Vehicle driving adjustment method and device, equipment and storage medium

Abstract

The invention discloses a vehicle driving adjustment method, device and equipment and a storage medium, and the method comprises the steps: obtaining vehicle driving data, and collecting the vehicle driving data through a vehicle-mounted sensor; dividing the vehicle driving data into a plurality of vehicle driving fragment data; selecting a longitudinal characteristic parameter corresponding to the longitudinal driving style and a transverse characteristic parameter corresponding to the transverse driving style according to the multiple pieces of vehicle driving fragment data; determining a target longitudinal driving style grade according to the longitudinal characteristic parameters; according to the transverse characteristic parameters, determining a target transverse driving style grade; according to the target longitudinal driving style grade, a vehicle power mode is adjusted; and adjusting a vehicle steering mode according to the target transverse driving style grade. The power mode and the steering mode of the vehicle can be independently and adaptively adjusted, and automatic matching of the vehicle driving mode and the current driving style and habit is achieved.

Description

Technical Field

[0001] The present application relates to the field of vehicle technology, and in particular to a vehicle driving adjustment method, device, equipment and storage medium. Background Art

[0002] In recent years, vehicles often have multiple driving modes, such as sports mode, automatic mode, economy mode, etc., but the current vehicle driving mode is usually set manually, resulting in the vehicle being unable to dynamically adapt to the driving habits of different drivers or the driving habits of the same driver at different time periods. This may cause the vehicle driving mode to not match the current driving style habits, affecting the driving experience. Summary of the invention

[0003] The present application aims to propose a vehicle driving adjustment method, device, equipment and storage medium, which can independently and adaptively adjust the vehicle's power mode and steering mode to achieve automatic matching of the vehicle's driving mode with the current driving style habits.

[0004] According to the first aspect of the present application, a vehicle driving adjustment method includes: Acquiring vehicle driving data, wherein the vehicle driving data is collected by a vehicle-mounted sensor; Dividing the vehicle driving data into a plurality of vehicle driving segment data; each of the vehicle driving segment data corresponds to the vehicle driving data within a vehicle driving time period; Selecting, according to the plurality of vehicle driving segment data, a longitudinal feature parameter corresponding to a longitudinal driving style and a lateral feature parameter corresponding to a lateral driving style; the longitudinal driving style represents a style feature of a driver manipulating a vehicle to move longitudinally, and the lateral driving style represents a style feature of a driver manipulating a vehicle to move laterally; determining a target longitudinal driving style level according to the longitudinal characteristic parameters; determining a target lateral driving style level according to the lateral characteristic parameters; According to the target longitudinal driving style level, a vehicle power mode is adjusted; according to the target lateral driving style level, a vehicle steering mode is adjusted.

[0005] According to some embodiments of the present application, determining a target longitudinal driving style level according to the longitudinal characteristic parameter; and determining a target lateral driving style level according to the lateral characteristic parameter include: The longitudinal characteristic parameters and the transverse characteristic parameters are subjected to dimensionality reduction processing by using a principal component analysis method to obtain longitudinal dimensionality reduction characteristic parameters and transverse dimensionality reduction characteristic parameters; The target longitudinal driving style level is determined according to the longitudinal dimension reduction feature parameters; and the target lateral driving style level is determined according to the lateral dimension reduction feature parameters.

[0006] According to some embodiments of the present application, determining a target longitudinal driving style level according to the longitudinal characteristic parameter; and determining a target lateral driving style level according to the lateral characteristic parameter include: Classifying all the longitudinal characteristic parameters using a clustering algorithm, and determining the longitudinal driving style level of each of the vehicle driving segment data according to the clustering result; determining the target longitudinal driving style level of the vehicle driving data according to the longitudinal driving style level of each of the vehicle driving segment data; All the lateral feature parameters are classified using a clustering algorithm, and the lateral driving style level of each of the vehicle driving segment data is determined according to the clustering result; and the target lateral driving style level of the vehicle driving data is determined according to the lateral driving style level of each of the vehicle driving segment data.

