Switching control method for steering mode and driving style of vehicle and related equipment

Abstract

The embodiment of the invention provides a vehicle steering mode and driving style switching control method and related equipment, and belongs to the technical field of vehicle control. The method comprises the following steps: acquiring driving data of vehicle driving, and extracting a plurality of kinematics fragments according to the driving data; for any kinematic fragment, determining a characteristic parameter corresponding to the kinematic fragment according to the driving data in the kinematic fragment; performing principal component analysis and clustering analysis on the characteristic parameters of the different kinematics fragments, and determining a driving style according to an analysis result; and matching a steering mode according to the driving style, and dynamically adjusting the steering mode according to the change of the kinematics fragment in the driving process. The embodiment of the invention aims at intelligently setting the steering mode matched with the driving habit of the driver and improving the driving experience.

Description

Technical Field

[0001] This application relates to the field of vehicle control technology, and in particular to a method and related equipment for switching vehicle steering modes and driving styles. Background Technology

[0002] In related technologies, with the development of intelligent driving technology, vehicle electric power steering systems are often equipped with multiple steering modes. Different steering modes have different steering force curves, providing drivers with different driving experiences. However, the current steering mode selection relies on manual settings by the driver. Most users are unaware of how to adjust the steering mode and the actual differences in experience, making it difficult for the default steering mode to match their driving habits and affecting the driving experience.

[0003] In summary, the technical problems existing in the relevant technologies need to be improved. Summary of the Invention

[0004] The main objective of this application is to propose a method and related equipment for switching vehicle steering modes and driving styles, aiming to achieve intelligent settings of steering modes that match the driver's driving habits and improve the driving experience.

[0005] To achieve the above objectives, one aspect of this application proposes a method for switching vehicle steering modes and driving styles, the method comprising: Acquire vehicle driving data, and extract several kinematic segments based on the driving data; For any of the aforementioned kinematic segments, the characteristic parameters corresponding to the kinematic segment are determined based on the driving data in the kinematic segment; Principal component analysis and cluster analysis are performed on the characteristic parameters of different kinematic segments, and driving style is determined based on the analysis results; The steering mode is matched according to the driving style, and the steering mode is dynamically adjusted according to the changes in the kinematic segments during driving.

[0006] In some embodiments, determining the driving style based on the analysis results includes: The sub-style of each kinematic segment is determined based on the analysis results; Determine the proportion of the kinematic segment corresponding to each substyle among all the kinematic segments, and determine the driving style based on the substyle with the highest proportion.

[0007] In some embodiments, after determining the driving style based on the analysis results, the method further includes: In response to a change in the substyle that has the largest proportion among all said kinematic segments, the driving style is updated according to the substyle that has the largest proportion after the change.

[0008] In some embodiments, dynamically adjusting the steering mode based on changes in the kinematic segments during driving includes: During the driving process, the most recent first preset number of consecutive kinematic segments are acquired and defined as the target kinematic segment; Determine the proportion of the kinematic segment corresponding to each sub-style in the target kinematic segment, and determine the temporary style based on the sub-style with the largest proportion. The selectable style type of the temporary style is consistent with the driving style. In response to a discrepancy between the temporary style and the driving style, and the proportion of the temporary style being greater than or equal to a preset percentage, the steering mode is updated according to the temporary style, wherein the steering mode is associated with the driving style.

[0009] In some embodiments, after updating the steering pattern according to the temporary style, the method further includes: In response to vehicle power-on and startup, the steering mode is updated again based on the association between the steering mode and the driving style, according to the currently set driving style.

[0010] In some embodiments, performing principal component analysis and cluster analysis on the feature parameters of different kinematic segments includes: The feature parameters are dimensionality reduced using principal component analysis (PCA) to obtain the principal component score matrix. Cluster analysis is performed on the kinematic segments based on the principal component score matrix.

[0011] In some embodiments, the clustering analysis of the kinematic segments based on the principal component score matrix includes: The number of clusters is set according to the number of style types of the driving style. Based on the number of clusters and the principal component score matrix, the kinematic segments are clustered using the K-means algorithm until the cluster centers no longer fluctuate, thus determining the cluster centers of each driving style.

