Vehicle control method, control equipment, vehicle and readable storage medium

Abstract

The invention discloses a vehicle control method, control equipment, a vehicle and a computer readable storage medium, and the vehicle control method comprises the steps: obtaining biological characteristic information, driving environment data and driving behavior data of a driver; based on the biological characteristic information, the driving environment data and the driving behavior data, the steering torque is dynamically calculated through a weighting algorithm; and the steering torque is sent to an electric power steering system controller so as to adjust the steering output torque of the vehicle. In this way, according to the technical scheme, accurate adjustment of the steering output torque of the vehicle can be achieved, so that the driving safety and individuation are improved.

Description

Technical Field

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

[0002] In existing car computer systems, users often log in through personal accounts and save the driver's general settings, driving modes, steering assist modes and other related settings in their personal accounts. Only when the user logs in to his or her personal account can the relevant settings of the entire vehicle match the driver's selection. When different drivers drive the same vehicle, different drivers need to manually set their favorite steering assist modes, and they need to reset them every time they switch drivers. And for most users, they can only choose a single steering assist mode, and the user's personalized experience is relatively simple. Summary of the invention

[0003] The purpose of this application is to provide a vehicle control method, a control device, a vehicle and a computer-readable storage medium, which can achieve precise adjustment of the vehicle steering output torque to improve driving safety and personalization.

[0004] To achieve the above objectives:

[0005] An embodiment of the present application provides a vehicle control method, including: obtaining a driver's biometric information, driving environment data, and driving behavior data; dynamically calculating a steering torque through a weighted algorithm based on the biometric information, driving environment data, and driving behavior data; and sending the steering torque to an electric power steering system controller to adjust the vehicle's steering output torque.

[0006] In one embodiment, based on biometric information and driving environment data, the steering torque is dynamically calculated by a weighted algorithm, including:

[0007] Determine a first steering assist factor based on the biometric information and the driving environment data, and determine a first steering torque according to the first steering assist factor and its corresponding weight coefficient;

[0008] Determine a second steering assist factor based on the driving behavior data and the driving environment data, and determine a second steering torque according to the second steering assist factor and its corresponding weight coefficient;

[0009] The steering torque is calculated according to the first steering torque and the second steering torque.

[0010] In one embodiment, the first steering assist factor includes at least one of facial information, gender, age, weight and facial expression in the biometric information, and at least one of road type and weather conditions in the driving environment data.

[0011] In one embodiment, the driving behavior data includes the steering wheel angle change rate and the vehicle speed, and the driving environment data also includes the road adhesion coefficient. The vehicle control method provided by the present application includes:

[0012] Calculate the friction compensation torque correction coefficient, the aligning torque correction coefficient and the damping torque correction coefficient according to the steering wheel angle change rate, the vehicle speed and the road adhesion coefficient;

[0013] adjusting the friction compensation torque according to the friction compensation torque correction coefficient, adjusting the aligning torque according to the aligning torque correction coefficient, and adjusting the damping torque according to the damping torque correction coefficient;

[0014] The second steering torque is calculated according to the friction compensation torque, the aligning torque and the damping torque.

[0015] In one embodiment, the driving behavior data also includes braking frequency. The vehicle control method provided by the present application includes:

[0016] The driving style score is calculated based on the steering wheel angle change rate and braking frequency. The driving style score is positively correlated with the steering wheel angle change rate and braking frequency.

[0017] According to the driving style score, the weight coefficient corresponding to the second steering power factor is dynamically adjusted within a preset range;

[0018] When the driving style score is higher than a preset threshold, the weight coefficient corresponding to the first steering power factor is reduced, and the weight coefficient corresponding to the second steering power factor is increased.

[0019] In one embodiment, it further comprises:

[0020] Matching the first steering assist factor to a preset assist curve, where the assist curve is generated by calibrating steering torque requirements of different biometric information and / or driving environment data of a real vehicle;

[0021] The steering torque corresponding to the first steering assist factor is determined according to the assist curve.

[0022] In one embodiment, it further comprises:

[0023] If the biometric information fails to be obtained or the driving environment data is abnormal, a basic power assist value is generated based on the linear relationship between vehicle speed and steering wheel torque to adjust the vehicle's steering output torque.