[0007] According to some embodiments of the present application, determining the target longitudinal driving style level of the vehicle driving data according to the longitudinal driving style level of each of the vehicle driving segment data includes: Counting the longitudinal driving style levels of all the vehicle driving segment data, and determining the longitudinal driving style level with the highest number as the target longitudinal driving style level; Determining the target lateral driving style level of the vehicle driving data according to the lateral driving style level of each of the vehicle driving segment data comprises: The lateral driving style levels of all the vehicle driving segment data are counted, and the lateral driving style level with the highest proportion in the number is determined as the target lateral driving style level.

[0008] According to some embodiments of the present application, the vehicle driving data includes the vehicle's average speed, maximum speed, speed standard deviation, average acceleration, maximum acceleration, acceleration standard deviation, average throttle pedal opening, throttle pedal opening standard deviation, average brake pedal opening, brake pedal opening standard deviation, average lateral acceleration, maximum lateral acceleration, lateral acceleration standard deviation, average steering wheel angular acceleration, and steering wheel angular acceleration standard deviation; The step of selecting the longitudinal characteristic parameter corresponding to the longitudinal driving style and the lateral characteristic parameter corresponding to the lateral driving style according to the plurality of vehicle driving segment data comprises the following steps: Selecting the average speed, maximum speed, speed standard deviation, average acceleration, maximum acceleration, acceleration standard deviation, average accelerator pedal opening, accelerator pedal opening standard deviation, average brake pedal opening and brake pedal opening standard deviation of the plurality of vehicle driving segment data as the longitudinal characteristic parameters; The average speed, maximum speed, speed standard deviation, average lateral acceleration, maximum lateral acceleration, lateral acceleration standard deviation, average steering wheel angular acceleration and steering wheel angular acceleration standard deviation of the multiple vehicle driving segment data are selected as the lateral characteristic parameters.

[0009] According to some embodiments of the present application, the longitudinal driving style level is divided into three categories, including longitudinal aggressive type, longitudinal general type and longitudinal robust type, and the lateral driving style level is divided into three categories, including lateral aggressive type, lateral general type and lateral robust type.

[0010] According to some embodiments of the present application, dividing the vehicle driving data into a plurality of vehicle driving segment data comprises the following steps: Obtain the start time of each idle state during vehicle driving; The vehicle driving data is divided into a plurality of vehicle driving segment data by taking the start time of each idle state as a time node.

[0011] According to a second aspect of the present application, the vehicle driving adjustment device comprises: An acquisition module, used to acquire vehicle driving data, wherein the vehicle driving data is collected by a vehicle-mounted sensor; A division module, used for dividing the vehicle driving data into a plurality of vehicle driving segment data; each of the vehicle driving segment data corresponds to the vehicle driving data within a vehicle driving time period; a selection module, configured to select, according to the plurality of vehicle driving segment data, a longitudinal feature parameter corresponding to a longitudinal driving style and a lateral feature parameter corresponding to a lateral driving style; the longitudinal driving style represents a style feature of a driver manipulating a vehicle to move longitudinally, and the lateral driving style represents a style feature of a driver manipulating a vehicle to move laterally; a determination module, configured to determine a target longitudinal driving style level according to the longitudinal characteristic parameters; and determine a target lateral driving style level according to the lateral characteristic parameters; The adjustment module is used to adjust the vehicle power mode according to the target longitudinal driving style level; and adjust the vehicle steering mode according to the target lateral driving style level.

[0012] An electronic device according to an embodiment of the third aspect of the present application includes a processor and a memory, wherein the memory stores programs or instructions that can be run on the processor, and when the program or instructions are executed by the processor, the steps of the vehicle driving adjustment method as described in any one of the embodiments of the first aspect are implemented.

[0013] According to the computer-readable storage medium of the fourth aspect embodiment of the present application, computer-executable instructions are stored, and the computer-executable instructions are used to execute the vehicle driving adjustment method as described in the first aspect embodiment above.

