Driving style identification method, system and equipment and medium

Abstract

The invention discloses a driving style identification method, system and device and a medium, and the method comprises the steps: obtaining the natural driving data of a target driver; performing driving behavior identification on the natural driving data to obtain a driving behavior identifier of the target driver; inputting the natural driving data into a driving style recognition model corresponding to the driving behavior identifier for style recognition to obtain intermediate driving style information output by the driving style recognition model; and performing style correction on the intermediate driving style information to obtain target driving style information of the target driver. The method can effectively improve the adaptive ability of driving style recognition and improve the recognition accuracy of the driving style. The invention relates to the technical field of driving style recognition.

Description

Technical Field

[0001] The present invention relates to the technical field of driving style recognition, and in particular to a driving style recognition method, system, device and medium. Background Art

[0002] As vehicle intelligence becomes the mainstream development of the global automotive industry, assisted driving is increasingly favored by consumers. Among them, the driver's driving style, which affects the effectiveness of assisted driving, has become one of the key focuses of relevant practitioners.

[0003] Currently, related technologies usually use offline data to train a driving style recognition model and use the driving style identified by the model as the driver's inherent driving style throughout the driving process. This method cannot adapt well to the driver's driving style under different driving behaviors, has limited adaptability, and the accuracy of driving style recognition is unsatisfactory.

[0004] Therefore, the problems existing in related technologies still need to be solved and optimized urgently. Summary of the Invention

[0005] The purpose of the present invention is to solve one of the technical problems existing in the related art to at least a certain extent.

[0006] To this end, an object of embodiments of the present invention is to provide a driving style recognition method, system, device, and medium, wherein the method can effectively improve the adaptability of driving style recognition and improve the recognition accuracy of driving style.

[0007] In order to achieve the above technical objectives, the technical solutions adopted in the embodiments of the present application include:

[0008] In a first aspect, an embodiment of the present application provides a driving style recognition method, comprising:

[0009] Obtaining natural driving data of the target driver;

[0010] Performing driving behavior recognition on the natural driving data to obtain a driving behavior identifier of the target driver;

[0011] inputting the natural driving data into a driving style recognition model corresponding to the driving behavior identifier to perform style recognition, and obtaining intermediate driving style information output by the driving style recognition model;

[0012] Style correction is performed on the intermediate driving style information to obtain target driving style information of the target driver.

[0013] In addition, the method according to the above embodiment of the present application may also have the following additional technical features:

[0014] Furthermore, in one embodiment of the present application, performing driving behavior recognition on the natural driving data to obtain the driving behavior identifier of the target driver includes:

[0015] Obtain driving behavior recognition model;

[0016] The natural driving data is input into the driving behavior recognition model to perform behavior recognition, and a driving behavior identifier output by the driving behavior recognition model is obtained.

[0017] Furthermore, in one embodiment of the present application, the driving behavior recognition model is constructed by the following steps:

[0018] Obtain several driving behavior rules;

[0019] Determining, based on all the driving behavior rules, a behavior membership function corresponding to each of the driving behavior rules;

[0020] The driving behavior recognition model is constructed based on all the behavior membership functions.

[0021] Furthermore, in one embodiment of the present application, inputting the natural driving data into a driving style recognition model corresponding to the driving behavior identifier to perform style recognition, and obtaining intermediate driving style information output by the driving style recognition model, includes:

[0022] Acquire a plurality of trained driving style recognition models, each of the trained driving style recognition models corresponding to a type of driving behavior;

[0023] According to the driving behavior identifier, all the trained driving style recognition models are screened to obtain a target style recognition model;

[0024] The natural driving data is input into the target style recognition model to perform style recognition to obtain the intermediate driving style information.

[0025] Furthermore, in one embodiment of the present application, obtaining a plurality of trained driving style recognition models includes:

[0026] Obtaining an original style recognition model and a driving training dataset, wherein the driving training dataset includes a plurality of sample driving data, and all of the sample driving data correspond to a type of driving behavior;

[0027] Extracting features and labels from the driving training data set to obtain a training feature set and a feature label corresponding to each target driving feature in the training feature set, wherein the target driving features under different types of driving behaviors are not completely the same;

[0028] According to all the feature labels, the training feature set is input into the original style recognition model to update parameters, thereby obtaining the trained driving style recognition model.

