A vehicle control method, a vehicle control device, a device, and a medium

Abstract

This application provides a vehicle control method, vehicle control device, equipment, and medium. The method includes: real-time acquisition of multi-dimensional data during vehicle operation, inputting the multi-dimensional data into a pre-trained driving scenario prediction model to determine the current driving scenario type of the vehicle; determining multiple initial driving parameters corresponding to the current driving scenario type from a preset parameter library, and adjusting the multiple initial driving parameters based on the user's currently set target driving mode to obtain target driving parameters; and controlling the vehicle based on the target driving parameters. Through this method and device, the user's personalized driving experience is fully satisfied, significantly improving the intelligence and comfort of driving.

Description

Technical Field

[0001] This application relates to the field of vehicle technology, and in particular to a vehicle control method, vehicle control device, equipment and medium. Background Technology

[0002] With the rapid development of automotive electronics technology, driving mode selection systems have become a standard feature in modern vehicles. Currently, most mainstream models adopt a preset driving mode design, which links the control signals of various subsystems through the vehicle bus network. When the driver selects a specific mode via the center console or steering wheel paddles, the vehicle will adjust the operating strategies of the relevant systems according to the preset parameter set.

[0003] Existing driving mode switching technologies mainly rely on the single-dimensional adjustment of vehicle dynamic parameters. Although these fixed mode switching schemes can provide differentiated driving experiences, their parameter adjustment strategies are all static preset values ​​and cannot be dynamically optimized according to actual driving scenarios. This makes it difficult for existing driving mode systems to achieve the optimal balance between driving performance and energy efficiency. Summary of the Invention

[0004] In view of this, the purpose of this application is to provide a vehicle control method, vehicle control device, equipment and medium that achieves deep integration of real-time road conditions, driving scenarios and user personal preferences, so that vehicle control parameters can dynamically adapt to different scenario requirements, which not only optimizes driving safety and handling stability, but also fully meets the user's personalized driving experience, and significantly improves the intelligence and comfort of driving.

[0005] In a first aspect, embodiments of this application provide a vehicle control method, the vehicle control method comprising: Multi-dimensional data during vehicle operation is collected in real time and input into a pre-trained driving scenario prediction model to determine the current driving scenario type of the vehicle. Multiple initial driving parameters corresponding to the current driving scenario type are determined from the preset parameter library, and the multiple initial driving parameters are adjusted based on the target driving mode currently set by the user to obtain the target driving parameters; The vehicle is controlled based on the target driving parameters.

[0006] Furthermore, the adjustment of the various initial driving parameters based on the user's currently set target driving mode to obtain the target driving parameters includes: Based on the target driving mode, at least one parameter to be adjusted corresponding to the target driving mode is determined from the plurality of initial driving parameters; The parameters to be adjusted are adjusted based on the weights corresponding to the target driving mode to obtain the target driving parameters.

[0007] Furthermore, after determining the current driving scenario type of the vehicle, the vehicle control method further includes: Obtain the confidence level corresponding to the current driving scenario type output by the driving scenario prediction model; If the confidence level is lower than a preset threshold, the historical driving scenario type determined in the previous moment will be used as the current driving scenario type.

[0008] Furthermore, the target driving parameters include power output coefficient, braking response coefficient, steering assist coefficient, and damping stiffness coefficient; the control of the vehicle based on the target driving parameters includes: The vehicle's engine fuel injection quantity or motor output torque is adjusted according to the power output coefficient. The braking assist level and / or the intervention threshold of the anti-lock braking system of the vehicle are adjusted according to the braking response coefficient. The power steering motor current of the vehicle's electric power steering system is adjusted according to the steering assist coefficient. The damping coefficient of the vehicle's electronic shock absorbers is adjusted according to the damping stiffness coefficient.

[0009] Furthermore, the vehicle control method also includes: The vehicle's current driving behavior is monitored in real time. When the current driving behavior deviates from the preset conditions corresponding to the target driving mode, at least one of the target driving parameters is temporarily corrected.