[0012] To achieve the above objectives, another aspect of this application proposes a vehicle steering mode and driving style switching control system, the system comprising: The data acquisition module is used to acquire vehicle driving data and extract several kinematic segments based on the driving data. The data processing module is used to determine the characteristic parameters corresponding to any given kinematic segment based on the driving data in the kinematic segment. The analysis module is used to perform principal component analysis and cluster analysis on the feature parameters of different kinematic segments, and determine the driving style based on the analysis results; An execution module is used to match a steering mode according to the driving style and dynamically adjust the steering mode according to changes in the kinematic segments during driving.

[0013] To achieve the above objectives, another aspect of this application provides a vehicle control device, the device including a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the method described above.

[0014] To achieve the above objectives, another aspect of this application provides a vehicle equipped with the vehicle control device described above and implementing the method described above.

[0015] The embodiments of this application include at least the following beneficial effects: This application provides a method and related equipment for switching vehicle steering modes and driving styles. This solution acquires vehicle driving data, extracts kinematic segments from the driving data, determines the corresponding feature parameters for any kinematic segment based on the driving data within it, and then performs principal component analysis and cluster analysis on the feature parameters. Based on the analysis results, the driving style is determined, thereby matching the steering mode according to the driving style. During subsequent driving, the steering mode is dynamically adjusted based on changes in the kinematic segments. Compared to steering modes that rely on manual settings by the driver, the method of this application analyzes driving data from actual vehicle operation on a kinematic segment basis, determines the driver's driving style through principal component analysis and cluster analysis, and then determines a suitable steering mode based on the driving style. This not only intelligently matches the steering mode to the driver but also improves the matching degree between the steering mode and the driver's driving habits, thus enhancing the driving experience. Attached Figure Description

[0016] Figure 1 This is a flowchart of a vehicle steering mode and driving style switching control method provided in an embodiment of this application; Figure 2 yes Figure 1 Partial flowchart of step S103; Figure 3 yes Figure 2 Flowchart of step S202; Figure 4 yes Figure 1 Another part of the flowchart for step S103; Figure 5 yes Figure 1 Partial flowchart of step S104; Figure 6 This is a schematic diagram of the structure of a vehicle steering mode and driving style switching control system provided in an embodiment of this application; Figure 7This is a schematic diagram of the vehicle control device provided in the embodiments of this application. Detailed Implementation

[0017] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of this application and are not intended to limit it. In the following description, when referring to the accompanying drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with those of this application; they are merely examples of apparatuses and methods consistent with some aspects of the embodiments of this application as detailed in the appended claims.

[0018] It is understood that the terms “first,” “second,” etc., used in this application may be used herein to describe various concepts, but unless otherwise stated, these concepts are not limited by these terms. These terms are only used to distinguish one concept from another. For example, without departing from the scope of the embodiments of this application, first information may also be referred to as second information, and similarly, second information may also be referred to as first information. Depending on the context, the words “if,” “when,” or “in response to a determination” as used herein may be interpreted as “when…” or “when…” or “in response to a determination.”

[0019] As used in this application, the terms "at least one", "multiple", "each", "any", etc., "at least one" includes one, two or more, "multiple" includes two or more, "each" refers to each of the corresponding multiples, and "any" refers to any one of the multiples.

[0020] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing embodiments of this application only and is not intended to limit this application.

[0021] In related technologies, with the development of intelligent driving technology, vehicle electric power steering systems are often equipped with multiple steering modes. Different steering modes have different steering force curves, providing drivers with different driving experiences. However, the current steering mode selection relies on manual settings by the driver. Most users are unaware of how to adjust the steering mode and the actual differences in experience, making it difficult for the default steering mode to match their driving habits and affecting the driving experience.