[0029] Furthermore, in one embodiment of the present application, performing style correction on the intermediate driving style information to obtain the target driving style information of the target driver includes:

[0030] Obtaining a historical update vector and historical driving style information, wherein the historical update vector is an update vector at a previous time point adjacent to a current time point, the current time point is a time point of the intermediate driving style information, and the historical driving style information is target driving style information at the same time point as the historical update vector;

[0031] The intermediate driving style information is updated according to the historical update vector and the historical driving style information to obtain the target driving style information.

[0032] Furthermore, in one embodiment of the present application, performing a relational update on the intermediate driving style information based on the historical update vector and the historical driving style information to obtain the target driving style information includes:

[0033] performing a time-dependent update on the historical update vector according to the historical driving style information to obtain a current update vector corresponding to the intermediate driving style information;

[0034] The intermediate driving style information is corrected and updated according to the current update vector to obtain the target driving style information.

[0035] In a second aspect, an embodiment of the present application provides a driving style recognition system, comprising:

[0036] a first processing unit, configured to obtain natural driving data of a target driver;

[0037] a second processing unit, configured to perform driving behavior recognition on the natural driving data to obtain a driving behavior identifier of the target driver;

[0038] a third processing unit, configured to input the natural driving data into a driving style recognition model corresponding to the driving behavior identifier to perform style recognition, and obtain intermediate driving style information output by the driving style recognition model;

[0039] The fourth processing unit is configured to perform style correction on the intermediate driving style information to obtain target driving style information of the target driver.

[0040] In a third aspect, an embodiment of the present application further provides an electronic device, including:

[0041] at least one processor;

[0042] at least one memory for storing at least one program;

[0043] When the at least one program is executed by the at least one processor, the at least one processor implements the above method.

[0044] In a fourth aspect, an embodiment of the present application further provides a computer-readable storage medium, which stores a program executable by a processor, and the program executable by the processor is used to implement the above method when executed by the processor.

[0045] The advantages and benefits of this application will be partially given in the following description, and partially become apparent from the following description, or learned through practice of this application:

[0046] Embodiments of the present application disclose a driving style recognition method, system, device, and medium. The method acquires natural driving data of a target driver; performs driving behavior recognition on the natural driving data to obtain a driving behavior identifier for the target driver; inputs the natural driving data into a driving style recognition model corresponding to the driving behavior identifier for style recognition, obtaining intermediate driving style information output by the driving style recognition model; and performs style correction on the intermediate driving style information to obtain target driving style information for the target driver. By inputting the natural driving data into the driving style recognition model corresponding to the driving behavior identifier for style recognition, the method can consider the driver's driving style under different driving behaviors, such as acceleration, deceleration, and turning, thereby effectively improving the adaptive capability of driving style recognition under different driver behaviors and improving the accuracy of driving style recognition. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following introduction is made to the drawings of the embodiments of the present application or the related technical solutions in the prior art. It should be understood that the drawings introduced below are only for the convenience of clearly expressing some embodiments of the technical solutions of the present application. For those skilled in the art, other drawings can be obtained based on these drawings without any creative work.

[0048] Figure 1 A flowchart of a driving style identification method provided in an embodiment of the present application;

[0049] Figure 2 A schematic diagram of a driving style recognition system provided in an embodiment of the present application;

[0050] Figure 3 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0051] The embodiments of the present application are described in detail below, and examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present application and are not to be construed as limiting the present application. For the step numbers in the following embodiments, they are provided only for the convenience of explanation and are not intended to limit the order of the steps. The order of execution of the steps in the embodiments can be adaptively adjusted according to the understanding of those skilled in the art.

[0052] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this application pertains. The terms used herein are for the purpose of describing the embodiments of this application only and are not intended to limit this application.

