Vehicle control method based on behavior habits and vehicle

Abstract

The present application provides a vehicle control method and vehicle based on behavioral habits. By obtaining the identity information of the target user, the behavioral habit model corresponding to the identity information is retrieved. When the number of behavioral habit models is at least two and the vehicle functions corresponding to the at least two behavioral habit models conflict, a first target behavioral habit model is determined from the at least two behavioral habit models with conflicting vehicle functions according to a preset usage logic, and a first behavioral habit prediction result corresponding to the first target behavioral habit model is obtained from the at least two behavioral habit prediction results. Vehicle adjustment is controlled according to the first behavioral habit prediction result. The method of determining the first target behavioral habit model from at least two behavioral habit models using the preset usage logic can achieve accurate selection of the behavioral habit model to be used when there is a conflict in vehicle functions, thereby avoiding the problem that the vehicle cannot be effectively adjusted when there is a conflict in vehicle functions.

Description

Technical Field

[0001] The present application relates to the field of vehicle control technology, and in particular to a vehicle control method and a vehicle based on behavioral habits. Background Art

[0002] Different users often have different behavioral habits when using the vehicle's functions, so it is necessary to adapt the corresponding behavioral habits to control vehicle adjustments.

[0003] However, the vehicle functions corresponding to the adapted behavioral habits in the related art may conflict with each other, making it impossible to effectively adjust the vehicle. Summary of the Invention

[0004] In view of this, the purpose of this application is to propose a vehicle control method and vehicle based on behavioral habits, so as to solve the problem in the prior art that the vehicle functions corresponding to the adapted behavioral habits may conflict, thereby making it impossible to effectively adjust the vehicle.

[0005] Based on the above objectives, the first aspect of the present application provides a vehicle control method based on behavioral habits, comprising:

[0006] Obtain the target user's identity information;

[0007] Retrieving a behavior habit model corresponding to the identity information and determining a number of behavior habit models, wherein the behavior habit model corresponds to adjusting at least one function of the vehicle;

[0008] When the number of behavior habit models is at least two, obtaining first current driving environment data corresponding to the target user, inputting the first current driving environment data into at least two behavior habit models, and having the at least two behavior habit models respectively output at least two corresponding behavior habit prediction results;

[0009] When a conflict in vehicle functions corresponding to at least two behavior habit models is determined based on the at least two behavior habit prediction results, obtaining preset usage logic of the at least two behavior habit models of the conflict in vehicle functions;

[0010] According to the preset usage logic, a first target behavior habit model is determined from at least two behavior habit models of the vehicle function conflict, and a first behavior habit prediction result corresponding to the first target behavior habit model is obtained from the at least two behavior habit prediction results, and vehicle adjustment is controlled according to the first behavior habit prediction result.

[0011] Optionally, the preset usage logic of the at least two behavioral habit models sets corresponding priorities for the at least two behavioral habit models;

[0012] The determining of a first target behavior habit model from at least two behavior habit models of conflicting vehicle functions according to the preset usage logic includes:

[0013] According to the priority, a behavior habit model with the highest priority is selected from at least two behavior habit models as the first target behavior habit model.

[0014] Optionally, after controlling vehicle adjustment according to the first behavioral habit prediction result, the method further includes:

[0015] Collecting a first training data set corresponding to the target user, the first training data set including first vehicle function status data and first driving environment data;

[0016] Training the first target behavior habit model using the first vehicle functional state data and the second driving environment data to obtain a trained first target behavior habit model;

[0017] The correspondence between the trained first target behavior habit model and the identity information corresponding to the target user is stored in a preset behavior habit model database, and the trained first target behavior habit model is used as the new first target behavior habit model. At the same time, the correspondence between the first target behavior habit model before training and the identity information corresponding to the target user is stored in the behavior habit model database.

[0018] Optionally, after storing the correspondence between the first target behavior habit model before training and the identity information corresponding to the target user in the behavior habit model database, the method further includes:

[0019] Acquire a current first target behavior habit model corresponding to the identity information of the target user;

[0020] When the current behavior habit model is the new first target behavior habit model and a switching instruction is received, the new first target behavior habit model is switched to the first target behavior habit model before training; or

[0021] When the current behavior habit model is the first target behavior habit model before training and a switching instruction is received, the first target behavior habit model before training is switched to the new first target behavior habit model.

[0022] Optionally, the method further includes:

[0023] When there is no identity information corresponding to the target user, create new identity information and obtain the preset behavior habit model;

[0024] Collecting a second training data set corresponding to the target user, the second training data set including second vehicle function status data and second driving environment data;

[0025] The preset behavior habit model is trained using the second vehicle functional status data and the second driving environment data to obtain a trained preset behavior habit model, and the correspondence between the trained preset behavior habit model and the new identity information corresponding to the target user is stored in a preset behavior habit model database.

[0026] Optionally, each behavior habit model is pre-stored in a preset behavior habit model database, and the method further includes:

[0027] Performing cluster analysis on each behavior habit model stored in the behavior habit model database to obtain multiple category groups;

[0028] displaying the behavioral habit models classified into the plurality of category groups;

[0029] Receive a selection instruction for a second target behavior habit model, obtain second current driving environment data, input the second current driving environment data into the second target behavior habit model, output a second behavior habit prediction result through the second target behavior habit model, and control vehicle adjustment according to the second behavior habit prediction result, wherein the second target behavior habit model is at least one of the behavior habit models classified into the multiple category groups.

[0030] Optionally, the cluster analysis is performed on each behavior habit model stored in the behavior habit model database to obtain a plurality of category groups, including:

[0031] Acquiring third driving environment data;

[0032] Performing predictions based on the third driving environment data using various behavior habit models to obtain multiple behavior habit prediction results;

[0033] combining the third driving environment data with each behavioral habit prediction result to obtain a plurality of classification features;

[0034] Cluster analysis is performed on multiple classification features to obtain multiple category groups.

[0035] Optionally, the third driving environment data is combined with each behavior habit prediction result to obtain multiple classification features, including:

[0036] combining the third driving environment data with each behavioral habit prediction result to form a plurality of initial classification features;

[0037] Performing dimensionality reduction processing on the multiple initial classification features to obtain multiple first-dimensional classification features;

[0038] The multiple first-dimensional classification features are mapped into multiple second-dimensional classification features, and the multiple second-dimensional classification features are used as the multiple classification features, where the dimension of the second-dimensional classification features is higher than the dimension of the second-dimensional classification features.

