Vehicle control method and device, vehicle and storage medium

Abstract

The embodiment of the invention provides a vehicle control method and device, a vehicle and a storage medium. The method comprises the following steps: acquiring first driving behavior data of a current vehicle; the driving style of the driver is determined according to the first driving behavior data, an auxiliary driving strategy suitable for the driver is determined according to the driving style, the type of the road where the vehicle is located and the risk response demand level corresponding to the vehicle, and the vehicle is controlled to run according to the auxiliary driving strategy. According to the technical scheme, a personalized driving assistance system can be realized, the driving experience can be optimized, the road safety can be improved, and the vehicle control can be dynamically optimized according to the driving style of the driver by adjusting the driving assistance strategy in real time, so that the safety of the driver can be more effectively guaranteed under different road conditions and risk environments, and the driving experience of the driver is improved. And meanwhile, the driving comfort and efficiency are improved.

Description

Technical Field

[0001] The present application relates to the field of vehicle technology, and in particular to a vehicle control method, device, vehicle, and storage medium. Background Art

[0002] As vehicles become more and more intelligent, intelligent assisted driving has gradually become popular, providing a positive impact on the driver's driving experience.

[0003] In existing related technologies, the configuration of intelligent assisted driving is mostly bound to the vehicle, and service providers rely on preset algorithms and thresholds to determine the corresponding assisted driving strategy.

[0004] However, a car is usually driven by multiple people at different times, and different drivers have different styles. This results in the inability to effectively apply the assisted driving strategies in the existing technology, which seriously affects the current driver experience. Summary of the invention

[0005] The embodiments of the present application provide a vehicle control method, device, vehicle, and storage medium to achieve the purpose of improving the comfort and efficiency of vehicle driving.

[0006] In a first aspect, an embodiment of the present application provides a vehicle control method, including:

[0007] Acquire first driving behavior data of the current vehicle;

[0008] determining the driving style of the driver according to the first driving behavior data;

[0009] Determining an assisted driving strategy suitable for the driver according to the driving style, the type of road on which the vehicle is located, and the risk response requirement level corresponding to the vehicle;

[0010] The vehicle operation is controlled according to the assisted driving strategy.

[0011] In one or more embodiments, determining the driving style of the driver according to the first driving behavior data includes:

[0012] The first driving behavior data is output to a predetermined driving style recognition model to obtain the driving style of the driver, wherein the driving style recognition model is obtained by training a preset machine learning algorithm model based on a plurality of driving behavior data.

[0013] In one or more embodiments, before determining the assisted driving strategy suitable for the driver according to the driving style, the type of road on which the vehicle is located, and the risk response requirement level corresponding to the vehicle, the method further includes:

[0014] Acquiring operation data of the vehicle and risk event data on a path of the vehicle;

[0015] A risk response requirement level corresponding to the vehicle is determined based on the operating data and the risk event data.

[0016] In one or more embodiments, the first driving behavior data includes: real-time driving behavior data and / or historical driving behavior data corresponding to the driver of the vehicle.

[0017] In one or more embodiments, the obtaining first driving behavior data of the current vehicle includes:

[0018] Obtaining driver information in the vehicle;

[0019] According to the driver information, historical driving behavior data corresponding to the driver is obtained.

[0020] In one or more embodiments, the operating data includes at least one of the following: engine parameters, brake system status, location, traffic flow, speed limit information, and driving trajectory;

[0021] The risk event data includes at least one of the following: trigger time, type and severity of at least one type of warning information.

[0022] In a second aspect, an embodiment of the present application provides a vehicle control device, including:

[0023] An acquisition module, used to acquire first driving behavior data of the current vehicle;

[0024] A first determining module, configured to determine the driving style of the driver according to the first driving behavior data;

[0025] A second determination module is used to determine an assisted driving strategy suitable for the driver according to the driving style, the type of road on which the vehicle is located, and the risk response requirement level corresponding to the vehicle;

[0026] A control module is used to control the operation of the vehicle according to the assisted driving strategy.

