] In a preferred embodiment, the system converts the tire pressure adjustment strategy into a specific control command and performs the adjustment operation. It first generates a control signal, converts the target tire pressure into a control voltage or PWM signal, and controls the operation of the air pressure regulating valve and the air pump.

[0283] Assume the target tire pressure is P target , the control voltage is V or the duty cycle of the pulse width modulation PWM signal is D, there is a functional relationship V=f1(P target ) or D=f2(P target ), used to convert the target tire pressure into a corresponding control signal to control the operation of the air pressure regulating valve and the air pump;

[0284] Closed-loop control algorithm: Let the real-time feedback tire pressure be P real , the adjustment amount of the control signal is ΔS, and the closed-loop control algorithm can be expressed as ΔS=f3(P target -P real ), by continuously adjusting the control signal according to the difference between the target tire pressure and the real-time tire pressure, so that P real Approaching P target ,Right now in Indicates the real-time tire pressure after the nth adjustment;

[0285] Predictive control: Assume that the natural change in tire pressure estimated based on temperature change is ΔP T , which is a function of the temperature change ΔT, namely ΔP T =f4(ΔT); When adjusting the control signal, consider this estimated change, and the adjusted control signal S adjusted For S adjusted =S initial +f5(ΔP T ), where S initial is the initial control signal without considering the temperature change, and f5 is the function for adjusting the control signal according to ΔPT.

[0286] In the preferred embodiment, process control is implemented, with an adjustment rate of 0.1 bar / minute in normal conditions and 0.3 bar / minute in emergency conditions. Precision control minimizes adjustment accuracy to 0.02 bar, with a steady-state error of less than 0.05 bar. Stability control prevents sudden pressure shocks, with each adjustment step less than 0.1 bar. Noise control optimizes air pump operating time and frequency to reduce noise interference. The preferred embodiment also supports multiple rounds of parallel adjustment. A resource allocation algorithm rationally allocates air pump and air path resources based on priority and urgency. A tiered strategy is used during large adjustments to avoid sudden changes in vehicle posture.

[0287] In a preferred embodiment, the tire pressure regulation actuator of the system includes multiple key components. The electronically controlled air pressure regulating valve responds quickly and can react in less than 0.1 seconds. Its regulation accuracy can reach ±0.01bar, which can accurately control the changes in tire pressure. The high-efficiency air pump has a strong inflation capacity, with an inflation volume of more than 15L per minute. At the same time, it has low power consumption of less than 200W, and can quickly replenish the tire pressure. The quick exhaust valve has extremely high exhaust efficiency, an exhaust capacity of more than 20L / minute, and a response time of less than 0.05 seconds, which can achieve a rapid reduction in tire pressure. The high-precision air pressure sensor measures accurately with an accuracy of ±0.01bar and a response time of less than 0.01 seconds. It can accurately monitor tire pressure in real time and provide accurate data basis for tire pressure regulation.

[0288] In a preferred embodiment, the system also continuously monitors and provides feedback on tire pressure adjustment effectiveness. Monitoring includes tire pressure stability, requiring that after adjustment, tire pressure remains stable near the target value, with a fluctuation range of no more than ±0.05 bar; driving performance changes, including acceleration response time and drive wheel slip for traction performance; braking distance and braking stability for braking performance; lateral acceleration and steering response time for handling performance; vertical acceleration and vibration spectrum for comfort performance; and fuel consumption and tire wear rate for economy performance; tire temperature changes, ensuring that tire temperature does not rise abnormally after adjustment; and abnormal condition detection, including pressure leaks and adjustment system malfunctions. Evaluation methods include before-and-after comparisons on the same road section, requiring a minimum 5% improvement in performance before and after adjustment; comparisons with historical data, striving to achieve the best historical performance under similar conditions; and comparisons with theoretical expectations, ensuring that actual results achieve at least 80% of theoretical expectations. The feedback mechanism is divided into short-term feedback, which directly adjusts current tire pressure control parameters with a response time of less than 30 seconds; medium-term feedback, which modifies the friction coefficient estimation model, with a cycle of approximately 24 hours; and long-term feedback, which optimizes the overall control strategy and updates the knowledge base, with a cycle of approximately 7-30 days.