[0039] Optionally, after displaying the behavioral habit models classified into the multiple category groups, the method further includes:

[0040] receiving scores for the behavior habit models stored in the behavior habit model database and classified into the plurality of category groups;

[0041] The behavioral habit models classified into the plurality of category groups are reclassified according to the scores of the behavioral habit models classified into the plurality of category groups.

[0042] Based on the same inventive concept, the second aspect of the present application provides a vehicle for executing the method described in the first aspect.

[0043] As can be seen from the above, the behavior-habit-based vehicle control method and vehicle provided in the present application obtain the identity information of the target user, retrieve the behavior habit model corresponding to the identity information, and determine the number of behavior habit models. The behavior habit model corresponds to adjusting at least one function of the vehicle. When the number of behavior habit models is at least two, first current driving environment data corresponding to the target user is obtained, the first current driving environment data is input into at least two behavior habit models, and at least two corresponding behavior habit prediction results are output by the at least two behavior habit models respectively. When it is determined based on the at least two behavior habit prediction results that the vehicle functions corresponding to the at least two behavior habit models conflict, the preset usage logic of the at least two behavior habit models with conflicting vehicle functions is obtained, and a first target behavior habit model is determined from the at least two behavior habit models with conflicting vehicle functions according to the preset usage logic. A first behavior habit prediction result corresponding to the first target behavior habit model is obtained from the at least two behavior habit prediction results, and vehicle adjustment is controlled according to the first behavior habit prediction result. The method of determining the first target behavior habit model from the at least two behavior habit models with conflicting vehicle functions using the preset usage logic can achieve accurate selection of the behavior habit model to be used when there is a conflict in vehicle functions, thereby avoiding the problem of the vehicle being unable to be effectively adjusted when there is a conflict in vehicle functions. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] In order to more clearly illustrate the technical solutions in this application or related technologies, the following briefly introduces the drawings required for use in the embodiments or related technical descriptions. Obviously, the drawings described below are merely embodiments of this application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0045] Figure 1 This is a flowchart of a vehicle control method based on behavioral habits according to an embodiment of the present application;

[0046] Figure 2 This is a schematic structural diagram of a vehicle control device based on behavioral habits according to an embodiment of the present application;

[0047] Figure 3 A schematic diagram of an electronic device according to an embodiment of the present application. DETAILED DESCRIPTION

[0048] In order to make the objectives, technical solutions and advantages of this application more clear, this application is further described in detail below in combination with specific embodiments and with reference to the accompanying drawings.

[0049] It should be noted that, unless otherwise defined, the technical terms or scientific terms used in the embodiments of the present application should have the usual meanings understood by people with ordinary skills in the field to which this application belongs. The "first", "second" and similar words used in the embodiments of the present application do not indicate any order, quantity or importance, but are only used to distinguish different components. "Include" or "comprise" and similar words mean that the elements or objects appearing before the word cover the elements or objects listed after the word and their equivalents, without excluding other elements or objects. "Connect" or "connected" and similar words are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "Up", "down", "left", "right" and the like are only used to indicate relative positional relationships. When the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0050] In the related art, different users often have different behavioral habits when using a vehicle. For example, different users (such as drivers or passengers) often have different driving / riding habits. Taking the driver as an example, when he starts the vehicle, he will generally ensure that the seat and rearview mirror are in a suitable configuration. Then, people who like to listen to music may immediately turn on the music player, people who like to listen to books may first open the audio book software, and others may directly open the map for navigation, or do nothing and drive away.

[0051] Furthermore, different drivers react differently to changes in temperature and humidity. For example, if the temperature reaches 30°C, some people might immediately turn on the air conditioning for cooling, while others might choose to open the windows. More specifically, at the same temperature, each person might set a different air conditioning temperature, air volume, and zone, as well as the window position and size.

[0052] Therefore, if the vehicle can adapt to the behavioral habits of each driver, it will greatly improve the driver's exclusive experience of the vehicle. However, the vehicle functions corresponding to the adapted behavioral habits may conflict, making it impossible to effectively adjust the vehicle.

[0053] Vehicle function conflict refers to the mutual exclusion of control states corresponding to vehicle functional components. For example, the vehicle functions corresponding to the same driver's behavior habits are opening the vehicle windows and turning on the vehicle air conditioner. At this time, there is a functional conflict between opening the vehicle windows and turning on the vehicle air conditioner.

[0054] In addition, the vehicle can also be controlled by an intelligent vehicle control system, which makes corresponding response adjustments based on preset conditions. However, the preset conditions are fixed, will not change, and cannot be applied to all users.

[0055] The embodiment of the present application proposes a vehicle control method based on behavioral habits, which uses a preset usage logic to determine a first target behavioral habit model from at least two behavioral habit models with conflicting vehicle functions. This method can accurately select the behavioral habit model to be used when there is a conflict in vehicle functions, thereby avoiding the problem that the vehicle cannot be effectively adjusted when there is a conflict in vehicle functions. Figure 1 Shown, including:

[0056] Step 101: Obtain the identity information of the target user.

[0057] In this step, the identity information refers to information used to distinguish the user's personal identity.

[0058] Identity information includes but is not limited to the user's name, date of birth, ID number, personal biometric information, address or telephone number.

[0059] For example, the identity information is a face code (Identity document, id), a camera is used to capture the face image of the target user, and then face recognition is performed based on the face image, so as to find the corresponding face code based on the obtained face recognition result.

[0060] Alternatively, the identity information is a fingerprint code (Identity document, ID). The fingerprint information of the target user is collected through a fingerprint collection sensor, and a neural network is used to pre-train fingerprint feature extraction to obtain a multi-layer feedforward neural network model (BP network, BackPropagation network) that can extract fingerprint features. Based on the fingerprint information, the fingerprint features are extracted through the multi-layer feedforward neural network model to obtain the fingerprint features of the target user, and then the corresponding fingerprint code is found according to the obtained fingerprint features.

[0061] The target user can be a driver or a passenger, but the driver is preferred here.

[0062] Step 102 retrieves a behavior habit model corresponding to the identity information and determines the number of behavior habit models, wherein the behavior habit model corresponds to adjusting at least one function of the vehicle.

[0063] In this step, the behavior habit model corresponding to the identity information is retrieved. The behavior habit model has been trained in advance, and the training process is as follows:

[0064] The training data set corresponding to the target user is collected by burying points, and the trained initial model is used as the behavioral habit model corresponding to the target user. The training data set includes vehicle function status data and driving environment data.

