Method and system for adjusting control parameters of traction control system and related equipment

Abstract

The invention relates to the technical field of vehicle control, and discloses a method and system for adjusting control parameters of a traction control system and related equipment.The method comprises the steps that current road condition information of a vehicle is obtained, and the current working condition corresponding to the vehicle is determined; all driving data of the vehicle from beginning to end under the current working condition are obtained, and the corresponding traction force control condition of the vehicle under the current working condition is determined; driving behavior parameters of a driver are obtained, and the weight of each control parameter of the traction force control system is determined; according to the current working condition, the traction force control condition and the weight, control parameters of a traction force control system are adjusted, and target control parameters are determined. The driving environment and the driving habit are considered when the control parameters of the traction control system are adjusted, so that the adjustment of the traction control system on the control parameters is more in line with the current use scene, and the efficiency of calibration matching work of the traction control system is improved.

Description

Background Art

[0002] The traction control system is used to prevent the wheels from spinning due to acceleration. Wheel slip usually occurs on smooth road surfaces, such as snow or puddles, where the wheels cannot generate enough traction to move the vehicle. The TCS (Traction Control System) can help better utilize the road adhesion coefficient. Especially when the driving wheels start to slip, it will sense and reduce the engine torque or apply braking pressure to help restore traction.

[0003] However, the control parameters of the existing TCS are mainly manually adjusted by engineers and cannot be adjusted according to the driving environment and driving habits during driving, resulting in the lack of generality of the manually adjusted control parameters. Summary of the Invention

[0004] In order to overcome the problem that the control parameters of the TCS cannot be adjusted according to the driving environment and driving habits during driving, resulting in the lack of generality of the manually adjusted control parameters, the present disclosure provides a method, a system and related devices for adjusting the control parameters of the traction control system.

[0005] In a first aspect, to solve the above technical problems, the present disclosure provides a method for adjusting the control parameters of the traction control system, including:

[0006] Obtain the current road condition information of the vehicle, and determine the current working condition of the vehicle based on the road condition information;

[0007] Obtain all the driving data of the vehicle from the start to the end under the current working condition, and determine the corresponding traction control situation of the vehicle under the current working condition based on the driving data;

[0008] Obtain the driving behavior parameters of the driver, and determine the weights of the respective control parameters of the traction control system based on the driving behavior parameters; wherein the weight characterizes the influence degree of the driver's driving style on the respective control parameters;

[0009] Adjust the control parameters of the traction control system according to the current working condition, the traction control situation and the weights to determine the target control parameters.

[0010] In a second aspect, the present disclosure provides an adjustment system for the control parameters of the traction control system, including:

[0011] A current working condition determination module, configured to obtain the current road condition information of the vehicle and determine the current working condition of the vehicle corresponding thereto based on the road condition information;

[0012] A traction control situation determination module, configured to obtain all driving data of a vehicle from the start to the end under the current working condition, and determine the corresponding traction control situation of the vehicle under the current working condition based on the driving data;

[0013] A weight determination module, configured to obtain the driving behavior parameters of a driver, and determine the weights of the respective control parameters of the traction control system based on the driving behavior parameters; wherein, the weight characterizes the influence degree of the driver's driving style on the respective control parameters;

[0014] A control parameter adjustment module, configured to adjust the control parameters of the traction control system according to the current working condition, the traction control situation and the weights, and determine the target control parameters.

[0015] In a third aspect, the present disclosure provides a computing device, including a memory, a processor, and a program stored on the memory and running on the processor, and when the processor executes the program, the steps of the method for adjusting the control parameters of the traction control system as described above are implemented.

[0016] In a fourth aspect, the present disclosure provides a computer-readable storage medium, in which instructions are stored, and when the instructions run on a terminal device, the terminal device is caused to execute the steps of the method for adjusting the control parameters of the traction control system as described above.

