Pedal feeling control method, computing device, vehicle and readable storage medium

Abstract

The invention discloses a pedal feeling control method, computing equipment, a vehicle and a storage medium. The method comprises the steps that character feature information of a current driver is acquired; matching a target pedal feeling strategy associated with the character feature information, wherein the target pedal feeling strategy is obtained after a preset model performs model training according to brake control data corresponding to the character feature information; and responding according to the target pedal feeling strategy. Thus, after the current driver is recognized, the pedal feeling strategy which is subjected to model training through the brake control data of the current driver and is output serves as the target pedal feeling strategy, and pedal feeling strategy configuration of the vehicle is completed; the current driver can more intuitively sense the response state of the vehicle braking system based on the braking control habit of the driver, and therefore the braking degree is accurately controlled. Therefore, the pedal feeling strategy configuration of the vehicle can be completed so as to accurately match the individual brake control habit of the driver, and the purpose of improving the driving experience is achieved.

Description

Technical Field

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

[0002] Brake pedal feel (pedal feel for short) has a significant impact on driving safety and handling performance. A good brake pedal feel allows the driver to more intuitively perceive the response state of the vehicle's braking system, thereby accurately controlling the braking force. This transmission of "road feel" not only enhances the driver's grasp of vehicle dynamics, but also helps the driver to quickly respond correctly in an emergency, effectively shortens the braking distance, and significantly reduces the probability of traffic accidents. In addition, brake pedal feel can also improve the overall driving experience. It makes every braking operation predictable and controllable, reduces the discomfort caused by sudden changes in braking force, and makes the driving process smoother and smoother. Especially when driving at high speeds or in complex road conditions, this delicate feedback is particularly important. It can help the driver predict in advance and adjust the speed in time to avoid unnecessary emergency braking, ensuring driving safety while also improving ride comfort. In short, brake pedal feel is an important bridge between the driver and the vehicle, and its quality directly affects driving safety, handling and comfort. By optimizing the design and adjustment of the braking system and improving the quality of the pedal feel, the comprehensive performance of the vehicle can be further enhanced, bringing the driver a more secure and pleasant driving experience. At present, some vehicles have multiple brake pedal feel strategies preset at the factory for drivers to choose in order to meet the different needs of different drivers for brake pedal feel.

[0003] However, the brake pedal feel strategy currently pre-set in the vehicle factory is difficult to accurately match the driver's personal braking control habits, so the driving experience needs to be improved. Therefore, how to configure the vehicle's pedal feel strategy to accurately match the driver's personal braking control habits is a technical problem that technicians in this field urgently need to solve. Summary of the invention

[0004] The purpose of this application is to provide a pedal feel control method, computing device, vehicle and computer-readable storage medium, which can realize the pedal feel strategy configuration of the vehicle to accurately match the driver's personal braking control habits and enhance the driving experience.

[0005] To achieve the above objectives: In a first aspect, an embodiment of the present application provides a method for controlling pedal feel, including: obtaining character feature information of the current driver; matching a target pedal feel strategy associated with the character feature information, the target pedal feel strategy being a preset model obtained after model training based on braking control data corresponding to the character feature information; and responding according to the target pedal feel strategy.

[0006] In one embodiment, before the step of obtaining the character feature information of the current driver, the method includes: obtaining the character feature information of the driver in response to meeting the pedal feel creation conditions; collecting the braking control data of the driver when driving the vehicle corresponding to the character feature information according to a preset data collection strategy, and constructing at least one data set, the braking control data including first corresponding information between deceleration and pedal force and / or second corresponding information between deceleration and pedal travel; training a preset model based on the braking control data in the data set to output a pedal feel strategy, the pedal feel strategy including corresponding relationship information between deceleration and pedal force and / or pedal travel; and storing the character feature information in association with the pedal feel strategy.

[0007] In one embodiment, a preset data collection strategy includes: when it is determined that the braking control state is a deep pedal state, the first braking control data at this time is collected according to a preset sampling frequency; when it is determined that the braking control state is a light pedal state, the second braking control data at this time is collected according to a preset sampling frequency; at least one data set is constructed based on the collected first braking operation data and the second braking control data.

[0008] In one embodiment, the pedal feel strategy includes at least one of the following: curve information of deceleration and pedal force that is convenient for fitting a quintic polynomial; curve information of deceleration and pedal travel that is convenient for fitting a quintic polynomial.

[0009] In one embodiment, the pedal feel creation condition includes at least one of the following: Receiving a preset instruction; Based on the character feature information of the current driver, determining that the current driver is a new driver; Determining that the new driver has driven the vehicle more than a preset number of times; It is determined that the cumulative driving time of the new driver for the vehicle exceeds a preset time.

[0010] In one embodiment, after the step of responding according to the target pedal feel strategy, the method includes: detecting that the current driver is changed to a new driver, and determining the attributes of the new driver; when the attribute determination result represents a temporary user, determining a temporary pedal feel strategy from at least one preset universal pedal feel strategy; and responding according to the temporary pedal feel strategy.

