Vehicle control method and device, electronic equipment and readable storage medium

Abstract

The embodiment of the invention provides a vehicle control method and device, electronic equipment and a readable storage medium, and the method comprises the steps: responding to a running state of a vehicle, and obtaining external environment data and running data of the vehicle and operation data of a driver of the vehicle; extracting external environment characteristics of the vehicle, driving characteristics of the vehicle and behavior characteristics of a driver from the external environment data, the driving data and the operation data; sorting the external environment features, the driving features and the behavior features based on the priorities corresponding to the external environment features, the driving features and the behavior features to obtain sorting results; generating a control strategy of the vehicle based on the sorting result and the driving portrait of the driver; and controlling the vehicle according to the control strategy. The technical problem that a vehicle control strategy is difficult to meet individual requirements of different users on driving experience and energy efficiency is solved.

Description

Technical Field

[0001] This application relates to the field of vehicle control, and more specifically, to a vehicle control method, apparatus, electronic device, and readable storage medium. Background Technology

[0002] With the increasing popularity of new energy vehicles, energy management and driving strategy formulation have become key technological areas for improving user experience and vehicle performance. Traditional vehicle control systems often focus on optimizing a single objective, such as fuel economy or driving stability. However, in new energy vehicles, especially those equipped with complex powertrains (such as plug-in hybrid electric vehicles and range-extended electric vehicles), achieving a dynamic balance between multiple objectives becomes particularly important. Existing control strategies for new energy vehicles mainly rely on preset operating conditions and fixed logic. Such control strategies are difficult to flexibly adjust according to real-time driving environments and personalized user needs, making it difficult to meet the diverse driving requirements of different users.

[0003] There is currently no good solution to the problem that the aforementioned vehicle control strategies cannot meet the personalized needs of different users for driving experience and energy efficiency. Summary of the Invention

[0004] This application provides a vehicle control method, device, electronic device, and readable storage medium to at least solve the technical problem that vehicle control strategies are difficult to meet the personalized needs of different users for driving experience and energy efficiency.

[0005] According to one aspect of the embodiments of this application, a vehicle control method is provided. The method includes: in response to the vehicle being in a driving state, acquiring external environment data, driving data, and driver operation data of the vehicle, wherein the external environment data is used to characterize the environmental conditions of the current environment in which the vehicle is located, the driving data is used to characterize the current driving performance of the vehicle, and the operation data is used to characterize the driver's operating habits; extracting external environment features, driving features, and driver behavior features from the external environment data, driving data, and operation data; sorting the external environment features, driving features, and behavior features according to their respective priorities to obtain a sorting result, wherein the priority is used to characterize the importance of the external environment features, driving features, and behavior features to the safe driving of the vehicle; generating a vehicle control strategy based on the sorting result and the driver's driving profile, wherein the control strategy is used to characterize the rules for controlling the vehicle's driving, and the driving profile is used to characterize the driver's driving style under different driving conditions; and controlling the vehicle according to the control strategy.

[0006] Furthermore, from external environment data, driving data, and operation data, the vehicle's external environment features, driving features, and driver behavior features are extracted, including: preprocessing the external environment data, driving data, and operation data to obtain preprocessed external environment data, driving data, and operation data, wherein the preprocessing includes at least noise reduction and standardization; extracting external environment features from the preprocessed external environment data, extracting driving features from the preprocessed driving data, and extracting behavior features from the preprocessed operation data.

[0007] Furthermore, based on the priorities corresponding to external environment features, driving features, and behavioral features respectively, the external environment features, driving features, and behavioral features are sorted to obtain the sorting results, including: determining the priorities corresponding to external environment features, driving features, and behavioral features respectively according to feature priority rules; and sorting the external environment features, driving features, and behavioral features in descending order of priority based on the priorities corresponding to external environment features, driving features, and behavioral features respectively, to obtain the sorting results.

[0008] Furthermore, based on the ranking results and the driver's driving profile, a vehicle control strategy is generated, including: obtaining driving styles associated with external environment features, driving features, and behavioral features from the driving profile according to the priority order of external environment features, driving features, and behavioral features in the ranking results; generating a first control strategy corresponding to the external environment features based on the driving styles associated with the external environment features; generating a second control strategy corresponding to the driving features based on the driving styles associated with the driving features; and generating a third control strategy associated with the behavioral features based on the driving styles associated with the behavioral features.

[0009] Furthermore, based on the control strategy, the vehicle is controlled, including: determining the execution order of the first control strategy, the second control strategy, and the third control strategy based on the sorting result; and executing the first control strategy, the second control strategy, and the third control strategy in the execution order to control the vehicle.

[0010] Furthermore, the method also includes: acquiring driving behavior data of the vehicle's driver under different driving conditions within a historical time period, wherein the driving conditions are used to characterize the external environment in which the vehicle is located and the driving state of the vehicle; determining the driver's driving style under different driving conditions based on the driving behavior data, wherein the driving style includes at least an aggressive style and a smooth style; and constructing a driving profile of the driver based on the driving style and the vehicle's operating mode under different driving conditions, wherein the operating mode includes at least the vehicle's driving mode and energy mode under different driving conditions.

[0011] Furthermore, the method also includes updating the driving profile at preset time intervals.

[0012] According to another aspect of the embodiments of this application, a vehicle control device is also provided. The device includes: an acquisition unit, configured to acquire external environment data, driving data, and driver operation data of the vehicle in response to the vehicle being in a driving state, wherein the external environment data is used to characterize the environmental conditions of the current environment in which the vehicle is located, the driving data is used to characterize the current driving performance of the vehicle, and the operation data is used to characterize the driver's operating habits; an extraction unit, configured to extract external environment features, driving features, and driver behavior features from the external environment data, driving data, and operation data; a sorting unit, configured to sort the external environment features, driving features, and behavior features according to their respective priorities to obtain a sorting result, wherein the priority is used to characterize the importance of the external environment features, driving features, and behavior features to the safe driving of the vehicle; a generation unit, configured to generate a vehicle control strategy based on the sorting result and a driver's driving profile, wherein the control strategy is used to characterize the rules for controlling the vehicle's driving, and the driving profile is used to characterize the driver's driving style under different driving conditions; and a control unit, configured to control the vehicle according to the control strategy.

[0013] According to another aspect of the embodiments of this application, an electronic device is also provided, including: a memory storing an executable program; and a processor for running the program, wherein the program executes the methods in various embodiments of this application when it runs.

[0014] According to another aspect of the embodiments of this application, a computer-readable storage medium is also provided, the computer-readable storage medium including a stored executable program, wherein, when the executable program is running, it controls the device where the computer-readable storage medium is located to perform the methods of various embodiments of this application.

[0015] According to another aspect of the embodiments of this application, a computer program product is also provided, including a computer program that, when executed by a processor, implements the methods of various embodiments of this application.