[0289] In a preferred embodiment, the adjustment results and the latest road condition data are uploaded to a cloud server, achieving a virtuous data cycle. Uploads include road condition updates, which are uploaded when local road conditions detect discrepancies with server data. These data include road surface type, status, coordinates, and timestamps; tire pressure adjustment records, including pre- and post-adjustment tire pressure values, adjustment reasons, road condition information, and vehicle status; adjustment effect evaluations, including performance change indicators and subjective evaluation scores; and abnormality reports, including detection of unmarked hazardous road conditions and tire pressure adjustment system anomalies. Upload strategies include regular uploads, which aggregate data every 30 km or three hours; timely uploads, which upload immediately upon detection of significant road condition changes or significant performance improvements; batch uploads, which upload non-critical data in batches when network conditions are good; and incremental uploads, which upload only changed data to reduce transmission volume. The data processing process includes anonymization to remove personal identification information, compression and encryption to ensure transmission security, prioritization to ensure critical data is transmitted first, and a transmission confirmation mechanism to ensure successful data upload. This approach allows individual vehicle experiences to be shared with the entire fleet, promoting collective learning and improving system prediction accuracy and control effectiveness.

[0290] Example 2

[0291] Please refer to Figure 5 This embodiment 2 provides a control system for improving the driving ability of an unmanned vehicle, including

[0292] The data processing unit collects road condition data in real time from the leading vehicle and uploads it to the cloud server after data preprocessing. The cloud server aggregates and analyzes the received data from multiple vehicles, synchronously building and updating a road condition map. The following vehicle requests road condition data from the server based on multi-dimensional information, including current location and driving direction. After verifying the road condition data, the following vehicle assigns a corresponding weight to it and then fuses the remote data with the local data to generate a final road condition estimate.

[0293] The parameter calculation unit is used to determine the base value based on the fused data according to the road surface type, and then calculate the road friction coefficient by combining correction factors including weather, road surface, tire, and vehicle status, and evaluate its credibility to adjust the control strategy. At the same time, a weather and road surface status mapping model is constructed to calculate the weather influencing factor for friction coefficient and tire pressure adjustment, and optimize the strategy in advance by predicting weather changes. A monitoring system is set up to process tire temperature data and establish a temperature-tire pressure model to adaptively control tire pressure according to different temperature ranges and handle abnormal situations. Multiple sensors are used to monitor load and vehicle speed, calculate load distribution and dynamic transfer, identify driving modes, and establish a mapping model to design tire pressure adjustment strategies. Finally, the tire pressure optimization model is built, combined with the working condition formula and temperature compensation solution, and multiple safety constraint checks are performed to obtain the optimal tire pressure.

[0294] The execution control unit is used to first determine the adjustment needs based on the optimal tire pressure, and combine it with the road condition optimization strategy to clarify the target tire pressure and adjustment parameters; then formulate differentiated tire pressure plans for each wheel and set constraints for different working conditions including acceleration and braking; control the adjustment execution process through closed-loop and predictive control methods; continuously monitor the tire pressure adjustment effect, and upload the adjustment effect and the latest road condition data to the cloud server.

[0295] Example 3

[0296] This embodiment 3 also provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, it can implement any step of a method for improving the driving ability of an unmanned vehicle.

[0297] The computer-readable storage medium may include: a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and other media that can store program codes.

[0298] For an introduction to the computer-readable storage medium provided in this application, please refer to the above method embodiment, and this application will not go into details here.