[0011] In one embodiment, when the attribute determination result characterizes a temporary user, the step of determining a temporary pedal feel strategy from at least one preset universal pedal feel strategy includes: when the attribute determination result characterizes a temporary user, obtaining population attributes of the temporary user and driving data of the temporary user when driving a vehicle; determining a corresponding driving style based on the population attributes and the driving data; and selecting a universal pedal feel strategy that matches the driving style from at least one preset universal pedal feel strategy as the temporary pedal feel strategy.

[0012] In a second aspect, an embodiment of the present application provides a computing device, comprising: a processor and a memory storing a computer program, and when the processor runs the computer program, the steps of the pedal feel control method described in any one of the above items are implemented.

[0013] In a third aspect, an embodiment of the present application provides a vehicle, and the vehicle includes the aforementioned computing device.

[0014] In a fourth aspect, 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 steps of the pedal feel control method described in any of the above items are implemented.

[0015] The pedal feel control method, computing device, vehicle and computer-readable storage medium provided in the embodiment of the present application include: obtaining the character feature information of the current driver; matching the target pedal feel strategy associated with the character feature information, the target pedal feel strategy is obtained by training the preset model according to the braking control data corresponding to the character feature information; responding according to the target pedal feel strategy. Through the technical solution of the present application, after identifying the current driver, the pedal feel strategy output by the model training of the braking control data of the current driver is used as the target pedal feel strategy to complete the pedal feel strategy configuration of the vehicle, so that the current driver can more intuitively perceive the response state of the vehicle braking system (i.e., a good brake pedal feel) based on his own braking control habits, so as to accurately control the braking degree, thereby not only enhancing the current driver's grasp of the vehicle dynamics, but also helping the current driver to quickly make a correct response in an emergency, so as to effectively shorten the braking distance (or braking distance) and significantly reduce the probability of traffic accidents. Therefore, the technical solution of the present application can complete the pedal feel strategy configuration of the vehicle to accurately match the driver's personal braking control habits, and achieve the purpose of improving the driving experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] The drawings herein are incorporated into the specification and constitute a part of the specification, illustrate embodiments consistent with the present application, and are used together with the specification to explain the principles of the present application. In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the description of the embodiments are briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without creative labor.

[0017] Figure 1 A schematic flow chart of a pedal feel control method provided in an embodiment of the present application.

[0018] Figure 2 This is a schematic diagram of the process framework of the pedal feel control method of this application example.

[0019] Figure 3 A schematic diagram of the structure of a computing device provided in an embodiment of the present application.

[0020] The realization of the purpose, functional features and advantages of this application will be further described in conjunction with the embodiments and with reference to the accompanying drawings. The above-mentioned drawings have shown clear embodiments of this application, which will be described in more detail later. These drawings and textual descriptions are not intended to limit the scope of the concept of this application in any way, but to illustrate the concept of this application to those skilled in the art by referring to specific embodiments. DETAILED DESCRIPTION

[0021] 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.

[0022] 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.

[0023] 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.

[0024] 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.

[0025] It should be noted that, in this article, step codes such as S11, S12, etc. are used for the purpose of expressing the corresponding content more clearly and concisely, and do not constitute a substantial limitation on the sequence. When implementing it specifically, those skilled in the art may execute S12 first and then S11, etc., but these should all be within the scope of protection of this application.

[0026] 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.

[0027] 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.

[0028] See also Figure 1 , is a pedal feel control method provided in an embodiment of the present application, which can be executed by a computing device provided in an embodiment of the present application, and the computing device can be implemented in software and / or hardware. The computing device is, for example, a vehicle-mounted terminal, a server, etc.

[0029] The present embodiment provides a method for controlling pedal feel, comprising the following steps (for example, steps S11 to S13): Step S11: Obtain the character feature information of the current driver.

[0030] In one implementation, the character feature information may represent a body feature that can uniquely identify a specific person.

[0031] In one embodiment, the character feature information includes face information, iris information, fingerprint information, etc. Optionally, the face information and iris information can be collected by a camera in the vehicle. Optionally, the fingerprint information can be collected by a fingerprint sensor in the vehicle.

[0032] In one embodiment, the current driver may represent a person at the primary cabin location of the vehicle.

[0033] In one embodiment, step S11: before obtaining the character feature information of the current driver, it may include: in response to meeting the pedal feel creation conditions, obtaining the driver's character feature information; according to a preset data collection strategy, collecting the braking control data of the driver when driving the vehicle corresponding to the character feature information, and constructing at least one data set, the braking control data including first corresponding information between deceleration and pedal force and / or second corresponding information between deceleration and pedal travel; training a preset model based on the braking control data in the data set to output a pedal feel strategy, the pedal feel strategy including the corresponding relationship information between deceleration and pedal force and / or pedal travel; and storing the character feature information in association with the pedal feel strategy.