[0014] In the embodiment of the present application, the vehicle driving mode is determined by analyzing and processing the vehicle driving data, so as to achieve a better match between the vehicle driving mode and the current driving style and habits, dynamically adapt to the driver's driving habits, and improve the driving experience satisfaction. In addition, the present application determines the driver's lateral driving style and longitudinal driving style separately, and then realizes independent adaptive adjustment of the vehicle's power mode and steering mode. Therefore, the aggressiveness of the vehicle's longitudinal driving operations such as acceleration and deceleration and the aggressiveness of lateral operations such as lane changing and steering do not need to be consistent in the same driving mode, but are adjusted according to the driver's actual lateral and longitudinal driving styles, which helps to accurately match the vehicle's driving mode with the current driving style and habits.

[0015] Other features and advantages of the present application will be set forth in the following description, and in part will become apparent from the description, or may be understood by practicing the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] The above and / or additional aspects and advantages of the present application will become apparent and easily understood from the description of the embodiments in conjunction with the following drawings, in which: Figure 1 is a flow chart of an embodiment of the vehicle driving adjustment method of the present application; Figure 2 is a schematic structural diagram of an embodiment of a vehicle driving adjustment device of the present application; Figure 3 It is a schematic diagram of the hardware structure of an embodiment of the electronic device of the present application. DETAILED DESCRIPTION

[0017] The embodiments of the present application are described in detail below, and examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present application, and cannot be understood as limiting the present application.

[0018] In the description of this application, if there is a description of first, second, etc., it is only for the purpose of distinguishing technical features, and cannot be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features or implicitly indicating the order of the indicated technical features.

[0019] In the description of the present application, it should be understood that the descriptions involving orientation, such as the orientation or positional relationship indicated as up, down, etc., are based on the orientation or positional relationship shown in the drawings, and are only for the convenience of describing the present application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be understood as a limitation on the present application.

[0020] In the description of this application, it should be noted that, unless otherwise clearly defined, terms such as setting, installing, connecting, etc. should be understood in a broad sense, and technicians in the relevant technical field can reasonably determine the specific meaning of the above terms in this application based on the specific content of the technical solution.

[0021] The technical solution of the present application will be clearly and completely described below in conjunction with the accompanying drawings. Obviously, the embodiments described below are only part of the embodiments of the present application, not all of the embodiments.

[0022] Figure 1 This is a flow chart of the vehicle driving adjustment method of the embodiment provided in the present application. The vehicle driving adjustment method is applied to an electronic device, which may be a vehicle, a vehicle-mounted terminal device, etc. Figure 1 , the embodiments of the present application are further described.

[0023] The present application embodiment provides a vehicle driving adjustment method, which comprises the following steps: Step 101: Acquire vehicle driving data, where the vehicle driving data is collected by a vehicle-mounted sensor; Step 102: Divide the vehicle driving data into a plurality of vehicle driving segment data; each vehicle driving segment data corresponds to the vehicle driving data within a vehicle driving time period; Step 103: selecting longitudinal characteristic parameters corresponding to the longitudinal driving style and transverse characteristic parameters corresponding to the transverse driving style according to the plurality of vehicle driving segment data; the longitudinal driving style represents the style characteristics of the driver controlling the vehicle to move longitudinally, and the transverse driving style represents the style characteristics of the driver controlling the vehicle to move transversely; Step 104: determining a target longitudinal driving style level according to the longitudinal characteristic parameters; determining a target lateral driving style level according to the lateral characteristic parameters; Step 105: Adjust the vehicle power mode according to the target longitudinal driving style level; and adjust the vehicle steering mode according to the target lateral driving style level.