[0299] It will be easily understood by those skilled in the art that the above is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A control method for improving the driving ability of an unmanned vehicle, characterized in that ,include: S1. The preceding vehicle collects road condition data in real time and uploads it to the cloud server after data preprocessing. The cloud server aggregates and analyzes the received data from multiple vehicles, and simultaneously builds and updates the road condition map. The following vehicle requests the server for road condition data ahead based on multi-dimensional information including current location and driving direction. After verifying the road condition data ahead, the following vehicle assigns a corresponding weight to it, and then fuses the remote data with the local data to generate the final road condition estimate; S2. Based on the fused data, a baseline value is first determined based on the road surface type. Correction factors, including weather, road surface, tire, and vehicle conditions, are then incorporated to calculate the road friction coefficient. The confidence level is then assessed to adjust the control strategy. A weather and road condition mapping model is constructed to calculate weather influencing factors for friction coefficient and tire pressure adjustment, enabling strategies to be optimized in advance by predicting weather changes. A monitoring system is built to process tire temperature data, establishing a temperature-tire pressure model to adaptively control tire pressure and handle abnormal conditions based on different temperature ranges. Multiple sensors are used to monitor load and speed, calculate load distribution and dynamic transfer, identify driving patterns, and establish a mapping model to design tire pressure adjustment strategies. Finally, a tire pressure optimization model is developed, combined with operating condition formulas and temperature compensation solutions, and multiple safety constraint checks are performed to determine the optimal tire pressure. S3. First, determine the need for adjustment based on the optimal tire pressure. Combined with the road condition optimization strategy, the target tire pressure and adjustment parameters are clearly defined. Then, differentiated tire pressure plans are developed for each wheel based on different operating conditions, including acceleration and braking, and constraints are set. The adjustment execution process is controlled through closed-loop and predictive control methods. The tire pressure adjustment effect is continuously monitored, and the adjustment results and the latest road condition data are uploaded to the cloud server.

2. The method for improving the driving ability of an unmanned vehicle according to claim 1, characterized in that: The specific method for determining the base value according to the road surface type in S2 and calculating the road friction coefficient by combining correction factors including weather, road surface, tire, and vehicle status is as follows: First, according to the road type, the basic friction coefficient μ is determined by looking up the table. base , different road surface types correspond to different value ranges; Calculate the correction factors for different influencing factors: Weather correction factor f weather :According to the calculation of precipitation and temperature, different conditions of precipitation and temperature will make f weather Take values ​​within a certain range: Road surface condition correction factor f surface : Considering the road surface slipperiness and roughness, let the road surface slipperiness be S, then: f surface =1-k s ×ΔS Among them, k s is the coefficient related to the road surface, and is the change in ΔS slipperiness Tire condition correction factor f tire : Considering the tire temperature T and the degree of wear, the temperature change will make f tire Produce corresponding changes, assuming the temperature change is ΔT, then: f tire =1+k t ×ΔT Among them, k t is the coefficient related to the tire; Vehicle state correction factor f vehicle : Considering the load W and speed v, the load change will make f vehicle Change, let the load change be ΔW f vehicle =1-k w ×ΔW Among them, k w is the coefficient related to the tire; The calculation formula of the road friction coefficient μ is as follows: μ=μ base ×f weather ×f surface ×f tire ×f vehicle Where μ represents the road friction coefficient after correction factors including weather, road surface, tire, and vehicle status.

3. The method for improving the driving ability of an unmanned vehicle according to claim 2, characterized in that: The weather influence factor is calculated in S2 for friction coefficient and tire pressure adjustment. The specific method of optimizing the strategy in advance by predicting weather changes is as follows: Assume that the precipitation intensity is I, the temperature is T, the wind speed is V, and the reference tire pressure is P ref , the actual tire pressure is P, and the tire pressure adjustment amount is ΔP; Taking into account the influence of precipitation, temperature and wind speed on weather factors, the f weather The calculation formula is: f weather (I,T,V)=a-bI-c(T-T0)-d(V-V0) Among them, a is the basic coefficient, which represents the initial value of the weather impact factor when there is no precipitation, the temperature is the reference temperature T0, and the wind speed is the reference wind speed V0; b is the precipitation influence coefficient, b>0, the greater the precipitation intensity I, f weather The more it is lowered; c is the temperature influence coefficient, which is used to quantify the influence degree of temperature on f weather When T > T0, for every certain increase in temperature, f weather changes by a multiple of c; when T < T0, the same principle applies; d is the wind speed influence coefficient. When the wind speed V deviates from the reference wind speed V0, f weather Change will occur; Assume that the tire pressure adjustment ΔP and the weather influence factor f weather There is a linear relationship between: ΔP=e×f weather Among them, e is the tire pressure adjustment coefficient, which reflects the influence of weather factors on the tire pressure adjustment amount; The calculation formula for the actual tire pressure P is: P=P ref +ΔP=P ref +e×f weather Right now: P=P ref +e×(a-bI-c(T-T0)-d(V-V0)) If the system predicts that the future precipitation intensity is I pred , temperature is T pred , wind speed is V pred , then the future weather impact factor can be calculated Then adjust the tire pressure adjustment amount ΔP in advance pred : And adjust the actual tire pressure to: P pred =P ref +ΔP pred .