[0010] Furthermore, the vehicle control method also includes: In response to the user's selection of any one of multiple temporary driving modes, a target temporary driving mode is determined, and the target driving parameters are adjusted based on the target temporary driving mode to obtain the adjusted driving parameters. The vehicle is then controlled using the adjusted driving parameters within a preset time period.

[0011] Furthermore, the vehicle control method also includes: The current driving scenario type, the target driving mode, and the target driving parameters are sent to the vehicle's display device so that the display device can display them in real time.

[0012] Secondly, embodiments of this application also provide a vehicle control device, the vehicle control device comprising: The driving scenario determination module is used to collect multi-dimensional data during the vehicle's driving process in real time, and input the multi-dimensional data into a pre-trained driving scenario prediction model to determine the current driving scenario type of the vehicle. The driving parameter determination module is used to determine multiple initial driving parameters corresponding to the current driving scenario type from a preset parameter library, and adjust the multiple initial driving parameters based on the target driving mode currently set by the user to obtain the target driving parameters; The vehicle control module is used to control the vehicle based on the target driving parameters.

[0013] Thirdly, embodiments of this application also provide an electronic device, including: a processor, a memory, and a bus. The memory stores machine-readable instructions executable by the processor. When the electronic device is running, the processor communicates with the memory via the bus. When the machine-readable instructions are executed by the processor, the steps of the vehicle control method described above are performed.

[0014] Fourthly, embodiments of this application also provide a computer-readable storage medium storing a computer program that, when executed by a processor, performs the steps of the vehicle control method described above.

[0015] This application provides a vehicle control method, vehicle control device, equipment, and medium. First, multi-dimensional data during vehicle operation is collected in real time and input into a pre-trained driving scenario prediction model to determine the current driving scenario type of the vehicle. Then, multiple initial driving parameters corresponding to the current driving scenario type are determined from a preset parameter library, and these initial driving parameters are adjusted based on the user's currently set target driving mode to obtain target driving parameters. Finally, the vehicle is controlled based on the target driving parameters.

[0016] This application accurately identifies the current driving scenario of the vehicle by collecting multi-dimensional data in real time and inputting it into a pre-trained driving scenario prediction model. It then retrieves matching initial driving parameters from a preset parameter library and makes personalized adjustments based on the user-defined target driving mode, ultimately obtaining the target driving parameters to control the vehicle. The vehicle control method provided in this application achieves a deep integration of real-time road conditions, driving scenarios, and user preferences, enabling vehicle control parameters to dynamically adapt to different scenario requirements. This optimizes driving safety and handling stability while fully satisfying the user's personalized driving experience, significantly improving driving intelligence and comfort.

[0017] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0018] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of this application and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 A flowchart of a vehicle control method provided in an embodiment of this application; Figure 2 This is a schematic diagram of the structure of a vehicle control device provided in an embodiment of this application; Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0020] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. The components of the embodiments of this application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely represents selected embodiments of this application. Based on the embodiments of this application, every other embodiment obtained by those skilled in the art without inventive effort falls within the scope of protection of this application.

[0021] First, the applicable scenarios for this application will be introduced. This application can be applied to the field of vehicle technology.

[0022] With the rapid development of automotive electronics technology, driving mode selection systems have become a standard feature in modern vehicles. Currently, most mainstream models adopt a preset driving mode design, which links the control signals of various subsystems through the vehicle bus network. When the driver selects a specific mode via the center console or steering wheel paddles, the vehicle will adjust the operating strategies of the relevant systems according to the preset parameter set.

[0023] Research has found that existing driving mode switching technologies mainly rely on the single-dimensional adjustment of vehicle dynamic parameters. Although these fixed mode switching schemes can provide differentiated driving experiences, their parameter adjustment strategies are all static preset values ​​and cannot be dynamically optimized according to actual driving scenarios. This makes it difficult for existing driving mode systems to achieve the optimal balance between driving performance and energy efficiency.

[0024] Based on this, the embodiments of this application provide a vehicle control method that achieves deep integration of real-time road conditions, driving scenarios and user preferences, enabling vehicle control parameters to dynamically adapt to different scenario requirements. This not only optimizes driving safety and handling stability, but also fully satisfies the user's personalized driving experience, significantly improving the intelligence and comfort of driving.