[0027] In one or more embodiments, the first determining module is specifically configured to:

[0028] The first driving behavior data is output to a predetermined driving style recognition model to obtain the driving style of the driver, wherein the driving style recognition model is obtained by training a preset machine learning algorithm model based on a plurality of driving behavior data.

[0029] In one or more embodiments, before determining the assisted driving strategy suitable for the driver according to the driving style, the type of road on which the vehicle is located, and the risk response requirement level corresponding to the vehicle, the second determination module is further used to:

[0030] Acquiring operation data of the vehicle and risk event data on a path of the vehicle;

[0031] A risk response requirement level corresponding to the vehicle is determined based on the operating data and the risk event data.

[0032] In one or more embodiments, the first driving behavior data includes: real-time driving behavior data and / or historical driving behavior data corresponding to the driver of the vehicle.

[0033] In one or more embodiments, the acquisition module is specifically used to:

[0034] Obtaining driver information in the vehicle;

[0035] According to the driver information, historical driving behavior data corresponding to the driver is obtained.

[0036] In one or more embodiments, the operating data includes at least one of the following: engine parameters, brake system status, location, traffic flow, speed limit information, and driving trajectory;

[0037] The risk event data includes at least one of the following: trigger time, type and severity of at least one type of warning information.

[0038] In a third aspect, an embodiment of the present application provides a vehicle, including: a memory, a processor;

[0039] The memory stores computer-executable instructions;

[0040] The processor executes the computer-executable instructions stored in the memory, so that the processor executes the above first aspect and / or various possible implementations of the first aspect.

[0041] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, in which computer-executable instructions are stored. When the computer-executable instructions are executed by a processor, they are used to implement the first aspect above and / or various possible implementations of the first aspect.

[0042] In a fifth aspect, an embodiment of the present application provides a computer program product, including a computer program, which, when executed by a processor, implements the above first aspect and / or various possible implementation methods of the first aspect.

[0043] The vehicle control method, device, vehicle, and storage medium provided in the embodiment of the present application obtain the first driving behavior data of the current vehicle, determine the driver's driving style based on the first driving behavior data, determine the assisted driving strategy suitable for the driver based on the driving style, the type of road the vehicle is on, and the risk response demand level corresponding to the vehicle; and control the vehicle operation according to the assisted driving strategy. In this technical solution, the driving behavior data of the current vehicle is directly obtained, the data is processed, and the driving style of the current driver is determined, and then the vehicle control can be dynamically optimized according to the driver's driving style, combined with the road conditions and risk environment, so as to more effectively protect the driver's safety and improve the comfort and efficiency of driving. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0045] Figure 1 A schematic diagram of the structure of a vehicle control system provided in an embodiment of the present application;

[0046] Figure 2 Schematic diagram of the process of the vehicle control method provided in the embodiment of the present application Figure 1 ;

[0047] Figure 3 Schematic diagram of the process of the vehicle control method provided in the embodiment of the present application Figure 2 ;

[0048] Figure 4 A schematic diagram of the structure of a vehicle control device provided in an embodiment of the present application;

[0049] Figure 5 A schematic diagram of the structure of a vehicle provided in an embodiment of the present application.

[0050] The above drawings have shown clear embodiments of the present application, which will be described in more detail later. These drawings and text descriptions are not intended to limit the scope of the present application in any way, but to illustrate the concept of the present application to those skilled in the art by referring to specific embodiments. DETAILED DESCRIPTION

[0051] Exemplary embodiments will be described in detail herein, examples of which are shown in the accompanying drawings. When the following description refers to the drawings, the same numbers in different drawings represent the same or similar elements unless otherwise indicated. The implementations described in the following exemplary embodiments do not represent all implementations consistent with the present application. Instead, they are merely examples of devices and methods consistent with some aspects of the present application as detailed in the appended claims.

[0052] First, the professional terms involved in the embodiments of the present application are explained:

[0053] T-Box (Telematics Box) is a device used for vehicle telematics and communication, which can transmit the vehicle's real-time data to the cloud server;

[0054] The Internet of Vehicles (IoV) refers to a network that connects vehicles to everything through information and communication technology.