[0022] In view of this, this application provides a method and related equipment for switching vehicle steering modes and driving styles. This solution acquires vehicle driving data, extracts kinematic segments from the driving data, determines the corresponding feature parameters for any kinematic segment based on the driving data within it, and then performs principal component analysis and cluster analysis on the feature parameters. Based on the analysis results, the driving style is determined, and a steering mode is matched according to the driving style. During subsequent driving, the steering mode is dynamically adjusted based on changes in the kinematic segments. Compared to steering modes that rely on manual settings by the driver, this method analyzes driving data from actual vehicle operation, taking kinematic segments as units, and determines the driver's driving style through principal component analysis and cluster analysis. Based on this driving style, a suitable steering mode is then determined. This not only intelligently matches the steering mode to the driver but also improves the matching degree between the steering mode and the driver's driving habits, thus enhancing the driving experience.

[0023] The vehicle steering mode and driving style switching control method provided in this application relates to the field of vehicle control technology. This method can be applied to a terminal, a server, or software running on either a terminal or a server. In some embodiments, the terminal can be a smartphone, tablet, laptop, desktop computer, smart speaker, smartwatch, or in-vehicle terminal, but is not limited to these. The server can be configured as an independent physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms. The server can also be a node server in a blockchain network. The software can be an application implementing the vehicle steering mode and driving style switching control method, but is not limited to the above forms.

[0024] This application can be used in a wide variety of general-purpose or special-purpose computer system environments or configurations. Examples include: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, and distributed computing environments including any of the above systems or devices. This application can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc., that perform specific tasks or implement specific abstract data types. This application can also be practiced in distributed computing environments where tasks are performed by remote processing devices connected via a communication network. In distributed computing environments, program modules can reside in local and remote computer storage media, including storage devices.

[0025] It should be noted that in all specific embodiments of this application, when processing data related to user identity or characteristics, such as user information, user behavior data (e.g., driving data), user historical data, and user location information, user permission or consent is obtained first. Furthermore, the collection, use, and processing of this data comply with relevant laws, regulations, and standards. In addition, when embodiments of this application require access to sensitive personal information of users, separate permission or consent from the user is obtained through pop-ups or redirection to a confirmation page. Only after obtaining the user's separate permission or consent is the necessary user-related data required for the normal operation of embodiments of this application obtained.

[0026] Figure 1 This is an optional flowchart of the vehicle steering mode and driving style switching control method provided in the embodiments of this application. Figure 1 The method may include, but is not limited to, steps S101 to S104.

[0027] Step S101: Obtain vehicle driving data and extract several kinematic segments based on the driving data.

[0028] To intelligently set a steering mode that matches the driver's driving habits, it is first necessary to analyze the driver's driving habits. This involves acquiring driving data during the vehicle's operation. This data can be obtained through the vehicle's sensor system or by accessing the in-vehicle system, and may include, but is not limited to, data such as vehicle speed, acceleration, accelerator pedal opening, brake pedal opening, steering wheel angle, and lateral acceleration.

[0029] Based on the data collection time, kinematic segments are divided. Specifically, in this embodiment, the segment between two adjacent idling states in the same driving trip is defined as a kinematic segment. Therefore, each kinematic segment includes multiple sets of driving data that change over time, thereby analyzing the driver's driving habits within a single time segment.

[0030] Step S102: For any kinematic segment, determine the characteristic parameters corresponding to the kinematic segment based on the driving data in the kinematic segment.

[0031] Optionally, the method in this embodiment will analyze driving habits based on feature parameters. These feature parameters are all parameters representing the changing characteristics of various driving data over a time period, and therefore do not target instantaneous dynamics. Please refer to Table 1 below.

[0032] Table 1 Feature Parameters

[0033] The characteristic parameters mainly include three dimensions: mean, maximum, and standard deviation. First, determine the number of data points within the corresponding kinematic segment's time interval, defined as N. If the sampling frequency is 1Hz, there is one data point per second. In this case, the number of data points can also be used to visually represent the duration T of the kinematic segment. Taking vehicle speed as an example, the average speed... The calculation can be referenced in the following formula (1): (1) Maximum speed Please refer to the following formula (2): (2) speed standard deviation You can refer to the following formula (3): (3) For other characteristic parameters, the calculation method for vehicle speed described above can be used as a reference. In addition, the parameters related to accelerator pedal opening, brake pedal opening, and steering wheel angle are different from parameters such as vehicle speed and are not meaningful throughout the entire kinematic segment. Therefore, the time used for their calculation can only be the trigger time. For example, the accelerator pedal opening trigger time can be used.

