Vehicle intelligent control method and device, electronic equipment, storage medium and program product

Abstract

Embodiments of the present application provide a vehicle intelligent control method and device, electronic equipment, storage medium and program product. The method comprises: in the process of driving a vehicle by a subject, collecting driving state data and external environment data of the vehicle in real time; analyzing the driving state data and the external environment data, and identifying a current driving scene; obtaining a mode switching behavior database constructed for the subject, wherein the mode switching behavior database is constructed based on historical driving state data, historical external environment data and historical behavior data of the subject, and is used to predict a driving mode to be switched to according to the driving scene; querying a target driving mode matched with the current driving scene from the mode switching behavior database, and controlling the vehicle to switch to the target driving mode. By using the method, the driving habits and preferences of the driver can be combined with the external environment and the vehicle state to perform vehicle intelligent control during driving.

Description

Technical Field

[0001] This application relates to the field of computer technology, and in particular to a vehicle intelligent control method, device, electronic device, storage medium, and program product. Background Technology

[0002] With technological advancements, some vehicles on the market are now equipped with memory functions to achieve intelligent vehicle control. These functions mainly fall into two categories: 1. Those capable of recording the driver's driving habits and preferences, but typically limited to adjusting simple settings such as seats, rearview mirrors, and air conditioning based on the recorded habits and preferences, such as CN110466457A - Vehicle Automatic Adjustment Method. They cannot intelligently recognize and automatically switch driving modes based on vehicle status and external environment while driving. 2. Those that make certain mode adjustments based on vehicle status and road conditions in the external environment, but these represent ideal vehicle control, such as CN114228499A - An Electric Vehicle Control Method, which cannot incorporate the driver's driving habits and preferences.

[0003] Therefore, traditional solutions that achieve intelligent vehicle control by equipping vehicles with memory functions cannot combine the driver's driving habits and preferences with the external environment and vehicle status during driving to achieve intelligent vehicle control. Summary of the Invention

[0004] This application provides a vehicle intelligent control method, device, electronic device, readable storage medium, and program product, which can achieve the effect of intelligent vehicle control by combining the driver's driving habits and preferences with the external environment and vehicle status during driving.

[0005] In a first aspect, embodiments of this application provide a vehicle intelligent control method, including:

[0006] During the process of the object driving the vehicle, real-time data on the vehicle's driving status and external environment are collected.

[0007] By analyzing driving status data and external environment data, the current driving scenario can be identified;

[0008] Obtain the mode switching behavior database built for the object, and query the target driving mode that matches the current driving scenario from the mode switching behavior database; the mode switching behavior database is built based on the object's historical driving status data, historical external environment data, and object's historical behavior data, and is used to predict the driving mode to be switched to based on the driving scenario.

[0009] Control the vehicle to switch to the target driving mode.

[0010] In one possible implementation, driving status data and external environment data are analyzed to identify the current driving scenario, including:

[0011] Perform condition analysis on driving status data to determine driving scenarios;

[0012] Analyze external environmental data to determine road condition scenarios;

[0013] It integrates driving scenarios and road condition scenarios to identify the current driving scenario.

[0014] In one possible implementation, a database for switching behavior in object construction patterns includes:

[0015] Obtain historical driving status data, historical external environment data, and historical behavior data of the vehicle during the historical time period in which the object was driving the vehicle;

[0016] Based on the object's historical behavior data, identify the mode switching behavior triggered by the object, and determine the driving mode switched to by each mode switching behavior, as well as the driving scenario represented by the historical driving state data and historical external environment data associated with each mode switching behavior.

[0017] Construct a mapping relationship between driving scenarios and driving modes for each mode switching behavior to obtain a mode switching behavior database built for each object.

[0018] In one possible implementation, determining the driving mode switched to by each mode switching behavior, and the driving scenario represented by historical driving state data and historical external environment data associated with each mode switching behavior, includes:

[0019] Determine the trigger time for each mode switching behavior;

[0020] For each behavior trigger moment, extract the driving mode switched to after the behavior trigger moment from the historical driving status data to obtain the driving mode switched to by the corresponding mode switching behavior at the behavior trigger moment.

[0021] From historical driving status data and historical external environment data, extract the historical driving status data and historical external environment data associated with the behavior triggering time. Based on the historical driving status data and historical external environment data associated with the behavior triggering time, determine the driving scenario associated with the mode switching behavior corresponding to the behavior triggering time.