[0055] Secondly, the technical background involved in the embodiments of the present application is described:

[0056] As vehicles become more intelligent, intelligent assisted driving is becoming more popular, providing a positive impact on the driver's driving experience. In existing related technologies, the configuration of intelligent assisted driving is mostly bound to the vehicle, and service providers rely on preset algorithms and thresholds to determine the corresponding assisted driving strategy.

[0057] However, due to the popularity of intelligent assisted driving, it is common for multiple people to drive in one car, and different drivers have different styles, such as aggressive, steady, and peaceful. At the same time, the need for accurate configuration can no longer be met based solely on driving style. The vehicle's T-Box data (including vehicle status, location, and other information), risk events during driving (such as collision warning, lane departure warning), real-time vehicle speed, road type (highway, urban road, etc.), and warning information all have a key impact on the effectiveness of intelligent assisted driving solutions.

[0058] However, it is difficult for the existing technology to integrate these factors to achieve dynamic and personalized configuration, and it is unable to fully adapt to complex and changing driving scenarios and different driver needs. That is, the existing technology has the following technical problems:

[0059] 1) Configuration uniformity and vehicle binding issues:

[0060] The intelligent assisted driving solution is preset by the manufacturer with fixed algorithms and thresholds based on the overall characteristics of the vehicle, and fails to fully consider the situation of multiple drivers in the same vehicle. Different drivers have unique driving styles and habits, and a one-size-fits-all configuration method cannot optimize intelligent assisted driving for individual differences. As a result, when multiple people drive the same vehicle, the intelligent assisted driving system is difficult to meet the actual needs of different drivers, reducing the practicality of the system and user experience.

[0061] 2) Lack of comprehensive consideration of multiple driving influencing factors:

[0062] Existing intelligent assisted driving technologies only rely on limited preset parameters when determining solutions, and fail to fully integrate key data during vehicle operation. The vehicle's T-Box data contains a wealth of real-time vehicle status information, such as the instant output of the power system, the dynamic response of the chassis, the vehicle's position and trajectory data on the driving route, risk events during driving, various warning information, real-time vehicle speed, road type (such as urban congested roads, highways, rural roads, etc., whose traffic rules and road conditions vary greatly), and other important factors have not been comprehensively weighed, making it difficult for intelligent assisted driving solutions to adapt to the safety and efficiency requirements of different road conditions and driving scenarios.

[0063] 3) Lack of dynamic adaptive adjustment mechanism:

[0064] Since it cannot track the changes in driver behavior and the dynamic changes in the driving environment in real time, the intelligent assisted driving system cannot adjust the configuration plan in time according to new situations during the driving process, and cannot ensure that the safety and comfort of driving are always in the best state during the entire driving process.

[0065] In response to the technical problems existing in the prior art, the inventor of this application has the following idea: based on the driver's driving behavior data, a judgment can be made to determine the driver's driving style, so as to achieve dynamic and personalized matching of intelligent assisted driving configuration solutions based on the driver's driving style, the current road conditions, and the vehicle's current risk response demand level, promote the transformation of intelligent assisted driving from static universal to dynamic personalized, and customize and dynamically adapt personalized intelligent assisted driving solutions.

[0066] The technical solution of the present application and how the technical solution of the present application solves the above-mentioned technical problems are described in detail below with specific embodiments. The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be repeated in some embodiments. The embodiments of the present application will be described below in conjunction with the accompanying drawings.

[0067] Figure 1 A schematic diagram of the structure of a vehicle control system provided in an embodiment of the present application, such as Figure 1 As shown, the vehicle control system can be composed of components inside the vehicle, including: a front camera, a driver recognition module, a central control module, a data acquisition and integration module, a driving style analysis module, an intelligent assisted driving solution matching module, and an intelligent assisted driving execution module.

[0068] Combination Figure 1 The architecture of the vehicle control system is shown, and the embodiments of the present application are described in detail below.