[0024] An embodiment of the present application also provides a control device, including: a processor and a memory storing a computer program, and when the processor runs the computer program, the vehicle control method steps described in any one of the above items are implemented.

[0025] An embodiment of the present application also provides a vehicle equipped with the above-mentioned control device.

[0026] An embodiment of the present application provides a computer-readable storage medium, in which a computer program is stored. When the computer program is executed by a processor, the vehicle control method steps described in any one of the above items are implemented.

[0027] The vehicle control method, control device, vehicle and computer-readable storage medium provided in the embodiments of the present application include: obtaining the driver's biometric information, driving environment data and driving behavior data; dynamically calculating the steering torque through a weighted algorithm based on the biometric information, driving environment data and driving behavior data; sending the steering torque to the electric power steering system controller to adjust the steering output torque of the vehicle. In this way, the technical solution of the present application can achieve precise adjustment of the vehicle's steering output torque to improve driving safety and comfort. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Figure 1 A flow chart of a vehicle control method provided in an embodiment of the present application.

[0029] Figure 2 A schematic diagram of the structure of a vehicle control system provided in an embodiment of the present application.

[0030] Figure 3 A schematic diagram of a process for determining a steering assist mode according to an example embodiment of the present application.

[0031] Figure 4 This is another schematic diagram of determining a steering assist mode according to an embodiment of the present application.

[0032] Figure 5 This is one of the schematic diagrams of the power assistance curve of an example embodiment of the present application.

[0033] Figure 6 This is the second schematic diagram of the power assistance curve of an example embodiment of the present application.

[0034] Figure 7 This is the third schematic diagram of the power assistance curve of an example embodiment of the present application.

[0035] Figure 8 This is the fourth schematic diagram of the assistance curve of an example embodiment of the present application.

[0036] Fig. 9 This is the fifth schematic diagram of the assistance curve of the embodiment of the present application.

[0037] Fig.10 This is the sixth schematic diagram of the assist curve of an example embodiment of the present application.

[0038] Fig.11 A schematic diagram of the structure of a control device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0039] Exemplary embodiments will be described in detail herein, examples of which are shown in the accompanying drawings. When the following description refers to the drawings, the same numbers in different drawings represent the same or similar elements unless otherwise indicated. The implementations described in the following exemplary embodiments do not represent all implementations consistent with the present application. Instead, they are merely examples of devices and methods consistent with some aspects of the present application as detailed in the appended claims.

[0040] It should be noted that, in this article, the terms "include", "comprises" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, an element defined by the sentence "includes a ..." does not exclude the existence of other identical elements in the process, method, article or device including the element. In addition, components, features, and elements with the same name in different embodiments of the present application may have the same meaning or different meanings, and their specific meanings need to be determined by their explanation in the specific embodiment or further combined with the context of the specific embodiment.

[0041] It should be understood that, although the terms first, second, third, etc. may be used to describe various information in this article, these information should not be limited to these terms. These terms are only used to distinguish the same type of information from each other. For example, without departing from the scope of this article, the first information may also be referred to as the second information, and similarly, the second information may also be referred to as the first information. Depending on the context, the word "if" as used herein can be interpreted as "at the time of..." or "when..." or "in response to determination". Furthermore, as used in this article, the singular forms "one", "one" and "the" are intended to also include plural forms, unless there is an opposite indication in the context. It should be further understood that the terms "comprising", "including" indicate that there are described features, steps, operations, elements, components, projects, kinds, and / or groups, but do not exclude the existence, occurrence or addition of one or more other features, steps, operations, elements, components, projects, kinds, and / or groups. The terms "or" and "and / or" used herein are interpreted as inclusive, or mean any one or any combination. Thus, “A, B, or C” or “A, B and / or C” means “any of the following: A; B; C; A and B; A and C; B and C; A, B, and C.” An exception to this definition will occur only when a combination of elements, functions, steps, or operations are inherently mutually exclusive in some manner.

[0042] It should be understood that, although the various steps in the flowchart in the embodiment of the present application are displayed in sequence according to the indication of the arrows, these steps are not necessarily performed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and it can be performed in other orders. Moreover, at least a portion of the steps in the figure may include a plurality of sub-steps or a plurality of stages, and these sub-steps or stages are not necessarily performed at the same time, but can be performed at different times, and their execution order is not necessarily performed in sequence, but can be performed in turn or alternately with other steps or at least a portion of the sub-steps or stages of other steps.