[0022] In one possible implementation, the vehicle intelligent control method further includes:

[0023] When an object data query command is received that is triggered by the database for mode switching behavior, the object data query command is authenticated.

[0024] Configure query permissions for the schema switching behavior database when the object data query command is authenticated.

[0025] In one possible implementation, the vehicle intelligent control method further includes:

[0026] When an object data editing command is received that is triggered by the database for mode switching behavior, the object data editing command is authenticated.

[0027] When object data editing commands are authenticated, configure editing permissions for the mode switching behavior database and collect updated data based on the editing permission settings;

[0028] Synchronize the updated data to the mode switching behavior database.

[0029] Secondly, embodiments of this application provide a vehicle intelligent control device, comprising:

[0030] The real-time data acquisition module is used to collect real-time data on the vehicle's driving status and external environment during the driving process.

[0031] The driving scenario recognition module is used to analyze driving status data and external environment data to identify the current driving scenario;

[0032] The driving mode determination module is used to obtain a mode switching behavior database built for the object, and to query the target driving mode that matches the current driving scenario from the mode switching behavior database. The mode switching behavior database is built based on the object's historical driving status data, historical external environment data, and object's historical behavior data, and is used to predict the driving mode to be switched to based on the driving scenario.

[0033] The driving mode switching module is used to control the vehicle to switch to the target driving mode.

[0034] Thirdly, embodiments of this application provide an electronic device, including: a memory and a processor;

[0035] The memory stores the instructions that the computer executes;

[0036] The processor executes computer execution instructions stored in memory, causing the processor to perform the first aspect and / or various possible implementations of the first aspect as described above.

[0037] Fourthly, embodiments of this application provide a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, are used to implement the first aspect and / or various possible implementations of the first aspect.

[0038] Fifthly, embodiments of this application provide a computer program product, including a computer program that, when executed by a processor, implements the first aspect and / or various possible implementations of the first aspect.

[0039] The vehicle intelligent control method, device, electronic device, readable storage medium, and program product provided in this application can collect vehicle driving status data and external environment data in real time during the driving process. By analyzing the driving status data and external environment data, the current driving scenario can be identified. Furthermore, a mode-switching behavior database constructed for the subject is obtained. This database is built based on the subject's historical driving status data (representing the subject's driving habits and preferences), historical external environment data (representing the external environment), and historical behavior data (representing the vehicle state), and is used to predict the driving mode to be switched to based on the driving scenario. Even further, a target driving mode matching the current driving scenario can be queried from the mode-switching behavior database, and the vehicle can be controlled to switch to the target driving mode. Based on this, using the above process, during driving, the vehicle can intelligently control the switching of driving modes based on the subject's driving status data, external environment data, and the mode-switching behavior database incorporating the subject's driving habits and preferences. Therefore, during driving, the driver's driving habits and preferences can be combined with the external environment and vehicle state for intelligent vehicle control. Attached Figure Description

[0040] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0041] Figure 1 A schematic diagram illustrating a scenario for the vehicle intelligent control method provided in this application;

[0042] Figure 2 A flowchart illustrating the vehicle intelligent control method provided in this application;

[0043] Figure 3 A flowchart illustrating the vehicle intelligent control method with automatic switching driving modes provided in this application;

[0044] Figure 4 A schematic diagram of the vehicle intelligent control device provided in this application;

[0045] Figure 5 A schematic diagram of the structure of the electronic device provided in this application.

[0046] The accompanying drawings illustrate specific embodiments of this application, which will be described in more detail below. These drawings and descriptions are not intended to limit the scope of the concept in any way, but rather to illustrate the concept of this application to those skilled in the art through reference to particular embodiments. Detailed Implementation

[0047] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0048] The vehicle intelligent control method provided in this application embodiment can be applied to, for example... Figure 1 In the application environment shown, multiple data acquisition devices 102 deployed on the vehicle are connected to the central processing unit 104 via network communication or electrical connection. Taking the application of vehicle intelligent control methods to the central processing unit 104 as an example, the central processing unit 104 can respond to vehicle intelligent control commands triggered by the object through the in-vehicle touch screen, and collect vehicle driving status data and external environment data in real time through the multiple data acquisition devices 102 installed on the vehicle. Furthermore, the central processing unit 104 can analyze the driving status data and external environment data to identify the current driving scenario, and then obtain a mode switching behavior database constructed for the object, querying the target driving mode matching the current driving scenario from the mode switching behavior database. The mode switching behavior database is constructed based on the object's historical driving status data, historical external environment data, and historical behavior data, and is used to predict the driving mode to be switched to based on the driving scenario. Finally, the central processing unit 104 can control the vehicle to switch to the target driving mode.