[0069] Figure 2Schematic diagram of the process of the vehicle control method provided in the embodiment of the present application Figure 1 ,like Figure 2 As shown, the method includes:

[0070] Step 21, obtaining first driving behavior data of the current vehicle;

[0071] In this step, in order to obtain an assisted driving strategy that can adapt to the driver, the driving style that affects the assisted driving strategy can be obtained first. The driving style can be obtained based on the driver's driving behavior data, that is, the driving behavior data of the driver in the current vehicle is obtained and recorded as the first driving behavior data.

[0072] In one possible implementation, the first driving behavior data may be data obtained based on the driver's operations when the driver is driving the vehicle and collected by relevant sensors such as a driving behavior collection unit, for example, the angle, frequency, rapidity, depth and duration of the steering operation, and the like of the accelerator and brake pedals.

[0073] Optionally, the first driving behavior data includes: real-time driving behavior data and / or historical driving behavior data corresponding to the driver of the vehicle.

[0074] In this implementation, the real-time driving behavior data may be data on the vehicle's response to the driver's operations, acquired in real time while the driver is driving the vehicle; the historical driving behavior data may be data on the vehicle's response to the driver's operations, acquired before the current time period.

[0075] Correspondingly, when the first driving behavior data includes: historical driving behavior data corresponding to the driver of the vehicle, a possible implementation of step 21 is: obtaining driver information in the vehicle; and obtaining historical driving behavior data corresponding to the driver based on the driver information.

[0076] In this implementation, it can be based on Figure 1 The front camera in the vehicle obtains the driver information of the main driver's seat, and then the driver recognition module obtains the driver information and identifies the driver. After that, the driving behavior data corresponding to the driver is obtained from the cloud database through the Internet of Vehicles technology as historical driving behavior data.

[0077] In actual implementation, the driver information may include: an identification of the driver, and accordingly, the driving behavior data corresponding to the identification is obtained in the database as the historical driving behavior data corresponding to the driver;

[0078] It may also include: the driver's facial expression information, and accordingly, the driving behavior data corresponding to the facial expression information is obtained in the database as the driver's corresponding historical driving behavior data, that is, the database stores the driver's driving behavior data under different expressions.

[0079] Step 22: determining the driving style of the driver according to the first driving behavior data;

[0080] In this step, since the first driver behavior data can reflect the driver's operation when driving the vehicle, the driver's driving style can be judged. Therefore, the first driving behavior data can be processed in real time to obtain the driver's driving style.

[0081] Optionally, a possible implementation of step 22 is: outputting the first driving behavior data to a predetermined driving style recognition model to obtain the driving style of the driver;

[0082] Among them, the driving style recognition model is obtained by training a preset machine learning algorithm model based on multiple driving behavior data.

[0083] In this implementation, it can be based on Figure 1 The first driving behavior data is processed by a driving behavior analysis module in the driving behavior analysis module, which pre-stores a trained driving style recognition model. The first driving behavior data is input into the driving style recognition model to obtain the driving style of the current driver.

[0084] Among them, the driver's driving style can be aggressive, steady, conservative, etc.

[0085] In a possible implementation, multiple driving behavior data can be acquired in advance, and each driving behavior data can be labeled to obtain the driving style corresponding to the driving behavior data. Then, based on the driving behavior data and the corresponding driving style, the preset machine learning algorithm model is trained until the accuracy of the driving style result obtained from the verification set (taking part of the driving behavior data and the corresponding driving style as an example) is greater than a first preset threshold, and then it is determined to generate a driving style recognition model.

[0086] The following are possible examples of driving styles in step 22 according to an embodiment of the present application:

[0087] Example 1, aggressive driving style: frequent acceleration, sudden braking, high speed, and high-speed steering;

[0088] Example 2, steady (or stable) driving style: driving habits are relatively stable, avoiding sudden acceleration and braking, and driving at a moderate speed;

[0089] Example 3, conservative driving style: The driving style is gentle, accelerating slowly, avoiding any possible risks, driving at a low speed, slowing down in advance, and turning carefully.