[0025] Please see Figure 1 , Figure 1 This is a flowchart illustrating a vehicle control method provided in an embodiment of this application. Figure 1 As shown in the embodiments of this application, the vehicle control method includes: S101, collect multi-dimensional data during the vehicle's driving process in real time, and input the multi-dimensional data into a pre-trained driving scenario prediction model to determine the current driving scenario type of the vehicle.

[0026] Regarding step S101 above, in specific implementation, multi-dimensional data generated by the vehicle during driving is collected in real time through onboard sensors. Here, according to the embodiments provided in this application, the multi-dimensional data may include road condition data, driving behavior data, vehicle status data, and environmental data. Specifically, road condition data is collected through onboard navigation (obtaining road type, congestion level, and speed limit information 500 meters to 3 kilometers ahead), a forward-facing camera (identifying lane lines, traffic density, pedestrians / obstacles), and millimeter-wave radar (detecting following distance and relative speed); driving behavior data is collected through a steering wheel angle sensor (collecting steering frequency and angle), an accelerator pedal sensor (collecting pedal depth and frequency), and a brake pedal sensor (collecting pedal force and response time); vehicle status data is collected through a battery management system (BMS, collecting remaining battery power and charging / discharging efficiency), a tire pressure monitoring system (TPMS, collecting tire pressure and tire temperature), and an engine / motor controller (collecting power output and speed); environmental data is collected through a rain sensor (collecting precipitation), a light sensor (collecting light intensity), and a temperature sensor (collecting ambient temperature). After collecting multi-dimensional data, the data is input into a pre-trained driving scenario prediction model to determine the current driving scenario type of the vehicle. As an example, the current driving scenario type can be core driving scenarios such as highway cruising, urban congestion, sharp bends, and driving in the rain; this application does not make specific limitations on this.

[0027] As an optional implementation, after acquiring multi-dimensional data, the collected raw multi-dimensional data can be denoised and normalized, and then the processed multi-dimensional data can be input into the driving scenario prediction model to predict driving scenarios.

[0028] As an optional embodiment, after determining the current driving scenario type in which the vehicle is currently located, the vehicle control method further includes: Obtain the confidence level of the current driving scenario type output by the driving scenario prediction model; if the confidence level is lower than a preset threshold, then the historical driving scenario type determined at the previous moment is taken as the current driving scenario type.

[0029] Here, to ensure the reliability of scene recognition, the driving scene prediction model outputs a confidence score along with the current driving scene type. This confidence score is then compared to a preset threshold. If the confidence score is lower than the threshold, it means the driving scene prediction model is unsure of the judgment result. In this case, it will not adopt this potentially erroneous result, but will conservatively use the historical driving scene type determined in the previous moment, and trigger a data supplementation operation, such as increasing the sensor's acquisition frequency to obtain more data for the next round of judgment. This effectively avoids sudden changes in vehicle parameters caused by brief recognition errors.

[0030] S102, determine multiple initial driving parameters corresponding to the current driving scenario type from the preset parameter library, and adjust the multiple initial driving parameters based on the target driving mode currently set by the user to obtain the target driving parameters.

[0031] Regarding S102 above, in specific implementation, a pre-set parameter benchmark library is established, which sets the optimal initial driving parameter set for each driving scenario. For example, for the "high-speed cruising" scenario, the benchmark parameters might be set as: power output 100%, braking response 90%, steering assist 80%, and damping stiffness 90%. After the scenario type is determined in step S101, this set of benchmark parameters is retrieved from the library. At this time, the currently set target driving mode, such as "energy saving priority" or "comfort priority," is obtained, thereby allowing for personalized adjustments to the initial parameters.

[0032] Specifically, regarding step S102 above, adjusting the various initial driving parameters based on the user's currently set target driving mode to obtain the target driving parameters includes: Step 1021: Based on the target driving mode, determine at least one parameter to be adjusted from the plurality of initial driving parameters corresponding to the target driving mode.

[0033] Step 1022: Adjust the parameters to be adjusted based on the weights corresponding to the target driving mode to obtain the target driving parameters.