[0024] In the embodiment of the present application, the vehicle driving mode is determined by analyzing and processing the vehicle driving data, so as to achieve a better match between the vehicle driving mode and the current driving style and habits, dynamically adapt to the driver's driving habits, and improve the driving experience satisfaction. In addition, the present application determines the driver's lateral driving style and longitudinal driving style separately, and then realizes independent adaptive adjustment of the vehicle's power mode and steering mode. Therefore, the aggressiveness of the vehicle's longitudinal driving operations such as acceleration and deceleration and the aggressiveness of lateral operations such as lane changing and steering do not need to be consistent in the same driving mode, but are adjusted according to the driver's actual lateral and longitudinal driving styles, which helps to accurately match the vehicle's driving mode with the current driving style and habits.

[0025] In the above step 101, vehicle driving data is acquired, and the vehicle driving data is collected by a vehicle-mounted sensor.

[0026] During vehicle driving, various on-board sensors will collect and record various vehicle driving data such as time, speed, acceleration and their mean, extreme values ​​and other derivative data and pedal operation conditions.

[0027] For example, the real-time speed of the vehicle can be obtained through the vehicle speed sensor, and the average speed, maximum speed, speed standard deviation, etc. can be obtained through data processing; the voltage value provided by the vehicle pedal can be obtained through the voltage sensor, and the vehicle accelerator pedal opening and brake pedal opening can be further deduced, and the accelerator pedal opening standard deviation, brake pedal opening standard deviation, etc. can be obtained through data processing; the real-time angular velocity of the vehicle steering wheel can be obtained through the angle sensor, and the average steering wheel angular acceleration, steering wheel angular acceleration standard deviation, etc. can be obtained through data processing.

[0028] In the above step 102, the vehicle driving data is divided into a plurality of vehicle driving segment data; each vehicle driving segment data corresponds to the vehicle driving data within a vehicle driving time period.

[0029] The above-mentioned dividing the vehicle driving data into a plurality of vehicle driving segment data may be dividing the vehicle driving data according to a certain rule to obtain a plurality of vehicle driving segment data.

[0030] For example, the vehicle driving data is divided according to time into vehicle driving segment data of multiple time periods, and each vehicle driving segment data includes the speed, acceleration and its mean, extreme value and other derivative data and the vehicle driving data such as pedal operation status in this time period.

[0031] In the above step 103, based on multiple vehicle driving segment data, longitudinal feature parameters corresponding to the longitudinal driving style and lateral feature parameters corresponding to the lateral driving style are selected; the longitudinal driving style represents the style characteristics of the driver controlling the vehicle to move longitudinally, and the lateral driving style represents the style characteristics of the driver controlling the vehicle to move laterally.

[0032] The longitudinal driving style may be the driving behavior characteristics of the driver along the forward and backward directions of the vehicle while the vehicle is traveling, and may include preferences and habits for acceleration, deceleration, braking, and other behaviors, reflecting the driver's control ability and driving habits over the vehicle's accelerator pedal and brake pedal.

[0033] The longitudinal driving style can be the driver's control characteristics along the lateral direction of the vehicle during the driving process, including steering, lane changing, lane keeping and other behaviors, which reflects the driver's control ability and driving habits of the vehicle steering wheel.

[0034] The above steps may be to respectively select characteristic parameters that can characterize the lateral and longitudinal driving styles as lateral and longitudinal characteristic parameters. Specifically, the longitudinal characteristic parameters may include vehicle driving data such as speed, acceleration, pedal operation, etc. during vehicle driving; the lateral characteristic parameters may include lateral acceleration and steering-related vehicle driving data during vehicle driving; the steering-related vehicle driving data may include data such as steering angle, steering speed, and steering acceleration.

[0035] In the above step 104, the target longitudinal driving style level is determined according to the longitudinal characteristic parameters; the target lateral driving style level is determined according to the lateral characteristic parameters; In the above steps, the horizontal and vertical characteristic parameters of multiple vehicle driving segments can be classified, and then the horizontal and vertical driving style of the driver can be judged according to the classification results of the horizontal and vertical characteristic parameters. The classification results of the above horizontal and vertical characteristic parameters include the number of classification levels determined after classification, and the proportion of each classification level in the whole data. Specifically, the above classification process of the horizontal and vertical characteristic parameters can be implemented by using a clustering algorithm.