[0049] The data acquisition devices 102 include, but are not limited to: vehicle speed sensor, acceleration sensor, steering wheel angle and torque sensor, brake pedal position sensor, front-facing camera, surround-view camera, positioning sensor, interior temperature sensor, exterior temperature sensor, interior audio sensor, radar, and vehicle battery detector. The central processing unit 104 can receive, fuse, and process massive amounts of data from the multiple data acquisition devices 102, run complex algorithms to make decisions, and ultimately issue control commands to the vehicle's actuators.

[0050] In one embodiment, a vehicle intelligent control method is provided. This embodiment uses the application of this vehicle intelligent control method to the central processing unit 104 as an example for illustration. Figure 2 As shown, the vehicle intelligent control method includes:

[0051] Step 202: During the process of the object driving the vehicle, collect the vehicle's driving status data and external environment data in real time.

[0052] The vehicle is equipped with multiple data acquisition devices, whose onboard sensors can form an onboard sensor network. These multiple data acquisition devices can collect driving status data that reflects the vehicle's condition and external environment data that reflects the vehicle's surrounding environment in real time and synchronously.

[0053] Optionally, after the vehicle is started, multiple data acquisition devices deployed in the vehicle will automatically collect data. During the process of the vehicle being driven, the central processing unit can collect real-time driving status data of the vehicle through multiple data acquisition devices, such as vehicle speed, acceleration, steering wheel angle and torque, brake pedal position, positioning, audio, etc. It can also collect real-time data on the vehicle's external environment, such as data on surrounding vehicles, weather, and roads.

[0054] Step 204: Analyze the driving status data and external environment data to identify the current driving scenario.

[0055] Optionally, the central processing unit can analyze driving status data and external environment data based on a multi-source data fusion algorithm. Specifically, it aligns the driving status data and external environment data in time, performs condition analysis on the driving status data to determine the driving scenario, and performs road condition analysis on the external environment data to determine the road condition scenario. Finally, it merges the driving scenario and the road condition scenario to identify the current driving scenario.

[0056] For example, in a certain scenario, the central processing unit can determine that the vehicle is at a stable speed V based on driving status data, and determine that there is a vehicle Y meters ahead of the vehicle based on external environment data, the distance between the two vehicles is within a safe range, and there are no strong interfering factors on the road. Based on this, the current driving scenario can be determined as "smooth following".

[0057] Step 206: Obtain the mode switching behavior database built for the object, and query the target driving mode that matches the current driving scenario from the mode switching behavior database.

[0058] The mode switching behavior database is built based on the object's historical driving status data, historical external environment data, and historical object behavior data. It is used to predict the driving mode to switch to based on the driving scenario. The object's historical behavior data can represent the object's driving habits and preferences.

[0059] Optionally, the central processing unit can obtain a mode switching behavior database built for the object, integrate the object's driving habits and preferences, and query the target driving mode that matches the current driving scenario from the mode switching behavior database built for the object.

[0060] Step 208: Control the vehicle to switch to the target driving mode.

[0061] Optionally, the central processing unit can issue commands to the vehicle's actuators to control them to switch to the target driving mode. Based on this, the vehicle's intelligence level can be effectively improved, the driver's workload reduced, driving safety and comfort enhanced, and vehicle performance optimized in different driving scenarios.

[0062] The aforementioned intelligent vehicle control method can collect real-time vehicle driving status data and external environment data during the driver's operation. By analyzing the driving status data and external environment data, the current driving scenario can be identified. Furthermore, a mode-switching behavior database is acquired, constructed based on the driver's historical driving status data (representing the driver's driving habits and preferences), historical external environment data (representing the external environment), and historical behavior data (representing the vehicle's state). This database is used to predict the driving mode to switch to based on the driving scenario. Even further, a target driving mode matching the current driving scenario can be queried from the mode-switching behavior database, and the vehicle can be controlled to switch to the target driving mode. Based on this, using the above process, during driving, the vehicle can intelligently control the switching of driving modes based on the driver's driving status data, external environment data, and the mode-switching behavior database incorporating the driver's driving habits and preferences. Therefore, intelligent vehicle control can be achieved by combining the driver's driving habits and preferences with the external environment and vehicle state during driving.