[0034] Step S103: Perform principal component analysis and cluster analysis on the characteristic parameters of different kinematic segments, and determine the driving style based on the analysis results.

[0035] Based on the extracted feature parameters, principal component analysis and cluster analysis can then be performed. Principal component analysis extracts relevant variables based on their contribution, and analysis based on these extracted variables can reduce the dimensionality of the feature parameters. Cluster analysis then classifies the processed feature parameters to extract driving styles.

[0036] refer to Figure 2 In step S103 of some embodiments, the principal component analysis and cluster analysis of the feature parameters of different kinematic segments includes: Step S201: The feature parameters are reduced in dimensionality using the principal component analysis algorithm to obtain the principal component score matrix.

[0037] Step S202: Perform cluster analysis on the kinematic segments based on the principal component score matrix.

[0038] Specifically, since the original data of the feature parameters have different units, directly processing the original data would result in significant dispersion, negatively impacting subsequent analysis. Therefore, it is necessary to first standardize the original data, converting it into a standardized matrix with a mean of 0 and a variance of 1 for each column. Then, the correlation coefficient is calculated based on the matrix formed from the original data, generating a correlation coefficient matrix. The eigenvalues ​​of this correlation coefficient matrix are then calculated, sorted from largest to smallest, and their corresponding eigencomponents are identified. Next, the contribution rate of each principal component and its cumulative contribution rate are calculated. When the cumulative contribution rate of the first two predetermined number of principal components reaches the required contribution rate, the information from the original data can be considered largely preserved, and these principal components are selected as the basis for classification. Finally, the loading matrix of the principal components is calculated, and the standardized matrix is ​​multiplied by the loading matrix to obtain the principal component score matrix, which is then used for subsequent cluster analysis.

[0039] Principal component analysis is used to reduce the dimensionality of feature parameters and extract principal components that retain key information, thereby optimizing the data structure. This is beneficial for classifying data in subsequent steps such as cluster analysis and improves the accuracy of determining driving style.

[0040] refer to Figure 3 Based on the above embodiments, in some embodiments, step S202 includes: Step S203: Set the number of clusters according to the number of driving style types. Based on the number of clusters and the principal component score matrix, perform cluster analysis on the kinematic segments using the K-means algorithm until the cluster centers no longer fluctuate, and determine the cluster centers for each driving style.

[0041] In this embodiment, driving styles can be set to include three types: aggressive, standard, and moderate. In other embodiments, more or different types of driving styles can be set. Correspondingly, the number of clusters is 3. First, an initial cluster center is randomly set for each category; the Euclidean distance between each kinematic segment and the cluster center is calculated; data with similar distances are grouped into the same category, and the center position of each category is calculated based on this, and the cluster center is reset according to the center position; the above steps of calculating Euclidean distance and updating cluster centers are repeated until the cluster centers no longer fluctuate, thus determining the cluster centers for the three driving styles. Since the cluster centers are actually determined step by step based on the analysis of the driver's kinematic segments, the cluster centers determined by drivers with different driving habits will also have certain differences, so that the classification of driving styles can fully adapt to each driver's driving habits.

[0042] Cluster analysis helps determine the style distribution of kinematic segments, which is beneficial for extracting and summarizing the driver's driving style and supporting subsequent steps to determine the corresponding steering mode, thus enabling intelligent matching of steering modes that conform to the driver's driving habits.

[0043] refer to Figure 4 In step S103 of some embodiments, determining the driving style based on the analysis results includes: Step S204: Determine the substyle of each kinematic segment based on the analysis results.

[0044] Step S205: Determine the proportion of the kinematic segment corresponding to each substyle in all kinematic segments, and determine the driving style based on the substyle with the highest proportion.

