Vehicle control strategy generation method and device, storage medium and electronic device

Abstract

The invention discloses a vehicle control strategy generation method and device, a storage medium and an electronic device.The method comprises the steps that driving styles of drivers are classified according to multiple sets of historical driving data to generate multiple driving styles, and the multiple sets of historical driving data comprise historical driving data of multiple drivers; obtaining vehicle state information of a target vehicle and historical driving data of a target driver driving the target vehicle; determining a target driving style corresponding to the target driver in a plurality of driving styles according to the vehicle state information and historical driving data of the target driver; and generating a vehicle control strategy of the target driver on the target vehicle according to the target driving style and the vehicle state information. By the adoption of the technical scheme, the problem that a unified vehicle control strategy cannot be completely suitable for drivers of various different driving styles due to the fact that the unified vehicle control strategy is used under the limiting working condition in the related technology is solved.

Description

Technical Field

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

[0002] Currently, in extreme operating conditions (such as low-friction, emergency obstacle avoidance, split-drive, and docking scenarios), the risk of vehicle instability increases dramatically due to deteriorated tire-road adhesion or a sudden increase in tire lateral force, threatening driving safety. Therefore, vehicle stability control under extreme conditions is extremely important. When a vehicle is in extreme conditions, both its lateral and longitudinal adhesion reach a critical state. To fully utilize tire adhesion and improve handling performance while ensuring vehicle stability, coordinated lateral and longitudinal control is required.

[0003] Furthermore, drivers with different driving styles control the vehicle differently under extreme conditions. For example, drivers with a more moderate driving style tend to use the brake pedal more often and brake earlier when cornering, with relatively smaller braking deceleration, compared to aggressive drivers.

[0004] There is currently no effective solution to the problem that using a uniform vehicle control strategy under extreme conditions may not be suitable for drivers with different driving styles.

[0005] Therefore, it is necessary to improve the relevant technology to overcome the aforementioned defects. Summary of the Invention

[0006] This application provides a method and apparatus for generating a vehicle control strategy, a storage medium, and an electronic device, to at least solve the problem that the use of a uniform vehicle control strategy under extreme conditions in related technologies may not be fully suitable for drivers with different driving styles.

[0007] According to one aspect of the embodiments of this application, a method for generating a vehicle control strategy is provided, comprising: classifying a driver's driving style according to multiple sets of historical driving data to generate multiple driving styles, wherein the multiple sets of historical driving data include: historical driving data of multiple drivers; acquiring vehicle status information of a target vehicle and historical driving data of a target driver driving the target vehicle; determining a target driving style corresponding to the target driver among the multiple driving styles according to the vehicle status information and the target driver's historical driving data; and generating a vehicle control strategy for the target driver on the target vehicle according to the target driving style and the vehicle status information.

[0008] In an exemplary embodiment, classifying a driver's driving style based on multiple sets of historical driving data includes: determining a longitudinal driver model and a lateral driver model corresponding to each set of historical driving data, wherein the longitudinal driver model is used to indicate a first average pedal opening and a first average pedal change rate for each set of historical driving data, and the lateral driver model is used to indicate a first average steering wheel angle change rate for each set of historical driving data, the first average pedal opening including a first average accelerator pedal opening and a first average brake pedal opening, and the first average pedal change rate including a first average accelerator pedal change rate and a first average brake pedal change rate; and performing cluster analysis on multiple first average pedal openings, multiple first average pedal change rates, and multiple average steering wheel angle change rates to determine the multiple driver styles.

[0009] In an exemplary embodiment, the vehicle state information of the target vehicle includes: basic state information of the target vehicle and key state information of the target vehicle, wherein the basic state information includes at least one of the following: vehicle speed information, wheel speed information, acceleration information, wheel angle information, vehicle body posture information, tire driving torque information, and tire braking torque information of the target vehicle; the acceleration information includes: longitudinal acceleration and lateral acceleration of the target vehicle; and the vehicle body posture information includes: yaw rate of the target vehicle. The key state information includes at least one of the following: tire force of the target vehicle, road adhesion coefficient of the target vehicle, and center of gravity sideslip angle of the target vehicle.

[0010] In one exemplary embodiment, obtaining key state information of the target vehicle includes: determining the tire angular acceleration of the target vehicle based on the wheel speed information; determining the longitudinal force of the target vehicle's tires based on the tire driving torque information, the tire braking torque information, and the tire angular acceleration; determining the lateral force of the target vehicle's tires based on the lateral acceleration, the yaw rate, and the wheel rotation angle information; and determining the vertical load of the target vehicle's tires based on the longitudinal acceleration and the lateral acceleration, wherein the tire forces of the target vehicle include: the longitudinal force of the tires, the lateral force of the tires, and the vertical load of the tires; and determining the sideslip angle of the target vehicle's center of gravity based on the vehicle speed information and the yaw rate.

[0011] In an exemplary embodiment, generating a vehicle control strategy for a target driver on a target vehicle based on the target driving style and the vehicle state information includes: determining driving parameters corresponding to the target driving style, wherein the driving parameters include at least: a second average pedal opening corresponding to the target driving style, a second average pedal change rate corresponding to the target driving style, and a second average steering wheel angle change rate corresponding to the target driving style, wherein the second average pedal opening includes: a second average accelerator pedal opening and a second average brake pedal opening, and the second average pedal change rate includes: a second average accelerator pedal change rate and a second average brake pedal change rate; determining an optimal speed sequence and an optimal path sequence corresponding to the target driving style based on an optimization algorithm, the driving parameters, and the vehicle state information; determining an optimal driving trajectory for the target driver of the target driving style based on the optimal speed sequence and the optimal path sequence; and generating the vehicle control strategy based on the optimal driving trajectory.

[0012] In an exemplary embodiment, determining the optimal speed sequence corresponding to the target driving style based on the optimization algorithm, the driving parameters, and the vehicle state information includes: determining the vehicle speed information in the vehicle state information as the first state vector of the optimization algorithm; determining the tire friction circle of the target vehicle based on the road adhesion coefficient and tire force in the vehicle state information; determining the tire friction circle, the tire driving torque information in the vehicle state information, and the tire braking torque information in the vehicle state information as the first constraint condition of the optimization algorithm; determining the second average pedal opening and the second average pedal change rate as the first supplementary constraint condition of the optimization algorithm; and calculating the optimal speed sequence corresponding to the target driving style based on the first state vector, the first constraint condition, and the first supplementary constraint condition.