[0043] It should be noted that in this article, step codes such as S1 and S2 are used for the purpose of expressing the corresponding content more clearly and concisely, and do not constitute a substantial limitation on the order. When implementing the step, those skilled in the art may execute S2 first and then S1, etc., but these should all be within the scope of protection of this application.

[0044] It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0045] In the subsequent description, the suffixes such as "module", "component" or "unit" used to represent elements are only used to facilitate the description of the present application, and have no specific meanings. Therefore, "module", "component" or "unit" can be used in a mixed manner.

[0046] See also Figure 1 , is a vehicle control method provided in an embodiment of the present application. The method can be executed by a control device provided in an embodiment of the present application. The control device can be implemented in software and / or hardware.

[0047] The vehicle control method provided in this embodiment includes the following steps (e.g., steps S1 to S3):

[0048] Step S1: Acquire the driver's biometric information, driving environment data, and driving behavior data.

[0049] Among them, biometric information is used to reflect the biometric characteristics of the vehicle driver, which can be collected and analyzed by in-vehicle sensors, including but not limited to facial information, gender, age, weight and facial expressions. For example, the driver's facial information can be collected by the in-vehicle camera, and the driver's gender, age, etc. can be further analyzed. The driver's weight information can be obtained through the seat pressure sensor. The driver's heart rate data can be detected by the in-vehicle infrared camera, and the driver's emotional and physiological state, such as calmness, excitement, anger, frustration, fatigue, etc., can be analyzed in combination with facial expressions.

[0050] Driving environment data is used to reflect the external driving environment of the vehicle, and can be collected / analyzed by external sensors or third-party servers, including but not limited to road type and weather conditions. For example, a vehicle camera installed outside the vehicle can be used to capture road images, and then a machine learning algorithm (such as a convolutional neural network) can be used to identify and classify the driving road surface as high-speed, muddy, or icy and snowy roads. Push information on weather conditions can be obtained in real time through the vehicle navigation system or weather application.

[0051] Driving behavior data is used to reflect the driver's way and habits of operating the vehicle, including but not limited to the steering wheel angle change rate, vehicle speed and braking frequency, which can be obtained and analyzed through in-vehicle sensors. The steering wheel angle change rate is the speed at which the steering wheel turns, that is, the change in the steering wheel angle per unit time. The braking frequency indicates the number of times the driver brakes per unit time, reflecting the driver's habit of sudden braking or speed control. For example, the steering wheel angle and its change rate, as well as the vehicle's speed information, can be monitored in real time through sensors installed in the vehicle; the frequency and intensity of vehicle braking can also be recorded using acceleration sensors.

[0052] In one embodiment, the vehicle control method may be based on Figure 2 The vehicle control system shown is implemented. The vehicle control system includes a large screen, a car module, an EPS (Electric Power Steering) controller, a face recognition module and an EPS steering execution module. Among them, the large screen can be used to prompt relevant information about the steering assist, such as recording the steering assist mode, the steering assist torque, recording the current driver information and the steering mode set by the driver. The driver can interact with the car module through the large screen. The face recognition module is used to input the driver information to the EPS controller after identifying the driver's facial information. The EPS controller is used to receive or send relevant information about the steering assist from the car module to the car module. The EPS controller is also connected to the EPS steering execution module, and after identifying the driver's output steering mode, it controls the EPS steering execution module to perform steering assist.

[0053] It should be noted that power steering is to reduce the force that the driver needs to apply when turning the steering wheel through the auxiliary system, thereby improving driving comfort and vehicle handling performance. The power steering system can automatically adjust the amount of power provided according to factors such as vehicle speed and steering angle, making steering easier when driving at low speeds (such as parking or turning around) and more stable and accurate when driving at high speeds.

[0054] like Figure 3As shown in the figure, when the driver drives the current vehicle for the first time, the in-car camera is activated to collect the driver's facial information, the facial information is entered, and the steering mode currently selected by the driver is obtained, such as comfort mode, standard mode, sports mode, custom mode, etc. If the driver chooses to enter facial information, the system will record the facial information and the steering mode selected by the driver. If the driver chooses not to enter facial information, the system will automatically set the steering power mode to "standard" mode.