[0053] Currently, related technologies typically train driving style recognition models using offline data and use the driving style identified by the model as the driver's inherent driving style throughout the entire driving process. However, in practice, the same driver may exhibit different driving styles throughout the entire driving process. This approach cannot effectively adapt to the driver's driving style under different driving behaviors and has limited adaptability, resulting in unsatisfactory driving style recognition accuracy. Furthermore, this approach fails to account for the relationship between the driver's "long-term driving habits" and "instantaneous preferences" in vehicle driving, which may lead to random estimation bias and low accuracy of the inherent driving style output by the model.

[0054] It should be noted that the above-mentioned related technologies are only used to assist in understanding the technical solutions of this application and do not mean that they belong to the disclosed prior art.

[0055] In view of this, embodiments of the present invention provide a driving style recognition method, system, device, and medium. The method performs style recognition by inputting natural driving data into a driving style recognition model corresponding to a driving behavior identifier. This method can consider the driver's driving style under different driving behaviors, such as acceleration, deceleration, and turning, thereby effectively improving the adaptive capability of driving style recognition under different driving behaviors and improving the accuracy of driving style recognition.

[0056] Furthermore, the method performs style correction on the intermediate driving style information output by the driving style recognition model. Specifically, the intermediate driving style information is updated by a current update vector with time-dependent characteristics. This method can fully consider the relationship between the driver's long-term driving style and instantaneous driving style, which is conducive to reducing the generation of random estimation bias in the recognition process (i.e., alleviating the negative impact of random fluctuations), thereby effectively improving the recognition accuracy of driving style.

[0057] Reference Figure 1 In an embodiment of the present application, a driving style recognition method includes:

[0058] Step 110: Obtaining natural driving data of the target driver;

[0059] In an embodiment of the present application, the natural driving data may be actual vehicle data under the current natural driving conditions when the target driver is driving the vehicle. Specifically, it may be obtained by collecting the vehicle CAN signal through a multi-channel CANoe device. The natural driving data includes but is not limited to the vehicle's speed, longitudinal acceleration, lateral acceleration, steering wheel angle, steering wheel speed, accelerator pedal opening, and brake pedal opening.

[0060] It is understandable that due to the influence of factors such as vehicle vibration and uneven road surface, the collected natural driving data may be mixed with random noise. Therefore, after obtaining the original natural driving data, the original natural driving data can be subjected to preprocessing operations such as outlier detection and removal, noise filtering, etc., so as to obtain preprocessed natural driving data. There are many specific preprocessing methods, which will not be repeated in this application.

[0061] Step 120: performing driving behavior recognition on the natural driving data to obtain a driving behavior identifier of the target driver;

[0062] In an embodiment of the present application, the acquired natural driving data can be identified to obtain the driving behavior of the target driver under the current natural driving conditions, which is recorded as a driving behavior identifier. The driving behavior identifier includes left-turn behavior, right-turn behavior, acceleration behavior, deceleration behavior, no obvious driving behavior, etc.

[0063] In some embodiments, performing driving behavior recognition on the natural driving data to obtain the driving behavior identifier of the target driver includes:

[0064] Obtain driving behavior recognition model;

[0065] The natural driving data is input into the driving behavior recognition model to perform behavior recognition, and a driving behavior identifier output by the driving behavior recognition model is obtained.

[0066] In an embodiment of the present application, a driving behavior recognition model can be constructed based on fuzzy reasoning, and natural driving data can be input into the driving behavior recognition model to identify the driving behavior corresponding to the natural driving data, thereby obtaining a driving behavior identifier.

[0067] In some embodiments, the driving behavior recognition model is constructed by the following steps:

[0068] Obtain several driving behavior rules;

[0069] Determining, based on all the driving behavior rules, a behavior membership function corresponding to each of the driving behavior rules;

[0070] The driving behavior recognition model is constructed based on all the behavior membership functions.