[0034] In this way, the technical solution of this embodiment can determine the personal characteristic information of a specific driver when creating a pedal sensing strategy, and collect the braking control data of the specific driver when stepping on the brake pedal (first corresponding information between deceleration and pedal force and / or second corresponding information between deceleration and pedal stroke) in a scenario where the specific driver naturally drives the vehicle, and construct a data set based on the collected braking control data, and input the data in the data set into a preset model so that the preset model outputs a pedal feel strategy (corresponding relationship information between deceleration and pedal force and / or pedal stroke) that matches the braking control habits of the specific driver, and associate the personal characteristic information of the specific driver with the pedal feel strategy for storage for subsequent matching and calling.

[0035] It should be understood that the brake control data includes but is not limited to first correspondence information between deceleration and pedal force and / or second correspondence information between deceleration and pedal travel, and may also include, for example, a third correspondence between pedal force and vehicle brake system response speed, a fourth correspondence between pedal travel and vehicle brake system response speed, etc. Specifically, the brake control data may represent various parameters that may affect pedal feel, and / or the correspondence between various parameters.

[0036] In one embodiment, the deceleration in the braking control data may be detected by an acceleration sensor in the vehicle. The pedal force in the braking control data may be detected by a force sensor installed at the brake pedal. The pedal travel in the braking control data may be detected by a displacement sensor at the brake pedal.

[0037] In one embodiment, the preset model may be, but is not limited to, a BP neural network model, for example, a support vector machine, a random forest, etc.

[0038] In one embodiment, the step of training a preset model based on the braking control data in the data set to output a pedal feel strategy may include: extracting features based on the braking control data in the data set to obtain feature parameters, which include pedal travel, pedal force, and deceleration; inputting the feature parameters into a BP neural network model for model training to output a pedal feel strategy.

[0039] In one embodiment, the pedal stroke may represent the pedal stroke from the time when the brake begins to contact the brake disc to the time when a certain braking force is reached after the brake pedal is depressed. The length of the pedal stroke will affect the sensitivity of the brake and the driver's braking feeling. The shorter the stroke, the faster the braking response, but too short a stroke may cause the brake to be too sensitive and affect comfort; the longer the stroke, the slower the braking response, but it may make the driver feel more comfortable.

[0040] In one embodiment, the pedal force can represent the resistance felt by the driver when stepping on the brake pedal. This resistance feeling is an important feedback of the driver to the response of the vehicle's braking system, and it represents the following meanings: (1) Braking response: The pedal force can reflect the response speed of the braking system; if the pedal force is moderate and increases linearly with the pedal travel, it usually means that the braking system is sensitive and can respond quickly to the driver's operation. (2) Braking force prediction: The size of the pedal force can help the driver predict the braking force of the vehicle; in emergency braking or when precise control of the braking force is required, this prediction ability is crucial to avoiding accidents; (3) Driving comfort: The size and change of the pedal force will affect driving comfort; if the pedal force is too small, it may cause the brake pedal to feel "soft", making the driver feel that the braking effect is poor; if the pedal force is too large, it may cause driver fatigue. (4) Safety: Appropriate pedal force helps ensure that the vehicle can provide reliable braking performance under various road conditions, thereby improving driving safety. (5) The relationship between subjective evaluation and objective data: Pedal force is the bridge between subjective evaluation of pedal feel and objective test data. By objectively measuring physical quantities such as pedal force and pedal travel, the driver's subjective feeling of pedal feel can be quantified. Therefore, pedal force is an important component of pedal feel, which not only affects the driving experience, but is also directly related to the vehicle's braking performance and driving safety.

[0041] In one embodiment, the pedal stroke, pedal force, and deceleration together constitute the evaluation dimension of pedal feel, which is an important factor affecting the driver's braking operation and the braking performance of the vehicle. The length of the pedal stroke directly affects the sensitivity and immediacy of the braking system. For example, a short initial pedal stroke can lead to a quick braking response, but it may also make the driver feel that the braking is too sensitive; while a longer pedal stroke may make the driver feel more comfortable, but it may reduce the immediacy of braking.

[0042] In one embodiment, the pedal feel strategy can improve the pedal feel, for example, by adjusting the pedal travel to ensure that while providing a good braking effect, the driving comfort and safety can also be ensured. Optionally, the pedal travel can be adjusted by, for example, adjusting the parameters of the braking system, such as the brake pedal lever ratio and the brake system idle travel, to adjust the pedal travel, thereby improving the pedal feel.

[0043] In one embodiment, in the pedal feel strategy output by the preset model, the adjustment of the pedal travel may take into account the response speed and pedal force of the braking system to ensure that appropriate braking performance is provided under different driving conditions.

[0044] In one embodiment, the pedal feel strategy may represent a manner of adjusting specific parameters of the pedal force and / or pedal travel based on the driver's braking condition on the brake pedal.

[0045] In one embodiment, the pedal feel strategy is used to adjust the pedal force and / or pedal travel suitable for the specific driver under different deceleration requirements to accurately adapt to the braking control habits of the specific driver. Optionally, when the vehicle responds according to the pedal feel strategy, the specific driver can obtain a good pedal feel.