[0013] In an exemplary embodiment, determining the optimal path sequence corresponding to the target driving style based on the optimization algorithm, the driving parameters, and the vehicle state information includes: determining the sideslip angle of the target vehicle based on the vehicle speed information and yaw rate in the vehicle state information; determining the sideslip angle and yaw rate as the second state vector of the optimization algorithm; determining the tire friction circle of the target vehicle based on the road adhesion coefficient and tire force in the vehicle state information; determining the tire friction circle as the second constraint condition of the optimization algorithm; determining the second average steering wheel angle change rate as the second supplementary constraint condition of the optimization algorithm; and calculating the optimal path sequence corresponding to the target driving style based on the second state vector, the second constraint condition, and the second supplementary constraint condition.

[0014] In an exemplary embodiment, generating the vehicle control strategy based on the optimal driving trajectory includes: performing a force analysis on the target vehicle based on the vehicle state information to determine the force analysis result corresponding to the target vehicle, wherein the force analysis result includes: longitudinal force balance, lateral force balance, and yaw moment balance corresponding to the target vehicle; and applying wheel angle constraints and tire friction circle constraints to the target vehicle based on the vehicle state information; determining the optimal tire adhesion utilization rate of the target vehicle based on the road adhesion coefficient and tire force of the target vehicle in the vehicle state information; determining the control variables of the optimal driving trajectory based on the force analysis result, the wheel angle constraints, the tire friction circle constraints, and the optimal tire adhesion utilization rate; and generating the vehicle control strategy based on the control variables and the optimal driving trajectory.

[0015] According to another aspect of the embodiments of this application, a vehicle control strategy generation apparatus is also provided, comprising: a classification module, configured to classify a driver's driving style according to multiple sets of historical driving data to generate multiple driving styles, wherein the multiple sets of historical driving data include: historical driving data of multiple drivers; an acquisition module, configured to acquire vehicle status information of a target vehicle and historical driving data of a target driver driving the target vehicle; a determination module, configured to determine a target driving style corresponding to the target driver among the multiple driving styles based on the vehicle status information and the target driver's historical driving data; and a generation module, configured to generate a vehicle control strategy for the target vehicle by the target driver based on the target driving style and the vehicle status information.

[0016] According to another aspect of the embodiments of this application, a computer-readable storage medium is also provided, wherein a computer program is stored in the computer program, and the computer program is configured to execute the above-described method for generating the vehicle control strategy when it is run.

[0017] According to another aspect of the embodiments of this application, an electronic device is also provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the method for generating the vehicle control strategy through the computer program.

[0018] According to another aspect of the embodiments of this application, a computer program product is also provided, including a computer program, wherein the computer program is executed by a processor to generate the vehicle control strategy.

[0019] This application's embodiments classify drivers' driving styles based on multiple sets of historical driving data from multiple drivers to generate various driving styles; acquire vehicle status information of the target vehicle and historical driving data of the target driver driving the target vehicle; determine the target driving style corresponding to the target driver among the various driving styles based on the vehicle status information and the target driver's historical driving data; and generate a vehicle control strategy for the target driver on the target vehicle based on the target driving style and vehicle status information. In other words, after determining multiple driving styles based on different drivers' historical driving data, this application's embodiments can determine the vehicle status information of the target vehicle and the target driver's historical driving data, and then determine the target driver's driving style based on the vehicle status information and the target driver's historical driving data, and generate a targeted vehicle control strategy for the target driver on the target vehicle based on the target driver's driving style. This application's embodiments can solve the problem that related technologies using a uniform vehicle control strategy under extreme conditions may not be fully suitable for drivers with different driving styles. Furthermore, it allows for the targeted determination of a vehicle control strategy suitable for the target driver based on their driving style. 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 of this application and, together with the description thereof, serve to explain this application and do not constitute an undue limitation thereof. In the drawings:

[0021] Figure 1 This is a hardware structure block diagram of a computer device for generating a vehicle control strategy according to an embodiment of this application.

[0022] Figure 2 This is a flowchart of a method for generating a vehicle control strategy according to an embodiment of this application;

[0023] Figure 3 This is a flowchart of a lateral and longitudinal coordinated control system and method for extreme operating conditions that takes into account driving style, according to an optional embodiment of this application;

[0024] Figure 4 This is a schematic diagram of a longitudinal driver model according to an optional embodiment of this application;

[0025] Figure 5 This is a schematic diagram of a lateral driver model according to an optional embodiment of this application;

[0026] Figure 6 This is a schematic diagram of force analysis of a vehicle dynamics reference model according to an optional embodiment of this application;

[0027] Figure 7This is a structural block diagram of a vehicle control strategy generation device according to an embodiment of this application. Detailed Implementation

[0028] 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.

[0029] It should be noted that the terms and terms such as "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 a 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.

[0030] The methods and embodiments provided in this application can be executed in a computer device or similar computing device. Taking running on a computer device as an example, Figure 1 This is a hardware structure block diagram of a computer device for a vehicle control strategy generation method according to an embodiment of this application. For example... Figure 1 As shown, a computer device may include one or more ( Figure 1 Only one is shown in the diagram. A processor 102 (which may include, but is not limited to, a microprocessor (MPU) or a programmable logic device (PLD)) and a memory 104 for storing data are also shown. In one exemplary embodiment, the computer device may further include a transmission device 106 for communication functions and an input / output device 108. Those skilled in the art will understand that... Figure 1 The structure shown is for illustrative purposes only and does not limit the structure of the computer device described above. For example, the computer device may also include components that are more... Figure 1 The more or fewer components shown, or having the same Figure 1 Equivalent functions or ratios shown Figure 1 The functions shown have more different configurations.

[0031] The memory 104 can be used to store computer programs, such as application software programs and modules, like the computer program corresponding to the vehicle control strategy generation method in this embodiment. The processor 102 executes various functional applications and data processing by running the computer program stored in the memory 104, which is equivalent to implementing the above-described method. The memory 104 may include high-speed random access memory and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory 104 may further include memory remotely located relative to the processor 102, and these remote memories can be connected to the PTZ camera via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0032] The transmission device 106 is used to receive or send data via a network. Specific examples of the network described above may include a wireless network provided by a communication provider for the computer equipment. In one example, the transmission device 106 includes a Network Interface Controller (NIC), which can connect to other network devices via a base station to communicate with the Internet. In another example, the transmission device 106 may be a Radio Frequency (RF) module used for wireless communication with the Internet.

[0033] This embodiment provides a method for generating a vehicle control strategy. Figure 2 This is a flowchart of a method for generating a vehicle control strategy according to an embodiment of this application, running in the aforementioned computer device. The process includes the following steps:

[0034] Step S202: Classify the driver's driving style according to multiple sets of historical driving data to generate multiple driving styles, wherein the multiple sets of historical driving data include: historical driving data of multiple drivers;

[0035] Among them, multiple drivers can include drivers with different driving styles.

[0036] Step S204: Obtain the vehicle status information of the target vehicle and the historical driving data of the target driver driving the target vehicle;

[0037] Step S206: Determine the target driving style corresponding to the target driver from the multiple driving styles based on the vehicle status information and the target driver's historical driving data;

[0038] Step S208: Generate the vehicle control strategy of the target driver for the target vehicle based on the target driving style and the vehicle status information.