[0017] The beneficial effects of the present disclosure are as follows: By calculating the current working condition of the vehicle through road condition information, calculating the traction control situation through driving data, and then calculating the weights of the respective control parameters through driving behavior parameters, the control parameters of the traction control system can be adjusted according to the current working condition, the traction control situation and the weights. When adjusting the control parameters of the traction control system, the present disclosure takes into account the driving environment (current working condition) and driving habits (weights), making the adjustment of the control parameters of the traction control system more in line with the current usage scenario and improving the efficiency of the calibration and matching work of the traction control system. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the technical solutions in the embodiments of the present disclosure or the prior art, the following further illustrates the present disclosure with reference to the drawings and embodiments.

[0019] Figure 1 It is a flowchart of the method for adjusting the control parameters of the traction control system according to an embodiment of the present disclosure;

[0020] Figure 2 It is a structural diagram of the system for adjusting the control parameters of the traction control system according to an embodiment of the present disclosure;

[0021] Figure 3 It is a structural diagram of the computing device according to an embodiment of the present disclosure. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0022] The following embodiments are further explanations and supplements to the present disclosure and do not constitute any limitation to the present disclosure.

[0023] The following describes a method, a system and related devices for adjusting control parameters of a traction control system according to embodiments of the present disclosure with reference to the accompanying drawings.

[0024] As Figure 1 shown, the embodiments of the present disclosure provide a method for adjusting control parameters of a traction control system, including:

[0025] S1. Obtain the current road condition information of the vehicle, and determine the current working condition of the vehicle based on the road condition information.

[0026] S2. Obtain all the driving data of the vehicle from the start to the end under the current working condition, and determine the corresponding traction control situation of the vehicle under the current working condition based on the driving data.

[0027] S3. Obtain the driving behavior parameters of the driver, and determine the weights of the control parameters of the traction control system based on the driving behavior parameters; wherein, the weight characterizes the influence degree of the driver's driving style on each control parameter.

[0028] S4. Adjust the control parameters of the traction control system according to the current working condition, the traction control situation and the weight, and determine the target control parameters.

[0029] In this embodiment, the current working condition of the vehicle is calculated through the road condition information, and the traction control situation is calculated through the driving data. Then, the weights of each control parameter are calculated through the driving behavior parameters. Thus, the control parameters of the traction control system can be adjusted according to the current working condition, the traction control situation and the weight. When adjusting the control parameters of the traction system, the present disclosure takes into account the driving environment and driving habits, making the adjustment of the control parameters of the traction control system more in line with the current usage scenario and improving the efficiency of the calibration and matching work of the traction control system.

[0030] Optionally, determining the current working condition of the vehicle corresponding to the road condition information includes:

[0031] Obtain the road condition image and the vehicle driving parameters in real time; wherein, the vehicle driving parameters include wheel speed, vehicle speed, acceleration, throttle, brake pedal state and steering wheel angle;

[0032] Based on the current road condition image, extract the features of the current road condition image through a convolutional neural network, and classify the features of the current road condition image through a random forest to determine the road surface type;

[0033] Based on the vehicle driving parameters, determine the vehicle motion state through a logistic regression algorithm;

[0034] Determine the current working condition based on the road surface type and vehicle motion state.

[0035] In this embodiment, the road condition image is from the road surface image collected by the vehicle's front-mounted on-vehicle camera.

[0036] In this embodiment, the road surface types include uniform road surface types such as dry and wet asphalt, snow surface, ice surface, ceramic tile, basalt, muddy ground or sandy ground (the four wheels of the vehicle are driving on the road surface with the same adhesion coefficient), or split (the wheels on the left and right sides of the vehicle are driving on the road surfaces with different adhesion coefficients) road surfaces.

[0037] In this embodiment, the vehicle motion states include acceleration, uniform motion, climbing and turning.

[0038] In this embodiment, the operation process of the convolutional neural network is as follows:

[0039] When the road condition image is input into the convolutional neural network CNN, first perform a convolution operation to extract the local features of the road condition image to obtain a feature map.

[0040] Secondly, activate the feature map through the non-linear activation function ReLU. After activation, reduce the dimension of the feature map through the pooling layer, and compress the dimension-reduced feature map into adjacent ones through the global average pooling layer to obtain a feature vector.