[0065] The vehicle function status data may include: 1) vehicle information: vehicle speed, steering wheel angle, seat status, window status, air conditioning status, etc.; 2) application information: navigation, music, etc.

[0066] Driving environment data can be: 1) weather information: temperature, humidity, air quality, sunny, rainy, foggy, etc.; 2) spatiotemporal information: time, location, season, etc.; 3) external vehicle information: road environment, traffic flow, etc.; 4) other information: vehicle intelligent monitoring system (VIMS) results, etc.

[0067] The behavior habit model represents a model for predicting a user's possible vehicle function operations based on driving environment data.

[0068] For example, the behavioral habit model predicts whether the car windows are open, if open, how wide the windows are open, how long the windows are open, or the order in which the windows are opened.

[0069] Alternatively, the behavioral habit model can predict whether the air conditioner is turned on, and if so, how the air conditioner mode is set, how the air conditioner temperature is adjusted, how long the air conditioner is turned on, or the order in which the air conditioner is turned on.

[0070] The behavior habit model corresponding to the identity information is retrieved, and the number of the behavior habit models is determined, so as to determine whether there is a possibility of vehicle function conflict based on the number of the behavior habit models.

[0071] Step 103: When the number of behavioral habit models is at least two, obtain the first current driving environment data corresponding to the target user, input the first current driving environment data into at least two behavioral habit models, and output at least two corresponding behavioral habit prediction results respectively through the at least two behavioral habit models.

[0072] In this step, when the number of behavioral habit models corresponding to the identity information of the target user is at least two, the first current driving environment data is input into at least the at least two behavioral habit models respectively, and the at least two behavioral habit models output the corresponding at least two behavioral habit models for judging whether there is a vehicle function conflict between the at least two behavioral habit models based on the at least two behavioral habit models.

[0073] Step 104 : When a conflict in vehicle functions corresponding to at least two behavior habit models is determined based on the at least two behavior habit prediction results, a preset usage logic of the at least two behavior habit models of the vehicle function conflict is obtained.

[0074] In this step, when the vehicle function conflict corresponding to at least two behavior habit models is determined based on at least two behavior habit prediction results, the preset usage logic of the at least two behavior habit models of the vehicle function conflict is obtained, wherein the preset usage logic is used to represent the usage rules of the at least two behavior habit models of the vehicle function conflict.

[0075] For example, the behavioral habit model can be a model for predicting the use of vehicle windows (window opening, window position, sunroof), a model for predicting the use of air conditioning (temperature, wind speed, air purification, negative ions, steering wheel heating), a model for predicting the use of ambient lights (switch, mode, brightness), a model for predicting the adjustment of driving position (seat position, rearview mirror, head-up display system (Head Up Display, HUD), the height of the head-up display system, the brightness of the head-up display system, the angle of the head-up display system), a model for predicting the use of in-vehicle applications (Application, APP), a model for predicting the setting of vehicle driving modes (off-road mode, four-wheel drive mode, steering mode, suspension mode, drive mode), a model for predicting the use of smart screen savers (start-up time, display mode, automatic switching time, pattern selection, screen saver switch), a model for predicting vehicle control settings (automatic parking, automatic headlights, smart start and stop, steering wheel power mode, head-up display system switch) or a model for predicting system settings (music settings (equalizer settings-treble settings, equalizer settings-mid-range settings, equalizer settings-bass settings), central control screen brightness, theme switching).

[0076] Vehicle functions represent the control states corresponding to vehicle functional components. For example, all control operations for vehicle windows include at least one of the following:

[0077] Control the opening of vehicle windows, the opening degree of windows, and the position of windows that need to be opened.

[0078] All control operations of the air conditioner include at least one of the following:

[0079] Control the vehicle's air conditioning, wind speed, temperature, and air conditioning mode.

[0080] Vehicle function conflict refers to the mutual exclusion of control states corresponding to vehicle functional components. For example, the vehicle functions corresponding to the same driver's behavior habits are opening the vehicle windows and turning on the vehicle air conditioner. At this time, there is a functional conflict between opening the vehicle windows and turning on the vehicle air conditioner.

[0081] Among them, whether there is a vehicle function conflict between at least two behavior habit models can be determined by inputting driving environment data into the at least two behavior habit models and judging whether there is a vehicle function conflict between the at least two behavior habit models based on the corresponding behavior habit prediction results.

[0082] For example, the driving environment data shows that the temperature reaches 30°, and the at least two behavioral habit models are respectively a model for predicting the use of air conditioning and a model for predicting the use of windows. The temperature reaching 30° is input into the model for predicting the use of air conditioning, and the output behavioral habit prediction result is to turn on the air conditioner. The temperature reaching 30° is input into the model for predicting the use of windows, and the output behavioral habit prediction result is to open the windows. At this time, the two behaviors of "opening the windows" and "turning on the air conditioner" are in conflict, and it is determined that there is a vehicle function conflict between the model for predicting the use of air conditioning and the model for predicting the use of windows.

[0083] As an optional embodiment, when predicting the behavior habits of the target user, the behavior habit model can be implemented using a decision tree model.

[0084] The usage of vehicle functions of the same category can be predicted by the same decision tree model. For example, the window opening, window position, and sunroof can be represented by a decision tree model for predicting window usage.

[0085] Among them, for different decision tree models, the required input data and quantity will be different. The selection of input data can be completed by the decision tree model or determined manually in advance.

[0086] It is understood that the above embodiment uses a decision tree model to predict the target user's behavior habits. However, in practice, other models can also be used to predict the target user's behavior habits. These models can be, for example, a random forest model, an iterative algorithm classifier (ADABOOST), a deep learning model (transformer), and so on.

[0087] And the vehicle functions corresponding to at least two behavioral habit models conflict with each other. For example, the prediction result of the model used to predict the use of windows is that the windows need to be opened, and the prediction result of the model used to predict the use of air conditioners is that the air conditioner needs to be turned on. At this time, the two behaviors of "opening the windows" and "turning on the air conditioner" are in conflict.

[0088] The preset usage logic is used to represent usage rules of at least two behavior habit models that conflict with vehicle functions.

[0089] For example, the at least two behavior habit models of vehicle function conflict are a model for predicting window usage and a model for predicting air conditioning usage, and one of the models for predicting window usage and air conditioning usage is selected for use.