[0039] Through the above steps, driving styles are categorized based on multiple sets of historical driving data from various drivers to generate multiple driving styles; vehicle status information of the target vehicle and historical driving data of the target driver are obtained; the target driving style corresponding to the target driver is determined from among the multiple driving styles based on the vehicle status information and the target driver's historical driving data; and a vehicle control strategy for the target driver on the target vehicle is generated based on the target driving style and vehicle status information. In other words, after determining multiple driving styles based on the historical driving data of different drivers, this application embodiment can determine the vehicle status information of the target vehicle and the historical driving data of the target driver, and then determine the target driver's driving style based on the vehicle status information and the target driver's historical driving data, and generate a targeted vehicle control strategy for the target driver on the target vehicle based on the target driver's driving style. This application embodiment can solve the problem that the unified vehicle control strategy used in related technologies under extreme conditions may not be fully suitable for drivers with multiple different driving styles. Furthermore, a vehicle control strategy suitable for the target driver is determined specifically based on the driver's driving style.

[0040] Optionally, step S202 above, which classifies the driver's driving style based on multiple sets of historical driving data, includes: determining a longitudinal driver model and a lateral driver model corresponding to each set of historical driving data, wherein the longitudinal driver model is used to indicate the first average pedal opening and the first average pedal change rate of each set of historical driving data, and the lateral driver model is used to indicate the first average steering wheel angle change rate of each set of historical driving data. The first average pedal opening includes: the first average accelerator pedal opening and the first average brake pedal opening, and the first average pedal change rate includes: the first average accelerator pedal change rate and the first average brake pedal change rate; performing cluster analysis on multiple first average pedal openings, multiple first average pedal change rates, and multiple average steering wheel angle change rates to determine the multiple driver styles.

[0041] Understandably, multiple driver styles can be determined using longitudinal and lateral driver models. Specifically, the longitudinal driver model simulates the driver's operation of the accelerator or brake pedal to ensure the vehicle's actual speed tracks the target speed. The lateral driver model, on the other hand, simulates the steering wheel angle required for the driver to traverse the target path, analyzing and simulating the driver's lateral target requirements.

[0042] The first average pedal change rate and the first average pedal opening of each set of historical driving data are determined by the longitudinal driver model, and the first average steering wheel angle change rate of each set of historical driving data is determined by the lateral driver model.

[0043] Then, cluster analysis is performed based on the first average pedal change rate, the first average steering wheel angle change rate, and the first average pedal opening to determine multiple driving styles through driving data of different driving styles.

[0044] Optionally, the vehicle state information of the target vehicle in step S204 includes: basic state information of the target vehicle and key state information of the target vehicle, wherein the basic state information includes at least one of the following: vehicle speed information, wheel speed information, acceleration information, wheel angle information, vehicle body posture information, tire driving torque information, and tire braking torque information of the target vehicle; the acceleration information includes: longitudinal acceleration and lateral acceleration of the target vehicle; the vehicle body posture information includes: yaw rate of the target vehicle; and the key state information includes at least one of the following: tire force of the target vehicle, road adhesion coefficient of the target vehicle, and center of gravity sideslip angle of the target vehicle.

[0045] The acquisition of key state information of the target vehicle includes: determining the tire angular acceleration of the target vehicle based on the wheel speed information; determining the longitudinal force of the target vehicle's tires based on the tire driving torque information, the tire braking torque information, and the tire angular acceleration; determining the lateral force of the target vehicle's tires based on the lateral acceleration, the yaw rate, and the wheel rotation angle information; and determining the vertical load of the target vehicle's tires based on the longitudinal acceleration and the lateral acceleration. The tire forces of the target vehicle include: the longitudinal force, the lateral force, and the vertical load. The vehicle speed and yaw rate are used to determine the sideslip angle of the target vehicle's center of gravity.

[0046] It is understandable that vehicle status information includes: basic status information of the target vehicle and key status information of the target vehicle. Among them, tire force and center of gravity sideslip angle in the key status information can be calculated from the basic status information.

[0047] Specifically:

[0048] (1) Determine tire force:

[0049] 1) Determine the longitudinal force of the tire:

[0050] Determine the tire angular acceleration based on the wheel speed information in the basic state information;

[0051] The longitudinal force of the tire is determined based on the tire driving torque information, tire braking torque information, and tire angular acceleration.

[0052] 2) Determine the lateral force of the tire:

[0053] The lateral force of the tire is determined based on the yaw rate and wheel rotation angle information in the basic state information.

[0054] 3) Determine the vertical load on the tire:

[0055] The vertical load on the tire is determined based on the longitudinal and lateral accelerations in the basic state information.

[0056] Among them, the longitudinal force, lateral force, and vertical load of the tire constitute the tire force.

[0057] (2) Determine the centroid sideslip angle:

[0058] The center of gravity sideslip angle is determined based on the vehicle speed and yaw rate information in the basic status information.

[0059] Optionally, step S208 above, which generates the vehicle control strategy for the target driver based on the target driving style and the vehicle state information, includes: determining driving parameters corresponding to the target driving style, wherein the driving parameters include at least: a second average pedal opening, a second average pedal change rate, and a second average steering wheel angle change rate corresponding to the target driving style, wherein the second average pedal opening includes: a second average accelerator pedal opening and a second average brake pedal opening, and the second average pedal change rate includes: a second average accelerator pedal change rate and a second average brake pedal change rate; determining the optimal speed sequence and the optimal path sequence corresponding to the target driving style based on an optimization algorithm, the driving parameters, and the vehicle state information; determining the optimal driving trajectory of the target driver for the target driving style based on the optimal speed sequence and the optimal path sequence; and generating the vehicle control strategy based on the optimal driving trajectory.

[0060] The process of determining the optimal speed sequence corresponding to the target driving style based on the optimization algorithm, the driving parameters, and the vehicle state information includes: determining the vehicle speed information in the vehicle state information as the first state vector of the optimization algorithm; determining the tire friction circle of the target vehicle based on the road adhesion coefficient and tire force in the vehicle state information; determining the tire friction circle, the tire driving torque information in the vehicle state information, and the tire braking torque information in the vehicle state information as the first constraint conditions of the optimization algorithm; determining the second average pedal opening and the second average pedal change rate as the first supplementary constraint conditions of the optimization algorithm; and calculating the optimal speed sequence corresponding to the target driving style based on the first state vector, the first constraint conditions, and the first supplementary constraint conditions.