[0090] Step 23: determining an assisted driving strategy suitable for the driver according to the driving style, the type of road the vehicle is on, and the risk response requirement level corresponding to the vehicle;

[0091] In this step, based on the current path information of the vehicle, the type of road the vehicle is on can be determined; and based on Figure 1 The T-box data collection unit in the data collection and integration module obtains the vehicle operation data, and the risk event data determined by the risk event data collection unit in the data collection and integration module jointly determine the risk response demand level.

[0092] Furthermore, since the driving style, the type of road the vehicle is on, and the corresponding risk response demand level of the vehicle all have an important impact on the vehicle's assisted driving strategy, it can be based on Figure 1 The intelligent assisted driving solution matching module in the system processes the driving style, the type of road the vehicle is on, and the risk response demand level corresponding to the vehicle to determine the assisted driving strategy suitable for the driver.

[0093] Among them, the road types can be: highways, congested roads, country roads, etc.; the risk response demand levels can be: high-risk response demand, medium-risk response demand, low-risk response demand, no-risk response demand, etc.

[0094] Optionally, step 23 may include the following possible implementations:

[0095] The first type is to determine the assisted driving strategy suitable for the driver in a preset mapping relationship based on the driving style, the type of road the vehicle is on, and the risk response requirement level corresponding to the vehicle. The mapping relationship records the correspondence between different driving styles, different road types, different risk response requirement levels, and different assisted driving strategies.

[0096] Based on the mapping relationship, the assisted driving strategy corresponding to the above-determined driving style, the type of road the vehicle is on, and the risk response requirement level corresponding to the vehicle can be obtained as an assisted driving strategy suitable for the driver.

[0097] The second method is to input the driving style, the type of road the vehicle is on, and the risk response requirement level corresponding to the vehicle into the assisted driving strategy prediction model to obtain an assisted driving strategy suitable for the driver. The assisted driving strategy prediction model can be based on different driving styles, different road types, different risk response requirement levels, and different assisted driving strategies. The preset machine learning algorithm model is trained until the accuracy of the assisted driving strategy results obtained from the verification set (taking some driving styles, some road types, some risk response requirement levels, and some assisted driving strategies as examples) is greater than the second preset threshold, and then it is determined to generate the assisted driving strategy prediction model.

[0098] The following is a possible example of the actual implementation of step 23 in the embodiment of the present application:

[0099] Example 1: Aggressive driving style + highway + high-risk response requirements. The corresponding assisted driving strategies can be as follows:

[0100] Speed ​​control: automatically limits the vehicle speed to meet the road speed limit or slightly below the speed limit; Adaptive cruise control: automatically adjusts the vehicle speed when a vehicle ahead is detected; Emergency brake intervention: when the vehicle determines that there is a risk of collision ahead (for example, the vehicle ahead brakes suddenly or a traffic accident occurs), the system will initiate emergency braking; Lane keeping assist: automatically adjusts the steering wheel to ensure that the vehicle is always in the center of the lane.

[0101] Example 2: Steady driving style + urban roads + medium risk response requirements. The corresponding assisted driving strategies can be as follows:

[0102] Low-speed driving assist: automatically adjusts the vehicle speed to ensure that the vehicle maintains a low speed on urban roads; automatic parking assist: when parking in a busy urban area, the parking assist function is automatically activated and the vehicle is automatically parked based on environmental perception data; pedestrian detection and emergency braking: monitors whether there are pedestrians in front through vision or sensors. If pedestrians suddenly enter the vehicle's path, the system will issue a warning and automatically brake if necessary; forward collision warning: when driving at low speeds, the system will continuously monitor obstacles ahead (such as other vehicles, obstacles, etc.), and when a potential collision risk is detected, it will issue a warning and prepare to brake.