[0090] Alternatively, when there is a conflict in vehicle functions, the model for predicting window usage or the model for predicting air conditioning usage is used.

[0091] Alternatively, the model for predicting window usage at a first preset time (for example, 30 minutes) is first used, and then the model for predicting air conditioning usage at a second preset time (for example, 20 minutes) is used; or the model for predicting air conditioning usage at a first preset time is first used, and then the model for predicting window usage at a second preset time is used, wherein the first preset time and the second preset time are set according to specific circumstances.

[0092] Step 105: Determine a first target behavior habit model from at least two behavior habit models of the vehicle function conflict according to the preset usage logic, obtain a first behavior habit prediction result corresponding to the first target behavior habit model from the at least two behavior habit prediction results, and control vehicle adjustment according to the first behavior habit prediction result.

[0093] In this step, a more accurate first target behavior habit model is determined according to the preset usage logic. The first target behavior habit model represents the final model for predicting the target user's behavior habits. Then, the first current driving environment data is input into the first target behavior habit model. The first target behavior habit model outputs the first behavior habit prediction result. The vehicle adjustment is controlled according to the first behavior habit prediction result. The first behavior habit prediction result represents the control state that the user may set for the vehicle's functional components. For example, the first behavior habit prediction result is to control the cockpit window to open, and the opening degree is set to one percent to sixty.

[0094] The method of determining the first target behavior habit model from at least two behavior habit models by using preset usage logic can accurately select the behavior habit model to be used when there is a conflict in vehicle functions, avoiding the problem that the vehicle cannot be effectively adjusted when there is a conflict in vehicle functions.

[0095] Through the above scheme, by obtaining the identity information of the target user, retrieving the behavior habit model corresponding to the identity information, and determining the number of behavior habit models, the behavior habit model corresponds to adjusting at least one function of the vehicle. When the number of behavior habit models is at least two, the first current driving environment data corresponding to the target user is obtained, the first current driving environment data is input into at least two behavior habit models, and the at least two behavior habit models respectively output the corresponding at least two behavior habit prediction results. When it is determined based on the at least two behavior habit prediction results that the vehicle functions corresponding to the at least two behavior habit models conflict, the preset usage logic of the at least two behavior habit models of the vehicle function conflict is obtained, and the first target behavior habit model is determined from the at least two behavior habit models of the vehicle function conflict according to the preset usage logic, and the first behavior habit prediction result corresponding to the first target behavior habit model is obtained from the at least two behavior habit prediction results. The vehicle adjustment is controlled according to the first behavior habit prediction result. The preset usage logic is used to determine the first target behavior habit model from at least two behavior habit models. This method can achieve accurate selection of the behavior habit model to be used when there is a conflict in vehicle functions, thereby avoiding the problem that the vehicle cannot be effectively adjusted when there is a conflict in vehicle functions.

[0096] In some embodiments, the preset usage logic of the at least two behavioral habit models sets corresponding priorities for the at least two behavioral habit models.

[0097] In step 105, determining a first target behavior habit model from at least two behavior habit models of conflicting vehicle functions according to the preset usage logic includes:

[0098] According to the priority, a behavior habit model with the highest priority is selected from at least two behavior habit models as the first target behavior habit model.

[0099] In the above scheme, when the preset usage logic represents priority, based on the priorities corresponding to at least two behavior habit models that conflict with vehicle functions, the behavior habit model with the highest priority among at least two behavior habit models is selected as the first target behavior habit model to be used in the end more accurately.

[0100] According to the preset priorities of the at least two behavioral habit models of vehicle function conflict, and in accordance with the priority order of the at least two behavioral habit models of vehicle function conflict, the prediction result output by the behavioral habit model with the highest priority among the at least two behavioral habit models of vehicle function conflict is used as the input of the behavioral habit model with the lowest priority to determine the final vehicle response result. For example, the at least two behavioral habit models of vehicle function conflict are a model for predicting window usage and a model for predicting air conditioning usage. The prediction result of the model for predicting window usage is that the window needs to be opened, and the prediction result of the model for predicting air conditioning usage is that the air conditioning needs to be turned on. At this time, the two behaviors of "opening the window" and "turning on the air conditioning" are in conflict.

[0101] When the model used to predict air conditioning use has a higher priority than the model used to predict window use, the input of the model used to predict window use will receive the information that the air conditioning should be turned on at this time. At this time, the action of "opening the window" will be judged as invalid. At this time, the air conditioning should be turned on and the action of "opening the window" will be judged as invalid as the final vehicle response result, and there will be no functional conflict between opening the window and turning on the air conditioning at the same time.

[0102] This enables self-correction between the behavioral habits predicted by each behavioral habit model.

[0103] In some embodiments, after controlling vehicle adjustment according to the first behavioral habit prediction result in step 105, the method further includes:

[0104] Step A1: Collect a first training data set corresponding to the target user, where the first training data set includes first vehicle function status data and first driving environment data.

[0105] Step A2: Use the first vehicle functional status data and the first driving environment data to train the first target behavior habit model to obtain a trained first target behavior habit model.

[0106] Step A3, storing the correspondence between the trained first target behavior habit model and the identity information corresponding to the target user into a preset behavior habit model database, and using the trained first target behavior habit model as the new first target behavior habit model, while storing the correspondence between the first target behavior habit model before training and the identity information corresponding to the target user in the behavior habit model database.

[0107] In the above solution, the preset behavior habit database can be stored in the cloud or in the vehicle, with the cloud being preferred.

[0108] When the model update function is turned on, the first training data set corresponding to the target user is collected by burying points and temporarily stored in the car computer or the cloud. The first training data set is uploaded to the cloud, and the first target behavior habit model is trained and updated using the first training data set in the cloud. The correspondence between the trained first target behavior habit model and the identity information corresponding to the target user is stored in a preset behavior habit model database and sent to the car computer. The first training data set includes first vehicle function status data and first driving environment data. The first vehicle function status data can be: 1) vehicle information: vehicle speed, steering wheel angle, seat status, window status, air conditioning status, etc., 2) application information: navigation, music, etc.

[0109] The first driving environment data can be: 1) weather information: temperature, humidity, air quality, sunny, rainy, foggy, etc.; 2) spatiotemporal information: time, location, season, etc.; 3) external vehicle information: road environment, traffic flow, etc.; 4) other information: vehicle intelligent monitoring system (VIMS) results, etc.