[0016] According to another aspect of the embodiments of this application, a computer program product is also provided, including a non-volatile computer-readable storage medium storing a computer program that, when executed by a processor, implements the methods in various embodiments of this application.

[0017] According to another aspect of the embodiments of this application, a computer program is also provided, which, when executed by a processor, implements the methods of the various embodiments of this application.

[0018] According to another aspect of the embodiments of this application, a vehicle is also provided, including: a memory storing an executable program; and a processor for running the program, wherein the program executes the methods in various embodiments of this application when it runs.

[0019] In this embodiment, in response to the vehicle being in motion, external environment data, driving data, and driver operation data are acquired. The external environment data characterizes the current environmental conditions of the vehicle, the driving data characterizes the vehicle's current driving performance, and the operation data characterizes the driver's operating habits. External environment features, driving features, and driver behavior features are extracted from the external environment data, driving data, and operation data. Based on the priorities corresponding to these features, the external environment features, driving features, and behavior features are ranked to obtain a ranking result. The priority represents the importance of these features to the safe driving of the vehicle. Based on the ranking result and the driver's driving profile, a vehicle control strategy is generated. The control strategy characterizes the rules for controlling vehicle movement, and the driving profile characterizes the driver's driving style under different driving conditions. The vehicle is then controlled according to the control strategy. In other words, in this embodiment, by acquiring the vehicle's external environment data, driving data, and driver operation data, the current external environment characteristics, driving characteristics, and driver operation characteristics of the vehicle can be extracted. Then, based on the importance of these characteristics to safe driving, they are ranked, resulting in a ranking. This allows for prioritizing features with a greater impact on safe driving when formulating vehicle control strategies. Subsequently, by combining the driver's user profile with the ranking results, a vehicle control strategy is generated. This strategy better matches the driver's personal driving habits while ensuring vehicle safety, ensuring that the driver receives a driving experience that meets their personal driving style and energy efficiency goals in various driving scenarios. This solves the technical problem in related technologies where vehicle control strategies struggle to meet users' personalized needs. Attached Figure Description

[0020] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings:

[0021] Figure 1 This is a flowchart of a vehicle control method according to an embodiment of this application;

[0022] Figure 2This is a structural diagram of a vehicle control system based on a user driving profile according to an embodiment of this application;

[0023] Figure 3 This is a flowchart of a vehicle control method based on a user driving profile according to an embodiment of this application;

[0024] Figure 4 This is a schematic diagram of a vehicle control device according to an embodiment of this application. Detailed Implementation

[0025] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort should fall within the scope of protection of the present application.

[0026] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0027] According to an embodiment of this application, an embodiment of a vehicle control method is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0028] This embodiment provides a vehicle control method. Figure 1 This is a flowchart of a vehicle control method according to an embodiment of this application, such as... Figure 1 As shown, the process includes the following steps:

[0029] Step S101: In response to the vehicle being in motion, acquire the vehicle's external environment data, driving data, and the driver's operation data.

[0030] In the technical solution provided by step S101 of this application, the external environment data is used to characterize the environmental conditions of the current environment in which the vehicle is located, the driving data is used to characterize the current driving performance of the vehicle, and the operation data is used to characterize the driver's driving habits.

[0031] In this embodiment, the vehicle's external environmental data can be detected in real time using onboard sensors, radar, and cameras. The vehicle's driving data is comprehensively monitored through a state monitoring system (e.g., battery management system, engine management system, tire pressure monitoring system) to determine the vehicle's driving performance. Driver operation data, such as driving mode settings, energy usage mode settings, and usage data of vehicle functions, such as air conditioning temperature, seat heating, and volume control, is obtained through the vehicle's Human Machine Interface (HMI) system. This is merely an example and does not limit the specific methods for obtaining external environmental data, driving data, and driver operation data.

[0032] Optionally, the aforementioned external environmental data includes, but is not limited to, real-time weather conditions in the vehicle's current environment (e.g., sunny, rainy, snowy, foggy, etc.), road conditions on the road the vehicle is currently traveling on (e.g., straight, curved, sloped, tunnel, etc.), traffic flow on the road the vehicle is currently traveling on (e.g., congested, smooth), and road signs on the road the vehicle is traveling on (e.g., speed limit signs, traffic lights, etc.). This external environmental data helps the vehicle's control system monitor and assess the safety and available resources of the driving environment in real time. For example, by using weather and road condition data, energy usage strategies can be planned in advance to avoid excessive consumption of battery resources in adverse environments.

[0033] Optionally, the aforementioned driving data is used to characterize the vehicle's current driving performance, such as vehicle speed, acceleration, steering angle, battery charge (State of Charge, SOC), tire pressure, engine speed, and coolant temperature. This driving data helps the vehicle's control system to promptly grasp the vehicle's performance status. For example, real-time monitoring of battery charge is crucial for determining when to switch to range-extending mode. Furthermore, vehicle speed and acceleration data are also critical for adjusting energy recovery strategies and improving energy efficiency.

[0034] Optionally, the aforementioned driver operation data focuses on recording the driver's operating habits, such as shifting habits, braking frequency, acceleration force, steering wheel operation habits, frequency of cruise control use, and air conditioning usage intensity. Understanding the driver's driving preferences and habits through this data helps adjust the vehicle's response characteristics, such as increasing power output in aggressive driving mode or optimizing energy economy in smooth driving mode.

[0035] In this step, by acquiring data on the vehicle's external environment, driving data, and the driver's operation data, a solid data foundation can be provided for subsequent decisions on vehicle control strategies.

[0036] Step S102: Extract the vehicle's external environment features, vehicle driving features, and driver behavior features from external environment data, driving data, and operation data.

[0037] In the technical solution provided in step S102 of this application, after acquiring the vehicle's external environment data, driving data, and operation data, the vehicle's external environment characteristics, driving characteristics, and driver behavior characteristics can be extracted from these data. The external environment characteristics at least characterize the weather conditions, road type, road surface conditions, and traffic congestion level of the vehicle's current environment; the driving characteristics at least characterize the vehicle's driving performance and dynamic behavior, helping the vehicle's control system understand the vehicle's actual needs. The driver behavior characteristics at least characterize the driver's operating habits and personalized settings.

[0038] In this embodiment, after acquiring the vehicle's external environment data, vehicle driving data, and driver operation data, the collected data can be preprocessed to obtain preprocessed external environment data, driving data, and operation data. This preprocessing includes, but is not limited to, denoising, standardization, and format conversion to ensure data quality and consistency. After obtaining the preprocessed external environment data, driving data, and operation data, the vehicle's external environment features can be extracted from the preprocessed external environment data, the vehicle's driving features can be extracted from the preprocessed driving data, and the driver's behavioral features can be extracted from the preprocessed operation data.