[0061] The process of determining the optimal path sequence corresponding to the target driving style based on the optimization algorithm, the driving parameters, and the vehicle state information includes: determining the center-of-gravity sideslip angle of the target vehicle based on the vehicle speed information and yaw rate in the vehicle state information; determining the center-of-gravity sideslip angle and the yaw rate as the second state vector of the optimization algorithm; determining the tire friction circle of the target vehicle based on the road adhesion coefficient and tire force in the vehicle state information; determining the tire friction circle as the second constraint condition of the optimization algorithm; determining the second average steering wheel angle change rate as the second supplementary constraint condition of the optimization algorithm; and calculating the optimal path sequence corresponding to the target driving style based on the second state vector, the second constraint condition, and the second supplementary constraint condition.

[0062] Understandably, the vehicle control strategy for the target driver is determined based on the target driving style and vehicle status information. Specifically:

[0063] 1) Determine the second average pedal opening, the second average pedal change rate, and the second average steering wheel angle change rate corresponding to the target driving style.

[0064] 2) Use the vehicle speed information in the basic state information as the first state vector of the optimization algorithm; determine the tire friction circle based on the adhesion coefficient and tire force in the key state information, and use the tire friction circle, the tire driving torque information and the tire braking torque information in the basic state information as the first constraint conditions of the optimization algorithm; use the second average pedal opening and the second average pedal change rate as the first supplementary constraint conditions of the optimization algorithm.

[0065] After determining the first state vector, the first constraint condition, and the first supplementary constraint condition, the optimal speed sequence corresponding to the target driving style can be determined through an optimization algorithm.

[0066] 3) Determine the centroid deviation angle based on the vehicle speed and yaw rate in the basic state information; use the centroid deviation angle and yaw rate as the second state vector of the optimization algorithm; determine the tire friction circle based on the tire force and road adhesion coefficient in the key state information, and use the tire friction circle as the second constraint condition; use the second average steering wheel angle change rate as the second supplementary constraint condition.

[0067] After determining the second state vector, the second constraint condition, and the second supplementary constraint condition, the optimal path sequence can be determined according to the optimization algorithm.

[0068] Then, the optimal driving trajectory is determined based on the optimal speed sequence and the optimal path sequence.

[0069] 4) After determining the optimal driving trajectory, the parameters of the target vehicle can be determined based on the optimal driving trajectory, thereby generating a vehicle control strategy.

[0070] The process of generating the vehicle control strategy based on the optimal driving trajectory includes: performing a force analysis on the target vehicle based on the vehicle state information to determine the force analysis results corresponding to the target vehicle, wherein the force analysis results include: longitudinal force balance, lateral force balance, and yaw moment balance corresponding to the target vehicle; and applying wheel angle constraints and tire friction circle constraints to the target vehicle based on the vehicle state information; determining the control variables of the optimal driving trajectory based on the force analysis results, the wheel angle constraints, the tire friction circle constraints, and the optimal tire adhesion utilization rate; and generating the vehicle control strategy based on the control variables and the optimal driving trajectory.

[0071] Understandably, after determining the optimal driving trajectory, it is necessary to determine the control variables corresponding to the optimal driving trajectory. Specifically:

[0072] (1) Perform force analysis on the target vehicle based on the three-degree-of-freedom vehicle dynamics reference model and vehicle state information.

[0073] The force analysis mainly includes: longitudinal force analysis, lateral force analysis, and yaw moment balance.

[0074] The vehicle status information includes tire longitudinal force, tire lateral force, and tire vertical load.

[0075] The stress analysis can be performed on each tire of the target vehicle.

[0076] (2) Based on the vehicle status information, the target vehicle is constrained by wheel angle and tire friction circle.

[0077] Based on vehicle status information such as wheel rotation angle and tire friction circle, wheel rotation angle constraints and tire friction circle constraints are applied to the target vehicle.

[0078] (3) Based on the force analysis results, the wheel angle constraint and the tire friction circle constraint, the control variables of the optimal driving trajectory are determined with the optimal tire adhesion utilization rate as the objective.

[0079] Obviously, the embodiments described above are only some embodiments of this application, and not all embodiments. To better understand the above method for generating vehicle control strategies, the process is described below with reference to embodiments, but this is not intended to limit the technical solutions of the embodiments of this application. Specifically:

[0080] Currently, regarding the issue that a uniform vehicle control strategy used in related technologies under extreme conditions may not be fully suitable for drivers with diverse driving styles, the following solutions exist in related technologies:

[0081] 1) A multi-style vehicle trajectory tracking and collision avoidance control method based on reinforcement learning, comprising the following main steps: Step 1, acquiring information about the vehicle to be controlled; Step 2, inputting the information into a pre-constructed optimal control model for vehicle trajectory tracking and collision avoidance based on a reinforcement learning framework, and outputting a multi-style parameterized policy network; Step 3, inputting the vehicle state, vehicle observation information, and style index coefficients from the information into the multi-style parameterized policy network, and outputting action quantities; Step 4, realizing multi-style vehicle trajectory tracking and collision avoidance control based on the action quantities. Optional embodiments of this application can achieve multi-style policy output for the same scenario, meeting the needs of multi-style driving. However, the above technical solution is only applicable to autonomous vehicles, and the multi-style policy is only designed for the passenger's riding experience, without considering the diversity of human drivers' driving styles. Furthermore, the reinforcement learning method used in the above technical solution requires sufficient training data to ensure the feasibility of the optimization results.

[0082] 2) A control system and method for extreme drifting of intelligent driving vehicles, the main steps of which include: Step 1, fusing and processing the collected vehicle data and calculating the vehicle's center of gravity sideslip angle; Step 2, predicting the vehicle's desired trajectory based on the vehicle dynamics model; Step 3, determining the vehicle's drift initiation state; Step 4, calculating the vehicle's drift dynamic balance state; Step 5, tracking the vehicle's desired trajectory and dynamic balance state; Step 6, controlling the vehicle based on the vehicle's desired trajectory and dynamic balance state. Optional embodiments of this application are applicable to intelligent electric vehicles with dual rear-wheel independent electric drive, where both motors have electric braking capabilities, effectively improving the vehicle's controllability and driving safety during drifting. However, the above technical solution is only applicable to intelligent electric vehicles with dual rear-wheel independent electric drive, where both motors have electric braking capabilities, and is not applicable to other powertrain configurations or vehicles without intelligent driving functions. Furthermore, it does not perform trajectory planning and motion control for different driving styles of actual drivers, and is only applicable to front-wheel steering vehicles, not vehicles with rear-wheel steering capabilities.

[0083] To address the problems existing in the technical solutions of the aforementioned related technologies, this application provides an optional embodiment of a lateral and longitudinal coordinated control system and method for extreme operating conditions that takes into account driving style. The purpose is to design an extreme operating condition control strategy based on the driver's driving style for vehicles that have both front-wheel steering and rear-wheel steering functions, thereby improving the vehicle handling performance under extreme operating conditions while ensuring driving safety and stability.