[0110] Among them, during the training and updating process of the first target behavior habit model, it is necessary to determine whether the first target behavior habit model in the training process meets the convergence conditions. Generally speaking, when the first target behavior habit model in the training process changes very little, it can be considered to be converged. At this time, the converged model (that is, the new first target behavior habit model) needs to be uniformly stored in the cloud and stored in the preset behavior habit model database to meet the subsequent analysis and clustering of a large number of stored behavior habit models.

[0111] The correspondence between the first target behavior habit model before training and the identity information corresponding to the target user is stored in the behavior habit model database, so that the target user can reuse the first target behavior habit model before updating.

[0112] In some embodiments, in step A3, after storing the correspondence between the first target behavior habit model before training and the identity information corresponding to the target user in the behavior habit model database, the method further includes:

[0113] Step B1: Obtain a current first target behavior habit model corresponding to the identity information of the target user.

[0114] Step B2: when the current behavior habit model is the new first target behavior habit model and a switching instruction is received, the new first target behavior habit model is switched to the first target behavior habit model before training; or

[0115] Step B3: When the current behavior habit model is the first target behavior habit model before training and a switching instruction is received, the first target behavior habit model before training is switched to the new first target behavior habit model.

[0116] In the above solution, multiple behavior habit models stored in the behavior habit model database can be switched.

[0117] If the current behavior habit model is the updated new first target behavior habit model, when a switching instruction is received, the updated new first target behavior habit model is switched to the first target behavior habit model before the update.

[0118] If the current behavior habit model is the first target behavior habit model before updating, when a switching instruction is received, the first target behavior habit model before updating is switched to the new first target behavior habit model after updating.

[0119] In some embodiments, the method further comprises:

[0120] Step C1: When there is no identity information corresponding to the target user, new identity information is created and a preset behavior habit model is obtained.

[0121] Step C2: collecting a second training data set corresponding to the target user, where the second training data set includes second vehicle function status data and second driving environment data.

[0122] Step C3, use the second vehicle functional status data and the second driving environment data to train the preset behavior habit model to obtain a trained preset behavior habit model, and store the correspondence between the trained preset behavior habit model and the new identity information corresponding to the target user into a preset behavior habit model database.

[0123] In the above scheme, if there is no identity information corresponding to the target user, it means that the user has not been stored or has never driven the vehicle. At this time, new identity information of the target user is created, and a default behavior habit model (i.e., a preset behavior habit model) is used. It is asked whether to train the default behavior habit model. After receiving the confirmation training instruction, a second training data set corresponding to the target user is collected. The preset behavior habit model is trained using the second training data set to obtain a trained preset behavior habit model. The correspondence between the trained preset behavior habit model and the new identity information corresponding to the target user is stored in the preset behavior habit model database, so that the next time the user uses the vehicle, the behavior habit can be adaptively adapted based on the correspondence between the stored preset behavior habit model and the new identity information corresponding to the target user, thereby controlling the vehicle for adjustment.

[0124] Among them, the second training data set includes second vehicle functional status data and second driving environment data. The second vehicle functional status data can be: 1) vehicle information: vehicle speed, steering wheel angle, seat status, window status, air conditioning status, etc., 2) application information: navigation, music, etc.

[0125] The second driving environment data can be: 1) weather information: temperature, humidity, air quality, sunny, rainy, foggy, etc.; 2) spatiotemporal information: time, location, season, etc.; 3) external vehicle information: road environment, traffic flow, etc.; 4) other information: vehicle intelligent monitoring system (VIMS) results, etc.

[0126] In some embodiments, each behavior habit model is pre-stored in a preset behavior habit model database, and the method further includes:

[0127] Step D1: performing cluster analysis on each behavior habit model stored in the behavior habit model database to obtain multiple category groups.

[0128] In step D2, the behavior habit models classified into the plurality of category groups are displayed.

[0129] Step D3, receiving the selection instruction of the second target behavior habit model, obtaining the second current driving environment data, inputting the second current driving environment data into the second target behavior habit model, outputting the second behavior habit prediction result through the second target behavior habit model, and controlling the vehicle adjustment according to the second behavior habit prediction result, wherein the second target behavior habit model is at least one of the behavior habit models classified into the multiple category groups.

[0130] In the above scheme, cluster analysis is performed on each behavior habit model stored in the behavior habit model database to obtain multiple category groups, and the behavior habit models classified into multiple category groups are displayed for recommendation to users who use the vehicle. Category groups include: comfort (playing light and soothing music, soft ambient lights, air-conditioning settings that match the weather, etc.), economy (opening windows when it is hot, changing driving modes, etc.), etc.

[0131] In addition, users can upload their own personalized and interesting behavior models to the cloud-based behavior model library for other users to try. However, if a user's uploaded behavior model shows extreme behavior (such as always turning the air conditioner to the coldest or hottest setting), there are clear management measures, such as limiting the air conditioner adjustment range or prohibiting such users from uploading habit models and promptly handling the relevant behavior models. Users can also download other users' behavior models from the cloud to experience them, which can enhance the user's driving experience and convenience.

[0132] In some embodiments, step D1 includes:

[0133] Step D11: Acquire third driving environment data.

[0134] Step D12: performing predictions based on the third driving environment data through various behavior habit models to obtain multiple behavior habit prediction results.

[0135] Step D13: combining the third driving environment data with each behavior habit prediction result to obtain multiple classification features.

[0136] Step D14: performing cluster analysis on the multiple classification features to obtain multiple category groups.

[0137] In the above scheme, the behavior habit model is represented by f(·). First, the third driving environment data and the output behavior corresponding to the behavior habit model are encoded, represented as X (i.e., the third driving environment data) and Y (i.e., the behavior habit prediction result), respectively. X includes the vehicle condition data under various circumstances. Different behavior habit models will have different responses to X, that is, Y = f(X). In this way, [X, Y] can be used as the feature of the behavior habit model for subsequent analysis.

[0138] Among them, the third driving environment data can be: 1) weather information: temperature, humidity, air quality, sunny, rainy, foggy, etc.; 2) spatiotemporal information: time, location, season, etc.; 3) external vehicle information: road environment, traffic flow, etc.; 4) other information: vehicle intelligent monitoring system (VIMS) results, etc.

[0139] Perform cluster analysis on [X, Y] corresponding to all behavioral habit models, and classify each behavioral habit model according to the final cluster center. At the same time, calculate the distance between the behavioral habit model and each cluster center and annotate the corresponding category score (such as comfort index, economic index, etc.).