[0063] In some optional embodiments, driving status data and external environment data are analyzed to identify the current driving scenario, including:

[0064] Perform condition analysis on driving status data to determine driving scenarios;

[0065] Analyze external environmental data to determine road condition scenarios;

[0066] It integrates driving scenarios and road condition scenarios to identify the current driving scenario.

[0067] Specifically, the central processing unit can perform condition analysis on driving status data. Based on data such as vehicle speed, acceleration, steering wheel angle and torque, brake pedal position, positioning, and audio, it can determine whether the vehicle is currently in any of the following driving scenarios: "stable," "rapid acceleration," "emergency braking," "cornering," or "frequent start-stop." The analysis of driving scenarios focuses on the user's operational intent and the vehicle's dynamic response.

[0068] Furthermore, the central processing unit can perform traffic condition analysis on external environmental data. Based on vehicle, road, and weather data collected by the front-facing and surround-view cameras, it analyzes the external environment and determines the traffic condition scenario as a combination of at least one of the following: "highway," "urban ring road," "downtown area," "mountain road," "sunny daytime," "nighttime," "rain, snow, fog, or haze," "smooth traffic," "slow traffic," or "congestion." The analysis of traffic condition scenarios focuses on objective environmental constraints.

[0069] Furthermore, the central processing unit can use rule-based decision trees or lightweight machine learning classifiers to integrate driving scenarios and road condition scenarios to identify the current driving scenario.

[0070] In this embodiment, the driving scenario obtained by fusing driving scenario and road condition scenario can describe both the driver's subjective state and objective environmental information, providing accurate and rich basis for subsequent prediction of the driving mode to be switched.

[0071] In one possible implementation, a database for switching behavior in object construction patterns includes:

[0072] Obtain historical driving status data, historical external environment data, and historical behavior data of the vehicle during the historical time period in which the object was driving the vehicle;

[0073] Based on the object's historical behavior data, identify the mode switching behavior triggered by the object, and determine the driving mode switched to by each mode switching behavior, as well as the driving scenario represented by the historical driving state data and historical external environment data associated with each mode switching behavior.

[0074] Construct a mapping relationship between driving scenarios and driving modes for each mode switching behavior to obtain a mode switching behavior database built for each object.

[0075] The object's historical behavior data includes: operation records and time of each time the object switches driving modes. The object can switch driving modes actively via physical buttons, touchscreen, or voice commands.

[0076] Specifically, when the vehicle is first started, the central processing unit can control multiple data acquisition devices deployed in the vehicle to begin data collection during the initial historical driving period of the vehicle, and store the collected data in a designated location. When the amount of collected data reaches a preset amount, the central processing unit can actively or passively respond to the vehicle intelligent control command triggered by the vehicle, and retrieve the vehicle's historical driving status data, historical external environment data, and the vehicle's historical behavior data from the stored data during the historical driving period of the vehicle. The central processing unit then performs timestamp alignment processing on the historical driving status data, historical external environment data, and historical behavior data of the vehicle.

[0077] After completing the timestamp alignment process, the central processing unit can identify the mode-switching behavior triggered by the object based on the object's historical behavior data. Using the trigger time of each mode-switching behavior as an anchor point, it determines the driving mode switched to by each behavior, as well as the associated historical driving state data and historical external environment data. Then, it performs driving scenario analysis on the historical driving state data associated with each mode-switching behavior to determine the driving scenario, and performs road condition scenario analysis on the historical external environment data associated with each behavior to determine the road condition scenario. By fusing the driving scenario and road condition scenario associated with each mode-switching behavior, it classifies and identifies the driving scenario represented by the historical driving state data and historical external environment data associated with each mode-switching behavior.

[0078] Furthermore, the central processing unit can construct a mapping relationship between driving scenarios and driving modes for each mode-switching behavior, using a large number of "scenario-behavior" samples to build a mode-switching behavior database for each object. This mode-switching behavior database can be a structured lookup table or a trained machine learning model (such as a classification model), which can learn the probability distribution of an object selecting different driving modes in different driving scenarios, realizing a personalized mapping from "driving scenario" to predicted "driving mode".