[0036] In the above step 105 , the vehicle power mode is adjusted according to the target longitudinal driving style level; and the vehicle steering mode is adjusted according to the target lateral driving style level.

[0037] In the above steps, the steering mode and power mode of the vehicle can be independently and adaptively adjusted based on the driver's lateral and longitudinal driving styles.

[0038] In some implementations, determining a target longitudinal driving style level according to the longitudinal characteristic parameters; determining a target lateral driving style level according to the lateral characteristic parameters includes: The principal component analysis method is used to reduce the dimension of the longitudinal characteristic parameters and the transverse characteristic parameters to obtain the longitudinal dimension reduction characteristic parameters and the transverse dimension reduction characteristic parameters; The target longitudinal driving style level is determined based on the longitudinal dimension reduction feature parameters; the target lateral driving style level is determined based on the lateral dimension reduction feature parameters.

[0039] In this embodiment, the principal component analysis method is used to perform dimensionality reduction processing on the horizontal and vertical feature parameters respectively on the basis of retaining the validity of the original information to the greatest extent, remove redundant information, make the data set more compact, filter out unimportant noise, and retain the most important data features at the same time, which helps to improve the signal-to-noise ratio of the data and help identify and extract the most important features, which is very useful for subsequent data analysis.

[0040] The principal component analysis method mentioned above is a statistical method for data dimensionality reduction. The idea is to extract variables that may be correlated during data processing, and transform them into a set of variables with fewer and linearly unrelated variables through orthogonal transformation to replace the original large number of variables with many types, and in this process, the information contained in the original data is retained as much as possible. In the calculation process, the new variable with the largest variance in all linear combinations is defined as the first principal component M1. If the first principal component cannot well reflect the information contained in the original data, the new variable with the largest variance in the remaining linear combinations is selected as the second principal component M2... and so on. When the cumulative contribution rate of the selected principal components exceeds 85%, it can be considered that the new variables have more completely reflected the information in the original data.

[0041] The specific steps of the principal component analysis are as follows: In this application, the following calculations need to be performed on two groups of characteristic parameters, namely, the longitudinal characteristic parameters and the transverse characteristic parameters.

[0042] Step 1: Standardize the data. Since the original data have different units, if the original data is directly analyzed without processing, the results will be more discrete, which will obviously have a certain impact on the classification. Therefore, the original data should be standardized to eliminate this impact. The specific method is to convert the original data matrix into a standardized matrix according to a certain method, so that the mean of each column of the matrix is ​​0 and the variance is l.

[0043] Let the original data matrix be X, where p is the number of short segments and n is the number of feature parameters:

[0044] The matrix X is standardized to obtain the standardized matrix Y. The standardization process is as follows:

[0045]

[0046]

[0047] Where r=1,2,3...p, j=1,2,3...n;

[0048] Step 2: Calculate the correlation coefficient. The calculation method of the correlation coefficient is as follows:

[0049] The corresponding correlation coefficient matrix is:

[0050] Step 3: Determine the eigenvector and eigenvalue of the correlation coefficient matrix according to the correlation coefficient matrix. Find the eigenvalue λ of the correlation coefficient matrix, arrange the eigenvalues ​​in descending order as λ1>λ2>λ3>…>λn, and find the eigenvectors corresponding to the eigenvalues ​​as ξr and ξrj, where ξrj represents the jth component of the eigenvector ξr.

[0051] Step 4: Calculate the contribution rate of the principal component. The calculation formula for the contribution rate of the jth principal component is:

[0052] According to the above formula, the contribution rate of each principal component is calculated in turn, and their cumulative contribution rate is calculated. When the cumulative contribution rate of the current m principal components reaches more than 85%, it can be considered that the information of the original data has been relatively completely retained, and these principal components are selected as the basis for classification.