[0045] By comparing the position of each kinematic segment relative to the three cluster centers, the sub-style of each kinematic segment can be determined. The style corresponding to the kinematic segment is defined as the sub-style, which is also one of three styles: aggressive, standard, or steady. The occurrence frequency of the three driving styles in all kinematic segments is counted, and the proportion of kinematic segments corresponding to each style in all kinematic segments is determined. The style that occurs most frequently is identified as the driver's driving style.

[0046] In addition, since determining the driving style requires a certain number of kinematic segments for analysis, a third preset number is set. When the number of kinematic segments recorded by the vehicle reaches the third preset number, such as 20, the driving style is then determined by the method of this embodiment. Before that, the driving style can be preset to the standard style.

[0047] By determining the driving style based on the sub-style of the kinematic segments, the information of the kinematic segments is summarized and generalized to support the subsequent steps in determining the corresponding steering mode, thereby achieving intelligent matching of the steering mode that conforms to the driver's driving habits.

[0048] In some embodiments, after step S103, the method further includes: In response to a change in the substyle that has the largest proportion among all kinematic segments, the driving style is updated based on the substyle that has the largest proportion after the change.

[0049] On the other hand, once a driving style is determined, a driver's driving habits may change. Therefore, during driving, the proportion of the three sub-styles in all kinematic segments is continuously counted to determine whether the sub-style with the largest proportion has changed. If it has changed, the driving style is updated according to the sub-style with the largest proportion after the change; if it has not changed, the current driving style is maintained.

[0050] Furthermore, for the statistics of kinematic segments, only data within the target time period can be extracted, such as the most recent month or the most recent six months; or a historical decay factor can be applied to the kinematic segments, so that the kinematic segments further away from the present have a lower influence, making the analysis of driving style more focused on the driver's latest driving habits.

[0051] By dynamically updating the driving style, the driving style can adapt to changes in the driver's driving habits in a timely manner, thereby improving the matching degree between the driving style and the driver's driving habits.

[0052] Step S104: Match the steering mode according to the driving style, and dynamically adjust the steering mode according to the changes in kinematic segments during driving.

[0053] Regarding steering modes, this embodiment sets three corresponding steering modes for three driving styles: Sport mode for aggressive driving, Standard mode for standard driving, and Comfort mode for smooth driving. There is a correlation between steering modes and driving modes, allowing the appropriate steering mode to be determined directly based on the driving style. Simultaneously, each of the three steering modes also has a preset steering force curve. Once the steering mode is determined, the vehicle will provide steering force according to the corresponding steering force curve when turning.

[0054] refer to Figure 5 In step S104 of some embodiments, dynamically adjusting the steering mode according to changes in kinematic segments during driving includes: Step S301: During the driving process, acquire the most recent first preset number of continuous kinematic segments and define them as target kinematic segments.

[0055] Step S302: Determine the proportion of the kinematic segment corresponding to each sub-style in the target kinematic segment, and determine the temporary style based on the sub-style with the largest proportion. The selectable style type of the temporary style is consistent with the driving style.

[0056] In step S303, in response to the inconsistency between the temporary style and the driving style, and the proportion of the temporary style being greater than or equal to a preset percentage, the steering mode is updated according to the temporary style, and the steering mode is related to the driving style.

[0057] The driving style and steering mode determined in the above steps are overall settings covering the entire driving process. However, in actual driving, users may suddenly encounter special conditions and have to switch to a different driving style. To address this, this embodiment also acquires the most recent first preset number of continuous kinematic segments during driving. The first preset number can be set to 8 or 10, etc., and these kinematic segments are defined as target kinematic segments.

[0058] By analyzing only the target kinematic segments, the number and proportion of sub-styles within these segments are determined. A temporary style is then determined based on the sub-style with the highest proportion, which is either aggressive, standard, or stable. If the driver's driving habits do not change temporarily, the temporary style should match the set driving style, requiring no special handling. If the temporary style differs from the driving style, and its proportion is greater than or equal to a preset percentage (e.g., 80%), it indicates a potential special operating condition. In this case, the steering mode is updated according to the special operating condition, and steering effort is provided based on the updated steering mode.