[0041] Finally, after the above convolution operation, activation and pooling operations on the road condition image, output the final feature vector.

[0042] In this embodiment, the operation process of the random forest is as follows:

[0043] First, define the decision tree and information gain.

[0044] Secondly, perform random forest training based on the defined decision tree and information gain to obtain the final decision tree model.

[0045] Finally, input the feature vector into the decision tree model to obtain the final classification result, that is, the road surface type.

[0046] In this embodiment, the process of the logistic regression algorithm is as follows:

[0047] First, preprocess the vehicle driving parameters, and input the features X = [ω, v, a, throttle, brake, δ], where ω is the wheel speed, v is the vehicle speed, a is the acceleration, throttle is the throttle opening, brake is the pedal depth, and δ is the steering wheel angle.

[0048] Secondly, define the label:

[0049] Encode the motion state into discrete categories y ∈ {0, 1, 2, 3, 4, 5}, corresponding to uniform motion, rapid acceleration, acceleration, rapid deceleration, deceleration, and start.

[0050] Secondly, perform data standardization, that is, perform Z-score standardization on continuous features to obtain the final standardized input features.

[0051] Secondly, construct a logistic regression model, including:

[0052] Define the hypothesis function: For class k, its probability is calculated by the Softmax function.

[0053] Define the loss function: Cross-Entropy Loss

[0054] Parameter optimization: Update the parameters by the gradient descent method.

[0055] Feature engineering and threshold setting: Adjust according to actual data requirements

[0056] For example: Sharp acceleration: a > 2.5 m / s² and throttle > 70%;

[0057] Sharp deceleration: a < -3.0 m / s² and brake > 60%;

[0058] Starting: vprev (vehicle speed in the previous cycle) = 0 and vcurrent (current vehicle speed) > 0;

[0059] Time window processing: Introduce time series features (determined according to actual requirements, the operating cycle of the functional program) to enhance the judgment of state continuity.

[0060] Finally, input the standardized input features into the constructed logistic regression model, output the probability distribution, and take the class corresponding to the maximum probability as the prediction result, that is, the vehicle motion state.

[0061] In this embodiment, the current working condition is jointly composed of the road surface type and the vehicle motion state, specifically as follows:

[0062] 1. Uniform road surface (dry and wet asphalt, ice and snow road surface, wet ceramic tile, wet basalt) stationary full-throttle straight start.

[0063] 2. Transition from a low-adhesion road surface to a high-adhesion road surface during stationary full-throttle start.

[0064] 3. Transition from a high-adhesion road surface to a low-adhesion road surface during stationary full-throttle start.

[0065] 4. Opposite road surface start and acceleration (15% slope, 20% slope, flat ground).

[0066] 5. High-adhesion road surface (dry and wet asphalt) cornering acceleration.

[0067] 6. Low-adhesion road surface (ice, snow) cornering acceleration.

[0068] 7. Ice start on a ramp (10% gradient).

[0069] Optionally, based on the driving data, determine the traction control situation corresponding to the vehicle under the current working conditions, including:

[0070] Based on the driving data, determine whether the traction control function meets the first requirement;

[0071] And whether the braking force meets the second requirement when the wheels slip;

[0072] And whether the braking force meets the third requirement when the wheels slip;

[0073] And whether the acceleration performance after the vehicle starts meets the fourth requirement.

[0074] In this embodiment, the first requirement specifically includes:

[0075] When it is recognized that the wheel slip degree (slip amount) during vehicle driving exceeds the threshold, determine whether the traction control function is activated within the required time T. If the traction control function is activated within time T, the traction control function meets the first requirement. If the traction control function is not activated within time T, the traction control function does not meet the first requirement;

[0076] In this embodiment, the second requirement specifically includes:

[0077] When the wheels slip, determine whether the wheel braking force response is timely and the braking force is appropriate. If the braking force response is timely and the braking force is appropriate, the braking force meets the second requirement. If the braking force response is not timely and the braking force is unreasonable, the braking force does not meet the second requirement.