[0053] Step 5: Calculate the principal component loading matrix Z. The calculation formula is:

[0054] Among them, j=1,2,3...n, q=1,2,3...m;

[0055] Step 6: Multiply the standardized feature parameter matrix by the principal component loading matrix to obtain the principal component score matrix:

[0056] In some implementations, determining a target longitudinal driving style level according to the longitudinal characteristic parameters; determining a target lateral driving style level according to the lateral characteristic parameters includes: All longitudinal characteristic parameters are classified by using a clustering algorithm, and the longitudinal driving style level of each vehicle driving segment data is determined according to the clustering result; according to the longitudinal driving style level of each vehicle driving segment data, the target longitudinal driving style level of the vehicle driving data is determined; All lateral characteristic parameters are classified by using a clustering algorithm, and the lateral driving style level of each vehicle driving segment data is determined according to the clustering result; according to the lateral driving style level of each vehicle driving segment data, the target lateral driving style level of the vehicle driving data is determined.

[0057] In this embodiment, a clustering algorithm is used to classify the transverse and longitudinal characteristic parameters, determine the transverse and longitudinal driving style level categories, and thus more accurately determine the transverse and longitudinal driving style levels during vehicle driving.

[0058] The above clustering algorithm can adopt the K-means clustering algorithm. K-means clustering is to divide samples into several categories based on the intrinsic relationship between data without knowing any sample labels in advance, so that the similarity between samples of the same category is high, and the similarity between samples of different categories is low. The core goal of K-means clustering is to divide a given data set into K clusters and give the center point corresponding to each sample data.

[0059] The specific steps of the above classification using the K-means clustering algorithm are as follows: In this application, the longitudinal characteristic parameters and the transverse characteristic parameters need to be classified using the K-means clustering algorithm respectively.

[0060] According to the actual situation, the number of clusters k is set to 3, and the initial cluster center is randomly set for each category. Under normal circumstances, the driving modes of general vehicles are divided into three modes: sports mode, automatic mode, and economic mode, so k can be set to 3 for reference. However, in some cases, it is expected to achieve more detailed adjustment of the driving mode, so k can be set to 5.

[0061] First, the Euclidean distance between each fragment and the centroid is calculated, and the calculation formula is:

[0062] The data with close distances are grouped into one category, and the center position of each category is calculated and set as the new cluster center. Then, according to the new cluster center, the above steps are repeated, and the new cluster center is continuously determined through repeated calculations until the center no longer fluctuates. Clustering is completed. For the horizontal and vertical driving style parameters, according to the clustering results, the driving style segments are divided into 3 categories.

[0063] For example, the longitudinal driving style level is divided into a longitudinal first style level, a longitudinal second style level, and a longitudinal third style level, and the lateral driving style level is divided into a lateral first style level, a lateral second style level, and a lateral third style level.

[0064] For example, based on the clustering results of the longitudinal feature parameters, it can be determined whether the longitudinal driving style level of this vehicle driving segment data is the longitudinal first style level, the longitudinal second style level, or the longitudinal third style level; and based on the clustering results of the lateral feature parameters, it can be determined whether the lateral driving style level of this vehicle driving segment data is the lateral first style level, the lateral second style level, or the lateral third style level.

[0065] In some implementations, determining a target longitudinal driving style level according to the longitudinal characteristic parameters; determining a target lateral driving style level according to the lateral characteristic parameters includes: The principal component analysis method is used to reduce the dimension of the longitudinal characteristic parameters and the transverse characteristic parameters to obtain the longitudinal dimension reduction characteristic parameters and the transverse dimension reduction characteristic parameters; All the dimension-reduced longitudinal feature parameters are classified using a clustering algorithm, and the longitudinal driving style level of each vehicle driving segment data is determined according to the clustering result; according to the longitudinal driving style level of each vehicle driving segment data, the target longitudinal driving style level of the vehicle driving data is determined; A clustering algorithm is used to classify all the reduced-dimensional lateral feature parameters, and the lateral driving style level of each vehicle driving segment data is determined according to the clustering result; and a target lateral driving style level of the vehicle driving data is determined according to the lateral driving style level of each vehicle driving segment data.