[0039] Optionally, regarding external environmental characteristics, data related to weather conditions, road type, road surface condition, and traffic congestion level can be identified from the preprocessed external environmental data. Then, the weather conditions, road type, road surface condition, and traffic congestion level of the vehicle's current environment can be extracted from the identified data. This is merely an example and does not limit the process of determining the vehicle's external environmental characteristics.

[0040] Optionally, based on driving characteristics, vehicle speed, acceleration, steering angle, braking force, etc., can be identified from the preprocessed driving data to determine the vehicle's current driving performance and dynamic behavior. Furthermore, the vehicle's battery SOC, remaining driving range, etc., can also be identified from the preprocessed driving data to determine the vehicle's energy usage mode and energy recovery strategy.

[0041] Optionally, based on the driver's behavioral characteristics, the pre-processed behavioral data can be used to determine the accelerator pedal opening, brake pedal usage frequency and force, steering wheel turning amplitude, etc., to determine the driver's driving habits. In addition, the pre-processed behavioral data can also be used to identify the driver's personalized settings for the vehicle's smart cockpit, such as energy mode selection, air conditioning temperature setting, multimedia volume setting, etc., without specific limitations here.

[0042] In this step, after acquiring the vehicle's external environment data, driving data, and operation data, the vehicle's external environment characteristics, driving characteristics, and driver behavior characteristics can be extracted from these data, providing a data foundation for the subsequent generation of vehicle control strategies.

[0043] Step S103: Based on the priority of the external environment features, driving features, and behavioral features respectively, sort the external environment features, driving features, and behavioral features to obtain the sorting results.

[0044] In the technical solution provided in step S103 of this application, a priority ranking mechanism is introduced. Based on the priorities corresponding to external environmental characteristics, driving characteristics, and behavioral characteristics, the vehicle's external environmental characteristics, driving characteristics, and behavioral characteristics are ranked to obtain a ranking result. This ensures that the vehicle control strategy prioritizes characteristics that have a greater impact on the vehicle's safe driving. The priority is used to characterize the importance of the corresponding characteristics (e.g., external environmental characteristics, driving characteristics, and behavioral characteristics) to the vehicle's safe driving.

[0045] In this embodiment, as described in step S102 above, the vehicle's external environment features characterize the weather conditions, road type, road surface conditions, and traffic congestion level of the current environment in which the vehicle is located; driving features characterize the vehicle's driving performance and dynamic behavior; and driver behavior features characterize the driver's operating habits and personalized settings. Each feature carries different impacts on driving experience, energy efficiency, and driving safety. To ensure that the vehicle's control strategy prioritizes driving safety, a priority concept is introduced. Through feature priority rules, corresponding priorities are assigned to external environment features, driving features, and behavioral features. Then, based on the priorities corresponding to the external environment features, driving features, and behavioral features, the external environment features, driving features, and behavioral features are prioritized and ranked to obtain a ranking result. This ranking result characterizes the order of importance of the external environment features, driving features, and behavioral features to the safe driving of the vehicle.

[0046] Optionally, the feature priority rule can follow the principle of: safety > economy > power > driving experience. That is, features related to vehicle driving safety have a higher priority than features related to vehicle economy, which in turn have a higher priority than features related to vehicle power, and features related to vehicle power have a higher priority than features related to vehicle driving experience. In other words, during the vehicle control strategy decision-making process, any feature that may threaten driving safety will be given the highest priority, followed by economic considerations to optimize energy efficiency, then power to ensure sufficient driving power, and finally driving experience to improve user satisfaction.

[0047] Optionally, based on the above feature priority rules, corresponding priorities can be assigned to external environment features, driving features, and behavioral features, and then the external environment features, driving features, and behavioral features can be sorted according to the corresponding priorities to obtain the sorting results.

[0048] For example, suppose that external environmental features have a greater impact on the safe driving of a vehicle, driving features have a second greater impact, and behavioral features have the least impact. According to the feature priority rules mentioned above, the priority of external environmental features can be set as the first priority, the priority of driving features as the second priority, and the priority of behavioral features as the third priority. The first priority is greater than the second priority, and the second priority is greater than the third priority. Then, based on the first priority of external environmental features, the second priority of driving features, and the third priority of behavioral features, the external environmental features, driving features, and behavioral features are sorted in descending order of priority to obtain the sorting result.

[0049] Step S104: Based on the sorting results and the driver's driving profile, generate the vehicle control strategy.

[0050] In the technical solution provided in step S104 of this application, the driving profile is constructed by collecting driving behavior data of drivers within a historical time period. The driving profile integrates the driver's behavioral habits, operating preferences and personality settings, including but not limited to driving style (e.g., aggressive style / smooth style), common road condition preferences (e.g., urban roads / highway roads), energy usage mode preferences (e.g., forced pure electric mode / forced power preservation mode / forced charging mode / oil-electric balance mode), etc.

[0051] In this embodiment, based on the ranking results of external environmental features, driving features, and behavioral features, the most important features for safe vehicle driving can be determined, and then combined with the driver's driving profile, a vehicle control strategy can be generated.

[0052] Optionally, a control strategy can be generated based on external environmental characteristics and the driving style or behavior habits in the driver profile that correspond to those external environmental characteristics. For example, if the external environmental characteristics indicate that the current weather is rainy or snowy, and the driving profile indicates a more aggressive driving style for such weather, the control strategy may include adjusting to a more conservative torque output and enhancing the intervention of the anti-lock braking system (ABS) to improve driving safety.

[0053] Optionally, a control strategy targeting the driving characteristics can be generated based on the driving characteristics and the associated behavioral habits in the driving profile. For example, when driving at high speeds, if the driving profile indicates that the driver tends to overtake quickly, the control strategy may include temporarily increasing the power system output to ensure that the vehicle's response speed meets the driver's needs.

[0054] Optionally, a control strategy can be generated based on the behavioral characteristics and the driving style associated with those characteristics in the driving profile. For example, if the driving profile indicates that the driver uses the brakes frequently in urban driving, the control strategy might include enhancing the energy recovery level to optimize energy efficiency.

[0055] Optionally, after generating multiple control strategies, the execution order of the generated strategies can be determined by prioritizing the results of external environment features, driving features, and behavioral features.

[0056] Step S105: Control the vehicle according to the control strategy.

[0057] In the technical solution provided by step S105 of this application, after obtaining the vehicle control strategy and the order of the control strategies according to step S104, each control strategy can be executed according to the execution order of the control strategies to control the vehicle.

[0058] Optionally, the execution of the control strategy is not fixed but dynamically adjusted based on the real-time driving environment and vehicle status. For example, when encountering a sudden deceleration requirement while driving at high speed, the control strategy can be adjusted in real time based on the energy recovery intensity and the braking system's response time to ensure both safe deceleration and maximum energy recovery. This dynamic adjustment capability can be achieved through a closed-loop control system, which can continuously correct and optimize control commands based on the deviation between the actual execution result and the expected target to achieve the best control effect.