[0103] Example 3: Conservative driving style + rural roads + low-risk response requirements. The corresponding assisted driving strategies can be as follows:

[0104] Lane Keeping Assist: On rural roads, the vehicle will keep the lane centered and correct the direction as appropriate during driving; Cruise Control: On rural roads with less traffic, cruise control can be automatically enabled according to the driver's settings to keep the vehicle at a constant speed and reduce the driver's control burden; Automatic high and low beam switching: When the vehicle speed is low and there are no other vehicles on the road, the high beam is automatically turned on to increase the visible range of the road; when encountering oncoming vehicles, it automatically switches to low beam to avoid interfering with other drivers; Steering Assist: When encountering sharp turns on rural roads, moderate steering assistance is provided to help the driver take turns more easily, especially at night or when visibility is unclear.

[0105] Step 24: Control the vehicle operation according to the assisted driving strategy.

[0106] In this step, after obtaining the auxiliary driving strategy suitable for the driver, based on Figure 1 The intelligent driving execution system in the vehicle executes the assisted driving strategy to control the vehicle operation.

[0107] Optionally, the specific implementation components may involve cruise control (adaptive cruise control unit), lane keeping assist unit, automatic emergency braking unit, etc.

[0108] The vehicle control method provided in the embodiment of the present application obtains the first driving behavior data of the current vehicle, determines the driving style of the driver according to the first driving behavior data, determines the assisted driving strategy suitable for the driver according to the driving style, the type of road the vehicle is on, and the risk response demand level corresponding to the vehicle; and controls the operation of the vehicle according to the assisted driving strategy. In this technical solution, the driving behavior data of the current vehicle is directly obtained, the data is processed, and the driving style of the current driver is determined, and then the vehicle control can be dynamically optimized according to the driving style of the driver, combined with the road conditions and risk environment, so as to more effectively protect the safety of the driver and improve the comfort and efficiency of driving.

[0109] Based on the above embodiments, Figure 3 Schematic diagram of the process of the vehicle control method provided in the embodiment of the present application Figure 2 ,like Figure 3 As shown, before step 23, the method further includes:

[0110] Step 31: Obtain the vehicle's operating data and risk event data on the vehicle's path;

[0111] In this step, the operation data of the vehicle during operation and the risk event data on the vehicle path have a certain impact on the driving safety of the vehicle. Therefore, firstly based on Figure 1 The data collection and integration module in obtains the vehicle's operating data and risk event data.

[0112] Optionally, the operating data includes at least one of the following: engine parameters, braking system status, location, traffic flow, speed limit information, and driving trajectory; and can be obtained through deep interaction between the data acquisition and integration module and the vehicle T-Box.

[0113] In this implementation, for example, if the vehicle's braking system is in an abnormal state or the vehicle is traveling too fast, the chance of danger may increase; heavy traffic and a mismatch between the speed limit information and the current vehicle speed may also increase the risk; and if the vehicle's driving trajectory deviates from the normal route, it may also be a potential risk factor.

[0114] Optionally, the risk event data includes at least one of the following: the trigger time of at least one type of warning information, the type of warning information, and the severity; it can be collected in real time by the data acquisition and integration module, and a statistical analysis can be performed on the frequency and correlation of the risk event data.

[0115] In this implementation, for example, if a collision warning or road slip warning occurs and the severity is high, it means that there is a high risk in the current environment.

[0116] Step 32: Determine the risk response requirement level corresponding to the vehicle based on the operation data and risk event data.

[0117] In this step, the operational data and risk event data obtained above are combined for fusion analysis, and a weight model can be established to obtain the risk response requirement level.

[0118] For example, if the vehicle's braking system has an abnormality and triggers a high-severity collision warning at the same time, or the vehicle is located in an accident-prone area, the risk response need level can be judged to be high-risk response need; if the traffic flow is low, the vehicle's driving trajectory is normal, and there is no serious warning information in the risk event data, the risk response need level can be judged to be low-risk response need.

[0119] For example, when the target key indicators (such as abnormal braking status, serious warning information, high traffic flow, etc.) exceed the corresponding preset thresholds, the risk response demand level can be judged as high-risk response demand; if the operating data and risk event data do not show obvious risk factors, the risk response demand level can be judged as low-risk response demand.