[0084] Figure 3 This is a flowchart of a lateral and longitudinal cooperative control system and method for extreme operating conditions, taking into account driving style, according to an optional embodiment of this application. Specifically:

[0085] Step S301: Obtain historical driving data of drivers with different driving styles and classify them according to driving style.

[0086] (1) Obtain driving data of drivers with different styles (i.e., various historical driving data of this application).

[0087] Acquire driving data from drivers with different driving styles, including at least the processes of straight-line acceleration and deceleration, turning or lane changing, and stabilization. Driving data should include at least the following information: vehicle position, speed, pedal input, and steering wheel angle.

[0088] (2) Establish a driver model.

[0089] The driver model consists of two parts: a longitudinal driver model and a lateral driver model.

[0090] Figure 4 This is a schematic diagram of a longitudinal driver model according to an optional embodiment of this application, such as... Figure 4 As shown:

[0091] Longitudinal driver model: This simulates the driver's operation of the accelerator or brake pedal, causing the car to respond longitudinally and ensuring that the vehicle's actual speed tracks the target speed. The PID controller corresponding to the driver and the relevant parameters in the vehicle speed feedback are determined using historical driving data.

[0092] Figure 5 This is a schematic diagram of a lateral driver model according to an optional embodiment of this application, such as... Figure 5 As shown:

[0093] Lateral Driver Model: This model simulates the steering wheel angle required for the driver to navigate the target path, analyzing and simulating the driver's lateral target requirements. Under extreme conditions, the driver needs to perform lateral control of the vehicle to follow the road curve while also controlling vehicle stability. Therefore, the steering angle output by the lateral driver model is determined by three parts: driver feedforward control, lateral feedback control based on the deviation between lateral displacement and the desired path, and yaw rate feedback control based on vehicle stability.

[0094] By controlling the steering wheel angle, the car can make lateral and yaw responses, thereby determining the lateral displacement and yaw rate.

[0095] (3) Driving style classification.

[0096] The average accelerator / brake pedal opening (i.e., the first average pedal opening of this application), the average accelerator / brake pedal change rate (i.e., the first average pedal change rate of this application), and the average steering wheel angle change rate (i.e., the average steering wheel angle change rate of this application) are used as key parameters characterizing the driver's operating characteristics. For driving data of drivers with different styles, the above key parameters are calculated separately, and cluster analysis is performed based on the calculation results to set classification criteria for various driving styles.

[0097] Step S302: Obtain the current vehicle status information and identify the current driver style.

[0098] (1) The basic vehicle status collected by the data acquisition equipment, including but not limited to:

[0099] Location information: X and Y coordinates of the vehicle's center of mass;

[0100] Vehicle speed information: Vehicle speed;

[0101] Wheel speed information: the rotational speed of each wheel;

[0102] Acceleration information: longitudinal acceleration, lateral acceleration;

[0103] Actual wheel rotation angle information (i.e., wheel rotation angle information in this application): front wheel rotation angle, rear wheel rotation angle;

[0104] Vehicle attitude information: yaw rate;

[0105] Drive torque information for each axle / wheel:

[0106] Braking torque information for each wheel:

[0107] (2) Based on the basic vehicle state information obtained in (1), estimate the key vehicle state information:

[0108] Tire force estimation includes estimation of longitudinal force, lateral force, and vertical load. The longitudinal tire force is estimated based on driving torque, braking torque, and wheel angular acceleration; the lateral tire force is estimated based on vehicle lateral acceleration, yaw rate, and front / rear wheel rotation angles; and the vertical tire load is estimated based on longitudinal acceleration and lateral acceleration.

[0109] Road surface adhesion coefficient estimation: Based on the relationship curve between tire adhesion coefficient and slip rate under different road surface conditions, the current road surface adhesion coefficient is estimated.

[0110] The center of gravity sideslip angle is estimated based on the longitudinal and lateral vehicle speeds.

[0111] (3) Based on the current vehicle status information obtained, the driving style of the current driver is identified by using fuzzy control and other methods.

[0112] Step S303, Trajectory Planning: Obtain the optimal trajectory corresponding to the current driving style.

[0113] Trajectory planning consists of two parts: velocity planning and path planning.

[0114] Different driving styles require different speeds and paths under extreme conditions; therefore, trajectory planning must be based on the current driver's driving style.

[0115] (1) Speed ​​planning:

[0116] The optimal speed sequence is solved iteratively by taking the shortest time to pass through a curve, with vehicle speed as the state variable and the constraints being the vehicle's maximum speed, the driving torque limit of the power system, the braking torque limit of the braking system, and the tire friction circle limit.

[0117] For the current driving style, the average accelerator / brake pedal opening and its rate of change range are used as supplementary constraints. The optimal speed sequence corresponding to the current driving style can be obtained by optimization.

[0118] (2) Path planning:

[0119] The optimal path sequence is solved by using convex optimization and other optimization methods, with the goal of navigating a curve with the minimum curvature, the yaw rate and the sideslip angle of the center of mass as state variables, and the road boundary and tire friction circle as constraints.

[0120] For the current driving style, the range of average steering wheel angle change rate is used as a supplementary constraint. Through optimization, the optimal path sequence corresponding to the current driving style can be obtained.

[0121] Step S304, Motion Control: Solve for the control variables corresponding to the optimal trajectory.

[0122] In order for the vehicle controlled by the driver to travel along the optimal trajectory, it is necessary to solve for the control variables corresponding to the optimal trajectory, and achieve the control objective through the actions of the vehicle drive system, braking system, and steering system.

[0123] Vehicle stability under extreme conditions is the primary concern. Since the actions of the vehicle's drive, braking, and steering systems rely on the forces between the tires and the ground to achieve changes in vehicle state, and because the horizontal force on the tires is limited by the vertical load, it is crucial to utilize each tire as evenly as possible according to the vertical load to avoid severe load on any one tire during vehicle movement, which could affect the vehicle's ultimate performance. Therefore, optimal tire grip utilization is the goal.

[0124] Optimal tire adhesion utilization objective function: Where the subscripts i = fl, fr, rl, rr represent the left front wheel, right front wheel, left rear wheel, and right rear wheel, respectively; μ ui For tire adhesion utilization rate; F x，i F is the longitudinal force of the tire. y，i F is the lateral force of the tire; z，i The vertical load on the tire; μ max This is the road surface adhesion coefficient.

[0125] A three-degree-of-freedom vehicle dynamics model with front-wheel and rear-wheel steering is used as the reference model, with the three degrees of freedom being longitudinal, lateral, and yaw degrees of freedom. Vehicle state information is used as state variables, mainly including vehicle position, speed, yaw rate, and sideslip angle. Front wheel steering angle, rear wheel steering angle, and driving / braking forces of each wheel are used as control variables. Furthermore, the constraints that the vehicle motion must satisfy include longitudinal force balance, lateral force balance, yaw moment balance, and tire friction circle constraints.