[0034] Regarding steps 1021-1022 above, in practical implementation, firstly, based on the user's selected target driving mode, determine which parameters need to be adjusted. For example, if "Energy Saving Mode" is selected, then "Power Output" is the parameter to be adjusted; if "Comfort Mode" is selected, then "Steering Assist" and "Damping Stiffness" may be parameters to be adjusted. Different modes correspond to different parameters to be adjusted. After determining the parameters to be adjusted, the weight corresponding to that mode is used for calculation. For example, if the user sets the "Energy Saving Priority" weight to 80%, then the power output parameter may be reduced by 20% from the baseline of 100%, adjusted to 80%. In this way, the scenario-based baseline parameters are combined with the user's long-term preferences to generate the target driving parameters.

[0035] S103, control the vehicle based on the target driving parameters.

[0036] Regarding step S103 above, in specific implementation, after determining the target driving parameters, the vehicle is controlled based on the target driving parameters.

[0037] Here, according to the embodiments provided in this application, the target driving parameters include power output coefficient, braking response coefficient, steering assist coefficient, and damping stiffness coefficient. Specifically, regarding step S103 above, controlling the vehicle based on the target driving parameters includes: Step 1031: Adjust the engine fuel injection quantity or motor output torque of the vehicle according to the power output coefficient.

[0038] Step 1032: Adjust the braking assist level and / or the intervention threshold of the anti-lock braking system of the vehicle according to the braking response coefficient.

[0039] Step 1033: Adjust the power steering motor current of the electric power steering system of the vehicle according to the steering assist coefficient.

[0040] Step 1034: Adjust the damping coefficient of the vehicle's electronic shock absorber according to the damping stiffness coefficient.

[0041] Here, this application concretizes the target driving parameters into executable control coefficients, mainly including: power output coefficient, braking response coefficient, steering assist coefficient, and damping stiffness coefficient. After determining these coefficient instructions, millisecond-level adjustments are made to each hardware system simultaneously. Specifically, based on the power output coefficient, the engine's fuel injection quantity or the drive motor's output torque is adjusted. For example, a coefficient of 0.5 reduces the motor's torque output capability by 50%. Based on the braking response coefficient, the amount of brake assist or the intervention timing of the anti-lock braking system (ABS) is adjusted. For example, a coefficient of 1.2 increases brake assist, making ABS intervention more sensitive and earlier. Based on the steering assist coefficient, the operating current of the power assist motor in the electric power steering (EPS) system is adjusted, thereby changing the steering wheel's feel. Based on the damping stiffness coefficient, the damping force of the electronic dampers is adjusted. For example, a coefficient of 0.8 reduces damping, improving ride comfort.

[0042] As an optional embodiment, the vehicle control method provided in this application further includes: The current driving scenario type, the target driving mode, and the target driving parameters are sent to the vehicle's display device so that the display device can display them in real time.

[0043] Here, in order to enhance the transparency of human-computer interaction, according to the above steps, this application also includes sending the current driving scenario type, the target driving mode selected by the user, and the target driving parameters that are currently in effect to the instrument panel or central control screen for real-time display, so that the driver can have a clear understanding of the vehicle status.

[0044] As an optional embodiment, the vehicle control method provided in this application further includes: The vehicle's current driving behavior is monitored in real time. When the current driving behavior deviates from the preset conditions corresponding to the target driving mode, at least one of the target driving parameters is temporarily corrected.

[0045] This application also possesses the ability to perceive and respond to the driver's temporary intentions. Regarding the steps described above, in practice, driving behavior, such as accelerator, brake, and steering operations, is monitored in real time and matched against preset conditions in the current driving mode. Once a deviation is detected, such as the driver continuously pressing the accelerator pedal deeply in energy-saving mode, with the accelerator opening >70% for more than 1 second, it is determined that the driver has a temporary strong power demand. Therefore, the target driving parameters are temporarily corrected, such as temporarily increasing the power output coefficient by 0.15, and a prompt is issued. This intelligently meets instantaneous dynamic needs without changing the user's preset mode.

[0046] As an optional embodiment, the vehicle control method provided in this application further includes: In response to the user's selection of any one of multiple temporary driving modes, a target temporary driving mode is determined, and the target driving parameters are adjusted based on the target temporary driving mode to obtain the adjusted driving parameters. The vehicle is then controlled using the adjusted driving parameters within a preset time period.