[0059] It should be noted that even if the steering mode is changed according to the temporary style, it will not affect the original driving style setting. The temporary style and the driving style are two different settings. This is because the driving style records the driver's long-term driving habits and should not be overridden by the short-term temporary style.

[0060] By determining the temporary style based on the most recent kinematic segments and identifying whether special conditions have been entered, the steering mode can be dynamically changed according to the actual driving conditions, improving the matching degree between the steering mode settings and the driver's needs, thereby enhancing the driving experience.

[0061] Based on the above embodiments, in some embodiments, after step S303, the method further includes: In response to vehicle power-on, the steering mode is updated based on the relationship between steering mode and driving style, according to the currently set driving style.

[0062] As described in the above embodiments, the temporary style only reflects short-term driving habits when facing special operating conditions. Therefore, after adjusting the steering mode according to the temporary style, if the vehicle is detected to be powered on and started, it means that the vehicle has finished the previous journey facing special operating conditions and is now at the beginning of a new journey. Therefore, resetting the corresponding steering mode according to the actual driving style is equivalent to resetting the steering mode settings.

[0063] By updating the steering mode according to the set driving style, the vehicle's steering mode setting is restored to a state that matches the driver's long-term driving habits, avoiding the problem of suddenly feeling uncomfortable with the steering feel after starting, and improving the driving experience.

[0064] This application embodiment analyzes driving data from actual vehicle operation in kinematic segments, determines the driver's driving style through principal component analysis and cluster analysis, and then determines a suitable steering mode based on the driving style. This not only intelligently matches the steering mode to the driver but also improves the matching degree between the steering mode and the driver's driving habits, thereby enhancing the driving experience.

[0065] The solutions of the embodiments of the present invention will be described in detail and explained below with reference to specific application examples: This application provides a method for switching vehicle steering mode and driving style, which can be applied to vehicles.

[0066] Acquire vehicle driving data, extract several kinematic segments based on the driving data; for any kinematic segment, determine the corresponding feature parameters of the kinematic segment based on the driving data in the kinematic segment.

[0067] Principal component analysis (PCA) is used to reduce the dimensionality of feature parameters, resulting in a principal component score matrix. The number of clusters is determined based on the number of driving style types. Using the number of clusters and the PCA score matrix, K-means algorithm is applied to cluster the kinematic segments until the cluster centers no longer fluctuate, thus determining the cluster centers for each driving style. The sub-style of each kinematic segment is determined based on the analysis results. The proportion of each sub-style's kinematic segment within all kinematic segments is then determined, and the driving style is determined based on the sub-style with the highest proportion. Subsequently, when the sub-style with the highest proportion among all kinematic segments changes, the driving style is updated based on the new sub-style with the highest proportion.

[0068] The steering mode is matched according to the driving style. Furthermore, during driving, the most recent first preset number of consecutive kinematic segments are acquired and defined as the target kinematic segment; the proportion of the kinematic segment corresponding to each sub-style in the target kinematic segment is determined; a temporary style is determined based on the sub-style with the highest proportion, and the selectable style type of the temporary style is the same as the driving style; when the temporary style is inconsistent with the driving style, and the proportion of the temporary style is greater than or equal to a preset percentage, the steering mode is updated according to the correlation between the steering mode and the driving style; subsequently, if the vehicle is powered on and started, the steering mode is updated again according to the currently set driving style based on the correlation between the steering mode and the driving style.

[0069] This application embodiment analyzes driving data from actual vehicle operation in kinematic segments, determines the driver's driving style through principal component analysis and cluster analysis, and then determines a suitable steering mode based on the driving style. This not only intelligently matches the steering mode to the driver but also improves the matching degree between the steering mode and the driver's driving habits, thereby enhancing the driving experience.

[0070] Please see Figure 6 This application also provides a vehicle steering mode and driving style switching control system that can implement the above method. The system includes: The data acquisition module is used to acquire vehicle driving data and extract several kinematic segments based on the driving data; The data processing module is used to determine the characteristic parameters corresponding to any kinematic segment based on the driving data in the kinematic segment. The analysis module is used to perform principal component analysis and cluster analysis on the feature parameters of different kinematic segments, and to determine the driving style based on the analysis results; The execution module is used to match the steering mode according to the driving style and dynamically adjust the steering mode according to changes in kinematic segments during driving.