[0079] For example, switching driving modes includes, but is not limited to, adjusting the vehicle's regenerative braking intensity, suspension mode, and driving gear.

[0080] In this embodiment, the driving habits and preferences of the subject are transformed into an explicit model. When the vehicle enters a similar driving scenario again, it can automatically identify and switch to a driving mode that matches the driving scenario through the mode switching behavior database. The subject does not need to manually operate the driving mode switching, which realizes the simplicity and intelligence of operation and can optimize the driving experience.

[0081] In one possible implementation, determining the driving mode switched to by each mode switching behavior, and the driving scenario represented by historical driving state data and historical external environment data associated with each mode switching behavior, includes:

[0082] Determine the trigger time for each mode switching behavior;

[0083] For each behavior trigger moment, extract the driving mode switched to after the behavior trigger moment from the historical driving status data to obtain the driving mode switched to by the corresponding mode switching behavior at the behavior trigger moment.

[0084] From historical driving status data and historical external environment data, extract the historical driving status data and historical external environment data associated with the behavior triggering time. Based on the historical driving status data and historical external environment data associated with the behavior triggering time, determine the driving scenario associated with the mode switching behavior corresponding to the behavior triggering time.

[0085] Specifically, the central processing unit can accurately pinpoint the trigger time for each mode switching behavior by monitoring signal transitions at the driving mode selection interface or recognizing specific voice commands. For each trigger time, the central processing unit can extract the driving mode switched to within a preset time window after the trigger time from historical driving status data, thus obtaining the driving mode switched to by the mode switching behavior corresponding to the trigger time.

[0086] Furthermore, the central processing unit can extract historical driving state data and historical external environment data associated with a preset time window prior to the behavior triggering time from historical driving state data and historical external environment data. The central processing unit can then perform driving scenario analysis based on the historical driving state data associated with the behavior triggering time to determine the driving scenario, and perform road condition scenario analysis based on the historical external environment data associated with the behavior triggering time to determine the road condition scenario. By fusing the driving scenario and the road condition scenario, the central processing unit can determine the driving scenario associated with the mode-switching behavior corresponding to the behavior triggering time.

[0087] In this embodiment, by accurately locating the trigger time of each mode-switching behavior, and performing time window segmentation and cause backtracking analysis before and after the mode-switching behavior, the confusion of data associations can be effectively avoided, greatly improving the accuracy and reliability of the constructed "scene-mode" mapping relationship. This ensures that when making predictions based on the mode-switching behavior database, the determined target driving mode is indeed the mode that the object truly intends to switch to in the current driving scenario, effectively optimizing the intelligent driving experience.

[0088] In one possible implementation, the vehicle intelligent control method further includes:

[0089] When an object data query command is received that is triggered by the database for mode switching behavior, the object data query command is authenticated.

[0090] Configure query permissions for the schema switching behavior database when the object data query command is authenticated.

[0091] The object data query command can be triggered by the vehicle's driver or by the vehicle's authorized service personnel, and can be triggered through the vehicle's infotainment system or a mobile app.

[0092] Specifically, when the central processing unit receives an object data query instruction triggered by the mode switching behavior database, the central processing unit can authenticate the object data query instruction. Authentication methods include, but are not limited to: in-vehicle biometrics (such as facial recognition, fingerprint), digital key authentication, or a password / verification code linked to the vehicle owner's account.

[0093] Furthermore, once the object data query command is authenticated, the central processing unit can configure query permissions for the mode switching behavior database. For example, the vehicle owner may have full query rights, viewing detailed data and analysis reports related to all driving scenarios and driving modes; visitors or maintenance technicians may be configured with limited query rights, only able to view anonymized statistical information or general mode logs related to vehicle diagnostics. Query results can be dynamically presented on the interface based on permissions.

[0094] In this embodiment, the database transparency of mode switching behavior can be ensured through the configuration process of query permissions. Furthermore, the tiered permission configuration not only satisfies the reasonable need of vehicle owners to understand their own data, but also ensures that data can be used within a controlled and limited scope in necessary scenarios such as maintenance and repair, effectively protecting data security.

[0095] In one possible implementation, the vehicle intelligent control method further includes:

[0096] When an object data editing command is received that is triggered by the database for mode switching behavior, the object data editing command is authenticated.