[0046] In one embodiment, pedal feel refers to the feedback force and feeling that the driver can feel when stepping on the brake pedal. It can mainly include factors such as pedal force, pedal travel, deceleration, etc. A good pedal feel is crucial to driving safety because it can help the driver accurately judge the braking force of the vehicle, thereby better controlling the vehicle speed and distance and reducing the risk of accidents. At the same time, it can also improve driving comfort and reduce the driver's fatigue during long-term driving. The evaluation of pedal feel can be carried out from multiple dimensions, including pedal force, pedal travel, pedal force and return lag, vehicle braking system response speed, and vehicle deceleration. These factors work together to affect the driver's perception of the vehicle's braking performance. For example, the relationship between pedal travel and vehicle deceleration, the relationship between pedal force and vehicle deceleration, and the hardness of the pedal are all important aspects of evaluating the brake pedal feel.

[0047] In one embodiment, the pedal feel strategy can determine target parameters and optimize the relevant components of the brake system based on the target parameters. For example, by reducing the gap between the brake disc and the brake pad, increasing the Young's modulus of the brake hose and the stiffness of the rubber reaction disc, the existing pedal feel can be significantly improved.

[0048] In one embodiment, the pedal feel creation condition includes at least one of the following: Receiving a preset instruction; Based on the character feature information of the current driver, determining that the current driver is a new driver; Determining that the new driver has driven the vehicle more than a preset number of times; It is determined that the cumulative driving time of the new driver for the vehicle exceeds a preset time.

[0049] In one implementation, the preset instruction may correspond to a pedal feel creation function.

[0050] In one embodiment, the preset instruction may be triggered by voice, or by a virtual function button, physical button, etc. of the vehicle.

[0051] The technical solution of this embodiment can directly determine whether the pedal feel creation conditions are met when a preset instruction is received, or it can determine whether the pedal feel creation conditions are met when a preset instruction is received and combined with other conditional factors (such as one or more of the driver attributes of the current driver, the number of times the new driver has driven, the cumulative driving time of the new driver, etc.).

[0052] In one embodiment, based on the character feature information of the current driver, the current driver is determined to be a new driver. Specifically, when the character feature information of the current driver does not match the pre-stored character feature information, the driver attribute of the current driver is determined to be a new driver.

[0053] The technical solution of this embodiment can directly determine that the pedal feel creation conditions are met when the current driver is determined to be a new driver, or it can determine whether the pedal feel creation conditions are met by combining other conditional factors (such as whether a preset instruction is received, the number of times the new driver has driven, the cumulative driving time of the new driver, etc.) when the current driver is determined to be a new driver.

[0054] In one implementation, the preset number of times can be set to any number as required.

[0055] The technical solution of this embodiment can directly determine that the pedal feel creation condition is met when it is determined that the number of times a new driver has driven the vehicle exceeds a preset number of times. In this way, the technical solution of this embodiment can configure a pedal feel strategy that meets the braking control habits of the new driver when the new driver has driven the vehicle multiple times, so as to improve the driving experience. In addition, the technical solution of this embodiment can also be to determine whether the pedal feel creation condition is met in combination with other conditional factors (such as whether a preset instruction is received, the cumulative driving time of the new driver, etc.) when the number of times a new driver has driven the vehicle exceeds a preset number of times.

[0056] In one embodiment, the preset duration can be set to any duration as required. Optionally, determining that the cumulative driving time of the new driver for the vehicle exceeds the preset duration can indicate that the new driver has used the vehicle for a long time.

[0057] The technical solution of this embodiment can directly determine that the pedal feel creation condition is met when it is determined that the cumulative driving time of the new driver exceeds the preset time. In this way, the technical solution of this embodiment can configure a pedal feel strategy that meets the braking control habits of the new driver when the new driver drives the vehicle for a long time, so as to improve the driving experience. In addition, the technical solution of this embodiment can also be to determine whether the pedal feel creation condition is met when the number of times the new driver drives the vehicle exceeds the preset number of times, combined with other conditional factors (such as whether the preset instruction is received, the number of times the new driver drives, etc.).

[0058] It should be understood that when the pedal feel creation conditions are constructed based on multiple conditional factors, it is possible to avoid accidentally triggering the pedal feel creation process to consume the vehicle's computing resources, and / or to more accurately grasp the triggering timing of the pedal feel creation process, so as to configure a pedal feel strategy that suits the braking control habits of new drivers in appropriate circumstances, thereby improving the driving experience.

[0059] In one embodiment, the preset data collection strategy may represent a specific method of constructing a data set matching the preset model according to the training requirements.