[0120] The vehicle control method provided in the embodiment of the present application obtains the vehicle's operating data and the risk event data on the vehicle's path, and determines the risk response requirement level corresponding to the vehicle based on the operating data and the risk event data. In this technical solution, the potential risks in the current driving environment can be evaluated in real time by combining the vehicle's operating data and the risk event data on the vehicle's path, so that the vehicle can dynamically determine the risk response requirement level of the vehicle based on the vehicle's real-time status and the surrounding risk events, thereby achieving accurate risk prediction and timely intervention, which not only improves the vehicle's ability to perceive potential dangers, but also can intelligently adjust the vehicle's assisted driving strategy for subsequent use based on actual conditions, which is of certain significance in improving driving safety and stability.

[0121] Based on the above method embodiment, Figure 4 A schematic diagram of the structure of a vehicle control device provided in an embodiment of the present application, such as Figure 4 As shown, the vehicle control device includes:

[0122] An acquisition module 41 is used to acquire first driving behavior data of the current vehicle;

[0123] A first determining module 42, configured to determine the driving style of the driver according to the first driving behavior data;

[0124] A second determination module 43 is used to determine an assisted driving strategy suitable for the driver according to the driving style, the type of road the vehicle is on, and the risk response requirement level corresponding to the vehicle;

[0125] The control module 44 is used to control the operation of the vehicle according to the auxiliary driving strategy.

[0126] In one or more embodiments, the first determining module 42 is specifically configured to:

[0127] The first driving behavior data is output to a predetermined driving style recognition model to obtain the driving style of the driver, wherein the driving style recognition model is obtained by training a preset machine learning algorithm model based on a plurality of driving behavior data.

[0128] In one or more embodiments, before determining the assisted driving strategy suitable for the driver according to the driving style, the type of road on which the vehicle is located, and the risk response requirement level corresponding to the vehicle, the second determining module 43 is further used to:

[0129] Acquiring operation data of the vehicle and risk event data on a path of the vehicle;

[0130] A risk response requirement level corresponding to the vehicle is determined based on the operating data and the risk event data.

[0131] In one or more embodiments, the first driving behavior data includes: real-time driving behavior data and / or historical driving behavior data corresponding to the driver of the vehicle.

[0132] In one or more embodiments, the acquisition module 41 is specifically used to:

[0133] Obtaining driver information in the vehicle;

[0134] According to the driver information, historical driving behavior data corresponding to the driver is obtained.

[0135] In one or more embodiments, the operating data includes at least one of the following: engine parameters, brake system status, location, traffic flow, speed limit information, and driving trajectory;

[0136] The risk event data includes at least one of the following: trigger time, type and severity of at least one type of warning information.

[0137] The vehicle control device provided in this embodiment can execute the vehicle control method provided in the above method embodiment. Its implementation principle and technical effects are similar, and will not be described in detail in this embodiment.

[0138] Figure 5 This is a schematic diagram of the structure of a vehicle provided in an embodiment of the present application. Figure 5 As shown, the vehicle provided in this embodiment includes: at least one processor 51 and a memory 52.

[0139] Optionally, the vehicle further includes a communication component 53 , wherein the processor 51 , the memory 52 and the communication component 53 are connected via a bus 54 .

[0140] In a specific implementation process, at least one processor 51 executes the computer-executable instructions stored in the memory 52, so that at least one processor 51 executes the above method.

[0141] The specific implementation process of the processor 51 can be found in the above method embodiment, and its implementation principle and technical effect are similar, so this embodiment will not be repeated here.

[0142] In the above embodiments, it should be understood that the processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), etc. A general-purpose processor may be a microprocessor or any conventional processor. The steps of the method disclosed in the invention may be directly implemented as being executed by a hardware processor, or may be executed by a combination of hardware and software modules in the processor.

[0143] The memory may include a high-speed memory (Random Access Memory, RAM), and may also include a non-volatile memory (Non-volatile Memory, NVM), such as at least one disk memory.

[0144] The bus may be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. The bus may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, the bus in the drawings of the present application is not limited to only one bus or one type of bus.

[0145] The present application also provides a computer program product, including a computer program, which implements the above method when executed by a processor.