[0126] Figure 6 This is a schematic diagram of the force analysis of a vehicle dynamics reference model according to an optional embodiment of this application, such as... Figure 6 As shown:

[0127] 1) Longitudinal force balance:

[0128] ma x =(F x,fl +F x,fr cosδ f +(F x,rl +F x,rr cosδ r -(F y,fl +F y,fr sinδ f -(F y,rl +F y,rr sinδ r ;

[0129] 2) Lateral force balance:

[0130] ma y =(F x,fl +F x,fr sinδ f -(F x,rl +F x,rr sinδ r +(F y,fl +F y,fr cosδ f -(F y,rl +F y,rr cosδ r ;

[0131] 3) Balancing of yaw moment:

[0132]

[0133] 4) Wheel angle control constraints:

[0134] Wheel angle constraints are twofold: firstly, the mechanical constraints of the steering system, and secondly, the constraints of the control strategy.

[0135] δ f,min ≤δ f ≤δ f,max ;δ r,min ≤δ r ≤δ r,max ;δ r =f(δ) f ,ω r ,…);

[0136] 5) Tire friction circle constraint:

[0137] Where, α fl α fr α rl α rr These are the sideslip angles for the left front wheel, right front wheel, left rear wheel, and right rear wheel, respectively; m is the vehicle mass; Iz Let a be the moment of inertia of the vehicle body about its vertical axis; x The longitudinal acceleration of the vehicle; a y This refers to the vehicle's lateral acceleration. δ is the yaw acceleration; f，max and δ f，min These are the maximum and minimum values ​​of the front wheel steering angle, respectively; δ r，max and δ r，min These are the maximum and minimum values ​​of the rear wheel steering angle, respectively; a This is the distance from the front axle to the vehicle's center of gravity; b d is the distance from the rear axle to the vehicle's center of gravity. w This refers to the wheel track between the left and right wheels.

[0138] Step S305: Send the optimal control strategy to the corresponding actuator for action.

[0139] Based on the current driving style of the driver, the optimal control strategy is sent to the vehicle's power and transmission system, steering system and other actuators to take action.

[0140] Through the optional embodiments of this application, for vehicles with both front-wheel steering and rear-wheel steering functions, the vehicle handling performance under extreme conditions can be improved by controlling the rear wheel steering angle, while ensuring driving safety and stability. The optional embodiments of this application are applicable to lateral and longitudinal coordinated control of different powertrain configurations, such as split-axle four-wheel drive and distributed four-wheel drive systems. Control strategies are designed for different driving styles, taking into account the driver's influence on the human-vehicle system response, to achieve optimal system response.

[0141] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods according to the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions to cause a terminal device (which may be a mobile phone, computer, server, or network device, etc.) to execute the methods of the various embodiments of this application.

[0142] This embodiment also provides a vehicle control strategy generation apparatus, which is used to implement the above embodiments and preferred embodiments; details already described will not be repeated. As used below, the term "module" can be a combination of software and / or hardware that implements a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0143] Figure 7 This is a structural block diagram of a vehicle control strategy generation apparatus according to an embodiment of this application. The apparatus includes:

[0144] The classification module 72 is used to classify the driver's driving style based on multiple sets of historical driving data to generate multiple driving styles, wherein the multiple sets of historical driving data include: historical driving data of multiple drivers;

[0145] The acquisition module 74 is used to acquire the vehicle status information of the target vehicle and the historical driving data of the target driver driving the target vehicle.

[0146] The determining module 76 is used to determine the target driving style corresponding to the target driver among the multiple driving styles based on the vehicle status information and the target driver's historical driving data;

[0147] The generation module 78 is used to generate a vehicle control strategy for the target driver on the target vehicle based on the target driving style and the vehicle status information.

[0148] This application's embodiments classify drivers' driving styles based on multiple sets of historical driving data from multiple drivers to generate various driving styles; acquire vehicle status information of the target vehicle and historical driving data of the target driver driving the target vehicle; determine the target driving style corresponding to the target driver among the various driving styles based on the vehicle status information and the target driver's historical driving data; and generate a vehicle control strategy for the target driver on the target vehicle based on the target driving style and vehicle status information. In other words, after determining multiple driving styles based on different drivers' historical driving data, this application's embodiments can determine the vehicle status information of the target vehicle and the target driver's historical driving data, and then determine the target driver's driving style based on the vehicle status information and the target driver's historical driving data, and generate a targeted vehicle control strategy for the target driver on the target vehicle based on the target driver's driving style. This application's embodiments can solve the problem that related technologies using a uniform vehicle control strategy under extreme conditions may not be fully suitable for drivers with different driving styles. Furthermore, it allows for the targeted determination of a vehicle control strategy suitable for the target driver based on their driving style.

[0149] In an exemplary embodiment, the classification module 72 is further configured to determine a longitudinal driver model and a lateral driver model corresponding to each set of historical driving data, wherein the longitudinal driver model is used to indicate the first average pedal opening and the first average pedal change rate of each set of historical driving data, and the lateral driver model is used to indicate the first average steering wheel angle change rate of each set of historical driving data. The first average pedal opening includes: a first average accelerator pedal opening and a first average brake pedal opening, and the first average pedal change rate includes: a first average accelerator pedal change rate and a first average brake pedal change rate. Cluster analysis is performed on multiple first average pedal openings, multiple first average pedal change rates, and multiple average steering wheel angle change rates to determine the multiple driver styles.

[0150] In an exemplary embodiment, the vehicle state information of the target vehicle in the acquisition module 74 includes: basic state information of the target vehicle and key state information of the target vehicle, wherein the basic state information includes at least one of the following: vehicle speed information, wheel speed information, acceleration information, wheel angle information, vehicle body posture information, tire driving torque information, and tire braking torque information of the target vehicle; the acceleration information includes: longitudinal acceleration and lateral acceleration of the target vehicle; the vehicle body posture information includes: yaw rate of the target vehicle; and the key state information includes at least one of the following: tire force of the target vehicle, road adhesion coefficient of the target vehicle, and center of gravity sideslip angle of the target vehicle.

[0151] In an exemplary embodiment, the acquisition module 74 is further configured to: determine the tire angular acceleration of the target vehicle based on the wheel speed information; determine the tire longitudinal force of the target vehicle based on the tire driving torque information, the tire braking torque information, and the tire angular acceleration; determine the tire lateral force of the target vehicle based on the lateral acceleration, the yaw rate, and the wheel rotation angle information; and determine the tire vertical load of the target vehicle based on the longitudinal acceleration and the lateral acceleration, wherein the tire force of the target vehicle includes: the tire longitudinal force, the tire lateral force, and the tire vertical load; and determine the center-of-gravity sideslip angle of the target vehicle based on the vehicle speed information and the yaw rate.