[0071] In the embodiment of the present application, the driving behavior rules include acceleration rules, deceleration rules, left turn rules and right turn rules as an example. At this time, the acceleration rule can specifically be the longitudinal acceleration a x Greater than 0.4m / s 2 ; The deceleration rule can be specifically the longitudinal acceleration a x Less than -0.4m / s 2 ; The specific left-turn rule can be the lateral acceleration a y Greater than 0.4m / s 2 Or the steering wheel angle θ is greater than 30°; and the left turn rule can be specifically the lateral acceleration a y Less than -0.4m / s 2 Or the steering wheel angle θ is less than -30°.

[0072] It is understandable that after all driving behavior rules are acquired, a corresponding behavior membership function may be determined based on each driving behavior rule.

[0073] For example, the behavior membership function of the acceleration rule can be expressed as:

[0074]

[0075] Among them, μ 加速 (a x ) is the behavioral membership function of the acceleration rule.

[0076] The behavior membership function of the deceleration rule can be expressed as:

[0077]

[0078] Among them, μ 减速 (a x ) is the behavioral membership function of the deceleration rule.

[0079] The behavior membership function of the left-turn rule can be expressed as:

[0080]

[0081] Among them, μ 左横 (a y ) is the behavior membership function of the left-turn rule regarding lateral acceleration; μ 左角 (θ) is the behavior membership function of the left-turn rule with respect to the steering wheel angle θ; μ 左转弯 is the overall behavior membership function of the left-turn rule.

[0082] The behavior membership function of the right-turn rule can be expressed as:

[0083]

[0084] Among them, μ 右横 (a y ) is the behavior membership function of the right-turn rule regarding lateral acceleration; μ 右角 (θ) is the behavior membership function of the right-turn rule with respect to the steering wheel angle θ; μ 右转弯 is the overall behavior membership function of the right-turn rule.

[0085] It can be understood that the specific numerical values ​​related to lateral acceleration, longitudinal acceleration, steering wheel angle, etc. in the examples of this application are only optional values ​​and are set only for ease of understanding; in actual applications, the specific numerical values ​​related to lateral acceleration, longitudinal acceleration, steering wheel angle, etc. can also be flexibly set, and this application will not go into details here.

[0086] In addition, after obtaining the behavior membership function of each driving behavior rule, a corresponding driving behavior recognition model can be constructed based on fuzzy reasoning based on all the obtained behavior membership functions. The driving behavior recognition model determines left-turn behavior, right-turn behavior, acceleration behavior, deceleration behavior, or no obvious driving behavior based on the input natural driving data (such as the vehicle's longitudinal acceleration, lateral acceleration, steering wheel angle, etc.), wherein no obvious driving behavior can be the output of the driving behavior recognition model when the input natural driving data does not conform to left-turn behavior, right-turn behavior, acceleration behavior, and deceleration behavior.

[0087] Step 130: Input the natural driving data into a driving style recognition model corresponding to the driving behavior identifier to perform style recognition, and obtain intermediate driving style information output by the driving style recognition model;

[0088] In an embodiment of the present application, after determining the driving behavior of the target driver, the natural driving data of the target driver can be input into a corresponding driving style recognition model for identification. The corresponding driving style recognition model is trained based on the training data of the corresponding driving behavior, thereby obtaining intermediate driving style information.

[0089] In some embodiments, inputting the natural driving data into a driving style recognition model corresponding to the driving behavior identifier to perform style recognition, and obtaining intermediate driving style information output by the driving style recognition model, includes:

[0090] Acquire a plurality of trained driving style recognition models, each of the trained driving style recognition models corresponding to a type of driving behavior;

[0091] According to the driving behavior identifier, all the trained driving style recognition models are screened to obtain a target style recognition model;

[0092] The natural driving data is input into the target style recognition model to perform style recognition to obtain the intermediate driving style information.

[0093] In an embodiment of the present application, the number of trained driving style recognition models is several. In the embodiment of the present application, the number of trained driving style recognition models is 4 as an example. The trained driving style recognition models can be a driving style recognition model corresponding to left-turning behavior, a driving style recognition model corresponding to right-turning behavior, an acceleration style recognition model corresponding to acceleration behavior, and an acceleration style recognition model corresponding to deceleration behavior.