[0047] In addition to automatic system correction, this application also supports direct driver intervention. Regarding the above steps, in practice, the driver can select a temporary driving mode with a single click using a specific control. In response to the user's selection of any of the multiple temporary driving modes, the target temporary driving mode is determined, and the target driving parameters are adjusted based on this mode, with a timer starting. After a preset duration, the parameters are automatically restored to their state before the temporary mode was selected, requiring no further driver intervention, making it convenient and quick. For example, when the driver selects a temporary power mode, the power output coefficient is temporarily increased to 80%, and automatically reverts to dynamic mode after 3 minutes.

[0048] Here, we will use a highway-to-city traffic jam section as an example to explain the embodiments of this application: First, multi-dimensional data was collected, including: road condition data: the vehicle navigation system indicated that the city entrance was 800 meters ahead, traffic density increased from 20 vehicles / km to 150 vehicles / km, and the speed limit decreased from 120km / h to 60km / h; the forward-facing camera detected narrower lane lines, and the millimeter-wave radar detected a shorter distance to the vehicle in front, decreasing from 100 meters to 30 meters; driving behavior data: the driver's accelerator pedal opening decreased from 30% to 10%, and the brake pedal pressing frequency increased from 0.2 times / minute to 1.5 times / minute; vehicle status data: the new energy vehicle's battery had 60% remaining charge, and the motor's current output power was 30kW; environmental data: the rain sensor detected 0mm of precipitation (sunny day), and the light intensity was 8000 lux (daytime).

[0049] Then, scene recognition is performed. After the scene recognition unit inputs the above data, it outputs "Highway to city congestion scene, confidence level 99%", matching the "road condition transition scene" in the preset scene library.

[0050] The target driving parameters are calculated by calling the baseline parameters of the "Road Condition Transition Scenario": power output 70%, braking response 120%, steering assist 90%, and damping stiffness 80%. The user presets "Energy Saving Priority" weight 60% and "Comfort Priority" weight 40%, then the parameters are adjusted as follows: power output 70%×(1-60%×0.2)=64.4% (energy saving weight reduces power), braking response 120% (no influence from comfort weight), steering assist 90%×(1+40%×0.1)=93.6% (comfort weight increases steering assist), and damping stiffness 80%×(1+40%×0.1)=83.2% (comfort weight reduces damping stiffness).

[0051] Dynamic correction: When the driver's brake pedal depressing frequency is detected to be continuously increasing (>2 times / minute), it is determined that the driver tends to decelerate frequently. The braking response coefficient is temporarily increased from 120% to 130%, and then gradually reduced back to 120% after 3 seconds.

[0052] Finally, the vehicle is controlled based on the target driving parameters. Powertrain: Motor output power is reduced from 30kW to 30kW × 64.4% ≈ 19.3kW to avoid unnecessary power consumption; Braking system: Brake assist is increased by 30%, and the ABS intervention threshold is reduced by 0.1g (gravitational acceleration), shortening braking distance; Steering system: EPS power assist motor current is increased from 12A to 12A × 93.6% ≈ 11.2A, improving steering lightness; Chassis system: Electronic shock absorber damping coefficient is reduced by 3.2%, filtering out minor bumps from urban roads.

[0053] The vehicle control method provided in this application firstly collects multi-dimensional data during the vehicle's driving process in real time and inputs the multi-dimensional data into a pre-trained driving scenario prediction model to determine the current driving scenario type of the vehicle; then, it determines multiple initial driving parameters corresponding to the current driving scenario type from a preset parameter library, and adjusts the multiple initial driving parameters based on the user's currently set target driving mode to obtain target driving parameters; finally, it controls the vehicle based on the target driving parameters.

[0054] This application accurately identifies the current driving scenario of the vehicle by collecting multi-dimensional data in real time and inputting it into a pre-trained driving scenario prediction model. It then retrieves matching initial driving parameters from a preset parameter library and makes personalized adjustments based on the user-defined target driving mode, ultimately obtaining the target driving parameters to control the vehicle. The vehicle control method provided in this application achieves a deep integration of real-time road conditions, driving scenarios, and user preferences, enabling vehicle control parameters to dynamically adapt to different scenario requirements. This optimizes driving safety and handling stability while fully satisfying the user's personalized driving experience, significantly improving driving intelligence and comfort.