[0140] Among them, the K-means clustering algorithm (K-means) can be used for clustering analysis, and the process is as follows:

[0141] Select any classification feature from all classification features as the cluster center for cluster analysis.

[0142] Obtaining the distance between each of the classification features other than the classification feature serving as the cluster center of the cluster analysis processing and the classification feature serving as the cluster center of the cluster analysis processing;

[0143] Assign each other classification feature to the nearest cluster center based on the total distance;

[0144] When all other classification features are assigned, the cluster center is updated, and the new distances between each new classification feature except the new classification feature as the updated cluster center and the new classification feature as the updated cluster center after cluster analysis are obtained, and new other classification features are assigned according to all the new distances;

[0145] When a preset termination condition of the cluster analysis process is reached, a plurality of category groups are obtained.

[0146] In addition, some behavioral habit models with obvious behaviors in the behavioral habit model database can be labeled in advance (such as radical, moderate, and economical) and used as labels for classification or clustering.

[0147] In some real-time examples, step D13 includes:

[0148] Step D131: combining the third driving environment data and each behavior habit prediction result to form a plurality of initial classification features.

[0149] Step D132: performing dimensionality reduction processing on the multiple initial classification features to obtain multiple first-dimensional classification features.

[0150] Step D133 : Mapping the multiple first-dimensional classification features to multiple second-dimensional classification features, using the multiple second-dimensional classification features as the multiple classification features, wherein the dimension of the second-dimensional classification features is higher than the dimension of the second-dimensional classification features.

[0151] In the above scheme, the initial dimension of [X, Y] is generally high. Here, the feature [X, Y] can be reduced in dimension through principal component analysis (PCA) or neural network methods, and mapped to a high-dimensional space that is more suitable for model category division. The result after mapping is recorded as m([X, Y]) (i.e., classification feature).

[0152] In some embodiments, after step D2, the method further comprises:

[0153] Step E1: receiving scores for the behavior habit models classified into the plurality of category groups stored in the behavior habit model database.

[0154] Step E2 : reclassifying the behavior habit models classified into the multiple category groups according to the scores of the behavior habit models classified into the multiple category groups.

[0155] In the above scheme, behavioral habit models classified into various category groups are recommended to users. After the user selects at least one of the recommended behavioral habit models, the behavioral habit model can be scored based on the user's own usage experience, and then the behavioral habit models classified into multiple category groups can be reclassified based on the user's score.

[0156] It should be noted that the method of the embodiment of the present application can be performed by a single device, such as a computer or server. The method of this embodiment can also be applied in a distributed scenario and performed by multiple devices working together. In such a distributed scenario, one of the multiple devices may only perform one or more steps of the method of the embodiment of the present application, and the multiple devices will interact with each other to complete the method.

[0157] It should be noted that the above description is limited to some embodiments of the present application. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims may be performed in an order different from that described in the above embodiments and still achieve the desired results. Furthermore, the processes depicted in the accompanying drawings do not necessarily require the specific order or sequential order shown to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0158] Based on the same inventive concept, corresponding to any of the above-mentioned embodiment methods, the present application also provides a vehicle control device based on behavioral habits.

[0159] refer to Figure 2 The vehicle control device based on behavioral habits includes:

[0160] Identity information acquisition module 201, used to obtain the identity information of the target user;

[0161] A behavior habit model retrieval module 202 is configured to retrieve a behavior habit model corresponding to the identity information and determine a number of behavior habit models, wherein the behavior habit model corresponds to adjusting at least one function of the vehicle;

[0162] The behavior habit prediction result output module 203 is configured to, when there are at least two behavior habit models, obtain first current driving environment data corresponding to the target user, input the first current driving environment data into at least two behavior habit models, and output at least two corresponding behavior habit prediction results via the at least two behavior habit models;

[0163] The preset usage logic acquisition module 204 is configured to acquire the preset usage logic of the at least two behavior habit models corresponding to the vehicle function conflict when a conflict of vehicle functions corresponding to the at least two behavior habit models is determined based on the at least two behavior habit prediction results;

[0164] The control and adjustment module 205 is used to determine a first target behavior habit model from at least two behavior habit models of vehicle function conflict according to the preset usage logic, and obtain a first behavior habit prediction result corresponding to the first target behavior habit model from the at least two behavior habit prediction results, and control vehicle adjustment according to the first behavior habit prediction result.

[0165] In some embodiments, the preset usage logic of the at least two behavioral habit models sets corresponding priorities for the at least two behavioral habit models;

[0166] The control and regulation module 205 is specifically configured to:

[0167] According to the priority, a behavior habit model with the highest priority is selected from the at least two behavior habit models with conflicting vehicle functions as the first target behavior habit model.

[0168] In some embodiments, the behavior-based vehicle control device further includes a model training module. After controlling the vehicle adjustment according to the first behavior habit prediction result, the model training module is specifically configured to:

[0169] Collecting a first training data set corresponding to the target user, the first training data set including first vehicle function status data and first driving environment data;

[0170] Training the first target behavior habit model using the first vehicle functional state data and the first driving environment data to obtain a trained first target behavior habit model;

[0171] The correspondence between the trained first target behavior habit model and the identity information corresponding to the target user is stored in a preset behavior habit model database, and the trained first target behavior habit model is used as the new first target behavior habit model. At the same time, the correspondence between the first target behavior habit model before training and the identity information corresponding to the target user is stored in the behavior habit model database.

[0172] In some embodiments, the behavior-habit-based vehicle control device further includes a model switching module. After storing the correspondence between the first target behavior habit model before training and the identity information corresponding to the target user in the behavior habit model database, the model switching module is specifically configured to:

[0173] Acquire a current first target behavior habit model corresponding to the identity information of the target user;

[0174] When the current behavior habit model is the new first target behavior habit model and a switching instruction is received, the new first target behavior habit model is switched to the first target behavior habit model before training; or

[0175] When the current behavior habit model is the first target behavior habit model before training and a switching instruction is received, the first target behavior habit model before training is switched to the new first target behavior habit model.

[0176] In some embodiments, the behavior-based vehicle control device further includes a storage module, which is specifically configured to:

[0177] When there is no identity information corresponding to the target user, create new identity information and obtain the preset behavior habit model;

[0178] Collecting a second training data set corresponding to the target user, the second training data set including second vehicle function status data and second driving environment data;

[0179] The preset behavior habit model is trained using the second vehicle functional status data and the second driving environment data to obtain a trained preset behavior habit model, and the correspondence between the trained preset behavior habit model and the new identity information corresponding to the target user is stored in a preset behavior habit model database.