[0152] In an exemplary embodiment, the generation module 78 is further configured to determine driving parameters corresponding to the target driving style, wherein the driving parameters include at least: a second average pedal opening corresponding to the target driving style, a second average pedal change rate corresponding to the target driving style, and a second average steering wheel angle change rate corresponding to the target driving style, wherein the second average pedal opening includes: a second average accelerator pedal opening and a second average brake pedal opening, and the second average pedal change rate includes: a second average accelerator pedal change rate and a second average brake pedal change rate; determine an optimal speed sequence and an optimal path sequence corresponding to the target driving style based on an optimization algorithm, the driving parameters, and the vehicle state information; determine an optimal driving trajectory for the target driver of the target driving style based on the optimal speed sequence and the optimal path sequence; and generate the vehicle control strategy based on the optimal driving trajectory.

[0153] In an exemplary embodiment, the generation module 78 is further configured to: determine the vehicle speed information in the vehicle state information as the first state vector of the optimization algorithm; determine the tire friction circle of the target vehicle based on the road adhesion coefficient and tire force in the vehicle state information; determine the tire friction circle, the tire driving torque information in the vehicle state information, and the tire braking torque information in the vehicle state information as the first constraint condition of the optimization algorithm; determine the second average pedal opening and the second average pedal change rate as the first supplementary constraint condition of the optimization algorithm; and calculate the optimal speed sequence corresponding to the target driving style based on the first state vector, the first constraint condition, and the first supplementary constraint condition.

[0154] In an exemplary embodiment, the generation module 78 is further configured to: determine the center-of-gravity sideslip angle of the target vehicle based on the vehicle speed information and yaw rate in the vehicle state information; determine the center-of-gravity sideslip angle and the yaw rate as the second state vector of the optimization algorithm; determine the tire friction circle of the target vehicle based on the road adhesion coefficient and tire force in the vehicle state information; determine the tire friction circle as the second constraint condition of the optimization algorithm; determine the second average steering wheel angle change rate as the second supplementary constraint condition of the optimization algorithm; and calculate the optimal path sequence corresponding to the target driving style based on the second state vector, the second constraint condition, and the second supplementary constraint condition.

[0155] In an exemplary embodiment, the generation module 78 is further configured to perform force analysis on the target vehicle based on the vehicle state information to determine the force analysis result corresponding to the target vehicle, wherein the force analysis result includes: longitudinal force balance, lateral force balance, and yaw moment balance corresponding to the target vehicle; and to impose wheel angle constraints and tire friction circle constraints on the target vehicle based on the vehicle state information; to determine the optimal tire adhesion utilization rate of the target vehicle based on the road adhesion coefficient and tire force of the target vehicle in the vehicle state information; to determine the control variables of the optimal driving trajectory based on the force analysis result, the wheel angle constraints, the tire friction circle constraints, and the optimal tire adhesion utilization rate; and to generate the vehicle control strategy based on the control variables and the optimal driving trajectory.

[0156] Optionally, in this embodiment, the storage medium may be configured to store a computer program for performing the following steps:

[0157] S1, classify the driver's driving style according to multiple sets of historical driving data to generate multiple driving styles, wherein the multiple sets of historical driving data include: historical driving data of multiple drivers;

[0158] S2, Obtain the vehicle status information of the target vehicle and the historical driving data of the target driver driving the target vehicle;

[0159] S3, determine the target driving style corresponding to the target driver from the multiple driving styles based on the vehicle status information and the target driver's historical driving data;

[0160] S4, Generate the vehicle control strategy of the target driver for the target vehicle based on the target driving style and the vehicle status information.

[0161] In one exemplary embodiment, the aforementioned computer-readable storage medium may include, but is not limited to, various media capable of storing computer programs, such as a USB flash drive, read-only memory (ROM), random access memory (RAM), portable hard disk, magnetic disk, or optical disk.

[0162] Specific examples in this embodiment can be found in the examples described in the above embodiments and exemplary implementations, and will not be repeated here.

[0163] Embodiments of this application also provide an electronic device including a memory and a processor, wherein the memory stores a computer program and the processor is configured to run the computer program to perform the steps in any of the above method embodiments.

[0164] Optionally, in this embodiment, the processor can be configured to perform the following steps via a computer program:

[0165] S1, classify the driver's driving style according to multiple sets of historical driving data to generate multiple driving styles, wherein the multiple sets of historical driving data include: historical driving data of multiple drivers;

[0166] S2, Obtain the vehicle status information of the target vehicle and the historical driving data of the target driver driving the target vehicle;

[0167] S3, determine the target driving style corresponding to the target driver from the multiple driving styles based on the vehicle status information and the target driver's historical driving data;

[0168] S4, Generate the vehicle control strategy of the target driver for the target vehicle based on the target driving style and the vehicle status information.

[0169] In one exemplary embodiment, the electronic device may further include a transmission device and an input / output device, wherein the transmission device is connected to the processor and the input / output device is connected to the processor.

[0170] Embodiments of this application also provide a computer program product, including a computer program that is executed by a processor through the steps of any of the above method embodiments.

[0171] Optionally, in this embodiment, the above-described computer program product can be executed by a processor using the following steps:

[0172] S1, classify the driver's driving style according to multiple sets of historical driving data to generate multiple driving styles, wherein the multiple sets of historical driving data include: historical driving data of multiple drivers;

[0173] S2, Obtain the vehicle status information of the target vehicle and the historical driving data of the target driver driving the target vehicle;

[0174] S3, determine the target driving style corresponding to the target driver from the multiple driving styles based on the vehicle status information and the target driver's historical driving data;

[0175] S4, Generate the vehicle control strategy of the target driver for the target vehicle based on the target driving style and the vehicle status information.

[0176] Specific examples in this embodiment can be found in the examples described in the above embodiments and exemplary implementations, and will not be repeated here.

[0177] Obviously, those skilled in the art should understand that the modules or steps of this application described above can be implemented using general-purpose computing devices. They can be centralized on a single computing device or distributed across a network of multiple computing devices. They can be implemented using computer-executable program code, and thus can be stored in a storage device for execution by a computing device. In some cases, the steps shown or described can be performed in a different order than those presented here, or they can be fabricated as separate integrated circuit modules, or multiple modules or steps can be fabricated as a single integrated circuit module. Thus, this application is not limited to any particular combination of hardware and software.

[0178] The above description is merely a preferred embodiment of this application and is not intended to limit this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the principles of this application should be included within the protection scope of this application.