[0059] Optionally, throughout the control process, the HMI (Human-Machine Interface) system not only presents the control strategy to the driver in an intuitive way—for example, displaying the current energy distribution status, driving mode, and estimated mileage on the instrument panel—but also collects real-time feedback and operating commands from the driver, such as switching driving modes and selecting energy modes. This ensures that the control strategy can respond to changes in user needs in real time, providing the best driving experience.

[0060] In steps S101 to S105 above, by acquiring the vehicle's external environment data, driving data, and driver operation data, the current external environment characteristics, driving characteristics, and driver operation characteristics of the vehicle can be extracted. Then, based on the importance of these characteristics to safe driving, they are ranked, resulting in a ranking. This allows for prioritizing features with a greater impact on safe driving when formulating vehicle control strategies. Subsequently, by combining the driver's user profile and the ranking results, a vehicle control strategy is generated. This strategy better matches the driver's personal driving habits while ensuring vehicle safety, ensuring that the driver receives a driving experience that meets their personal driving style and energy efficiency goals in various driving scenarios. This addresses the technical problem in related technologies where vehicle control strategies struggle to meet users' personalized needs.

[0061] The control method for the aforementioned vehicle described in this application is further described below.

[0062] As an optional implementation, step S102 involves extracting the vehicle's external environment features, vehicle driving features, and driver behavior features from the external environment data, driving data, and operation data. This includes: preprocessing the external environment data, driving data, and operation data to obtain preprocessed external environment data, driving data, and operation data, wherein the preprocessing includes at least noise reduction and standardization; extracting external environment features from the preprocessed external environment data; extracting driving features from the preprocessed driving data; and extracting behavior features from the preprocessed operation data.

[0063] In this embodiment, since the collected data may be subject to various interferences, such as external electromagnetic interference and random noise from the sensor itself, preprocessing can be performed on the acquired external environment data, driving data, and operational data. This preprocessing includes denoising and standardization. Denoising typically involves using digital signal processing techniques, such as low-pass filtering and median filtering, to remove or reduce noise signals while retaining valid information. Standardization is necessary because different types of sensor data may have different dimensions and ranges. Standardization (also known as data normalization) facilitates unified data processing and is generally achieved through methods such as min-max normalization and Z-score normalization, ensuring that all data are on similar scales, which is beneficial for subsequent feature extraction and analysis.

[0064] Optionally, after obtaining the preprocessed external environment data, external environment features such as weather conditions (sunny, rainy / snowy), road type (urban roads, highways), and traffic congestion can be extracted from the preprocessed external environment data. These features have a direct impact on vehicle performance and safety. For example, in rainy or snowy weather, it is necessary to identify slippery road surfaces in order to generate a more conservative driving control strategy.

[0065] Optionally, after obtaining the preprocessed driving data, vehicle features such as speed, acceleration, steering angle, and braking force can be extracted from the preprocessed driving data. These vehicle features reflect the current dynamic performance of the vehicle.

[0066] Optionally, after obtaining the preprocessed operation data, the driver's behavioral habits, such as operation frequency (frequent lane changes, acceleration or deceleration) and personalized settings preferences for the intelligent cockpit system, can be extracted from the preprocessed operation data. These features help to understand the driver's driving preferences and habits, thereby formulating control strategies that better meet the driver's needs.

[0067] As an optional implementation, step S103 involves sorting the external environment features, driving features, and behavioral features based on their respective priorities to obtain a sorting result. This includes: determining the priorities of the external environment features, driving features, and behavioral features according to feature priority rules; and sorting the external environment features, driving features, and behavioral features in descending order of priority based on their respective priorities to obtain a sorting result.

[0068] In this embodiment, after obtaining the external environment features, driving features, and behavioral features, the external environment features, driving features, and behavioral features can be sorted according to their respective priorities to obtain a sorting result.

[0069] Optionally, the priorities of external environmental features, driving features, and behavioral features can be determined according to feature priority rules. These rules follow the principle of: safety > economy > power > driving experience, meaning that driving safety is always the primary consideration under all circumstances. Therefore, external environmental features directly related to safety (e.g., rain / snow, sharp curves, obstacles ahead) or driving features (e.g., high-speed driving, rapid acceleration, or sudden braking) will be given the highest priority. Maximizing energy efficiency, while ensuring safety, is another key objective of drive strategy optimization. Therefore, external environmental features affecting vehicle economy (e.g., road gradient, traffic congestion) and driving features (e.g., vehicle speed, power output mode) will be given higher priority. Driving experience is a crucial aspect of improving user satisfaction. While ensuring safety, driver behavioral features (e.g., aggressive or smooth driving) and personalized settings (e.g., driving mode selection, energy mode selection) will be considered to provide a personalized driving experience.

[0070] Optionally, based on the aforementioned feature priority rules, the collected external environment features, driving features, and behavioral features will be sorted in descending order of priority, with the highest priority features listed first. This is because, when resources are limited or when decisions need to be made among multiple tasks, the system should prioritize processing and adjusting those features that have the greatest impact on the overall vehicle performance to achieve the best control effect.

[0071] For example, if the current weather conditions are severe (e.g., rain or snow), this external environmental characteristic will be given the highest priority. The system may prioritize adjusting the vehicle's Electric Power Steering (ESP) system settings to enhance tire grip and ensure driving safety. Simultaneously, if the system detects that the driver frequently overtakes or changes lanes quickly, this characteristic will be given a higher priority, and the system may adjust its power output strategy to provide a more timely power response to meet the driver's dynamic needs.

[0072] Optionally, the ranking of external environmental features, driving features, and behavioral features is dynamic and can be continuously updated based on real-time environmental changes and driver actions. This means that when the vehicle encounters new driving conditions, such as switching from urban roads to highways, or the weather changes from sunny to rainy, the priority of each feature can be reassessed immediately, and the ranking results can be updated to ensure the real-time nature and adaptability of the strategy.

[0073] Optionally, by prioritizing external environmental characteristics, driving characteristics, and behavioral characteristics, the system ensures that when formulating control strategies, it can prioritize the factors that have the greatest impact on vehicle safety, economy, and driving experience, demonstrating a high degree of intelligence and adaptability.

[0074] As an optional implementation, step S104 generates a vehicle control strategy based on the ranking results and the driver's driving profile, including: obtaining driving styles associated with external environment features, driving features, and behavioral features from the driving profile according to the priority order of external environment features, driving features, and behavioral features in the ranking results; generating a first control strategy corresponding to the external environment features based on the driving styles associated with the external environment features; generating a second control strategy corresponding to the driving features based on the driving styles associated with the driving features; and generating a third control strategy associated with the behavioral features based on the driving styles associated with the behavioral features.