[0078] In this embodiment, the third requirement specifically includes:

[0079] When the wheels slip, determine whether the wheel speed can be controlled to a reasonable speed. If the wheel speed can be controlled to a reasonable speed, the braking force meets the third requirement. If the wheel speed cannot be controlled to a reasonable speed, the braking force does not meet the third requirement.

[0080] In this embodiment, the fourth requirement specifically includes:

[0081] When the drive shaft wheels slip, torque reduction needs to be achieved through MTC (Machine Type Communication) torque control. The drive shaft will respond to the torque requested by MTC. It is necessary to ensure that the requested torque is smooth and without fluctuations, and enable the vehicle to have sufficient acceleration performance using the current adhesion coefficient, that is, to determine whether the vehicle reaches a preset value within a preset time after starting. If the vehicle reaches the preset value within the preset time after starting, the acceleration performance of the vehicle after starting meets the fourth requirement. If the vehicle does not reach the preset value within the preset time after starting, the acceleration performance of the vehicle after starting does not meet the fourth requirement.

[0082] Optionally, based on the driving behavior parameters, determine the weights of the various control parameters of the traction control system, including:

[0083] Extract the characteristics of the driving behavior parameters to determine the behavior characteristics;

[0084] Classify the behavior characteristics to determine the driving style category of the driver;

[0085] Based on the driving style category, assign preset weights to each control parameter of the traction control system through a decision tree algorithm.

[0086] In this embodiment, the process of the decision tree algorithm is as follows:

[0087] First, perform feature definition. The core of the driving style category is to extract behavior characteristics from driving behavior data. For example:

[0088] Acceleration behavior: Number of hard accelerations, average acceleration, maximum acceleration.

[0089] Deceleration behavior: Number of hard brakes, average deceleration, proportion of braking duration.

[0090] Steering behavior: Average speed during turning, lateral acceleration during turning.

[0091] Stability: Speed variance, lane keeping frequency, number of lane departures.

[0092] Time characteristics: Driving duration during peak hours, proportion of night driving.

[0093] Other indicators: Average vehicle speed, number of speeding violations, mean / variance of the distance from the vehicle ahead.

[0094] Data sources: On-vehicle sensors (OBD-II), GPS trajectory data, mobile phone accelerometers, on-vehicle cameras, etc.

[0095] Secondly, preprocess the collected data, including:

[0096] Missing value processing: Delete or fill in missing values (such as filling with the mean).

[0097] Outlier handling: Filter out abnormal driving segments based on quantiles or standard deviations (such as values where acceleration exceeds physical limits).

[0098] Standardization / Normalization: Standardize (such as Z-Score) or normalize (Min-Max) continuous features.

[0099] Secondly, label definition:

[0100] Define driving style categories according to business requirements (supervised labels are required), for example:

[0101] Aggressive: Frequent hard acceleration / hard braking, speeding, and frequent lane changes.

[0102] Conservative: Smooth acceleration, low-speed driving, and fewer lane changes.

[0103] Neutral: Between the two.

[0104] Secondly, feature selection and importance analysis

[0105] The decision tree itself can output feature importance, but initial screening of behavioral features is still required:

[0106] Filter method: Use variance threshold, mutual information to eliminate low-correlation behavioral features.

[0107] Domain knowledge: Combine knowledge of driving behavior to select behavioral features with clear physical meanings (such as the number of hard brakes).

[0108] Secondly, build a decision tree model, including:

[0109] Algorithm selection: Use CART (Classification and Regression Trees) or C4.5 algorithm, supporting classification tasks.

[0110] Key parameters:

[0111] Splitting criterion: Gini Impurity or Information Gain.

[0112] Tree depth control: max_depth (to prevent overfitting).

[0113] Minimum number of samples in leaf nodes: min_samples_leaf (to avoid noise interference).

[0114] Model optimization and parameter tuning:

[0115] Grid Search: Optimize hyperparameters (such as max_depth, min_samples_split).

[0116] Pruning: Simplify the tree structure through cost complexity pruning (ccp_alpha).