[0071] It is understood that the content of the above method embodiments is applicable to this system embodiment. The specific functions implemented in this system embodiment are the same as those in the above method embodiments, and the beneficial effects achieved are also the same as those achieved in the above method embodiments.

[0072] Reference Figure 7 The present invention also provides a vehicle control device, including a memory, a processor, and a program stored in the memory and executable on the processor. When the program is executed by the processor, it implements the method described in the above embodiments.

[0073] Taking the example of a processor and memory in a vehicle controller being connected via a bus, the memory, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs and non-transitory computer-executable programs. Furthermore, the memory may include high-speed random access memory, and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some embodiments, the memory may optionally include memory remotely located relative to the control processor, and these remote memories can be connected to the control device via a network.

[0074] The non-transient software program and instructions required to implement the control method of the above embodiments are stored in memory. When executed by a processor, the control method of the above embodiments is executed. For example, executing... Figure 1 The method steps S101 to S104 are described above. It is understood that the content of the above method embodiments is applicable to this device, and the specific functions implemented in this device embodiment are the same as those in the above method embodiments, and the beneficial effects achieved are also the same as those achieved in the above method embodiments.

[0075] Memory, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs and non-transitory computer-executable programs. Furthermore, memory may include high-speed random access memory, and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some embodiments, memory may optionally include memory remotely located relative to the processor, and these remote memories can be connected to the processor via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0076] This invention also provides a vehicle, including the vehicle control device described in the above embodiments.

[0077] Vehicles equipped with recirculating ball steering systems include various types of vehicles such as heavy trucks, buses, loaders, mining dump trucks, special off-road vehicles, and airport tractors. The vehicle must have an electric motor capable of outputting power or acting as a generator to store mechanical energy. When the vehicle is a new energy vehicle, it can be a hybrid or a pure electric vehicle.

[0078] Since the vehicle applies all the technical solutions of the above-described system or vehicle control device, it has at least all the beneficial effects brought about by the technical solutions of the above embodiments, which will not be repeated here.

[0079] This application provides a method and related equipment for switching vehicle steering modes and driving styles. The solution acquires vehicle driving data, extracts kinematic segments from the data, determines the corresponding feature parameters for each kinematic segment based on the driving data, performs principal component analysis and cluster analysis on the feature parameters, determines the driving style based on the analysis results, and then matches the steering mode to the driving style. During subsequent driving, the steering mode is dynamically adjusted based on changes in the kinematic segments. Compared to manually set steering modes by the driver, this method analyzes actual vehicle driving data on a kinematic segment basis, determines the driver's driving style through principal component analysis and cluster analysis, and then determines a suitable steering mode based on that driving style. This intelligently matches the steering mode to the driver while improving the matching degree between the steering mode and the driver's driving habits, thus enhancing the driving experience.

[0080] The embodiments described in this application are for the purpose of more clearly illustrating the technical solutions of the embodiments of this application, and do not constitute a limitation on the technical solutions provided by the embodiments of this application. As those skilled in the art will know, with the evolution of technology and the emergence of new application scenarios, the technical solutions provided by the embodiments of this application are also applicable to similar technical problems.

[0081] Those skilled in the art will understand that the technical solutions shown in the figures do not constitute a limitation on the embodiments of this application, and may include more or fewer steps than shown, or combine certain steps, or different steps.

[0082] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0083] Those skilled in the art will understand that all or some of the steps in the methods disclosed above, as well as the functional modules / units in the systems and devices, can be implemented as software, firmware, hardware, or suitable combinations thereof.

[0084] The terms “first,” “second,” “third,” “fourth,” etc. (if present) in the specification and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms “comprising” and “having,” and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0085] It should be understood that in this application, "at least one (item)" means one or more, and "more than" means two or more. "And / or" is used to describe the relationship between related objects, indicating that three relationships can exist. For example, "A and / or B" can represent three cases: only A exists, only B exists, and both A and B exist simultaneously, where A and B can be singular or plural. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. "At least one (item) of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one (item) of a, b, or c can represent: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, and c can be single or multiple.