[0055] like Figure 4 As shown in the figure, when the driver is not driving the current vehicle for the first time, the driver's facial information is obtained and recognized. If the facial recognition is successful, the system will check whether the facial information is in the information library. If the facial information is in the information library, the system will output the steering assist mode previously selected by the driver. If the facial information is not in the information library, the system will output the default steering assist mode, that is, the "standard" mode. If the facial recognition fails, the system will directly output the default steering assist mode. Finally, EPS performs corresponding operations according to the selected or default steering assist mode.

[0056] In one embodiment, the vehicle control method further includes:

[0057] If the biometric information fails to be obtained or the driving environment data is abnormal, a basic power assist value is generated based on the linear relationship between vehicle speed and steering wheel torque to adjust the vehicle's steering output torque.

[0058] When face recognition fails, the acquisition of biometric information fails; or when the environmental sensor data is abnormal, the driving environment data is abnormal. At this time, the steering assist mode is switched to the default standard mode. In the standard mode, a basic assist value is generated based on the linear relationship between vehicle speed and steering wheel torque, and EPS performs vehicle steering assist based on the basic assist value. For example, the coefficient of vehicle speed is set to k v , set the steering wheel torque coefficient to k t , then the basic assistance value = k v *Vehicle speed + k t * Steering wheel torque. k v and k t The specific value of can be determined through actual vehicle calibration.

[0059] It is worth mentioning that the priority of the standard mode is higher than other steering assistance modes, such as comfort mode, standard mode, sports mode, custom mode, and the power assistance mode based on the weighted algorithm to dynamically calculate the steering torque. In this way, when facing system failures or uncertain factors, the basic functions of the steering assistance system can still be guaranteed to operate normally through preset alternative solutions, reflecting a high degree of safety priority and technical reliability.

[0060] Step S2: Based on the biometric information, driving environment data and driving behavior data, the steering torque is dynamically calculated through a weighted algorithm.

[0061] In one embodiment, the corresponding steering assist factor and its corresponding weight coefficient are determined according to the biometric information, driving environment data and driving behavior data, each steering assist factor corresponds to an assist torque value, and then the assist torque value and its corresponding weight system are dynamically calculated through a weighted algorithm to obtain the steering torque of the current vehicle.

[0062] In one embodiment, based on biometric information and driving environment data, the steering torque is dynamically calculated by a weighted algorithm, including:

[0063] Determine a first steering assist factor based on the biometric information and the driving environment data, and determine a first steering torque according to the first steering assist factor and its corresponding weight coefficient;

[0064] Determine a second steering assist factor based on the driving behavior data and the driving environment data, and determine a second steering torque according to the second steering assist factor and its corresponding weight coefficient;

[0065] The steering torque is calculated according to the first steering torque and the second steering torque.

[0066] In this embodiment, the steering torque of the vehicle consists of two parts, namely the first steering torque and the second steering torque. The first steering torque is the basic power torque, which can be determined by biometric information and driving environment data. The calculation formula of the first steering torque is:

[0067]

[0068] Where, T n represents the first steering torque, i represents the first steering assist factor, including at least one of facial information, gender, age, weight and facial expression in the biometric information, and at least one of road type and weather conditions in the driving environment data; T represents the assist torque value corresponding to the first steering assist factor, and α represents the weight coefficient corresponding to the first steering assist factor.

[0069] The second steering torque can be determined by the driving behavior data and the driving environment data. The calculation formula of the second steering torque is:

[0070] T m =β(T f +T h -T s )

[0071] Where, T m represents the second steering torque, T f represents the friction compensation torque, Th Represents the aligning torque, T s represents the damping torque, and β represents the weight coefficient corresponding to the second steering assist factor.