[0117] Cross-validation: Avoid overfitting and ensure generalization.

[0118] Finally, input the specific driving style category of the driver into the constructed decision tree model. The decision tree model will associate the weights of the control parameters, that is, the influence weights of different driving styles on the control parameters of the traction control system are different.

[0119] Optionally, adjust the control parameters of the traction control system according to the current working condition, traction control situation, and weights to determine the target control parameters, including:

[0120] Based on the current working condition, traction control situation, and weights, autonomously learn and adjust the control parameters of the potential control system through the fuzzy logic control algorithm, and output the target control parameters.

[0121] The main control parameters of the traction control system in this embodiment are: 1) The slip amount corresponding to different adhesion coefficient road surfaces and vehicle speeds; 2) The relevant parameters for controlling the wheel braking force (initial value, gain, and PID control related parameters); 3) The relevant parameters for controlling the drive shaft torque (initial value, gain, and PID control related parameters).

[0122] The specific fuzzy logic control algorithm in this embodiment is as follows:

[0123] 1. Fuzzification

[0124] Objective: Convert accurate input data into fuzzy linguistic variables.

[0125] Key tool: Membership Function, which defines the degree to which an input value belongs to a certain fuzzy set (membership degree between 0 and 1).

[0126] 2. Rule Base & Fuzzy Inference

[0127] Rule base construction: Based on the "IF-THEN" rules of the model input signal, simulate human decision-making logic.

[0128] Example rule:

[0129] IF the driver's driving style is aggressive AND the required acceleration performance is strong, THEN the gradient of the torque command change increases.

[0130] Inference mechanism:

[0131] Fuzzy logic operation: Combine the membership degrees of multiple conditions using fuzzy logic operators (e.g., AND is minimized, OR is maximized).

[0132] Activation rule: Calculate the membership degree of the condition part (antecedent) of each rule to determine the strength of the rule output.

[0133] 3. Aggregation & Defuzzification

[0134] Aggregation: Combine the output fuzzy sets of all activated rules into a comprehensive fuzzy set.

[0135] Common methods: Take the maximum value of the outputs of each rule or weighted average.

[0136] Defuzzification: Convert the aggregated fuzzy output into an exact control quantity.

[0137] Common methods:

[0138] Centroid method: Calculate the centroid position of the fuzzy set.

[0139] Max-Membership method: Select the output value corresponding to the point with the highest membership degree.

[0140] Therefore, when the current working condition, traction control situation, and weight are obtained, through the result of fuzzy logic control, the target control parameter is obtained, and the control parameter is automatically updated and adjusted according to the target control parameter.

[0141] In this embodiment, the above process involves multiple models, such as convolutional neural network, decision tree model, fuzzy logic control model, etc. The relevant machine learning algorithm parameters and training data involved in the above models can be stored, and the data in the dataset will be updated and optimized based on the actual situation and fed back to the above several models for iteration.

[0142] As Figure 2 shown, the present disclosure provides a system for adjusting control parameters of a traction control system, including:

[0143] Current working condition determination module, configured to obtain the current road condition information of the vehicle and determine the corresponding current working condition of the vehicle based on the road condition information;

[0144] Traction control situation determination module, configured to obtain all driving data of the vehicle from the start to the end under the current working condition and determine the corresponding traction control situation of the vehicle under the current working condition based on the driving data;

[0145] A weight determination module, configured to obtain driving behavior parameters of a driver, and based on the driving behavior parameters, determine weights of respective control parameters of a traction control system; wherein, the weights characterize the influence degree of the driver's driving style on the respective control parameters.

[0146] A control parameter adjustment module, configured to adjust the control parameters of the traction control system according to the current working condition, the traction control situation and the weights, and determine target control parameters.

[0147] Optionally, the current working condition determination module is specifically configured to:

[0148] Obtain road condition images and vehicle driving parameters in real time; wherein, the vehicle driving parameters include wheel speed, vehicle speed, acceleration, throttle, brake pedal state and steering wheel angle.