[0094] It is understood that model screening can be based on the driving behavior indicated by the driving behavior identifier, selecting a driving style recognition model corresponding to the driving behavior, which is recorded as the target style recognition model. Natural driving data is then input into the target style recognition model for style recognition, thereby obtaining intermediate driving style information for the target driver under the current natural driving conditions.

[0095] In some embodiments, obtaining a plurality of trained driving style recognition models includes:

[0096] Obtaining an original style recognition model and a driving training dataset, wherein the driving training dataset includes a plurality of sample driving data, and all of the sample driving data correspond to a type of driving behavior;

[0097] Extracting features and labels from the driving training data set to obtain a training feature set and a feature label corresponding to each target driving feature in the training feature set, wherein the target driving features under different types of driving behaviors are not completely the same;

[0098] According to all the feature labels, the training feature set is input into the original style recognition model to update parameters, thereby obtaining the trained driving style recognition model.

[0099] In an embodiment of the present application, the original style recognition model can be a recurrent neural network (RNN) model, specifically a long short-term memory (LSTM) model. Specifically, for any original style recognition model, a number of sample driving data for a certain driving behavior type can be obtained. The sample driving data can be driving data publicly available on the internet or collected with the driver's consent.

[0100] It is understood that, because the data features corresponding to driving styles under different driving behaviors are different, the feature extraction in the embodiments of the present application can be based on the driving behavior type corresponding to the sample driving data, and the data features of each sample driving data in the driving training dataset can be extracted. Specifically, for acceleration and deceleration behaviors, the statistical values ​​of vehicle speed, longitudinal acceleration, accelerator pedal opening, and brake pedal opening (such as mean, standard deviation, and maximum value) can be extracted from the sample driving data as characteristic parameters characterizing their driving styles, which are recorded as target driving features; or, for left-turn and right-turn behaviors, the statistical values ​​of vehicle speed, longitudinal acceleration, lateral acceleration, steering wheel angle, steering wheel speed, accelerator pedal opening, and brake pedal opening (such as mean, standard deviation, and maximum value) can be extracted from the sample driving data as characteristic parameters characterizing their driving styles, which are also recorded as target driving features.

[0101] It should be noted that after extracting the target driving features from each sample driving data, a training feature set can be constructed based on all target driving features. Furthermore, label extraction can first be performed by reducing the dimensionality of all target driving features using principal component analysis (PCA) to obtain a number of reduced target driving features. Then, all reduced target driving features are clustered using a clustering algorithm (such as the K-means clustering algorithm) to obtain a number of clusters, each corresponding to a cluster label. Finally, based on the cluster labels, each reduced target driving feature within the cluster is labeled to obtain a feature label corresponding to each target driving feature.

[0102] Step 140: Perform style correction on the intermediate driving style information to obtain target driving style information of the target driver.

[0103] In an embodiment of the present application, after the driving style recognition model outputs the intermediate driving style information, the intermediate driving style information may be corrected to obtain the final target driving style information.

[0104] In some embodiments, performing style correction on the intermediate driving style information to obtain target driving style information of the target driver includes:

[0105] Obtaining a historical update vector and historical driving style information, wherein the historical update vector is an update vector at a previous time point adjacent to a current time point, the current time point is a time point of the intermediate driving style information, and the historical driving style information is target driving style information at the same time point as the historical update vector;

[0106] In this embodiment of the present application, the time point of the intermediate driving style information is the predicted time point output by the driving style recognition model. The update vector can be the weighted probability of the target driving style information at a previous time point, and the historical driving style information can be the target driving style information at a previous time point, for example, the target driving style information at a previous time point adjacent to the current time point.

[0107] The intermediate driving style information is updated according to the historical update vector and the historical driving style information to obtain the target driving style information.

[0108] Furthermore, the updating the intermediate driving style information according to the historical update vector and the historical driving style information to obtain the target driving style information includes:

[0109] performing a time-dependent update on the historical update vector according to the historical driving style information to obtain a current update vector corresponding to the intermediate driving style information;

[0110] The intermediate driving style information is corrected and updated according to the current update vector to obtain the target driving style information.