Claims

1. A method for generating a vehicle control strategy, characterized in that, include: The driving styles of drivers are classified based on multiple sets of historical driving data to generate multiple driving styles. The multiple sets of historical driving data include the historical driving data of multiple drivers. Obtain the vehicle status information of the target vehicle and the historical driving data of the target driver who is driving the target vehicle; Based on the vehicle status information and the target driver's historical driving data, the target driving style corresponding to the target driver is determined from the multiple driving styles; The vehicle control strategy for the target vehicle by the target driver is generated based on the target driving style and the vehicle status information.

2. The method for generating a vehicle control strategy according to claim 1, characterized in that, Drivers' driving styles are categorized based on multiple sets of historical driving data, including: Determine the longitudinal driver model and the lateral driver model corresponding to each set of historical driving data. The longitudinal driver model is used to indicate the first average pedal opening and the first average pedal change rate of each set of historical driving data. The lateral driver model is used to indicate the first average steering wheel angle change rate of each set of historical driving data. The first average pedal opening includes: the first average accelerator pedal opening and the first average brake pedal opening. The first average pedal change rate includes: the first average accelerator pedal change rate and the first average brake pedal change rate. Cluster analysis is performed on multiple first average pedal openings, multiple first average pedal change rates, and multiple average steering wheel angle change rates to determine the multiple driver styles.

3. The method for generating a vehicle control strategy according to claim 1, characterized in that, The vehicle status information of the target vehicle includes: The target vehicle's basic state information and key state information, wherein the basic state information includes at least one of the following: the target vehicle's speed information, the target vehicle's wheel speed information, the target vehicle's acceleration information, the target vehicle's wheel angle information, the target vehicle's body posture information, the target vehicle's tire driving torque information, and the target vehicle's tire braking torque information; the acceleration information includes the target vehicle's longitudinal acceleration and lateral acceleration; and the body posture information includes the target vehicle's yaw rate. The key status information includes at least one of the following: the tire force of the target vehicle, the road adhesion coefficient of the target vehicle, and the center of gravity sideslip angle of the target vehicle.

4. The method for generating a vehicle control strategy according to claim 3, characterized in that, Obtaining key status information of the target vehicle, including: The tire angular acceleration of the target vehicle is determined based on the wheel speed information; The longitudinal force of the target vehicle's tires is determined based on the tire driving torque information, the tire braking torque information, and the tire angular acceleration; the lateral force of the target vehicle's tires is determined based on the lateral acceleration, the yaw rate, and the wheel rotation angle information; and the vertical load of the target vehicle's tires is determined based on the longitudinal acceleration and the lateral acceleration. The tire forces of the target vehicle include: the longitudinal force, the lateral force, and the vertical load. The sideslip angle of the target vehicle's center of gravity is determined based on the vehicle speed information and the yaw rate.

5. The method for generating a vehicle control strategy according to claim 1, characterized in that, Generate a vehicle control strategy for the target driver based on the target driving style and the vehicle status information, including: Determine the driving parameters corresponding to the target driving style, wherein the driving parameters include at least: the second average pedal opening corresponding to the target driving style, the second average pedal change rate corresponding to the target driving style, and the second average steering wheel angle change rate corresponding to the target driving style, wherein the second average pedal opening includes: the second average accelerator pedal opening and the second average brake pedal opening, and the second average pedal change rate includes: the second average accelerator pedal change rate and the second average brake pedal change rate. The optimal speed sequence and optimal path sequence corresponding to the target driving style are determined based on the optimization algorithm, the driving parameters, and the vehicle state information. The optimal driving trajectory of the target driver with the target driving style is determined based on the optimal speed sequence and the optimal path sequence. The vehicle control strategy is generated based on the optimal driving trajectory.

6. The method for generating a vehicle control strategy according to claim 5, characterized in that, Determining the optimal speed sequence corresponding to the target driving style based on the optimization algorithm, the driving parameters, and the vehicle state information includes: The vehicle speed information in the vehicle status information is determined as the first state vector of the optimization algorithm; The tire friction circle of the target vehicle is determined based on the road surface adhesion coefficient and tire force in the vehicle status information. The tire friction circle, the tire driving torque information in the vehicle status information, and the tire braking torque information in the vehicle status information are determined as the first constraint conditions of the optimization algorithm. The second average pedal opening and the second average pedal change rate are determined as the first supplementary constraint conditions of the optimization algorithm; The optimal speed sequence corresponding to the target driving style is calculated based on the first state vector, the first constraint, and the first supplementary constraint.

7. The method for generating a vehicle control strategy according to claim 5, characterized in that, Determining the optimal path sequence corresponding to the target driving style based on the optimization algorithm, the driving parameters, and the vehicle state information includes: The center-of-gravity sideslip angle of the target vehicle is determined based on the vehicle speed information and yaw rate in the vehicle status information. The centroid sideslip angle and the yaw rate are determined as the second state vector of the optimization algorithm; The tire friction circle of the target vehicle is determined based on the road surface adhesion coefficient and tire force in the vehicle status information. The tire friction circle is defined as the second constraint condition of the optimization algorithm; The second average rate of change of steering wheel angle is determined as the second supplementary constraint condition of the optimization algorithm; The optimal path sequence corresponding to the target driving style is calculated based on the second state vector, the second constraint, and the second supplementary constraint.

8. The method for generating a vehicle control strategy according to claim 5, characterized in that, The vehicle control strategy is generated based on the optimal driving trajectory, including: Force analysis is performed on the target vehicle based on the vehicle status information to determine the force analysis results corresponding to the target vehicle. The force analysis results include: longitudinal force balance, lateral force balance, and yaw moment balance corresponding to the target vehicle; and wheel angle constraints and tire friction circle constraints are applied to the target vehicle based on the vehicle status information. The optimal tire adhesion utilization rate of the target vehicle is determined based on the road adhesion coefficient and tire force of the target vehicle in the vehicle status information. The control variables for the optimal driving trajectory are determined based on the force analysis results, the wheel angle constraint, the tire friction circle constraint, and the optimal tire adhesion utilization rate. The vehicle control strategy is generated based on the control variables and the optimal driving trajectory.

9. A vehicle control strategy generation device, characterized in that, include: The classification module is used to classify the driver's driving style based on multiple sets of historical driving data to generate multiple driving styles. The multiple sets of historical driving data include: historical driving data of multiple drivers. The acquisition module is used to acquire the vehicle status information of the target vehicle and the historical driving data of the target driver driving the target vehicle; The determination module is used to determine the target driving style corresponding to the target driver among the multiple driving styles based on the vehicle status information and the target driver's historical driving data; The generation module is used to generate a vehicle control strategy for the target driver on the target vehicle based on the target driving style and the vehicle status information.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored program, wherein the program, when executed, performs the method described in any one of claims 1 to 8.

11. An electronic device comprising a memory and a processor, characterized in that, The memory stores a computer program, and the processor is configured to execute the method described in any one of claims 1 to 8 through the computer program.

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