[0149] Based on the current road condition image, extract features of the current road condition image through a convolutional neural network, and classify the features of the current road condition image through a random forest to determine the road surface type.

[0150] Based on the vehicle driving parameters, determine the vehicle motion state through a logistic regression algorithm.

[0151] Based on the road surface type and the vehicle motion state, determine the current working condition.

[0152] Optionally, the traction control situation determination module is specifically configured to:

[0153] Based on the driving data, determine whether the traction control function meets the first requirement.

[0154] And whether the braking force meets the second requirement when the wheels slip.

[0155] And whether the braking force meets the third requirement when the wheels slip.

[0156] And whether the acceleration performance after the vehicle starts meets the fourth requirement.

[0157] Optionally, the weight determination module is specifically configured to:

[0158] Extract features of the driving behavior parameters to determine behavior features.

[0159] Classify the behavior features to determine the driver's driving style category.

[0160] Based on the driving style category, assign preset weights to each control parameter of the traction control system through a decision tree algorithm.

[0161] Optionally, the control parameter adjustment module is specifically configured to:

[0162] Based on the current working condition, traction control situation, and weight, the control parameters of the potential control system are autonomously learned and adjusted through a fuzzy logic control algorithm to output the target control parameters.

[0163] A computing device according to an embodiment of the present disclosure includes a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the computer program, the method for adjusting the control parameters of the traction control system as described above is implemented. That is to say, a computing device according to an embodiment of the present disclosure may include, but is not limited to: a processor and a memory; the memory is used to store the computer program; the processor is used to execute the method for adjusting the control parameters of the traction control system shown in any embodiment of the present disclosure by calling the computer program.

[0164] In an alternative embodiment, a computing device is provided, as Figure 3 shown, Figure 3 The computing device 4000 shown includes: a processor 4001 and a memory 4003. Among them, the processor 4001 and the memory 4003 are connected, such as connected through a bus 4002. Optionally, the computing device 4000 may further include a transceiver 4004, and the transceiver 4004 may be used for data interaction between this computing device and other computing devices, such as sending and / or receiving data, etc. It should be noted that in practical applications, the transceiver 4004 is not limited to one, and the structure of this computing device 4000 does not constitute a limitation on the embodiments of the present disclosure.

[0165] The processor 4001 may be a CPU (Central Processing Unit), a general-purpose processor, a DSP (Digital Signal Processor), an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array), or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. It can implement or execute various exemplary logic blocks, modules, and circuits described in combination with the disclosure content of the present disclosure. The processor 4001 may also be a combination for implementing computing functions, such as a combination including one or more microprocessors, a combination of a DSP and a microprocessor, etc.

[0166] The bus 4002 may include a path for transmitting information between the above components. The bus 4002 can be a PCI (Peripheral Component Interconnect) bus, an EISA (Extended Industry Standard Architecture) bus, or the like. The bus 4002 can be divided into an address bus, a data bus, a control bus, etc. For the sake of representation, Figure 3 only a thick line is used to represent the bus 4002 in Figure 3 , but it does not mean that there is only one bus or one type of bus.

[0167] The memory 4003 can be a ROM (Read Only Memory) or other types of static storage devices that can store static information and instructions, a RAM (Random Access Memory) or other types of dynamic storage devices that can store information and instructions, or it can also be an EEPROM (Electrically Erasable Programmable Read Only Memory), a CD-ROM (Compact Disc Read Only Memory), or other optical disc storage, optical disc storage (including compact discs, laser discs, optical discs, digital versatile discs, Blu-ray discs, etc.), magnetic disk storage media, or other magnetic storage devices, or any other medium that can be used to carry or store the desired program code in the form of instructions or data structures and can be accessed by a computer, but is not limited thereto.

[0168] The memory 4003 is used to store the application program code (computer program) for executing the present disclosure solution and is controlled by the processor 4001 for execution. The processor 4001 is used to execute the application program code stored in the memory 4003 to implement the content shown in the foregoing method embodiments.

[0169] Among them, the computing device can also be a terminal device, and the terminal device can be any device on which an application can be installed, including at least one of a smart phone, a tablet computer, a laptop computer, a desktop computer, a smart speaker, a smart watch, a smart TV, and a smart vehicle device.