[0111] In the embodiment of the present application, the intermediate driving style information may record the initial probability distribution of several driving styles corresponding to the natural driving data of the target driver. In the embodiment of the present application, taking the driving styles including aggressive, normal, and mild as an example, the functional expression of the intermediate driving style information may be:

[0112] P initial =[p0,p1,p2]

[0113] Among them, P initial is the intermediate driving style information; p0 is the initial probability distribution of the aggressive driving style; p1 is the initial probability distribution of the normal driving style; and p2 is the initial probability distribution of the mild driving style.

[0114] It is understandable that the timing-dependent update can be to fuse historical information into the update vector to suppress random noise in the real-time data. The current update vector has timing dependency, which can be specifically achieved through the weighted probability of historical driving style and historical update vector.

[0115] Specifically, the intermediate driving style information at the initial moment is updated using the initial update vector to obtain the target driving style information at the initial moment. The initial update vector can be specifically expressed as:

[0116] M initial =[p ′ 0,p ′ 1,p ′ 2]

[0117] Among them, M initial is the initial update vector; p ′ 0 is the initial update weight of the aggressive driving style; p ′ 1 is the initial update weight of normal driving style; p ′ 2 is the initial update weight for the mild driving style.

[0118] It should be noted that the initial update weights of the aggressive driving style, the normal driving style, and the mild driving style of the initial update vector are set to have equal probability, and their sum is 1.

[0119] The current update vector can be integrated with historical information and used to update the current intermediate driving style information, which can be specifically expressed as:

[0120]

[0121] Among them, M (t) is the current update vector, that is, the update vector at time point t; λ is the forgetting factor, λ∈(0,1); M (t-1) is the historical update vector, that is, the update vector at time point t-1; is the historical driving style information, that is, the target driving style information at time t-1.

[0122] It should be noted that the correction update can be to use the current update vector as the updated weight parameter to adjust the intermediate driving style to obtain the target driving style information. The target driving style information can include the target probability distribution of the aggressive driving style, the target probability distribution of the normal driving style, and the target probability distribution of the mild driving style.

[0123] For example, the target driving style information at time point t can be specifically expressed as:

[0124]

[0125] Among them, P correct is the target probability distribution of the target driving style information at time point t; Target probability for aggressive driving style; The target probability of normal driving style; Target probability for a moderate driving style.

[0126] For example, at time point t, the target probability distribution of the aggressive driving style can be expressed as:

[0127]

[0128] in, is the updated weight of the aggressive driving style at time point t.

[0129] It's worth noting that the target probability distributions for the normal and mild driving styles are similar to the aforementioned target probability distributions for the aggressive driving style and can be derived simply by analogy. This application will not elaborate further here. Furthermore, after obtaining the target probability distributions for all driving styles, the target probability distributions for all driving styles can be integrated to obtain target driving style information. This target driving style information is expressed in a similar format to the aforementioned intermediate driving style information and can be derived simply by analogy.

[0130] It should be noted that in actual applications, after obtaining the target driving style information, the driving style type with the highest target probability distribution is often determined as the final driving style recognition result. Furthermore, the driving style types used in this application are for illustration only and are not intended to limit this application. The actual number and types of driving style types can be flexibly set based on actual circumstances.

[0131] A driving style recognition system proposed according to an embodiment of the present application is described in detail below with reference to the accompanying drawings.

[0132] Reference Figure 2 A driving style recognition system proposed in an embodiment of the present application includes:

[0133] The first processing unit 101 is used to obtain natural driving data of the target driver;

[0134] The second processing unit 102 is configured to perform driving behavior recognition on the natural driving data to obtain a driving behavior identifier of the target driver;

[0135] a third processing unit 103, configured to input the natural driving data into a driving style recognition model corresponding to the driving behavior identifier to perform style recognition, and obtain intermediate driving style information output by the driving style recognition model;

[0136] The fourth processing unit 104 is configured to perform style correction on the intermediate driving style information to obtain target driving style information of the target driver.