[0180] In some embodiments, each behavior habit model is pre-stored in a preset behavior habit model database, and the behavior habit-based vehicle control device further includes a selection adjustment module, which includes:

[0181] a cluster analysis processing unit, configured to perform cluster analysis on each behavior habit model stored in the behavior habit model database to obtain a plurality of category groups;

[0182] a display unit, configured to display the behavior habit models classified into the plurality of category groups;

[0183] A selection adjustment unit is used to receive a selection instruction for a second target behavior habit model, obtain second current driving environment data, input the second current driving environment data into the second target behavior habit model, output a second behavior habit prediction result through the second target behavior habit model, and control vehicle adjustment according to the second behavior habit prediction result, wherein the second target behavior habit model is at least one of the behavior habit models classified into the multiple category groups.

[0184] In some embodiments, the cluster analysis processing unit includes:

[0185] A driving environment data acquisition subunit, configured to acquire third driving environment data;

[0186] a prediction subunit, configured to perform predictions using various behavior habit models based on the third driving environment data to obtain a plurality of behavior habit prediction results;

[0187] a classification feature acquisition subunit, configured to combine the third driving environment data with each behavior habit prediction result to obtain a plurality of classification features;

[0188] The cluster analysis processing subunit is used to perform cluster analysis on multiple classification features to obtain multiple category groups.

[0189] In some embodiments, the classification feature acquisition subunit is specifically configured to:

[0190] combining the third driving environment data with each behavioral habit prediction result to form a plurality of initial classification features;

[0191] Performing dimensionality reduction processing on the multiple initial classification features to obtain multiple first-dimensional classification features;

[0192] The multiple first-dimensional classification features are mapped into multiple second-dimensional classification features, and the multiple second-dimensional classification features are used as the multiple classification features, where the dimension of the second-dimensional classification features is higher than the dimension of the second-dimensional classification features.

[0193] In some embodiments, the behavior-habit-based vehicle control device further includes a category updating module. After displaying the category of the at least one behavior habit model, the category updating module is specifically configured to:

[0194] receiving scores for the behavior habit models stored in the behavior habit model database and classified into the plurality of category groups;

[0195] The behavioral habit models classified into the plurality of category groups are reclassified according to the scores of the behavioral habit models classified into the plurality of category groups.

[0196] For the convenience of description, the above devices are described as being divided into various modules according to their functions. Of course, when implementing this application, the functions of each module can be implemented in the same or multiple software and / or hardware.

[0197] The device of the above embodiment is used to implement the corresponding vehicle control method based on behavioral habits in any of the above embodiments, and has the beneficial effects of the corresponding method embodiment, which will not be repeated here.

[0198] Based on the same inventive concept, corresponding to any of the above-mentioned embodiments and methods, the present application also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and runnable on the processor, wherein when the processor executes the program, the vehicle control method based on behavioral habits described in any of the above embodiments is implemented.

[0199] Figure 3 A more specific hardware structure diagram of an electronic device provided in this embodiment is shown. The device may include: a processor 301, a memory 302, an input / output interface 303, a communication interface 304, and a bus 305. The processor 301, the memory 302, the input / output interface 303, and the communication interface 304 are communicatively connected to each other within the device via the bus 305.

[0200] The processor 301 can be implemented using a general-purpose CPU (Central Processing Unit), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of this specification.

[0201] The memory 302 can be implemented in the form of ROM (Read Only Memory), RAM (Random Access Memory), static storage devices, dynamic storage devices, etc. The memory 302 can store an operating system and other application programs. When the technical solutions provided in the embodiments of this specification are implemented through software or firmware, the relevant program codes are stored in the memory 302 and called and executed by the processor 301.

[0202] The input / output interface 303 is used to connect to input / output modules to implement information input and output. The input / output modules can be configured as components in the device (not shown in the figure) or can be externally connected to the device to provide corresponding functions. Input devices may include a keyboard, mouse, touch screen, microphone, various sensors, etc., and output devices may include a display, speaker, vibrator, indicator light, etc.

[0203] The communication interface 304 is used to connect to a communication module (not shown) to enable communication between the device and other devices. The communication module can communicate via a wired method (such as USB, network cable, etc.) or a wireless method (such as mobile network, WIFI, Bluetooth, etc.).

[0204] The bus 305 comprises a pathway for transmitting information between various components of the device (eg, the processor 301 , the memory 302 , the input / output interface 303 , and the communication interface 304 ).

[0205] It should be noted that although the above device only shows the processor 301, memory 302, input / output interface 303, communication interface 304, and bus 305, in a specific implementation, the device may also include other components necessary for normal operation. In addition, those skilled in the art will understand that the above device may only include the components necessary to implement the embodiments of this specification, and does not necessarily include all the components shown in the figure.

[0206] The electronic device of the above embodiment is used to implement the corresponding vehicle control method based on behavioral habits in any of the above embodiments, and has the beneficial effects of the corresponding method embodiment, which will not be repeated here.

[0207] Based on the same inventive concept, corresponding to any of the above-mentioned embodiment methods, the present application also provides a computer-readable storage medium, which stores computer instructions, and the computer instructions are used to enable the computer to execute the vehicle control method based on behavioral habits as described in any of the above embodiments.

[0208] The computer-readable media of this embodiment include permanent and non-permanent, removable and non-removable media that can be used to store information by any method or technology. The information can be computer-readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, read-only compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device.

[0209] The computer instructions stored in the storage medium of the above embodiment are used to enable the computer to execute the vehicle control method based on behavioral habits as described in any of the above embodiments, and have the beneficial effects of the corresponding method embodiments, which will not be repeated here.

[0210] Based on the same inventive concept, this embodiment provides a vehicle corresponding to the behavior-habit-based vehicle control device or electronic device or storage medium of any of the above-mentioned embodiments, and the vehicle is equipped with a behavior-habit-based vehicle control device or electronic device or storage medium that can implement the behavior-habit-based vehicle control method of any of the above-mentioned embodiments.

[0211] Those skilled in the art should understand that the discussion of any of the above embodiments is merely illustrative and is not intended to imply that the scope of the present application (including the claims) is limited to these examples. Within the scope of the present application, the technical features in the above embodiments or different embodiments may be combined, the steps may be implemented in any order, and there are many other variations of the different aspects of the embodiments of the present application as described above, which are not provided in detail for the sake of simplicity.