[0170] It should be noted that Figure 3 the computing device shown is only an example and should not impose any limitations on the functions and usage scope of the embodiments of the present disclosure.

[0171] A computer-readable storage medium according to an embodiment of the present disclosure has a computer program stored thereon, and when the computer program is executed by a processor, the above-mentioned method for adjusting control parameters of the traction control system is implemented.

[0172] Optionally, the computer-readable storage medium may be a read-only memory (ROM), a random access memory (RAM), a compact disc read-only memory (CD-ROM), magnetic tape, a floppy disk, an optical data storage device, etc.

[0173] In an exemplary embodiment, a computer program product or a computer program is further provided. The computer program product or the computer program includes computer instructions, and the computer instructions are stored in a computer-readable storage medium. The processor of the computing device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computing device executes the above-mentioned method for adjusting control parameters of the traction control system.

[0174] Computer program code for performing the operations of the present disclosure may be written in one or more programming languages or combinations thereof. The above-mentioned programming languages include object-oriented programming languages - such as Java, Smalltalk, C++, and also include conventional procedural programming languages - such as the "C" language or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, executed as a stand-alone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network - including a local area network (LAN) or a wide area network (WAN) - or may be connected to an external computer (for example, by using an Internet service provider to connect through the Internet).

[0175] It should be understood that the flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functions, and operations of the possible implementations of the methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagram may represent a module, a program segment, or a part of code that contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order from that marked in the accompanying drawings. For example, two consecutive blocks shown may actually be executed substantially in parallel, and they may sometimes be executed in the reverse order, depending on the functions involved. It should also be noted that each block in the block diagram and / or flowchart, as well as the combination of blocks in the block diagram and / or flowchart, may be implemented by a dedicated hardware-based system for performing the specified functions or operations, or may be implemented by a combination of dedicated hardware and computer instructions.

[0176] The computer-readable storage medium provided by the embodiments of the present disclosure may be, but is not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the above. More specific examples of the computer-readable storage medium may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EEPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present disclosure, the computer-readable storage medium may be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0177] The above computer-readable storage medium carries one or more programs that, when executed by the computing device, cause the computing device to perform the methods shown in the above embodiments.

[0178] The above description is only a preferred embodiment of the present disclosure and an explanation of the applied technical principles. Those skilled in the art should understand that the scope of disclosure involved in the present disclosure is not limited to the technical solutions formed by the specific combination of the above technical features, and should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the above disclosure concept. For example, the technical solutions formed by mutually replacing the above features with the (but not limited to) technical features having similar functions disclosed in the present disclosure.

[0179] It should be noted that the terms "first", "second", etc. in the description and claims of this application are used to distinguish similar objects, rather than to limit a specific order or sequence. The order of use of similar objects can be interchanged when appropriate, so that the embodiments of the present application described here can be implemented in an order other than the illustrated or described order.

[0180] Those skilled in the art know that the present disclosure can be implemented as a system, a method, or a computer program product. Therefore, the present disclosure can be specifically implemented in the following forms, that is, it can be entirely hardware, entirely software (including firmware, resident software, microcode, etc.), or a combination of hardware and software, which is generally referred to as "circuit", "module", or "system" in this article. In addition, in some embodiments, the present disclosure can also be implemented in the form of a computer program product in one or more computer-readable media, which contains computer-readable program code.

[0181] Although the embodiments of the present disclosure have been shown and described above, it can be understood that the above embodiments are exemplary and should not be construed as limiting the present disclosure. Those of ordinary skill in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of the present disclosure.

Claims

1. A method for adjusting control parameters of a traction control system, characterized in that, including: Obtain the current road condition information of the vehicle, and determine the current working condition of the vehicle based on the road condition information; Obtain all the driving data of the vehicle from the start to the end under the current working condition, and determine the traction control situation corresponding to the vehicle under the current working condition based on the driving data; Obtain the driving behavior parameters of the driver, and determine the weights of the respective control parameters of the traction control system based on the driving behavior parameters; wherein, the weights characterize the influence degree of the driver's driving style on the respective control parameters; Adjust the control parameters of the traction control system according to the current working condition, the traction control situation, and the weights, and determine the target control parameters.