[0060] In one embodiment, the preset data collection strategy may include: when the brake control state is determined to be a deep pedal state, collecting the first brake control data at this time according to the preset sampling frequency, and when the brake control state is determined to be a light pedal state, collecting the second brake control data at this time according to the preset sampling frequency; and constructing at least one data set based on the collected first brake operation data and the second brake control data. In this way, the technical solution of this embodiment can monitor the driver's deep pedaling action and light pedaling action on the brake pedal when the driver is driving the vehicle, and collect the first brake control data according to the preset sampling frequency when the deep pedaling action is detected, and collect the second brake control data according to the preset sampling frequency when the light pedaling action is detected, and can construct one or more data sets based on the collected first brake control data and the collected second brake control data, respectively, or can construct one data set based on the collected first brake control data and the collected second brake control data.

[0061] The technical solution of this embodiment can generalize data to predict the driver's braking control data under various degrees of pedaling through the BP neural network model and the driver's braking control data when the pedal is deeply stepped on and the braking control data when the pedal is lightly stepped on, so as to determine the relationship between the pedal travel and the vehicle's deceleration under various pedaling conditions, and the relationship between the pedal force and the vehicle's deceleration under various pedaling conditions, and then obtain the pedal feel strategy. In this way, the technical solution of this embodiment can provide a pedal feel strategy that adapts to the driver's personal braking control habits to enhance the driving experience.

[0062] It should be understood that the first brake control data and the second brake control data can refer to the definition of the brake control data mentioned above. Here, the first and second are used to describe the brake control data, which are only used to distinguish each other. The first brake control data represents the brake control data corresponding to the deep stepping action, and the second brake control data represents the brake control data corresponding to the light stepping action.

[0063] In one embodiment, the situation in which the braking control state is determined to be a deep pedal state or the situation in which a deep pedal action is detected includes but is not limited to at least one of the following: the current pedal stroke reaches the limit value or maximum proportion of the pedal stroke; the current pedal force on the brake pedal exceeds the first pedal force threshold.

[0064] In one embodiment, the situation in which the braking control state is determined to be a light pedal state or the situation in which a light pedal action is detected includes but is not limited to at least one of the following: the current pedal stroke reaches a preset pedal stroke (the preset pedal stroke is less than the limit value of the pedal stroke); the stroke ratio of the current pedal stroke to the limit value of the pedal stroke is less than or equal to a stroke ratio threshold (for example, 50%); the pedaling force on the brake pedal is within a preset pedaling force range, and the maximum value within the preset pedaling force range is less than the first pedaling force threshold.

[0065] In one embodiment, the limit value or maximum ratio of the pedal stroke, the first pedaling force threshold, the preset pedal stroke, the stroke ratio threshold, and the preset pedaling force range can be set according to actual needs or the vehicle's initial design.

[0066] In one embodiment, the preset sampling frequency can be set according to the acquisition accuracy requirement. Optionally, the determination of the data sampling frequency is usually based on the Nyquist theorem, which states that in order to reconstruct a continuous signal without distortion, the sampling frequency needs to be at least twice the highest frequency component in the signal.

[0067] In one embodiment, the preset sampling frequency can be set to a common sampling frequency of equipment in a vehicle, which can avoid some technical barriers. For example, the sampling frequency of most equipment in a vehicle is 2000 Hz, so the preset sampling frequency for collecting brake control data can also be set to 2000 Hz.

[0068] In one embodiment, the preset sampling frequency can be configured according to experimental tests. Specifically, if the preset sampling frequency is too high, the sampling sample size will be large, the memory usage will be too large, and the storage requirement will also be correspondingly high.

[0069] In one embodiment, the step of constructing at least one data set based on the collected first braking operation data and the second braking control data includes: aggregating and recording the collected first braking control data into at least one first data set according to the data set recording frequency; aggregating and recording the collected second braking control data into at least one second data set according to the data set recording frequency; and aggregating all the first data sets and all the second data sets into a final data set in response to meeting the data collection end condition.

[0070] In one embodiment, the data set recording frequency may represent the frequency of aggregating multiple brake control data. For example, when the preset sampling frequency is 2000 Hz, the data set recording frequency may be 50 Hz. Thus, the sampling frequency of the brake control data is 2000 Hz, one data set is recorded every 50 Hz, and 40 data sets can be recorded in 1 second. This setting can improve the acquisition accuracy and fit the data more effectively on the one hand, and can better reflect the actual driving situation on the other hand.

[0071] In one embodiment, the data collection end condition includes but is not limited to at least one of the following: A first accumulated time for collecting the first brake manipulation data reaches a first time threshold; The second accumulated time for collecting the second brake manipulation data reaches a second time threshold; A first accumulated data amount of the first brake manipulation data collected reaches a first quantity threshold; A second accumulated data amount of the collected second brake manipulation data reaches a second quantity threshold.

[0072] In one embodiment, the first duration threshold and the second duration threshold may be the same or different.

[0073] In one embodiment, the first quantity threshold and the second quantity threshold may be the same or different.

[0074] In one embodiment, the aforementioned data collection end condition may represent a limit setting on the data amount of the data set used for model training.