4. The method for improving the driving ability of an unmanned vehicle according to claim 2, characterized in that: The specific method of constructing the monitoring system in S2 to process tire temperature data is as follows: Assume the tire temperature series is T tire ={T tire1 ,T tire2 ,…,T tiren }; Data is filtered by median filtering method: Assume the filter window size m, for each measurement point i, the filtered value T′ tirei is the median of the m values ​​in the window, that is, By calculating the tire temperature change rate: To predict the warming trend, where Δt is the measurement time interval; Assume that the temperature set of the local area of ​​the tire is: T tire-local ={T tirej1 ,T tirej2 ,…,T tirejn } Calculate local temperature difference ΔT tire-local =max(T tire-local )-min(T tire-local ) When the local temperature difference exceeds the set threshold, it is considered abnormal; Based on the temperature measurement values ​​at different positions of the tire, a mapping relationship T between tire temperature and position is constructed. tire (x,y) is used to generate a tire temperature distribution heat map to detect potential hot spots.

5. The method for improving the driving ability of an unmanned vehicle according to claim 2, characterized in that: The specific method for establishing the temperature-tire pressure model in S2 and adaptively controlling the tire pressure according to different temperature ranges and handling abnormal situations is as follows: Build an ideal gas model: Among them, P1 and T tire1 are the initial tire pressure and initial tire temperature, in the same unit, the temperature is in Kelvin, P2 and T tire2 The current tire pressure and current tire temperature; Then the modified model of the temperature-tire pressure relationship model is: Where k is the correction factor, f(T tire2 ) is the high temperature compensation function, which is about the current tire temperature T tire2 function; When the tire temperature T tire When in the standard range, tire pressure P=P base , P base is the base tire pressure; If T tire If the tire pressure is lower than the lower limit of the standard range, adjust the tire pressure to P=P base (1-α low ), α low is the low temperature compensation coefficient, used to compensate for the rubber hardening effect; If T tire When the tire pressure is higher than the upper limit of the standard range but not reaching the dangerous value, the tire pressure is adjusted to P = P base (1+α high ), α high It is a high temperature compensation coefficient to prevent excessive expansion of the tire; If T tire If the dangerous threshold is exceeded, the high temperature alarm is triggered and the tire pressure is adjusted to P=P base (1-α danger ), α danger A larger reduction factor.

6. The method for improving the driving ability of an unmanned vehicle according to claim 2, characterized in that: The specific method for solving the optimal tire pressure in S2 is: The tire pressure optimization problem is modeled as a constrained optimization problem: The goal is to maximize the ground friction function F(P,T tire ): Among them, F z is the ground friction, P is the tire pressure, T tire is the tire temperature, P min and P max are the minimum and maximum tire pressures allowed, T tire_min and T tire_max are the safe minimum and maximum tire temperatures, respectively; Establish a model relationship between tire pressure P and ground contact area A, considering temperature T tire Impact: Where k is the proportional constant, F z is the ground friction, α is the temperature compensation coefficient, T tire_0 is the reference temperature.

7. The method for improving the driving ability of an unmanned vehicle according to claim 1, characterized in that: The specific method of differentiating tire pressure solutions and setting constraints in S3 is: Calculate the tire pressure correction value under each working condition, assuming that under acceleration condition Under braking condition Under turning conditions Off-road conditions Then, the differentiated tire pressure control can be expressed by the following formula: For the target tire pressure Pi of the i-th wheel: The differentiated constraint scheme is specifically as follows: Safety limit: For adjacent wheels i and j, |P i -P j |≤0.5bar; Performance optimization: Assume that the adhesion of each wheel is According to the adhesion distribution, the optimization goal is: Among them, η i is the weight coefficient of each wheel adhesion, and P i The relationship between adhesion and tire pressure must be met; Comfort considerations: By establishing a comfort evaluation function C(P1, P2, P3, P4), the effect of tire pressure differences on comfort is limited. That is, setting C min is the minimum acceptable value of comfort, then there is a constraint: C(P1,P2,P3,P4)≥C min .