[0097] When object data editing commands are authenticated, configure editing permissions for the mode switching behavior database and collect updated data based on the editing permission settings;

[0098] Synchronize the updated data to the mode switching behavior database.

[0099] Among them, the object data editing command can be triggered by the vehicle driver, the vehicle's authorized service personnel, or automatically when new data is collected.

[0100] Optionally, when the central processing unit receives an object data editing instruction triggered against the mode switching behavior database, the central processing unit can authenticate the object data editing instruction. If the object data editing instruction is successfully authenticated, the central processing unit can grant editing permissions to the mode switching behavior database and generate an editing page, from which updated data based on the editing permission settings can be collected. Furthermore, the central processing unit can synchronize the updated data to the mode switching behavior database. The updated data will not directly overwrite the data in the mode switching behavior database, but will instead serve as a new, high-weighted sample, triggering incremental data learning or fine-tuning.

[0101] For example, with the owner's authorization, the latest mode switching behavior database can be stored locally in the vehicle and synchronized to the cloud to ensure better application when entering the same or similar driving scenarios again.

[0102] In this embodiment, the updability of the mode switching behavior database can be ensured through the configuration process of edit permissions. Based on this, updates to records of driver habits and preferences are allowed, making the mode switching behavior database no longer invisible but interactive and learnable. This significantly improves transparency and control, facilitating continuous optimization of the intelligent driving experience.

[0103] In a specific application scenario, such as Figure 3 The diagram shows a flowchart of a vehicle intelligent control method that can automatically switch driving modes, which mainly includes the following interactive flow:

[0104] The central processing unit collects historical driving status data, historical external environment data, and historical behavior data of the vehicle during historical periods driven by the target vehicle through multiple data acquisition devices deployed on the vehicle. Then, based on the collected data, it analyzes the mode-switching behaviors triggered by the target vehicle and determines the driving mode switched to for each mode-switching behavior, as well as the driving scenario represented by the historical driving status data and historical external environment data associated with each mode-switching behavior. Furthermore, it constructs a mapping relationship between the driving scenario and driving mode for each mode-switching behavior, resulting in a mode-switching behavior database built specifically for the target vehicle.

[0105] During the driving process, the central processing unit can collect real-time driving status data and external environment data through multiple data acquisition devices deployed on the vehicle. It then analyzes this data to identify the current driving scenario. Furthermore, it queries the mode switching behavior database for a target driving mode that matches the current driving scenario and issues instructions to the vehicle's actuators to control the vehicle to switch to the target driving mode.

[0106] It should be understood that although the steps in the flowcharts of the above embodiments are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the above embodiments may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0107] Based on the same inventive concept, this application also provides a vehicle intelligent control device for implementing the vehicle intelligent control method described above. The solution provided by this vehicle intelligent control device is similar to the implementation scheme described in the vehicle intelligent control method above. Therefore, the specific limitations in one or more device embodiments provided below can be found in the limitations of the vehicle intelligent control method described above, and will not be repeated here.

[0108] In one embodiment, such as Figure 4 As shown, a vehicle intelligent control device 400 is provided, comprising:

[0109] The real-time data acquisition module 402 is used to collect real-time driving status data and external environment data of the vehicle during the driving process of the object.

[0110] The driving scene recognition module 404 is used to analyze driving status data and external environment data to identify the current driving scene;

[0111] The driving mode determination module 406 is used to obtain the mode switching behavior database built for the object, and to query the target driving mode that matches the current driving scenario from the mode switching behavior database. The mode switching behavior database is built based on the object's historical driving status data, historical external environment data, and object's historical behavior data, and is used to predict the driving mode to be switched to according to the driving scenario.

[0112] The driving mode switching module 408 is used to control the vehicle to switch to the target driving mode.

[0113] The aforementioned intelligent vehicle control device can collect real-time vehicle driving status data and external environment data during the driver's operation. By analyzing this data, it identifies the current driving scenario. Furthermore, it acquires a mode-switching behavior database built for the driver. This database is constructed based on the driver's historical driving status data (representing the driver's driving habits and preferences), historical external environment data (representing the external environment), and historical behavior data (representing the vehicle's state). This database is used to predict the driving mode to switch to based on the driving scenario. Even further, it can query the mode-switching behavior database for a target driving mode that matches the current driving scenario and control the vehicle to switch to that target mode. Based on this process, during driving, the vehicle can intelligently switch driving modes based on the driver's driving status data, external environment data, and the mode-switching behavior database incorporating the driver's driving habits and preferences. Therefore, intelligent vehicle control can be achieved by combining the driver's driving habits and preferences with the external environment and vehicle state during driving.