[0075] In this embodiment, after analyzing external environmental data, driving data, and driver operation data, and prioritizing external environmental features, driving features, and behavioral features based on multiple considerations such as safety, economy, and driving experience, a vehicle control strategy can be generated based on the ranking results and the driver's driving profile. This driving profile includes an individual's driving habits, preferences, and tendencies towards vehicle operation (e.g., a smooth driver prioritizes comfort, while an aggressive driver may seek stronger power performance). The construction of the driving profile provides the foundation for generating personalized control strategies.

[0076] Optionally, a driving style matching the current environmental conditions can be extracted from the driver's driving profile based on the priority of external environmental features in the ranking results. For example, if the system identifies rainy or snowy weather and determines that safety is the top priority in such an environment, the system will generate a primary control strategy, such as reducing torque output to prevent slippage, enhancing the ABS braking system, and adjusting the vehicle's stability control program to ensure driving safety on slippery roads.

[0077] Optionally, for specific driving characteristics, such as high-speed driving or urban congestion, a second control strategy can be generated based on driving behaviors associated with these characteristics in the driving profile. For example, for high-speed driving, a control strategy can be generated to optimize the vehicle's aerodynamic performance and improve fuel economy; for congested traffic conditions, energy recovery can be enhanced to reduce braking frequency, while optimizing power distribution to maintain vehicle smoothness.

[0078] Optionally, a third control strategy can be generated based on the driver's behavioral characteristics, such as frequent braking and preferred driving modes (economy, sport, and standard modes). This strategy aims to fine-tune vehicle performance to better meet the driver's expectations; for example, in aggressive driving mode, priority will be given to ensuring rapid power response, while in economy driving mode, the focus may be on minimizing energy consumption.

[0079] Optionally, the above method not only enables real-time response to changes in the external environment and driving conditions, but also generates customized control strategies based on each driver's individual style and preferences. In this way, the vehicle can achieve safe, economical, and comfortable driving in various driving scenarios, greatly improving the user experience and overall vehicle performance.

[0080] As an optional implementation, step S105, controlling the vehicle based on the control strategy, includes: determining the execution order of the first control strategy, the second control strategy, and the third control strategy based on the sorting result; and executing the first control strategy, the second control strategy, and the third control strategy in the execution order to control the vehicle.

[0081] In this embodiment, after obtaining the first control strategy, the second control strategy, and the third control strategy, the execution order of the first control strategy, the second control strategy, and the third control strategy can be determined according to the priority order in the sorting results of the aforementioned external environment characteristics, driving characteristics, and behavioral characteristics, so as to ensure that the control strategies related to vehicle driving safety are executed first.

[0082] Optionally, in multi-task processing, control strategies can be ranked based on determined feature ranking results. These strategies typically include a first control strategy targeting external environmental features, a second control strategy targeting driving features, and a third control strategy targeting behavioral features. The ranking principle follows the priorities of safety, economy, and drivability, while also considering the user's personalized needs.

[0083] For example, if the current weather is identified as rainy or snowy (external environmental characteristic), then control strategies that ensure vehicle stability and anti-skid performance (e.g., adjusting torque output, enhancing the ABS braking system) will be given the highest priority and executed as the first control strategy. Secondly, if the vehicle is traveling on a highway (driving characteristic), then strategies that optimize power output response and energy efficiency (e.g., temporarily increasing power system output, adjusting energy recovery levels) may serve as the second control strategy. Finally, considering the driver's aggressive driving style (behavioral characteristic), strategies that adjust the vehicle's dynamic response to provide an enhanced driving experience (e.g., adjusting driving mode settings, improving vehicle power response) will serve as the third control strategy.

[0084] Optionally, after determining the execution order of the first control strategy, the second control strategy, and the third control strategy, control commands can be issued to the actuators of the vehicle one by one to ensure that the first control strategy, the second control strategy, and the third control strategy are executed accurately. The execution of the control strategy is a dynamic process involving fine control of key actuators such as the engine, drive motor, generator, and braking system.

[0085] For example, if a first control strategy related to safety is implemented first, instructions can be sent to the vehicle's actuators, such as adjusting the sensitivity of the electronic stability program or automatically reducing power output to prevent tire slippage. Subsequently, a second control strategy related to driving economy is implemented, such as optimizing the energy recovery system or adjusting the operating modes of the engine and electric motor to achieve the best energy efficiency ratio. Finally, a third control strategy is implemented based on the driver's individual needs, such as adjusting the vehicle's acceleration curve or steering sensitivity.

[0086] Optionally, while executing the control strategy, vehicle status data and environmental information can be continuously collected and compared with the expected results to generate real-time feedback. If deviations from expectations are detected, the system will quickly adjust the strategy to ensure that the vehicle is always in the optimal control state.

[0087] Optionally, by implementing a control strategy based on driving profiles and environmental conditions, prioritizing driving characteristics, new energy vehicles can automatically adjust to meet the needs of safe, economical, and personalized driving in complex and ever-changing driving scenarios. This process not only demonstrates the system's intelligence and adaptive capabilities but also ensures that drivers can obtain the best driving experience under various operating conditions, while optimizing energy utilization and improving overall vehicle performance.

[0088] As an optional implementation, the method further includes: acquiring driving behavior data of the vehicle's driver under different driving conditions within a historical time period, wherein the driving conditions are used to characterize the external environment in which the vehicle is located and the driving state of the vehicle; determining the driver's driving style under different driving conditions based on the driving behavior data, wherein the driving style includes at least an aggressive style and a smooth style; and constructing a driving profile of the driver based on the driving style and the vehicle's operating mode under different driving conditions, wherein the operating mode includes at least the vehicle's driving mode and energy mode under different driving conditions.

[0089] In this embodiment, driving behavior data of the vehicle under different driving conditions can be collected over a period of time (e.g., the most recent month, year, or a specific driving cycle). This data comes from onboard sensors, radar, cameras, and navigation systems, and covers multi-dimensional information such as vehicle speed, acceleration, braking frequency, steering angle, energy consumption mode, and driving mode selection.

[0090] Optionally, driving conditions are used to describe the driving conditions of a vehicle in different environments and states, including but not limited to road type (city streets, highways), time (morning and evening rush hours, late at night), weather conditions (sunny days, rain and snow), terrain (flat ground, slopes), and whether there are traffic restrictions (such as speed limit zones, construction areas).

[0091] Optionally, based on historical driving behavior data, machine learning algorithms or statistical analysis methods can be used to identify the driver's driving habits and behavior patterns under different driving conditions. For example, by analyzing acceleration and braking frequency, it is possible to identify whether the driver tends to drive aggressively (rapid acceleration, frequent braking) or smoothly (gentle acceleration, slow braking).