[0086] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of the units described above is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0087] The units described above as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0088] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0089] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes multiple instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing programs, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0090] The preferred embodiments of the present application have been described above with reference to the accompanying drawings, but this does not limit the scope of the claims of the present application. Any modifications, equivalent substitutions, and improvements made by those skilled in the art without departing from the scope and substance of the embodiments of the present application shall be within the scope of the claims of the present application.

Claims

1. A method of switching control of a steering mode and a driving style of a vehicle, characterized by, The method comprises the following steps: Obtaining driving data of vehicle driving, and extracting a plurality of kinematic segments from the driving data; For any kinematic segment, determining the characteristic parameters corresponding to the kinematic segment according to the driving data in the kinematic segment; Performing principal component analysis and cluster analysis on the characteristic parameters of different kinematic segments, and determining a driving style according to the analysis result; Matching a steering mode according to the driving style, and dynamically adjusting the steering mode according to the change of the kinematic segment during driving.

2. The method of claim 1, wherein, The determination of the driving style according to the analysis result comprises: Determining a sub-style of each kinematic segment according to the analysis result; Determining the proportion of the kinematic segment corresponding to each sub-style in all kinematic segments, and determining the driving style according to the sub-style with the largest proportion.

3. The method of claim 2, wherein, After determining the driving style according to the analysis result, the method further comprises: In response to a change in the sub-style with the largest proportion in all kinematic segments, updating the driving style according to the sub-style with the largest changed proportion.

4. The method of claim 2, wherein, The dynamic adjustment of the steering mode according to the change of the kinematic segment during driving comprises: During driving, obtaining a first preset number of continuous kinematic segments, defined as target kinematic segments; Determining the proportion of the kinematic segment corresponding to each sub-style in the target kinematic segments, determining a temporary style according to the sub-style with the largest proportion, and the selectable style type of the temporary style is consistent with the driving style; In response to the temporary style being inconsistent with the driving style and the proportion of the temporary style being greater than or equal to a preset percentage, updating the steering mode according to the temporary style, and the steering mode has a correlation with the driving style.

5. The method of claim 4, wherein, After updating the steering mode according to the temporary style, the method further comprises: In response to the vehicle being powered on, updating the steering mode according to the current set driving style again according to the correlation between the steering mode and the driving style.

6. The method according to any one of claims 1 to 5, characterized in that, The principal component analysis and cluster analysis on the characteristic parameters of different kinematic segments comprise: Performing dimensionality reduction processing on the characteristic parameters through a principal component analysis algorithm to obtain a principal component score matrix; Performing cluster analysis on the kinematic segments according to the principal component score matrix.

7. The method of claim 6, wherein, The cluster analysis on the kinematic segments according to the principal component score matrix comprises: Setting the number of clusters according to the number of style types of the driving style, and performing cluster analysis on the kinematic segments through a K-means algorithm based on the number of clusters and the principal component score matrix, until the cluster center no longer fluctuates, to determine the cluster center of each driving style.

8. A vehicle steering mode and driving style switching control system characterized by comprising: The system comprises: A data acquisition module, configured to obtain driving data of vehicle driving, and extract a plurality of kinematic segments from the driving data; A data processing module, configured to determine the characteristic parameters corresponding to the kinematic segment according to the driving data in the kinematic segment for any kinematic segment; An analysis module, configured to perform principal component analysis and cluster analysis on the characteristic parameters of different kinematic segments, and determine a driving style according to the analysis result. An execution module is configured to match a steering mode according to the driving style, and dynamically adjust the steering mode according to changes of the kinematic segment during driving.

9. A vehicle control device characterized by comprising: The device comprises a memory and a processor, the memory stores a computer program, and the processor realizes the method in any one of claims 1 to 7 when executing the computer program.

10. A vehicle characterized by comprising: The vehicle is configured with the vehicle control device in claim 9.

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