[0114] In one possible implementation, the driving scene recognition module is further configured to:

[0115] Perform condition analysis on driving status data to determine driving scenarios;

[0116] Analyze external environmental data to determine road condition scenarios;

[0117] It integrates driving scenarios and road condition scenarios to identify the current driving scenario.

[0118] In one possible implementation, the vehicle intelligent control device further includes a database construction module, which is configured to:

[0119] Obtain historical driving status data, historical external environment data, and historical behavior data of the vehicle during the historical time period in which the object was driving the vehicle;

[0120] Based on the object's historical behavior data, identify the mode switching behavior triggered by the object, and determine the driving mode switched to by each mode switching behavior, as well as the driving scenario represented by the historical driving state data and historical external environment data associated with each mode switching behavior.

[0121] Construct a mapping relationship between driving scenarios and driving modes for each mode switching behavior to obtain a mode switching behavior database built for each object.

[0122] In one possible implementation, the database building module is also configured to:

[0123] Determine the trigger time for each mode switching behavior;

[0124] For each behavior trigger moment, extract the driving mode switched to after the behavior trigger moment from the historical driving status data to obtain the driving mode switched to by the corresponding mode switching behavior at the behavior trigger moment.

[0125] From historical driving status data and historical external environment data, extract the historical driving status data and historical external environment data associated with the behavior triggering time. Based on the historical driving status data and historical external environment data associated with the behavior triggering time, determine the driving scenario associated with the mode switching behavior corresponding to the behavior triggering time.

[0126] In one possible implementation, the vehicle intelligent control device further includes a data query module, which is configured to:

[0127] When an object data query command is received that is triggered by the database for mode switching behavior, the object data query command is authenticated.

[0128] Configure query permissions for the schema switching behavior database when the object data query command is authenticated.

[0129] In one possible implementation, the vehicle intelligent control device further includes a data editing module, which is configured to:

[0130] When an object data editing command is received that is triggered by the database for mode switching behavior, the object data editing command is authenticated.

[0131] When object data editing commands are authenticated, configure editing permissions for the mode switching behavior database and collect updated data based on the editing permission settings;

[0132] Synchronize the updated data to the mode switching behavior database.

[0133] Each module in the above-mentioned device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in the processor of a computer device in hardware form or independent of it, or stored in the memory of a computer device in software form, so that the processor can call and execute the operations corresponding to each module.

[0134] Figure 5 A schematic diagram of the structure of the electronic device provided in this application. Figure 5 As shown, the electronic device 500 provided in this embodiment includes at least one processor 501 and a memory 502. Optionally, the electronic device 500 further includes a communication component 503. The processor 501, memory 502, and communication component 503 are connected via a bus 504.

[0135] In a specific implementation, at least one processor 501 executes computer execution instructions stored in memory 502, causing at least one processor 501 to perform the above-described method.

[0136] The specific implementation process of processor 501 can be found in the above method embodiments, and its implementation principle and technical effect are similar. It will not be repeated here.

[0137] In the above embodiments, it should be understood that the processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), etc. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the method disclosed in this invention can be directly implemented by a hardware processor, or implemented by a combination of hardware and software modules within the processor.

[0138] The memory may include random access memory (RAM) and may also include non-volatile memory (NVM), such as at least one disk storage device.

[0139] The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be categorized as address buses, data buses, control buses, etc. For ease of illustration, the buses shown in the accompanying drawings are not limited to a single bus or a single type of bus.

[0140] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the above-described method.

[0141] This application also provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the above-described method.

[0142] The aforementioned readable storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. The readable storage medium can be any available medium accessible to a general-purpose or special-purpose computer.

[0143] An exemplary readable storage medium is coupled to a processor, enabling the processor to read information from and write information to the readable storage medium. Of course, the readable storage medium can also be a component of the processor. The processor and the readable storage medium can reside in an Application Specific Integrated Circuit (ASIC). Alternatively, the processor and the readable storage medium can exist as discrete components in the device.