[0072] Therefore, the expression of steering torque is:

[0073]

[0074] In one embodiment, the driving behavior data includes the steering wheel angle change rate and the vehicle speed, and the driving environment data also includes the road adhesion coefficient. The vehicle control method provided by the present application includes:

[0075] Calculate the friction compensation torque correction coefficient, the aligning torque correction coefficient and the damping torque correction coefficient according to the steering wheel angle change rate, the vehicle speed and the road adhesion coefficient;

[0076] adjusting the friction compensation torque according to the friction compensation torque correction coefficient, adjusting the aligning torque according to the aligning torque correction coefficient, and adjusting the damping torque according to the damping torque correction coefficient;

[0077] The second steering torque is calculated according to the friction compensation torque, the aligning torque and the damping torque.

[0078] Among them, the friction compensation torque is used to offset the mechanical friction in the steering system to ensure that the force feedback when the driver operates the steering wheel is more linear. The return torque is used to help the steering wheel automatically return to the center position after turning, improving the handling stability. The damping torque reduces the shaking or excessive rotation of the steering wheel through the damping effect, enhancing the driving smoothness. The road adhesion coefficient is used to reflect the friction between the tire and the ground, which depends on the road type (such as dry, slippery or covered with ice and snow) and the tire condition.

[0079] In this embodiment, the friction compensation torque correction coefficient is determined according to the steering wheel angle change rate and the road adhesion coefficient. When the vehicle is driving at high speed or on a low adhesion road, the friction compensation torque correction coefficient is increased to offset the friction lag effect. The return torque correction coefficient is determined according to the vehicle speed and the steering wheel angle change rate. When the vehicle is at a low speed or a large angle, the return torque correction coefficient is increased to enhance the return effect. The damping torque correction coefficient is determined according to the steering wheel angle change rate and the vehicle lateral acceleration. When the vehicle is in high-frequency shaking or intense driving, the damping torque correction coefficient is increased to suppress steering wheel oscillation.

[0080] Next, the friction compensation torque is adjusted according to the friction compensation torque correction coefficient, the return torque is adjusted according to the return torque correction coefficient, and the damping torque is adjusted according to the damping torque correction coefficient. Based on the adjusted friction compensation torque, return torque and damping torque, the second steering torque is calculated. The method of the present application responds to changes in the driving environment in real time through a dynamic correction function to improve the consistency of steering feel. Especially on low-adhesion roads or in emergency steering, friction compensation and damping torque can be automatically enhanced to prevent loss of control. In addition, the weight coefficient can also be adjusted according to the driver's habits (such as steering sensitivity preference). In this way, friction compensation, return torque, and damping torque are associated with real-time driving parameters through a dynamic correction function, thereby achieving refined control of steering assistance, taking into account both controllability and safety.

[0081] The vehicle control method monitors and analyzes driving behavior and environmental data in real time, and dynamically adjusts the parameters of the power steering system to improve driving safety and comfort. Especially in complex road conditions, it can effectively improve the vehicle's handling performance and make the driving experience smoother.

[0082] In one embodiment, the driving behavior data also includes braking frequency. The vehicle control method provided by the present application includes:

[0083] The driving style score is calculated based on the steering wheel angle change rate and braking frequency. The driving style score is positively correlated with the steering wheel angle change rate and braking frequency.

[0084] According to the driving style score, the weight coefficient corresponding to the second steering power factor is dynamically adjusted within a preset range;

[0085] When the driving style score is higher than a preset threshold, the weight coefficient corresponding to the first steering power factor is reduced, and the weight coefficient corresponding to the second steering power factor is increased.

[0086] In this embodiment, the driver's driving style score is calculated based on the collected steering wheel angle change rate and braking frequency. The driving style score is positively correlated with the steering wheel angle change rate and braking frequency, that is, if the driver frequently turns the steering wheel quickly or often brakes suddenly, his driving style score will be higher. A linear weighted model can be used to establish a formula for driving style scoring, that is, driving style score = w 1 * Steering wheel angle change rate + w 2 *Braking frequency. The influence coefficients of steering wheel angle change rate and braking frequency on driving style score can be determined by actual vehicle calibration. 1 and w 2 At the same time, the value range of the driving style score can be limited to the interval [0, 1] to facilitate subsequent threshold judgment.