[0066] In this implementation, the longitudinal feature parameters and the transverse feature parameters are clustered after dimensionality reduction processing, which reduces data complexity and improves the data processing speed of the clustering step.

[0067] In some implementations, determining a target longitudinal driving style level of vehicle driving data according to the longitudinal driving style level of each vehicle driving segment data includes: The longitudinal driving style levels of all vehicle driving segment data are counted, and the longitudinal driving style level with the highest number is determined as the target longitudinal driving style level; Determining a target lateral driving style level of the vehicle driving data according to the lateral driving style level of each vehicle driving segment data includes: The lateral driving style levels of all vehicle driving segment data are counted, and the lateral driving style level with the highest proportion in the number is determined as the target lateral driving style level.

[0068] In this implementation, according to the quantitative proportion of each type of longitudinal driving style level in all vehicle driving segment data, the driver's lateral and longitudinal driving styles can be determined respectively, thereby guiding the setting of the driving mode.

[0069] For example, the vehicle driving data generated by a certain driver during driving is divided into 1000 vehicle driving segment data, and the number of each type of horizontal and longitudinal driving style levels corresponding to the 1000 vehicle driving segment data is counted, and the horizontal and longitudinal driving style levels with the highest number are determined as the target horizontal and longitudinal driving style levels. Specifically, when the longitudinal driving style level is divided into the first longitudinal style level, the second longitudinal style level and the third longitudinal style level, and the lateral driving style level is divided into the first lateral style level, the second lateral style level and the third lateral style level, when there are 1000 vehicle driving segment data, the longitudinal driving style level statistics result is 700 first longitudinal style level segments, 200 second longitudinal style level segments and 100 third longitudinal style level segments, so the first longitudinal style level is determined as the target longitudinal driving style level; when there are 1000 vehicle driving segment data, the lateral driving style level statistics result is 300 first lateral style level segments, 500 second lateral style level segments and 200 third lateral style level segments, so the second lateral style level is determined as the target lateral driving style level.

[0070] In some embodiments, the vehicle driving data includes the vehicle's average speed, maximum speed, speed standard deviation, average acceleration, maximum acceleration, acceleration standard deviation, average accelerator pedal opening, accelerator pedal opening standard deviation, average brake pedal opening, brake pedal opening standard deviation, average lateral acceleration, maximum lateral acceleration, lateral acceleration standard deviation, average steering wheel angular acceleration, and steering wheel angular acceleration standard deviation; Selecting longitudinal characteristic parameters corresponding to the longitudinal driving style and transverse characteristic parameters corresponding to the transverse driving style according to a plurality of vehicle driving segment data includes the following steps: The average speed, maximum speed, speed standard deviation, average acceleration, maximum acceleration, acceleration standard deviation, average accelerator pedal opening, accelerator pedal opening standard deviation, average brake pedal opening and brake pedal opening standard deviation of multiple vehicle driving segment data are selected as longitudinal characteristic parameters; The average speed, maximum speed, speed standard deviation, average lateral acceleration, maximum lateral acceleration, lateral acceleration standard deviation, average steering wheel angular acceleration and steering wheel angular acceleration standard deviation of multiple vehicle driving segment data are selected as lateral characteristic parameters.

[0071] In this implementation, specific categories of the lateral and longitudinal characteristic parameters are selected according to the actual conditions of the lateral and longitudinal motion processes of the vehicle, so as to achieve more accurate judgment of the lateral and longitudinal driving styles.

[0072] The above segment time can be obtained by the following formula:

[0073] Wherein, N represents the number of data in each kinematic segment. Since the sampling frequency is 1 Hz, that is, one set of data per second, the duration of each kinematic segment can be expressed by the number of data contained in the corresponding segment.

[0074] The above average speed v a It can be obtained by the following formula:

[0075] in, v i Represents the vehicle speed at each moment, collected by the vehicle sensor, and i is a positive integer.