[0144] The division of units is merely a logical functional division; in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be indirect coupling or communication connection through some interfaces, devices, or units, and may be electrical, mechanical, or other forms.

[0145] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0146] In addition, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0147] If a function is implemented as 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 this invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0148] Those skilled in the art will understand that all or part of the steps of the above-described method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When executed, the program performs the steps of the above-described method embodiments; and the aforementioned storage medium includes various media capable of storing program code, such as ROM, RAM, magnetic disks, or optical disks.

[0149] Finally, it should be noted that other embodiments of the invention will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This invention is intended to cover any variations, uses, or adaptations of the invention that follow the general principles of the invention and include common knowledge or customary techniques in the art not disclosed herein, and is not limited to the precise structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of the invention is limited only by the appended claims.

Claims

1. A vehicle intelligent control method, characterized in that, include: During the process of the object driving the vehicle, the vehicle's driving status data and external environment data are collected in real time; The driving status data and the external environment data are analyzed to identify the current driving scenario; Obtain a mode switching behavior database constructed for the object, and query the target driving mode that matches the current driving scenario from the mode switching behavior database; the mode switching behavior database is constructed based on the object's historical driving status data, historical external environment data, and object's historical behavior data, and is used to predict the driving mode to be switched to according to the driving scenario; Control the vehicle to switch to the target driving mode.

2. The method according to claim 1, characterized in that, The analysis of the driving status data and the external environment data to identify the current driving scenario includes: Perform condition analysis on the driving status data to determine the driving scenario; The external environmental data is used to analyze road conditions and determine the road condition scenario; By combining the driving scenario and the road condition scenario, the current driving scenario is identified.

3. The method according to claim 1, characterized in that, The database for object construction mode switching behavior includes: Acquire the historical driving status data of the vehicle, historical external environment data, and historical behavior data of the object during the historical time period in which the object drives the vehicle; Based on the object's historical behavior data, identify the mode switching behavior triggered by the object, and determine the driving mode switched to by each mode switching behavior, as well as the driving scenario represented by the historical driving state data and historical external environment data associated with each mode switching behavior. Construct a mapping relationship between driving scenarios and driving modes for each of the aforementioned mode switching behaviors to obtain a mode switching behavior database for the object.

4. The method according to claim 3, characterized in that, The determination of the driving mode switched to by each mode switching behavior, and the driving scenario represented by historical driving state data and historical external environment data associated with each mode switching behavior, includes: Determine the trigger time for each of the aforementioned mode switching behaviors; For each of the aforementioned behavior trigger times, the driving mode switched to after the behavior trigger time is extracted from the historical driving state data to obtain the driving mode switched to by the mode switching behavior corresponding to the behavior trigger time. From the historical driving state data and the historical external environment data, extract the historical driving state data and historical external environment data associated with the behavior triggering time before the behavior triggering time. Based on the historical driving state data and historical external environment data associated with the behavior triggering time before the behavior triggering time, determine the driving scenario associated with the mode switching behavior corresponding to the behavior triggering time.

5. The method according to claim 1, characterized in that, The method further includes: When an object data query instruction triggered by the database for the mode switching behavior is received, the object data query instruction is authenticated; If the object data query command is authenticated, configure query permissions for the mode switching behavior database.

6. The method according to claim 1, characterized in that, The method further includes: When an object data editing instruction is received that is triggered by the database of the mode switching behavior, the object data editing instruction is authenticated; If the object data editing instruction is authenticated, configure editing permissions for the mode switching behavior database and collect updated data based on the editing permission settings; The updated data is synchronized to the mode switching behavior database.

7. A vehicle intelligent control device, characterized in that, include: The real-time data acquisition module is used to collect the vehicle's driving status data and external environment data in real time during the process of the object driving the vehicle. The driving scenario recognition module is used to analyze the driving status data and the external environment data to identify the current driving scenario; The driving mode determination module is used to obtain a mode switching behavior database constructed for the object, and query the target driving mode that matches the current driving scenario from the mode switching behavior database; the mode switching behavior database is constructed based on the object's historical driving status data, historical external environment data, and object's historical behavior data, and is used to predict the driving mode to be switched to according to the driving scenario. The driving mode switching module is used to control the vehicle to switch to the target driving mode.

8. An electronic device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 6.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 6.

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