[0092] Optionally, in addition to basic driving style, the driving profile also includes the driver's preference for vehicle operating mode under different driving conditions, such as preferring the economy driving mode (ECO mode) in urban congestion and the sport mode (SPORT mode) on open roads; as well as the choice of energy mode, such as preferring to use the power-saving mode when traveling long distances and preferring the pure electric mode (EV mode) for daily short trips.

[0093] Optionally, machine learning techniques can be used to learn and construct driver profiles from large amounts of historical data. This involves in-depth data mining and pattern recognition to capture subtle differences and patterns in driver behavior. Profile construction is not only based on averages from individual drives but also focuses on dynamic trends in driving behavior to adapt to changes in driver development and habits.

[0094] Optionally, the driving profile can dynamically adjust the control strategy based on the driver's driving style and preferences, as well as the current driving conditions. For example, for a driver identified as having an aggressive driving style, the system may automatically switch to Sport driving mode under high-speed driving conditions, increasing power response speed and appropriately adjusting the energy recovery level to provide a better driving experience.

[0095] Optionally, by collecting and analyzing historical driving behavior data, different driving styles can be identified, and personalized driving profiles of drivers can be constructed. This enables the dynamic strategy adaptation system for new energy vehicles to more accurately understand the driver's needs and preferences, thereby generating the most suitable control strategy under various driving conditions. This method significantly enhances the system's adaptability to driver habits and its responsiveness to the driving environment, laying a solid foundation for improving driving safety, economy, and user experience.

[0096] As an optional implementation, the method further includes updating the driving profile at preset time intervals.

[0097] In this embodiment, the driving profile is not static. As vehicle and driver data accumulates, the driving profile can be updated at preset time intervals, ensuring the timeliness and accuracy of the driving profile. This enables continuous optimization of the dynamic strategy adaptation of new energy vehicles to adapt to changes in driver behavior and preferences over time.

[0098] Optionally, after the driver profile is updated, the vehicle's control strategy can be adjusted based on the new profile information. For example, if the driver's driving style shifts from smooth to aggressive, the intensity of electric braking can be increased, and the response speed of power output can be increased to adapt to the performance requirements of an aggressive driving style.

[0099] Optionally, by regularly updating the driver profile, the system can continuously adapt to changes in driver behavior, improving the personalization and effectiveness of the strategy. This process not only enhances the system's adaptability but also improves the driving experience and energy efficiency, representing a key aspect of intelligent and human-centered design.

[0100] The above technical solutions of the embodiments of this application will be further illustrated below with reference to preferred embodiments.

[0101] Figure 2 This is a structural diagram of a vehicle control system based on a user driving profile according to an embodiment of this application, such as... Figure 2 The diagram illustrates the various modules and information flow paths within this user-driven vehicle control system. This structural diagram provides a clear understanding of how the system collects and processes information, ultimately generating and executing control strategies. Figure 2 As shown, the user profile-based dynamic control strategy adaptation system includes: an in-vehicle sensing module, an in-vehicle radar module, an in-vehicle video module, a user driving personalization setting module, a big data processing module, a database, a vehicle domain control unit, and actuators.

[0102] The vehicle-mounted sensing module, vehicle-mounted radar module, and vehicle-mounted video module serve as data acquisition modules, used to perceive the vehicle's surrounding environment. For example, various sensors (temperature sensors, pressure sensors) in the vehicle-mounted sensing module collect information on the vehicle's internal state and external environment, such as vehicle speed, acceleration, battery SOC status, external temperature, humidity, and road conditions, providing real-time data support for decision-making. The vehicle-mounted radar module, including lidar, ultrasonic radar, and millimeter-wave radar, detects obstacles, other vehicles, and pedestrians around the vehicle, providing the system with environmental information from a distance and beyond direct field of view. The high-definition camera and image processing unit in the vehicle-mounted video module identify traffic signs, road signs, pedestrian behavior, etc., providing visual information for decision-making.

[0103] Optionally, after obtaining the above data, visual algorithms, machine learning algorithms, and other methods can be used to obtain accurate information such as road conditions, traffic lights, and weather conditions in the current driving environment.

[0104] Optionally, the user's personalized driving settings module obtains key user personalization settings (driving modes (economy mode, standard mode, sport mode), energy modes (forced pure electric mode, forced power saving mode, forced charging mode, fuel-electric balance mode, etc.) and navigation map information through the intelligent cockpit human-machine interaction. Utilizing the high-precision map of the intelligent driving assistance system, lane levels can be accurately identified, including data such as road curvature, gradient, and speed limits.

[0105] The big data processing module includes a high-performance computing unit, which receives and integrates information from the vehicle-mounted sensing module, radar module, video module, and user-defined settings. It uses artificial intelligence (AI) algorithms to perform data fusion processing and analysis, and stores the data in a categorized manner in the vehicle database for later use.

[0106] The database is a high-capacity, high-reliability data storage unit used to store processed data and driving profiles for decision-making algorithms and historical data analysis.

[0107] As the central processing unit, the vehicle domain control unit can generate optimal control strategies adapted to different driving scenarios based on the output of the AI ​​big data processing module and information from the vehicle database. For example, it prioritizes complex data across multiple tasks, following principles of safety, economy, and drivability while also considering personalized user settings. Based on the priority of these tasks and the current powertrain operating mode (pure electric drive mode / range-extended electric mode), it calculates the desired torque and vehicle speed based on the driver's real-time needs. Simultaneously, it comprehensively evaluates the capabilities of the motor (drive and power generation) and battery (discharge and charge), makes the optimal decision, and then transmits the decision results to the domain control interface in a format acceptable to the target actuator, ultimately for the actuator to execute.

[0108] Optionally, the actuator is used to execute the control strategy decided by the central algorithm unit, while displaying vehicle status and control information to the driver.

[0109] Optionally, the aforementioned user profile-based dynamic control strategy adaptation system achieves dynamic control of new energy vehicles through comprehensive information collection, precise data analysis, and intelligent strategy generation, in order to adapt to different driving scenarios and personalized needs and provide the best driving experience.

[0110] Figure 3 This is a flowchart of a vehicle control method based on a user driving profile, according to an embodiment of this application. Figure 3As shown, it includes the following steps:

[0111] Step S301, Information Input.

[0112] In this embodiment, the input information includes: driving style information, current road condition information, weather condition information, navigation destination charging station distribution information, and user personalized settings information. Specifically, driving style information indicates whether the driver's driving style is aggressive or smooth; current road condition information indicates whether the vehicle is currently traveling on urban roads or highways; weather condition information indicates the current weather conditions of the vehicle's environment, such as sunny, rainy, or snowy; navigation destination charging station distribution information indicates available charging station resources near the navigation destination, helping to plan the use of remaining battery power; and user personalized settings information indicates the driver's personalized settings within the smart cockpit, including driving mode, energy mode, and V2X function settings.