[0075] The technical solution of this embodiment can achieve faster and more effective data fitting to output a pedal feel strategy through a preset model by setting a suitable sampling frequency and a suitable data volume for model training while ensuring the acquisition accuracy, and can ensure that the output pedal feel strategy can better match the actual driving situation of the corresponding driver (such as braking control habits), so that when the vehicle completes the configuration or responds according to the pedal feel strategy corresponding to the specific driver, the driving experience of the specific driver can be improved.

[0076] In one embodiment, the pedal feel strategy includes at least one of the following: curve information of deceleration and pedal force that is convenient for fitting a quintic polynomial; curve information of deceleration and pedal travel that is convenient for fitting a quintic polynomial.

[0077] In one embodiment, a quintic polynomial is a mathematical model, and the technical solution of this embodiment can well describe and simulate the shape of the pedal feel curve through a quintic polynomial. This polynomial contains six unknown coefficients (C0 to C5), and the values ​​of these coefficients can be determined by solving a set of equations. The establishment of the set of equations can be based on the dynamic characteristics of the pedal feel curve under different decelerations, including the relationship between the pedal force and the deceleration, and / or the relationship between the pedal travel and the deceleration.

[0078] In one embodiment, the technical solution of this embodiment can respectively characterize the relationship between deceleration and pedal force, and the relationship between deceleration and pedal travel through different curve information, and has the following advantages: (1) Intuitiveness: The curve characterizing the pedal feel can intuitively display the pedal force and pedal travel under different decelerations, so that relevant personnel can quickly understand the performance of the braking system, which can facilitate vehicle maintenance or testing; (2) Comparison between simulation and actual vehicle testing: When the simulation results of the vehicle braking system model established by simulation software such as SimulationX are compared with the actual test results, the accuracy of verification and comparison can be improved based on the pedal feel curve.

[0079] In one embodiment, the step of associating and storing the character feature information with the pedal feel strategy may include: associating the driver feature information with the pedal feel strategy to obtain pedal feel associated information; storing the pedal feel associated information in the vehicle, and / or sending the pedal feel associated information to a cloud server so that the cloud server stores the pedal feel associated information. The technical solution of this embodiment not only enables the vehicle to subsequently determine the target pedal feel strategy from the pedal feel associated information stored in itself according to the character feature information of the current driver, but also enables the vehicle to subsequently determine the target pedal feel strategy from the pedal feel associated information stored in the cloud according to the character feature information of the current driver. Among them, sending the pedal feel associated information to the cloud server so that the cloud server stores the pedal feel associated information can avoid occupying the storage space of the vehicle, and can also enable the driver corresponding to the pedal feel associated information to change any vehicle or any different vehicle of the same model, and can be configured or responded based on the target pedal feel strategy obtained from the cloud server.

[0080] Step S12: Matching a target pedal feel strategy associated with the character feature information, where the target pedal feel strategy is obtained by training a preset model based on the braking control data corresponding to the character feature information.

[0081] In one embodiment, step S12: matching the target pedal feel strategy associated with the character characteristic information may include: matching the target pedal feel strategy associated with the character characteristic information based on the pedal feel associated information and the character characteristic information stored in the vehicle and / or the cloud server.

[0082] Step S13: Respond according to the target pedal feel strategy.

[0083] In one embodiment, step S13: after responding according to the target pedal feel strategy, it may include: detecting that the current driver is changed to a new driver, and determining the attributes of the new driver; when the attribute determination result represents a temporary user, determining a temporary pedal feel strategy from at least one preset universal pedal feel strategy; and responding according to the temporary pedal feel strategy. In this way, the technical solution of this embodiment can provide a quick configuration function for the pedal feel strategy when the driver of the vehicle is changed from the owner or the owner's authorized person to other temporary vehicle users, so that the temporary vehicle users can select the universal pedal feel strategy for temporary configuration, thereby improving the driving experience of the temporary vehicle users.

[0084] In one embodiment, it is detected that the current driver is changed to a new driver, and the attributes of the new driver are determined, which can include: obtaining current character feature information of the current driver; when the current task feature information is different from the historical character feature information stored in the vehicle when it was last powered on, determining that the current driver is changed to a new driver; obtaining the new driver's vehicle usage information, the vehicle usage information including at least one of the number of times the vehicle is driven, the cumulative driving time of the vehicle, etc.; determining whether the vehicle usage information meets the temporary vehicle usage conditions; if so, determining the character attributes of the current driver as a temporary user; if not, triggering the pedal feel creation function according to the current task feature information.

[0085] In one embodiment, the temporary vehicle use conditions include but are not limited to at least one of the following: Determining that the number of times the new driver has driven the vehicle is less than or equal to a preset number of times; It is determined that the cumulative driving time of the new driver for the vehicle is less than or equal to a preset time.

[0086] In one embodiment, the universal pedal feel strategy may be a brake pedal feel strategy determined based on big data and associated with crowd attributes. In addition, the universal pedal feel strategy may also be a factory preset pedal feel strategy.

[0087] In one implementation, the universal pedal feel strategy is universal or general, and any driver can select and use it in conjunction.