[0137] It can be understood that the contents of the above method embodiments are all applicable to the present system embodiments, the functions specifically implemented by the present system embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0138] Reference Figure 3 , an embodiment of the present application further provides an electronic device, including:

[0139] at least one processor 201;

[0140] At least one memory 202, configured to store at least one program;

[0141] When the at least one program is executed by the at least one processor 201 , the at least one processor 201 implements the above method embodiment.

[0142] Similarly, it can be understood that the contents of the above method embodiments are applicable to the present device embodiments, the functions specifically implemented by the present device embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0143] An embodiment of the present application further provides a computer-readable storage medium, in which a program executable by the processor 201 is stored. The program executable by the processor 201 is used to implement the above-mentioned method embodiment when executed by the processor 201.

[0144] Similarly, the contents of the above method embodiments are applicable to the computer-readable storage medium embodiments. The functions specifically implemented by the computer-readable storage medium embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0145] An embodiment of the present application further provides a computer program product, including a computer program, which implements the steps in the above-mentioned method embodiments when executed by a processor.

[0146] Those skilled in the art will understand that all or part of the processes in the above-mentioned embodiment methods can be implemented by instructing related hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods.

[0147] In some optional embodiments, the functions / operations mentioned in the block diagram may not occur in the order mentioned in the operation diagram. For example, depending on the functions / operations involved, the two boxes shown in succession may actually be executed substantially simultaneously or the boxes can sometimes be executed in reverse order. In addition, the embodiments presented and described in the flow chart of the present application are provided in an exemplary manner for the purpose of providing a more comprehensive understanding of the technology. The disclosed method is not limited to the operations and logic flows presented herein. Optional embodiments are contemplated in which the order of the various operations is changed and the sub-operations described as a part of a larger operation are performed independently.

[0148] In addition, although the present application is described in the context of functional modules, it should be understood that, unless otherwise stated, one or more of the functions and / or features may be integrated into a single physical device and / or software module, or one or more functions and / or features may be implemented in separate physical devices or software modules. It is also understood that a detailed discussion of the actual implementation of each module is not necessary for understanding the present application. More specifically, given the properties, functions, and internal relationships of the various functional modules in the devices disclosed herein, the actual implementation of the module will be understood within the routine skills of an engineer. Therefore, a person skilled in the art can implement the present application as set forth in the claims using ordinary techniques without undue experimentation. It is also understood that the specific concepts disclosed are merely illustrative and are not intended to limit the scope of the present application, which is determined by the full scope of the appended claims and their equivalents.

[0149] If the function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the embodiment method of the present application. The aforementioned storage medium includes: various media that can store program codes, such as 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.

[0150] The logic and / or steps represented in the flowcharts or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing the logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (e.g., a computer-based system, a system including a processor, or other system that can fetch and execute instructions from an instruction execution system, apparatus, or device). For purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0151] More specific examples (a non-exhaustive list) of computer-readable media include the following: an electrical connection with one or more wires (electronic devices), a portable computer disk cartridge (magnetic devices), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), a fiber optic device, and a portable compact disc read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering, or processing in another suitable manner as necessary, and then stored in a computer memory.

[0152] It should be understood that various parts of the present application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented using software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented using hardware, as in another embodiment, any one of the following technologies known in the art or a combination thereof can be used to implement: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application-specific integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0153] In the above description of this specification, reference to the terms "one embodiment / example," "another embodiment / example," or "certain embodiments / examples" means that the specific features, structures, materials, or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described can be combined in any appropriate manner in any one or more embodiments or examples.

[0154] Although the embodiments of the present application have been shown and described, those skilled in the art will appreciate that various changes, modifications, substitutions, and variations may be made to the embodiments without departing from the principles and intent of the present application, and that the scope of the present application is defined by the claims and their equivalents.