[0087] If the calculated driving style score does not exceed the preset threshold, the weight coefficient corresponding to the second steering power factor is dynamically adjusted within the preset range, that is, dynamic driving behavior is prioritized. For example, for aggressive drivers (high scores), the sensitivity of the steering power is increased; while for mild drivers (low scores), the steering power is kept low to provide a more stable feeling. The driving style score is used to balance static preferences and dynamic needs in real time to avoid sudden changes in steering feel. If the driving style score exceeds the preset threshold, it means that the driver's operation is more intense or unstable. Increase the dynamic weight during aggressive driving, enhance the control response, and reduce the risk of steering delay. This can be done by reducing the weight coefficient α corresponding to the first steering power factor and correspondingly increasing the weight coefficient β corresponding to the second steering power factor. Specifically, α+β=1 can be set, and the values ​​of α and β can be adjusted according to a linear relationship.

[0088] In this way, by quantifying driving behavior characteristics (steering wheel angle change rate, braking frequency) and combining them with dynamic weight adjustment strategies, intelligent adaptation of the steering assist system is achieved. This method not only improves the personalized driving experience, but also ensures the control safety and real-time performance in complex scenarios.

[0089] In one embodiment, it further comprises:

[0090] Matching the first steering assist factor to a preset assist curve, where the assist curve is generated by calibrating steering torque requirements of different biometric information and / or driving environment data of a real vehicle;

[0091] The steering torque corresponding to the first steering assist factor is determined according to the assist curve.

[0092] like Figures 5 to 10 As shown in FIG. 1 , it is a power assist curve matching each first steering assist factor. Specifically, Figure 5 The power assist curves for female and male drivers are shown. In actual driving, most men prefer a heavier steering feel to increase the driving feel, and their steering assist value is correspondingly smaller; while most women prefer a lighter driving experience, so they need a larger steering assist value. Gender information can be identified by camera or obtained from user settings. Based on sample data, the relationship between gender and steering assist torque curve is obtained through user surveys and actual vehicle calibration. In actual applications, the system calls the corresponding curve according to the identified driver's gender, calculates the power assist torque value of gender factors, and determines the weighting coefficient through a large amount of sample data.

[0093] Figure 6The power assist curves corresponding to drivers of different age groups are shown. Different age groups have different requirements for steering feel: young people prefer a sporty and slightly heavier feel, the elderly pursue lightness, and the middle-aged are somewhere in between. Age information is also obtained through the camera or user settings, and the age groups are defined as young people (18-35 years old), middle-aged people (35-50 years old) and elderly people (over 50 years old). The relationship between age and steering assist torque curve is obtained through sample data and actual vehicle calibration, so as to determine the power assist torque value and its weighting coefficient of the age factor.

[0094] Figure 7 The power assist curves corresponding to drivers of different weight ranges are shown. Weight can reflect the driver's preference for steering feel to a certain extent. Weight data can be obtained through seat pressure sensors or user health information. The weight range is defined based on sample data, and the relationship between weight and steering torque curve is obtained through research and actual vehicle calibration, and then the weight factor power assist torque value is calculated and the weighting coefficient is determined.

[0095] Figure 8 The power assist curves corresponding to drivers with different emotions are shown. Mood states affect drivers' demands for steering feel. For example, when a driver is in a bad mood, he or she prefers a lighter feel, but when a driver is happy, the demand is not obvious. The in-car camera is used to identify, capture and analyze the driver's emotional changes to determine the optimal power assist value. The relationship between mood and power assist curve can be obtained through sample data surveys and actual vehicle calibration, and the power assist torque value and its weighted coefficient of mood factors can be calculated.

[0096] Fig. 9 The power assist curves corresponding to different driving surfaces are shown. The road conditions have a significant impact on the need for handling. A heavier steering wheel is required on high-speed roads to ensure safety, while muddy and bumpy roads require easy passage. Road surface information is obtained through the camera and the corresponding power assist curve is called. Based on sample data surveys and actual vehicle calibration, a relationship diagram between the road surface and the power assist curve is drawn to determine the power assist torque value and its weighted coefficient of the road surface factor.

[0097] Fig.10 The power assist curves corresponding to different weather conditions are shown. Weather conditions will also affect the steering feel requirements. On sunny days, the road conditions are good, and the steering assist value is large; on rainy days, the road is slippery, and the steering assist value needs to be reduced to ensure safety. Weather information is obtained by the vehicle computer, and the corresponding power assist curve is called. Through sample data research and actual vehicle calibration, the relationship diagram between weather and power assist curve is obtained, and finally the power assist value of weather factor power assist torque and its weighted coefficient are determined.