8. The method for improving the driving ability of an unmanned vehicle according to claim 7, characterized in that: The specific calculation method of the tire pressure correction value under each working condition is as follows: Acceleration conditions: On a good road surface, let the driving wheel be j, then Among them, β1>0 is the proportional coefficient; On wet roads, Among them, β2>0 is the proportionality coefficient; Braking conditions: Assume the coordinates of the vehicle's center of mass are (x c ,y c ), the position coordinates of each wheel are (x i ,y i ),i=(1,2,3,4), where i represents four wheels; the load transfer coefficient is k load , calculate the tire pressure correction value caused by the positive pressure change of each wheel based on the center of mass position and load transfer First calculate the distance of each wheel relative to the center of mass: di=(xi-xc)²+(yi-yc)² The positive pressure change of each wheel is obtained based on the load transfer relationship, and the tire pressure correction value is obtained based on the tire characteristics: Among them, γ i is the proportionality coefficient related to the tire; Turning condition: Let the outer wheel of the vehicle be m and the inner wheel be n when turning. Among them, β3>0 and the inner and outer tire pressure difference is satisfied Then, for the i-th tire, the calculation formula for the corrected tire pressure under cornering conditions is: Off-road conditions: For all wheels i, where β4∈[0.3,0.4].

9. The method for improving the driving ability of an unmanned vehicle according to claim 2, characterized in that: The specific method of controlling the adjustment execution process by the closed-loop and predictive control method in S3 is: Assume the target tire pressure is P target , the control voltage is V or the duty cycle of the pulse width modulation PWM signal is D, there is a functional relationship V=f1(P target ) or D=f2(P target ), used to convert the target tire pressure into a corresponding control signal to control the operation of the air pressure regulating valve and the air pump; Closed-loop control algorithm: Let the real-time feedback tire pressure be P real , the adjustment amount of the control signal is ΔS, and the closed-loop control algorithm can be expressed as ΔS=f3(P target -P real ), by continuously adjusting the control signal according to the difference between the target tire pressure and the real-time tire pressure, so that P real Approaching P target ,Right now in Indicates the real-time tire pressure after the nth adjustment; Predictive control: Assume that the natural change in tire pressure estimated based on temperature change is ΔP T , which is a function of the temperature change ΔT, namely ΔP T =f4(ΔT); When adjusting the control signal, consider this estimated change, and the adjusted control signal S adjusted For S adjusted =S initial +f5(ΔP T ), where S initial is the initial control signal without considering the temperature change, and f5 is the function for adjusting the control signal according to ΔPT.

10. A control system for improving the driving ability of an unmanned vehicle, characterized in that: include: The data processing unit is used to collect road condition data in real time from the preceding vehicle and upload it to the cloud server after data preprocessing; The cloud server aggregates and analyzes the received data from multiple vehicles, and simultaneously builds and updates the road condition map. The following vehicle requests the server for road condition data ahead based on multi-dimensional information including current location and driving direction. After verifying the road condition data ahead, the following vehicle assigns a corresponding weight to it, and then fuses the remote data with the local data to generate the final road condition estimate; The parameter calculation unit is used to determine the base value based on the fused data according to the road surface type, calculate the road friction coefficient by combining correction factors including weather, road surface, tire, and vehicle status, and evaluate its credibility to adjust the control strategy; At the same time, a weather and road condition mapping model is constructed to calculate weather influencing factors for friction coefficient and tire pressure adjustment, and to optimize strategies in advance by predicting weather changes. A monitoring system is built to process tire temperature data and establish a temperature-tire pressure model to adaptively control tire pressure according to different temperature ranges and handle abnormal situations. Multiple sensors are used to monitor load and speed, calculate load distribution and dynamic transfer, and establish a mapping model to design tire pressure adjustment strategies after identifying driving patterns. Finally, the tire pressure optimization model is built, combined with the operating condition formula and temperature compensation solution, and multiple safety constraint checks are performed to determine the optimal tire pressure. The execution control unit is used to first determine the adjustment needs based on the optimal tire pressure, and combine it with the road condition optimization strategy to clarify the target tire pressure and adjustment parameters; then formulate differentiated tire pressure plans for each wheel and set constraints for different working conditions including acceleration and braking; control the adjustment execution process through closed-loop and predictive control methods; continuously monitor the tire pressure adjustment effect, and upload the adjustment effect and the latest road condition data to the cloud server.

Images