[0055] Please see Figure 2 , Figure 2 This is a schematic diagram of the structure of a vehicle control device provided in an embodiment of this application. Figure 2 As shown, the vehicle control device 200 includes: The driving scenario determination module 201 is used to collect multi-dimensional data during the vehicle's driving process in real time, and input the multi-dimensional data into a pre-trained driving scenario prediction model to determine the current driving scenario type of the vehicle. The driving parameter determination module 202 is used to determine multiple initial driving parameters corresponding to the current driving scenario type from a preset parameter library, and adjust the multiple initial driving parameters based on the target driving mode currently set by the user to obtain the target driving parameters; The vehicle control module 203 is used to control the vehicle based on the target driving parameters.

[0056] Furthermore, when the driving parameter determination module 202 is used to adjust the various initial driving parameters based on the user's currently set target driving mode to obtain target driving parameters, the driving parameter determination module 202 is also used to: Based on the target driving mode, at least one parameter to be adjusted corresponding to the target driving mode is determined from the plurality of initial driving parameters; The parameters to be adjusted are adjusted based on the weights corresponding to the target driving mode to obtain the target driving parameters.

[0057] Furthermore, after determining the current driving scenario type of the vehicle, the driving scenario determination module 201 is also used to: Obtain the confidence level corresponding to the current driving scenario type output by the driving scenario prediction model; If the confidence level is lower than a preset threshold, the historical driving scenario type determined in the previous moment will be used as the current driving scenario type.

[0058] Furthermore, the target driving parameters include power output coefficient, braking response coefficient, steering assist coefficient, and damping stiffness coefficient; when the vehicle control module 203 controls the vehicle based on the target driving parameters, the vehicle control module 203 is also used for: The vehicle's engine fuel injection quantity or motor output torque is adjusted according to the power output coefficient. The braking assist level and / or the intervention threshold of the anti-lock braking system of the vehicle are adjusted according to the braking response coefficient. The power steering motor current of the vehicle's electric power steering system is adjusted according to the steering assist coefficient. The damping coefficient of the vehicle's electronic shock absorbers is adjusted according to the damping stiffness coefficient.

[0059] Furthermore, the vehicle control module 203 is also used for: The vehicle's current driving behavior is monitored in real time. When the current driving behavior deviates from the preset conditions corresponding to the target driving mode, at least one of the target driving parameters is temporarily corrected.

[0060] Furthermore, the vehicle control module 203 is also used for: In response to the user's selection of any one of multiple temporary driving modes, a target temporary driving mode is determined, and the target driving parameters are adjusted based on the target temporary driving mode to obtain the adjusted driving parameters. The vehicle is then controlled using the adjusted driving parameters within a preset time period.

[0061] Furthermore, the vehicle control device 200 also includes a display module, which is used for: The current driving scenario type, the target driving mode, and the target driving parameters are sent to the vehicle's display device so that the display device can display them in real time.

[0062] Please see Figure 3 , Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Figure 3 As shown, the electronic device 300 includes a processor 310, a memory 320, and a bus 330.

[0063] The memory 320 stores machine-readable instructions executable by the processor 310. When the electronic device 300 is running, the processor 310 and the memory 320 communicate via the bus 330. When the machine-readable instructions are executed by the processor 310, they can perform the operations described above. Figure 1The specific implementation of the vehicle control method in the illustrated method embodiment can be found in the method embodiment, and will not be repeated here.

[0064] This application also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, can perform the above-described actions. Figure 1 The specific implementation of the vehicle control method in the illustrated method embodiment can be found in the method embodiment, and will not be repeated here.

[0065] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0066] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. The apparatus embodiments described above are merely illustrative. For example, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. Furthermore, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Additionally, the shown or discussed mutual couplings, direct couplings, or communication connections may be through some communication interfaces; indirect couplings or communication connections between devices or units may be electrical, mechanical, or other forms.

[0067] 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.