[0098] In summary, through comprehensive consideration of factors such as gender, age, weight, mood, road surface and weather, and through the use of real-vehicle calibration and extensive data research, precise adjustment of the steering assist torque is achieved, thereby improving driving safety, comfort and personalized experience.

[0099] Step S3: Send the steering torque to the electric power steering system controller to adjust the steering output torque of the vehicle.

[0100] Here, the calculated steering torque is sent to the EPS controller via the CAN bus. After receiving the steering torque, the EPS controller adjusts the steering assist provided by the motor according to the value, thereby changing the steering wheel's rotation resistance. By generating different steering torque values ​​under different driving conditions, the EPS system can dynamically provide precise steering assist, which can increase the assist when necessary to make steering easier, or reduce the assist when necessary to allow the driver to feel more road feel. The EPS controller can also continuously monitor the actual driving status of the vehicle and feed this information back to the vehicle computer so that the vehicle computer can make continuous fine-tuning, greatly improving driving safety and comfort.

[0101] The vehicle control method, control device, vehicle and computer-readable storage medium of the embodiment of the present application, the vehicle control method, includes: obtaining the driver's biometric information, driving environment data and driving behavior data; dynamically calculating the steering torque through a weighted algorithm based on the biometric information, driving environment data and driving behavior data; sending the steering torque to the electric power steering system controller to adjust the vehicle's steering output torque. In this way, the technical solution of the present application can achieve precise adjustment of the vehicle's steering output torque to improve driving safety and personalization.

[0102] Based on the same inventive concept as the above embodiments, the present application embodiment provides a control device, such as Fig.11 As shown, the device includes: a processor 310 and a memory 311 storing a computer program; wherein, Fig.11 The processor 310 shown in the figure is not used to indicate that the number of the processor 310 is one, but is only used to indicate the positional relationship of the processor 310 relative to other devices. In actual applications, the number of the processor 310 may be one or more; similarly, Fig.11 The memory 311 shown in the figure has the same meaning, that is, it is only used to refer to the position relationship of the memory 311 relative to other devices. In practical applications, the number of memories 311 can be one or more. When the processor 310 runs the computer program, the vehicle control method applied to the above device is implemented.

[0103] The device may also include: at least one network interface 312. The various components in the device are coupled together via a bus system 313. It is understood that the bus system 313 is used to achieve connection and communication between these components. In addition to the data bus, the bus system 313 also includes a power bus, a control bus, and a status signal bus. However, for the sake of clarity, Fig.11Various buses are labeled as bus system 313.

[0104] The memory 311 may be a volatile memory or a nonvolatile memory, or may include both volatile and nonvolatile memories. The nonvolatile memory may be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), a magnetic random access memory (FRAM), a flash memory, a magnetic surface memory, an optical disk, or a compact disc read-only memory (CD-ROM); the magnetic surface memory may be a disk memory or a tape memory. The volatile memory may be a random access memory (RAM), which is used as an external cache. By way of example and not limitation, many forms of RAM are available, such as static random access memory (SRAM), synchronous static random access memory (SSRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDRSDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous link dynamic random access memory (SLDRAM, SyncLink Dynamic Random Access Memory), and direct RAM bus random access memory (DRRAM, Direct Rambus Random Access Memory).The memory 311 described in the embodiments of the present application is intended to include but is not limited to these and any other suitable types of memories.

[0105] The memory 311 in the embodiment of the present application is used to store various types of data to support the operation of the device. Examples of these data include: any computer program for operating on the device, such as an operating system and an application program, etc. Among them, the operating system includes various system programs, such as a framework layer, a core library layer, a driver layer, etc., for implementing various basic services and processing hardware-based tasks. The application program may include various applications, such as a media player (MediaPlayer), a browser (Browser), etc., for implementing various application services. Here, the program implementing the method of the embodiment of the present application may be included in the application program.

[0106] Based on the same inventive concept as the aforementioned embodiment, this embodiment also provides a vehicle, which is equipped with the aforementioned control device.