[0088] In one embodiment, when the attribute determination result characterizes a temporary user, the step of determining a temporary pedal feel strategy from at least one preset universal pedal feel strategy may include: when the attribute determination result characterizes a temporary user, obtaining crowd attributes of the temporary user and driving data of the temporary user when driving a vehicle; determining a corresponding driving style based on the crowd attributes and the driving data; and selecting a universal pedal feel strategy that matches the driving style from at least one preset universal pedal feel strategy as a temporary pedal feel strategy.

[0089] The technical solution of this embodiment can quickly predict the driving style of the temporary user according to the population attributes of the temporary user and the driving data of the temporary user when driving the vehicle, so as to quickly select and recommend a temporary pedal feel strategy suitable for the temporary user from at least one preset universal pedal feel strategy, so that the temporary vehicle user can quickly select the universal pedal feel strategy for temporary configuration, thereby improving the driving experience of the temporary vehicle user.

[0090] In one embodiment, the driving data includes at least one of braking intensity, braking frequency, instantaneous vehicle speed, etc.

[0091] In one embodiment, the crowd attributes may represent various attribute labels for people, including but not limited to gender labels, age labels, etc.

[0092] In one embodiment, the crowd attribute may be automatically determined by collecting image information of temporary users through a camera and performing image recognition, or may be automatically set in response to a user setting operation.

[0093] In one embodiment, the driving style includes one of leisurely driving, regular driving, aggressive driving, etc.

[0094] A pedal feel control method provided in this embodiment includes: step S11: obtaining the character feature information of the current driver; step S12: matching the target pedal feel strategy associated with the character feature information, the target pedal feel strategy being obtained by training the preset model according to the braking control data corresponding to the character feature information; step S13: responding according to the target pedal feel strategy. Through the technical solution of this embodiment, after identifying the current driver, the pedal feel strategy output by the model training of the braking control data of the current driver is used as the target pedal feel strategy to complete the pedal feel strategy configuration of the vehicle, so that the current driver can more intuitively perceive the response state of the vehicle braking system (i.e., a good brake pedal feel) based on his own braking control habits, thereby accurately controlling the braking degree, thereby not only enhancing the current driver's grasp of the vehicle dynamics, but also helping the current driver to quickly make a correct response in an emergency, thereby effectively shortening the braking distance (or braking distance) and significantly reducing the probability of traffic accidents. Therefore, the technical solution of this embodiment can complete the pedal feel strategy configuration of the vehicle to accurately match the driver's personal braking control habits, thereby achieving the purpose of improving the driving experience.

[0095] Based on the same inventive concept as the above embodiments, the following example illustrates a specific application scenario of a pedal feel control method to explain the above embodiments in detail: The effect achieved in this example is to rely on the BP neural network model to memorize the training data and output a pedal feel strategy that conforms to the driver under the memory model. See also Figure 2 , the specific implementation path of this example is: (1) When the camera recognizes the driver, it collects the driver's information (i.e., personal characteristics) and stores it in the in-vehicle storage chip. For example, after the cockpit camera recognizes person A, it sends person A to the in-vehicle storage chip for storage.

[0096] (2) When the driver currently steps on the brake pedal, the brake control data when the brake pedal is pressed deeply and lightly are recorded respectively. The brake control data includes the relationship between the deceleration and the pedal force and the relationship between the deceleration and the pedal travel.

[0097] The data sampling frequency is 2000 Hz, one data set is recorded every 50 Hz, and 40 data sets can be recorded in 1 second.

[0098] (3) The BP neural network model is trained based on the currently sampled data set. Specifically, the input parameters of the BP neural network model are: deceleration, pedal force, and pedal travel. The output of the BP neural network model is the pedal feel curve (i.e., pedal feel strategy), which is fitted using a quintic polynomial to fit the pedal feel that matches the current driver's driving style (i.e., braking control habits), and is associated with the current driver's driver information and recorded in the in-vehicle storage chip. When the driver steps on the brake pedal again or the next time the driver is identified as driving the vehicle, the vehicle will automatically switch to a pedal feel that matches the driver's style.

[0099] For example, the pedal feel sensor monitors the relevant conditions of the brake pedal in real time, and the sensing data obtained by the pedal feel sensor is sampled at a frequency of 2000Hz to record the relationship between deceleration and pedal force and the relationship between deceleration and pedal travel, and the data set is constructed and imported into the BP neural network model, so that after the BP neural network model is trained with the data set to obtain relevant data, the pedal feel curve is obtained by fitting using a quintic polynomial. The pedal feel curve is memorized as pedal feel mode A and stored in the in-vehicle storage chip in association with portrait A. Later, when the driver corresponding to portrait A drives the vehicle, pedal feel mode A can be called from the in-vehicle storage chip to control the execution output of the brake pedal.

[0100] This example requires that the pedal feel sensor has a sampling frequency of 2000 Hz, and the camera in the cockpit has a high-precision recognition function, which can collect the driver's facial feature points to form storage data (driver information) and memorize it.