2. The method according to claim 1, wherein The determining the current working condition corresponding to the vehicle based on the road condition information includes: Obtain the road condition image in real time, as well as the vehicle driving parameters; wherein, the vehicle driving parameters include wheel speed, vehicle speed, acceleration, throttle opening, brake pedal state, and steering wheel angle; Based on the current road condition image, extract the features of the current road condition image through a convolutional neural network, and classify the features of the current road condition image through a random forest to determine the road surface type; Based on the vehicle driving parameters, determine the vehicle motion state through a logistic regression algorithm; Determine the current working condition based on the road surface type and the vehicle motion state.

3. The method according to claim 1, characterized in that, The determining the traction control situation corresponding to the vehicle under the current working condition based on the driving data includes: Based on the driving data, determine whether the traction control function meets the first requirement; and whether the braking force meets the second requirement when the wheels slip; and whether the braking force meets the third requirement when the wheels slip; and whether the acceleration performance after the vehicle starts meets the fourth requirement.

4. The method according to claim 1, characterized in that, The determining the weights of the respective control parameters of the traction control system based on the driving behavior parameters includes: Extract the features of the driving behavior parameters to determine the behavior features; Classify the behavior features to determine the driving style category of the driver; Based on the driving style category, assign preset weights to each control parameter of the traction control system through a decision tree algorithm.

5. The method according to claim 1, wherein The adjusting the control parameters of the traction control system according to the current working condition, the traction control situation, and the weights, and determining the target control parameters includes: Based on the current working condition, the traction control situation, and the weights, autonomously learn and adjust the control parameters of the potential control system through a fuzzy logic control algorithm, and output the target control parameters.

6. Adjustment system for traction control system control parameters, characterized in that, including: A current working condition determination module, configured to obtain the current road condition information of the vehicle, and determine the current working condition corresponding to the vehicle based on the road condition information; A traction control situation determination module, configured to obtain all the driving data of the vehicle from the start to the end under the current working condition, and determine the traction control situation corresponding to the vehicle under the current working condition based on the driving data; A weight determination module, configured to obtain the driving behavior parameters of the driver, and determine the weights of the respective control parameters of the traction control system based on the driving behavior parameters; wherein, the weights characterize the influence degree of the driver's driving style on the respective control parameters; A control parameter adjustment module, configured to adjust control parameters of a traction control system according to the current working condition, the traction control situation, and the weight, and determine target control parameters.

7. The method according to claim 6, wherein The current working condition determination module is specifically configured to: Obtain a road condition image and vehicle driving parameters in real time; wherein, the vehicle driving parameters include wheel speed, vehicle speed, acceleration, throttle, brake pedal state, and steering wheel angle; Based on the current road condition image, extract features of the current road condition image through a convolutional neural network, and classify the features of the current road condition image through a random forest to determine the road surface type; Based on the vehicle driving parameters, determine the vehicle motion state through a logistic regression algorithm; Based on the road surface type and the vehicle motion state, determine the current working condition.

8. The method according to claim 6, characterized in that The traction control situation determination module is specifically configured to: Based on the driving data, determine whether the traction control function meets the first requirement; And whether the braking force meets the second requirement when the wheels slip; And whether the braking force meets the third requirement when the wheels slip; And whether the acceleration performance after the vehicle starts meets the fourth requirement.

9. A computing device, comprising a memory, a processor, and a program stored on the memory and running on the processor, characterized in that, When the processor executes the program, it implements the steps of the method for adjusting the control parameters of the traction control system according to any one of claims 1-5.

10. A computer-readable storage medium, characterized in that, Instructions are stored in a computer-readable storage medium, and when the instructions are run on a terminal device, the terminal device is caused to execute the steps of the method for adjusting the control parameters of the traction control system according to any one of claims 1-5.

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