[0107] Based on the same inventive concept as the above-mentioned embodiment, this embodiment further provides a computer-readable storage medium, in which a computer program is stored. The computer-readable storage medium may be a ferromagnetic random access memory (FRAM), a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), a flash memory, a magnetic surface memory, an optical disk, or a compact disc read-only memory (CD-ROM); it may also be various devices including one or any combination of the above-mentioned memories, such as a mobile phone, a computer, a tablet device, a personal digital assistant, etc. When the computer program stored in the computer-readable storage medium is executed by the processor, the above-mentioned vehicle control method is implemented. For the specific steps implemented when the computer program is executed by the processor, please refer to Figure 1 The vehicle control method shown.

[0108] The technical features of the above-described embodiments may be arbitrarily combined. To make the description concise, not all possible combinations of the technical features in the above-described embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0109] In this document, the terms "comprises," "comprising," or any other variations thereof, are intended to cover a non-exclusive inclusion of elements other than those listed and may also include additional elements not expressly listed.

[0110] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A vehicle control method, characterized in that: include: Obtain driver's biometric information, driving environment data and driving behavior data; dynamically calculating the steering torque through a weighted algorithm based on the biometric information, the driving environment data, and the driving behavior data; The steering torque is sent to an electric power steering system controller to adjust the steering output torque of the vehicle.

2. The method according to claim 1, characterized in that The dynamically calculating the steering torque by a weighted algorithm based on the biometric information and the driving environment data includes: determining a first steering assist factor based on the biometric information and the driving environment data, and determining a first steering torque according to the first steering assist factor and its corresponding weight coefficient; determining a second steering assist factor based on the driving behavior data and the driving environment data, and determining a second steering torque according to the second steering assist factor and its corresponding weight coefficient; The steering torque is calculated according to the first steering torque and the second steering torque.

3. The method according to claim 2, characterized in that The first steering assist factor includes at least one of facial information, gender, age, weight and facial expression in the biometric information, and at least one of road surface type and weather conditions in the driving environment data.

4. The method according to claim 2, characterized in that: The driving behavior data includes a steering wheel angle change rate and a vehicle speed, and the driving environment data also includes a road adhesion coefficient. The method includes: Calculating a friction compensation torque correction coefficient, a aligning torque correction coefficient and a damping torque correction coefficient according to the steering wheel angle change rate, the vehicle speed and the road adhesion coefficient; adjusting the friction compensation torque according to the friction compensation torque correction coefficient, adjusting the aligning torque according to the aligning torque correction coefficient, and adjusting the damping torque according to the damping torque correction coefficient; The second steering torque is calculated according to the friction compensation torque, the aligning torque and the damping torque.

5. The method according to claim 4, characterized in that The driving behavior data also includes braking frequency, and the method includes: Calculating a driving style score according to the steering wheel angle change rate and the braking frequency, wherein the driving style score is positively correlated with the steering wheel angle change rate and the braking frequency; dynamically adjusting a weight coefficient corresponding to the second steering assist factor within a preset range according to the driving style score; When the driving style score is higher than a preset threshold, the weight coefficient corresponding to the first steering assist factor is reduced, and the weight coefficient corresponding to the second steering assist factor is increased.

6. The method according to claim 2, characterized in that Also includes: Matching the first steering assist factor to a preset assist curve, wherein the assist curve is generated by calibrating steering torque requirements of different biometric information and / or driving environment data of an actual vehicle; The steering torque corresponding to the first steering assist factor is determined according to the assist curve.

7. The method according to claim 1, characterized in that Also includes: If the biometric information acquisition fails or the driving environment data is abnormal, a basic power assist value is generated based on the linear relationship between the vehicle speed and the steering wheel torque to adjust the steering output torque of the vehicle.

8. A control device, characterized in that: include: A processor and a memory storing a computer program, wherein when the processor runs the computer program, the steps of the vehicle control method according to any one of claims 1 to 7 are implemented.

9. A vehicle, characterized in that: The control device according to claim 8 is provided.

10. A computer-readable storage medium, characterized in that: A computer program is stored, and when the computer program is executed by a processor, the steps of the vehicle control method according to any one of claims 1 to 7 are implemented.

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