[0068] In addition, the functional units in the various embodiments of this application 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.

[0069] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a processor-executable, non-volatile, computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion 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 described in the various embodiments of this application. 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.

[0070] Finally, it should be noted that the above-described embodiments are merely specific implementations of this application, used to illustrate the technical solutions of this application, and not to limit them. The scope of protection of this application is not limited thereto. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that any person skilled in the art can still modify or easily conceive of changes to the technical solutions described in the foregoing embodiments, or make equivalent substitutions for some of the technical features, within the scope of the technology disclosed in this application. Such modifications, changes, or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be covered within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A vehicle control method characterized by, The vehicle control method includes: Multi-dimensional data during vehicle operation is collected in real time and input into a pre-trained driving scenario prediction model to determine the current driving scenario type of the vehicle. Multiple initial driving parameters corresponding to the current driving scenario type are determined from the preset parameter library, and the multiple initial driving parameters are adjusted based on the target driving mode currently set by the user to obtain the target driving parameters; The vehicle is controlled based on the target driving parameters.

2. The vehicle control method according to claim 1, characterized in that, The adjustment of the various initial driving parameters based on the user's currently set target driving mode to obtain the target driving parameters includes: Based on the target driving mode, at least one parameter to be adjusted corresponding to the target driving mode is determined from the plurality of initial driving parameters; The parameters to be adjusted are adjusted based on the weights corresponding to the target driving mode to obtain the target driving parameters.

3. The vehicle control method according to claim 1, characterized in that, After determining the current driving scenario type of the vehicle, the vehicle control method further includes: Obtain the confidence level corresponding to the current driving scenario type output by the driving scenario prediction model; If the confidence level is lower than a preset threshold, the historical driving scenario type determined in the previous moment will be used as the current driving scenario type.

4. The vehicle control method according to claim 1, characterized in that, The target driving parameters include power output coefficient, braking response coefficient, steering assist coefficient, and damping stiffness coefficient. The control of the vehicle based on the target driving parameters includes: The vehicle's engine fuel injection quantity or motor output torque is adjusted according to the power output coefficient. The braking assist level and / or the intervention threshold of the anti-lock braking system of the vehicle are adjusted according to the braking response coefficient. The power steering motor current of the vehicle's electric power steering system is adjusted according to the steering assist coefficient. The damping coefficient of the vehicle's electronic shock absorbers is adjusted according to the damping stiffness coefficient.

5. The vehicle control method according to claim 1, characterized in that, The vehicle control method further includes: The vehicle's current driving behavior is monitored in real time. When the current driving behavior deviates from the preset conditions corresponding to the target driving mode, at least one of the target driving parameters is temporarily corrected.

6. The vehicle control method according to claim 1, characterized in that, The vehicle control method further includes: In response to the user's selection of any one of multiple temporary driving modes, a target temporary driving mode is determined, and the target driving parameters are adjusted based on the target temporary driving mode to obtain the adjusted driving parameters. The vehicle is then controlled using the adjusted driving parameters within a preset time period.

7. The vehicle control method according to claim 1, characterized in that, The vehicle control method further includes: The current driving scenario type, the target driving mode, and the target driving parameters are sent to the vehicle's display device so that the display device can display them in real time.

8. A vehicle control device, characterized in that, The vehicle control device includes: The driving scenario determination module is used to collect multi-dimensional data during the vehicle's driving process in real time, and input the multi-dimensional data into a pre-trained driving scenario prediction model to determine the current driving scenario type of the vehicle. The driving parameter determination module is used to determine multiple initial driving parameters corresponding to the current driving scenario type from a preset parameter library, and adjust the multiple initial driving parameters based on the target driving mode currently set by the user to obtain the target driving parameters; The vehicle control module is used to control the vehicle based on the target driving parameters.

9. An electronic device, characterized in that, include: The device includes a processor, a memory, and a bus, wherein the memory stores machine-readable instructions executable by the processor, and when the electronic device is running, the processor communicates with the memory via the bus, and the machine-readable instructions are executed by the processor to perform the steps of the vehicle control method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, performs the steps of the vehicle control method as described in any one of claims 1 to 7.

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