[0155] The above is a specific description of the preferred implementation of the present application, but the present application is not limited to the embodiments. Those skilled in the art may make various equivalent modifications or substitutions without violating the spirit of the present application, and these equivalent modifications or substitutions are all included in the scope defined by the claims of the present application.

Claims

1. A driving style recognition method, characterized in that: include: Obtaining natural driving data of the target driver; Performing driving behavior recognition on the natural driving data to obtain a driving behavior identifier of the target driver; inputting the natural driving data into a driving style recognition model corresponding to the driving behavior identifier to perform style recognition, and obtaining intermediate driving style information output by the driving style recognition model; Style correction is performed on the intermediate driving style information to obtain target driving style information of the target driver.

2. The method according to claim 1, characterized in that The performing driving behavior recognition on the natural driving data to obtain the driving behavior identifier of the target driver includes: Obtain driving behavior recognition model; The natural driving data is input into the driving behavior recognition model to perform behavior recognition, and a driving behavior identifier output by the driving behavior recognition model is obtained.

3. The method according to claim 2, characterized in that The driving behavior recognition model is constructed by the following steps: Obtain several driving behavior rules; Determining, based on all the driving behavior rules, a behavior membership function corresponding to each of the driving behavior rules; The driving behavior recognition model is constructed based on all the behavior membership functions.

4. The method according to claim 1, wherein The step of inputting the natural driving data into a driving style recognition model corresponding to the driving behavior identifier to perform style recognition, and obtaining intermediate driving style information output by the driving style recognition model, includes: Acquire a plurality of trained driving style recognition models, each of the trained driving style recognition models corresponding to a type of driving behavior; According to the driving behavior identifier, all the trained driving style recognition models are screened to obtain a target style recognition model; The natural driving data is input into the target style recognition model to perform style recognition to obtain the intermediate driving style information.

5. The method according to claim 4, characterized in that The step of obtaining a plurality of trained driving style recognition models includes: Obtaining an original style recognition model and a driving training dataset, wherein the driving training dataset includes a plurality of sample driving data, and all of the sample driving data correspond to a type of driving behavior; Extracting features and labels from the driving training data set to obtain a training feature set and a feature label corresponding to each target driving feature in the training feature set, wherein the target driving features under different types of driving behaviors are not completely the same; According to all the feature labels, the training feature set is input into the original style recognition model to update parameters, thereby obtaining the trained driving style recognition model.

6. The method according to claim 1, characterized in that The performing style correction on the intermediate driving style information to obtain the target driving style information of the target driver includes: Obtaining a historical update vector and historical driving style information, wherein the historical update vector is an update vector at a previous time point adjacent to a current time point, the current time point is a time point of the intermediate driving style information, and the historical driving style information is target driving style information at the same time point as the historical update vector; The intermediate driving style information is updated according to the historical update vector and the historical driving style information to obtain the target driving style information.

7. The method according to claim 6, characterized in that The updating the intermediate driving style information according to the historical update vector and the historical driving style information to obtain the target driving style information includes: performing a time-dependent update on the historical update vector according to the historical driving style information to obtain a current update vector corresponding to the intermediate driving style information; The intermediate driving style information is corrected and updated according to the current update vector to obtain the target driving style information.

8. A driving style recognition system, characterized in that: include: a first processing unit, configured to obtain natural driving data of a target driver; a second processing unit, configured to perform driving behavior recognition on the natural driving data to obtain a driving behavior identifier of the target driver; a third processing unit, configured to input the natural driving data into a driving style recognition model corresponding to the driving behavior identifier to perform style recognition, and obtain intermediate driving style information output by the driving style recognition model; The fourth processing unit is configured to perform style correction on the intermediate driving style information to obtain target driving style information of the target driver.

9. An electronic device, characterized in that: include: at least one processor; at least one memory for storing at least one program; When the at least one program is executed by the at least one processor, the at least one processor implements the method according to any one of claims 1 to 7.

10. A computer-readable storage medium storing a program executable by a processor, characterized in that: The program executable by the processor is used to implement the method according to any one of claims 1 to 7 when executed by the processor.

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