[0101] The technical solution of this example, based on the existing switching pedal feel strategy, can create a new pedal feel strategy in real time according to the BP neural network model and the memory model for the current driver's driving. The created pedal feel strategy can allow the driver to more intuitively perceive the response state of the vehicle's braking system, so as to accurately control the braking force. It not only enhances the driver's grasp of the vehicle's dynamics, but also helps the driver to respond quickly and correctly in an emergency, effectively shortening the braking distance and significantly reducing the probability of traffic accidents. In addition, the pedal feel can also enhance the overall driving experience, especially when driving at high speeds or in complex road conditions, which can help the driver predict in advance and adjust the speed in time, ensuring driving safety while also improving ride comfort.

[0102] The technical solution of this example can combine the BP neural network model, integrate the cockpit camera, collect pedal feel data (brake control data), organize the fitting output and create a new pedal feel curve, which is automatically stored in the storage chip in the car to match the braking control habits of a specific driver. Based on the same inventive concept as the above embodiments, the present application embodiment provides a computing device, such as Figure 3 As shown, the device includes: a processor 310 and a memory 311 storing a computer program; wherein, Figure 3 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, Figure 3 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 pedal feel 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, Figure 3 Various buses are labeled as bus system 313.

[0104] The memory 311 may be a volatile memory or a non-volatile memory, or may include both volatile and non-volatile memories. The non-volatile 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 disc, 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 RAMbus 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 used to operate on the device, such as an operating system and an application program. Among them, the operating system includes various system programs, such as a framework layer, a core library layer, a driver layer, etc., which are used to implement various basic services and handle hardware-based tasks. The application program may include various applications, such as a media player (MediaPlayer), a browser (Browser), etc., which are used to implement various application services. Here, the program that implements 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 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 disc, 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 method is implemented. For the specific steps implemented when the computer program is executed by the processor, please refer to Figure 1 The description of the illustrated embodiment will not be repeated here.

[0107] 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.

[0108] 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.

[0109] 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 method for controlling pedal feel, characterized in that: include: Get the character feature information of the current driver; Matching a target pedal feel strategy associated with the character feature information, wherein the target pedal feel strategy is obtained by training a preset model according to the braking control data corresponding to the character feature information; Respond according to the target pedal feel strategy.

2. The method according to claim 1, characterized in that Before the step of obtaining the character feature information of the current driver, the method includes: In response to meeting the pedal feel creation condition, acquiring the character feature information of the driver; According to a preset data collection strategy, the braking control data of the driver corresponding to the character feature information when driving the vehicle is collected, and at least one data set is constructed, wherein the braking control data includes first corresponding information of deceleration and pedal force and / or second corresponding information of deceleration and pedal travel; Performing model training on the preset model according to the brake manipulation data in the data set to output a pedal feel strategy, wherein the pedal feel strategy includes information on a correspondence between a deceleration and a pedal force and / or a pedal stroke; The character feature information is associated with the pedal feel strategy and stored.

3. The method according to claim 2, characterized in that The preset data collection strategy includes: When it is determined that the brake control state is a deep pedal state, the first brake control data at this time is collected according to a preset sampling frequency; and when it is determined that the brake control state is a light pedal state, the second brake control data at this time is collected according to the preset sampling frequency; At least one of the data sets is constructed based on the collected first braking operation data and the second braking control data.

4. The method according to claim 2, characterized in that: The pedal feel strategy includes at least one of the following: Curve information of deceleration and pedal force that is conducive to fitting a quintic polynomial; The curve information of deceleration and pedal stroke is conducive to fitting the quintic polynomial.

5. The method according to claim 2, characterized in that: The pedal feel creation condition includes at least one of the following: Receiving a preset instruction; Based on the character feature information of the current driver, determining that the current driver is a new driver; determining that the number of times the new driver has driven the vehicle exceeds a preset number of times; It is determined that the cumulative driving time of the vehicle by the new driver exceeds a preset time.

6. The method according to any one of claims 1 to 5, characterized in that: After the step of responding according to the target pedal feel strategy, the method further comprises: detecting that the current driver is changed to a new driver, and determining the attributes of the new driver; When the attribute determination result characterizes a temporary user, determining a temporary pedal feel strategy from at least one preset universal pedal feel strategy; Respond according to the temporary pedal feel strategy.

7. The method according to claim 6, characterized in that The step of determining a temporary pedal feel strategy from at least one preset universal pedal feel strategy when the attribute determination result characterizes a temporary user includes: When the attribute determination result represents the temporary user, obtaining the crowd attributes of the temporary user and the driving data of the temporary user when driving a vehicle; Determining a corresponding driving style according to the crowd attributes and the driving data; The universal pedal feel strategy matching the driving style is selected from at least one preset universal pedal feel strategy as the temporary pedal feel strategy.

8. A computing 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 pedal feel control method according to any one of claims 1 to 7 are implemented.

9. A vehicle, characterized in that: The vehicle comprises the computing device of claim 8.

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 pedal feel control method according to any one of claims 1 to 7 are implemented.

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