[0212] In addition, for simplicity of description and discussion, and in order not to make the embodiment of the application difficult to understand, the known power supply / ground connection with integrated circuit (IC) chip and other components may or may not be shown in the accompanying drawings provided. In addition, the device can be shown in the form of a block diagram to avoid making the embodiment of the application difficult to understand, and this also takes into account the following fact, that is, the details of the embodiment of these block diagram devices are highly dependent on the platform to be implemented in the embodiment of the application (that is, these details should be fully within the scope of understanding of those skilled in the art). When specific details (for example, circuit) are set forth to describe exemplary embodiments of the application, it will be apparent to those skilled in the art that the embodiment of the application can be implemented without these specific details or when these specific details are changed. Therefore, these descriptions should be considered to be illustrative rather than restrictive.

[0213] Although the present invention has been described in conjunction with specific embodiments thereof, many alternatives, modifications, and variations of these embodiments will be apparent to those skilled in the art based on the foregoing description. For example, other memory architectures (e.g., dynamic RAM (DRAM)) may utilize the embodiments discussed.

[0214] The embodiments of the present application are intended to cover all such substitutions, modifications, and variations that fall within the broad scope of the appended claims. Therefore, any omissions, modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the embodiments of the present application should be included in the scope of protection of this application.

Claims

1. A vehicle control method based on behavioral habits, characterized in that: include: Obtain the target user's identity information; Retrieving a behavior habit model corresponding to the identity information and determining a number of behavior habit models, wherein the behavior habit model corresponds to adjusting at least one function of the vehicle; When the number of behavior habit models is at least two, obtaining first current driving environment data corresponding to the target user, inputting the first current driving environment data into at least two behavior habit models, and having the at least two behavior habit models respectively output at least two corresponding behavior habit prediction results; When a conflict in vehicle functions corresponding to at least two behavior habit models is determined based on the at least two behavior habit prediction results, obtaining preset usage logic of the at least two behavior habit models of the conflict in vehicle functions; According to the preset usage logic, a first target behavior habit model is determined from at least two behavior habit models of the vehicle function conflict, and a first behavior habit prediction result corresponding to the first target behavior habit model is obtained from the at least two behavior habit prediction results, and vehicle adjustment is controlled according to the first behavior habit prediction result.

2. The method according to claim 1, characterized in that The preset usage logic of the at least two behavioral habit models is to set corresponding priorities for the at least two behavioral habit models; The determining of a first target behavior habit model from at least two behavior habit models of conflicting vehicle functions according to the preset usage logic includes: According to the priority, a behavior habit model with the highest priority is selected from the at least two behavior habit models with conflicting vehicle functions as the first target behavior habit model.

3. The method according to claim 1, characterized in that After controlling vehicle adjustment according to the first behavioral habit prediction result, the method further includes: Collecting a first training data set corresponding to the target user, the first training data set including first vehicle function status data and first driving environment data; Training the first target behavior habit model using the first vehicle functional state data and the first driving environment data to obtain a trained first target behavior habit model; The correspondence between the trained first target behavior habit model and the identity information corresponding to the target user is stored in a preset behavior habit model database, and the trained first target behavior habit model is used as the new first target behavior habit model. At the same time, the correspondence between the first target behavior habit model before training and the identity information corresponding to the target user is stored in the behavior habit model database.

4. The method according to claim 3, characterized in that After storing the correspondence between the first target behavior habit model before training and the identity information corresponding to the target user in the behavior habit model database, the method further includes: Acquire a current first target behavior habit model corresponding to the identity information of the target user; When the current first target behavior habit model is the new first target behavior habit model and a switching instruction is received, the new first target behavior habit model is switched to the first target behavior habit model before training; or When the current first target behavior habit model is the first target behavior habit model before training and a switching instruction is received, the first target behavior habit model before training is switched to the new first target behavior habit model.

5. The method according to claim 1, wherein The method further comprises: When there is no identity information corresponding to the target user, create new identity information and obtain a preset behavior habit model; Collecting a second training data set corresponding to the target user, the second training data set including second vehicle function status data and second driving environment data; The preset behavior habit model is trained using the second vehicle functional status data and the second driving environment data to obtain a trained preset behavior habit model, and the correspondence between the trained preset behavior habit model and the new identity information corresponding to the target user is stored in a preset behavior habit model database.

6. The method according to claim 1, wherein Each behavior habit model is stored in a preset behavior habit model database in advance, and the method further includes: Performing cluster analysis on each behavior habit model stored in the behavior habit model database to obtain multiple category groups; displaying the behavioral habit models classified into the plurality of category groups; Receive a selection instruction for a second target behavior habit model, obtain second current driving environment data, input the second current driving environment data into the second target behavior habit model, output a second behavior habit prediction result through the second target behavior habit model, and control vehicle adjustment according to the second behavior habit prediction result, wherein the second target behavior habit model is at least one of the behavior habit models classified into the multiple category groups.

7. The method according to claim 6, characterized in that The cluster analysis process is performed on each behavior habit model stored in the behavior habit model database to obtain multiple category groups, including: Acquiring third driving environment data; Performing predictions based on the third driving environment data using various behavior habit models to obtain multiple behavior habit prediction results; combining the third driving environment data with each behavioral habit prediction result to obtain a plurality of classification features; Cluster analysis is performed on multiple classification features to obtain multiple category groups.

8. The method according to claim 7, characterized in that The third driving environment data is combined with each behavior habit prediction result to obtain multiple classification features, including: combining the third driving environment data with each behavioral habit prediction result to form a plurality of initial classification features; Performing dimensionality reduction processing on the multiple initial classification features to obtain multiple first-dimensional classification features; The multiple first-dimensional classification features are mapped into multiple second-dimensional classification features, and the multiple second-dimensional classification features are used as the multiple classification features, where the dimension of the second-dimensional classification features is higher than the dimension of the second-dimensional classification features.

9. The method according to claim 6, characterized in that After displaying the behavioral habit models classified into the plurality of category groups, the method further includes: receiving scores for the behavior habit models stored in the behavior habit model database and classified into the plurality of category groups; The behavioral habit models classified into the plurality of category groups are reclassified according to the scores of the behavioral habit models classified into the plurality of category groups.

10. A vehicle, characterized in that: Used to perform the method according to any one of claims 1 to 9.

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