[0113] Step S302, database storage.

[0114] In this embodiment, the information input in step S301 will be stored in the single-vehicle database to provide comprehensive data support for subsequent vehicle control strategy decisions.

[0115] Step S303, vehicle domain controller processing.

[0116] In this embodiment, the domain control unit plays a core role. It can retrieve information from the database and perform preliminary screening and preprocessing of the information through the multi-task preprocessing module to ensure the validity and applicability of the data.

[0117] Optionally, multi-task priority decision is used to prioritize input information (such as user personalization settings, rain and snow, slope, congestion, aggressive driving, and charging stations), with safety-related data chains having the highest priority, followed by user personalization settings, including driving mode, energy mode, and V2X function settings, as well as vehicle performance requirements information.

[0118] Optionally, the central decision-making algorithm unit is the core of the decision-making process. Based on the results of multi-task prioritization, combined with the current powertrain operating mode and vehicle state (e.g., battery SOC), it uses rule-based algorithms and neural network predictive planning decision-making algorithms to generate specific control commands. The vehicle domain control unit transmits the decided control commands to the execution unit through the domain control interface. For example... Figure 3As shown, the domain control interface may include at least: HMI human-machine interface, Intelligent Central Control (ICC) interface, engine control interface, drive motor control interface, charge motor control interface, and chassis control interface.

[0119] Step S304: The execution unit executes the control instructions.

[0120] In this embodiment, the execution units include an HMI (Human-Machine Interface), an instrument cluster control unit (ICC), an engine EMS (Electric Power Management System) control unit, a drive motor MCU (Microcontroller Unit), a generator MCU (Microcontroller Unit), and an integrated power braking system (IPB). These execution units perform corresponding operations based on the received control strategy, such as adjusting power output, energy recovery intensity, and throttle response speed, to achieve a safe, economical, and comfortable driving experience. Upon receiving a control command, the execution unit can execute the control command.

[0121] Optionally, after executing control commands, real-time data on vehicle operation is continuously collected and compared with the expected results to generate real-time feedback. If the actual operation deviates from the expectation, the system will quickly adjust the control strategy to ensure the vehicle operates in optimal condition. This feedback mechanism allows the system to continuously adjust and optimize the control strategy based on changes in the driving environment and the driver's real-time needs, achieving seamless adaptation of dynamic strategies.

[0122] Steps S301 to S304 outline the complete process of the vehicle control method based on user driving profiles. From information collection and storage to AI-based decision analysis, and then to strategy execution and dynamic optimization, each step is closely linked, forming a closed-loop intelligent control framework. This method significantly improves the adaptability and control efficiency of new energy vehicles under complex operating conditions, and through personalized adjustments, provides drivers with a safer, more economical, and more comfortable driving experience.

[0123] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties. Furthermore, the collection, use and processing of the relevant data must comply with the relevant laws, regulations and standards of the relevant countries and regions, and corresponding operation entry points are provided for users to choose to authorize or refuse.

[0124] According to an embodiment of this application, a vehicle control device is provided. It should be noted that the vehicle control device can be used to execute the above-described vehicle control method.

[0125] Figure 4 This is a schematic diagram of a vehicle control device according to an embodiment of this application. Figure 4 As shown, the vehicle control device 400 may include: an acquisition unit 401, an extraction unit 402, a sorting unit 403, a generation unit 404, and a control unit 405.

[0126] The acquisition unit 401 is used to acquire external environment data, driving data and driver operation data of the vehicle in response to the vehicle being in a driving state. The external environment data is used to characterize the environmental conditions of the current environment in which the vehicle is located, the driving data is used to characterize the current driving performance of the vehicle, and the operation data is used to characterize the driver's operating habits.

[0127] Extraction unit 402 is used to extract the vehicle's external environment features, vehicle driving features, and driver behavior features from external environment data, driving data, and operation data.

[0128] The sorting unit 403 is used to sort the external environment features, driving features and behavioral features based on their respective priorities to obtain sorting results. The priority is used to characterize the importance of the external environment features, driving features and behavioral features to the safe driving of the vehicle.

[0129] The generation unit 404 is used to generate a vehicle control strategy based on the sorting results and the driver's driving profile. The control strategy is used to characterize the rules for controlling the vehicle's driving, and the driving profile is used to characterize the driver's driving style under different driving conditions.

[0130] Control unit 405 is used to control the vehicle according to the control strategy.

[0131] Optionally, the extraction unit 402 is further configured to: preprocess the external environment data, driving data, and operation data to obtain preprocessed external environment data, driving data, and operation data, wherein the preprocessing includes at least noise reduction processing and standardization processing; extract external environment features from the preprocessed external environment data, extract driving features from the preprocessed driving data, and extract behavioral features from the preprocessed operation data.

[0132] Optionally, the sorting unit 403 is further configured to: determine the priorities of external environment features, driving features and behavioral features respectively according to feature priority rules; and sort the external environment features, driving features and behavioral features in descending order of priority based on the priorities of the external environment features, driving features and behavioral features respectively, to obtain the sorting result.

[0133] Optionally, the generation unit 404 is further configured to: obtain driving styles associated with external environment features, driving features, and behavioral features from the driving profile according to the priority order of external environment features, driving features, and behavioral features in the sorting results; generate a first control strategy corresponding to the external environment features based on the driving styles associated with the external environment features; generate a second control strategy corresponding to the driving features based on the driving styles associated with the driving features; and generate a third control strategy associated with the behavioral features based on the driving styles associated with the behavioral features.

[0134] Optionally, the control unit 405 is further configured to: determine the execution order of the first control strategy, the second control strategy, and the third control strategy based on the sorting result; and execute the first control strategy, the second control strategy, and the third control strategy in the execution order to control the vehicle.

[0135] Optionally, the device 400 is further configured to: acquire driving behavior data of the vehicle's driver under different driving conditions within a historical time period, wherein the driving conditions are used to characterize the external environment in which the vehicle is located and the driving state of the vehicle; determine the driver's driving style under different driving conditions based on the driving behavior data, wherein the driving style includes at least an aggressive style and a smooth style; and construct a driving profile of the driver based on the driving style and the vehicle's operating mode under different driving conditions, wherein the operating mode includes at least the vehicle's driving mode and energy mode under different driving conditions.

[0136] Optionally, the device 400 is also used to update the driving profile at preset time intervals.

[0137] In the vehicle control device described in this application, by acquiring external environmental data, driving data, and driver operation data, the device can extract the current external environmental characteristics, driving characteristics, and driver operation characteristics. Then, based on the importance of these characteristics to safe driving, they are ranked, resulting in a ranking. This allows for prioritizing features with a greater impact on safe driving when formulating vehicle control strategies. Subsequently, by combining the driver's user profile with the ranking results, a vehicle control strategy is generated. This strategy better matches the driver's personal driving habits while ensuring vehicle safety, ensuring a driving experience that meets the driver's personal driving style and energy efficiency goals in various driving scenarios. This solves the technical problem in related technologies where vehicle control strategies fail to meet users' personalized needs.