[0146] The present application also provides a computer-readable storage medium, in which computer-executable instructions are stored. When a processor executes the computer-executable instructions, the above method is implemented.

[0147] The above-mentioned 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 memory, flash memory, magnetic disk or optical disk. The readable storage medium can be any available medium that can be accessed by a general or special-purpose computer.

[0148] An exemplary readable storage medium is coupled to a processor so that the processor can read information from the readable storage medium 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 be located in an application specific integrated circuit (Application Specific Integrated Circuits, referred to as: ASIC). Of course, the processor and the readable storage medium can also exist in the device as discrete components.

[0149] The division of units is only a logical function division, and there may be other divisions in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interface, device or unit, which can be electrical, mechanical or other forms.

[0150] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0151] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0152] If the function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods of each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc. Various media that can store program codes.

[0153] Those skilled in the art can understand that all or part of the steps of implementing the above-mentioned method embodiments can be completed by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When the program is executed, the steps of the above-mentioned method embodiments are executed; and the aforementioned storage medium includes: ROM, RAM, disk or optical disk and other media that can store program codes.

[0154] Finally, it should be noted that those skilled in the art will readily conceive of other embodiments of the present invention after considering the specification and practicing the invention disclosed herein. The present invention is intended to cover any variations, uses or adaptations of the present invention, which follow the general principles of the present invention and include common knowledge or customary technical means in the art not disclosed by the present invention, are not limited to the precise structure described above and shown in the drawings, and may be modified and changed in various ways without departing from the scope thereof. The scope of the present invention is limited only by the appended claims.

Claims

1. A vehicle control method, characterized in that: include: Acquire first driving behavior data of the current vehicle; determining the driving style of the driver according to the first driving behavior data; Determining an assisted driving strategy suitable for the driver according to the driving style, the type of road on which the vehicle is located, and the risk response requirement level corresponding to the vehicle; The vehicle operation is controlled according to the assisted driving strategy.

2. The method according to claim 1, characterized in that The determining the driving style of the driver according to the first driving behavior data includes: The first driving behavior data is output to a predetermined driving style recognition model to obtain the driving style of the driver, wherein the driving style recognition model is obtained by training a preset machine learning algorithm model based on a plurality of driving behavior data.

3. The method according to claim 1, characterized in that Before determining the assisted driving strategy suitable for the driver according to the driving style, the type of road on which the vehicle is located, and the risk response requirement level corresponding to the vehicle, the method further includes: Acquiring operation data of the vehicle and risk event data on a path of the vehicle; A risk response requirement level corresponding to the vehicle is determined based on the operating data and the risk event data.

4. The method according to any one of claims 1 to 3, characterized in that: The first driving behavior data includes: real-time driving behavior data and / or historical driving behavior data corresponding to the driver of the vehicle.

5. The method according to claim 4, characterized in that The obtaining of first driving behavior data of the current vehicle includes: Obtaining driver information in the vehicle; According to the driver information, historical driving behavior data corresponding to the driver is obtained.

6. The method according to claim 3, characterized in that The operating data includes at least one of the following: engine parameters, brake system status, location, traffic flow, speed limit information, and driving trajectory; The risk event data includes at least one of the following: trigger time, type and severity of at least one type of warning information.

7. A vehicle control device, characterized in that: include: An acquisition module, used to acquire first driving behavior data of the current vehicle; A first determining module, configured to determine the driving style of the driver according to the first driving behavior data; A second determination module is used to determine an assisted driving strategy suitable for the driver according to the driving style, the type of road on which the vehicle is located, and the risk response requirement level corresponding to the vehicle; A control module is used to control the operation of the vehicle according to the assisted driving strategy.

8. A vehicle, characterized in that: include: Memory, processor; The memory stores computer-executable instructions; The processor executes the computer-executable instructions stored in the memory, so that the processor performs the method according to any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-executable instructions, which are used to implement the method according to any one of claims 1 to 6 when executed by a processor.

10. A computer program product, characterized in that The invention comprises a computer program, which implements the method according to any one of claims 1 to 6 when being executed by a processor.

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