[0138] Embodiments of this application also provide an electronic device, including: a memory storing an executable program; and a processor for running the program, wherein the program executes the methods in various embodiments of this application when it runs.

[0139] Embodiments of this application also provide a computer-readable storage medium including a stored executable program, wherein, when the executable program is running, it controls the device where the computer-readable storage medium is located to perform the methods of various embodiments of this application.

[0140] Embodiments of this application also provide a computer program product, including a computer program that, when executed by a processor, implements the methods of various embodiments of this application.

[0141] Embodiments of this application also provide a computer program product, including a non-volatile computer-readable storage medium for storing a computer program that, when executed by a processor, implements the methods in various embodiments of this application.

[0142] Embodiments of this application also provide a computer program that, when executed by a processor, implements the methods described in the various embodiments of this application.

[0143] Embodiments of this application also provide a vehicle, including: a memory storing an executable program; and a processor for running the program, wherein the program executes the methods described in various embodiments of this application when it runs.

[0144] In the above embodiments of this application, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0145] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. The device embodiments described above are merely illustrative; for example, the division of units can be a logical functional division, and in actual implementation, there may be other division methods. For instance, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual coupling, direct coupling, or communication connection may be through some interfaces; the indirect coupling or communication connection between units or modules may be electrical or other forms.

[0146] 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 units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0147] Furthermore, 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. The integrated unit can be implemented in hardware or as a software functional unit.

[0148] If the integrated unit 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 application, in essence, or the part that contributes to the prior art, or all or 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 described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as a USB flash drive, read-only memory (ROM), random access memory (RAM), portable hard drive, magnetic disk, or optical disk.

[0149] The above description is only a preferred embodiment of this application. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of this application, and these improvements and modifications should also be considered within the scope of protection of this application.

Claims

1. A control method of a vehicle, characterized by, The method comprises the following steps: in response to the vehicle being in a driving state, acquiring external environment data of the vehicle, driving data and operation data of a driver of the vehicle, wherein the external environment data is used to represent the environmental conditions of the environment in which the vehicle is currently located, the driving data is used to represent the current driving performance of the vehicle, and the operation data is used to represent the operation habits of the driver; extracting external environment features of the vehicle, driving features of the vehicle and behavior features of the driver from the external environment data, the driving data and the operation data; sorting the external environment features, the driving features and the behavior features based on the corresponding priority of the external environment features, the driving features and the behavior features, to obtain a sorting result, wherein the priority is used to represent the importance of the external environment features, the driving features and the behavior features to the safe driving of the vehicle; generating a control strategy of the vehicle based on the sorting result and a driving image of the driver, wherein the control strategy is used to represent the rules for controlling the driving of the vehicle, and the driving image is used to represent the driving style of the driver under different driving conditions; controlling the vehicle according to the control strategy.

2. The method of claim 1, wherein, The method comprises the following steps: extracting external environment features of the vehicle, driving features of the vehicle and behavior features of the driver from the external environment data, the driving data and the operation data, comprising: preprocessing the external environment data, the driving data and the operation data to obtain preprocessed external environment data, driving data and operation data, wherein the preprocessing at least includes denoising processing and standardization processing; 3. The method of claim 1, wherein, extracting the external environment features from the preprocessed external environment data, the driving features from the preprocessed driving data, and the behavior features from the preprocessed operation data. sorting the external environment features, the driving features and the behavior features based on the corresponding priority of the external environment features, the driving features and the behavior features, to obtain a sorting result, comprising: determining the priority of the external environment features, the driving features and the behavior features according to the feature priority rules; 4. The method of claim 1, wherein, sorting the external environment features, the driving features and the behavior features in descending order of priority based on the priority of the external environment features, the driving features and the behavior features, to obtain the sorting result. generating a control strategy of the vehicle based on the sorting result and a driving image of the driver, comprising: obtaining the driving style associated with the external environment features, the driving features and the behavior features from the driving image according to the priority order of the external environment features, the driving features and the behavior features in the sorting result; generate a first control strategy corresponding to the external environment feature based on a driving style associated with the external environment feature, generate a second control strategy corresponding to the driving feature based on a driving style associated with the driving feature, and generate a third control strategy associated with the behavior feature based on a driving style associated with the behavior feature.

5. The method of claim 4, wherein, control the vehicle based on the control strategy, including: determine an execution order of the first control strategy, the second control strategy, and the third control strategy based on the sorting result; execute the first control strategy, the second control strategy, and the third control strategy in the execution order to control the vehicle.

6. The method of claim 1, wherein, The method further includes: obtain driving behavior data of the driver of the vehicle in different driving conditions in a historical time period, wherein the driving conditions are used to represent the external environment in which the vehicle is located and the driving state of the vehicle; determine driving styles of the driver in the different driving conditions based on the driving behavior data, wherein the driving styles at least include aggressive style and stable style; construct a driving portrait of the driver based on the driving styles and operating modes of the vehicle in the different driving conditions, wherein the operating modes at least include driving modes and energy modes of the vehicle in the different driving conditions.

7. The method of claim 6, wherein, The method further includes: update the driving portrait at a preset time interval.

8. A control device of a vehicle characterized by comprising: comprise: an acquisition unit configured to, in response to the vehicle being in a driving state, acquire external environment data of the vehicle, driving data of the vehicle, and operation data of a driver of the vehicle, wherein the external environment data is used to represent an environmental condition of an environment in which the vehicle is currently located, the driving data is used to represent a driving performance of the vehicle at present, and the operation data is used to represent an operation habit of the driver; an extraction unit configured to extract external environment features of the vehicle, driving features of the vehicle, and behavior features of the driver from the external environment data, the driving data, and the operation data; a sorting unit configured to sort the external environment features, the driving features, and the behavior features based on respective priorities of the external environment features, the driving features, and the behavior features, to obtain a sorting result, wherein the priorities are used to represent importance degrees of the external environment features, the driving features, and the behavior features to safe driving of the vehicle; a generation unit configured to generate a control strategy of the vehicle based on the sorting result and a driving portrait of the driver, wherein the control strategy is used to represent a rule for controlling driving of the vehicle, and the driving portrait is used to represent driving styles of the driver in different driving conditions; a control unit configured to control the vehicle according to the control strategy.

9. An electronic device, comprising: comprise: a memory storing an executable program; a processor configured to run the program, wherein the program performs the method of any one of claims 1 to 7 when running.

10. A computer-readable storage medium, characterized in that, The computer readable storage medium comprises a stored executable program, wherein the executable program, when executed, controls a device in which the storage medium is located to perform the method of any one of claims 1 to 7.

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