[0076] Maximum speed above v max It can be obtained by the following formula:

[0077] The above speed standard deviation S v It can be obtained by the following formula:

[0078] The above acceleration can be obtained by the vehicle sensor at each moment of the longitudinal acceleration of the vehicle a i The above average acceleration a a It can be obtained by the following formula:

[0079] in, a i1 is the instantaneous acceleration value of the vehicle at that moment, T a It is the duration of the acceleration behavior during the vehicle's driving process. It should be noted that in order to avoid the interference of low-speed driving section data on clustering, only the absolute value of acceleration is calculated. a i At 0.15 m / s 2 Above, and T a Data longer than 3 seconds.

[0080] The above maximum acceleration a max It can be obtained by the following formula:

[0081] The above acceleration standard deviation S a It can be obtained by the following formula:

[0082] The above average accelerator pedal opening t a It can be obtained by the following formula:

[0083] in, t i is the accelerator pedal opening of the vehicle at that moment, T t is the total time that the accelerator pedal of the vehicle is activated in this kinematic segment.

[0084] The standard deviation of the above accelerator pedal opening S t It can be obtained by the following formula:

[0085] The above average brake pedal opening b a It can be obtained by the following formula:

[0086] in, b i is the accelerator pedal opening of the vehicle at that moment, T b is the total time that the vehicle's brake pedal is activated in this kinematic segment.

[0087] The above brake pedal opening standard deviation S b It can be obtained by the following formula:

[0088] The above average lateral acceleration l a It can be obtained by the following formula:

[0089] in, l i is the instantaneous lateral acceleration value of the vehicle at that moment,T l It refers to the duration of lateral movement of the vehicle during driving.

[0090] The above maximum lateral acceleration l max It can be obtained by the following formula:

[0091] The above lateral acceleration standard deviation S l It can be obtained by the following formula:

[0092] The above average steering wheel angular acceleration ω a It can be obtained by the following formula:

[0093] in, ω i is the steering wheel angular acceleration of the vehicle at that moment, collected by the vehicle sensor, T ω is the total time the vehicle performs steering maneuvers in this kinematic segment.

[0094] The standard deviation of the above steering wheel angular acceleration S ω It can be obtained by the following formula:

[0095] In some embodiments, the longitudinal driving style level is divided into three categories, including longitudinal aggressive type, longitudinal general type and longitudinal robust type, and the lateral driving style level is divided into three categories, including lateral aggressive type, lateral general type and lateral robust type.

[0096] In this implementation, based on the clustering results, the mean of the average acceleration and other data of the three cluster centers can be calculated for the longitudinal driving data, and the corresponding three categories can be classified as longitudinal aggressive type, longitudinal general type, and longitudinal robust type of longitudinal driving style according to the mean from large to small; and the mean of the average lateral acceleration and other values ​​of the three cluster centers can be calculated for the lateral driving data, and the corresponding three categories can be classified as lateral aggressive type, lateral general type, and lateral robust type of lateral driving style according to the mean from large to small.

[0097] In some implementations, the vehicle power mode is adjusted according to the target longitudinal driving style level; the vehicle steering mode is adjusted according to the target lateral driving style level. Specifically, the longitudinal aggressive type, longitudinal general type, and longitudinal robust type in the target longitudinal driving style level respectively adjust the vehicle power mode to the sport mode, the general mode, and the economic mode; the lateral aggressive type, lateral general type, and lateral robust type in the target lateral driving style level respectively adjust the vehicle steering mode to the sport mode, the general mode, and the economic mode.

[0098] In some implementations, dividing the vehicle driving data into a plurality of vehicle driving segment data comprises the following steps: Obtain the start time of each idle state during vehicle driving; The vehicle driving data is divided into a plurality of vehicle driving segment data by taking the start time of each idle state as a time node.

[0099] In this embodiment, the vehicle driving data is divided into multiple vehicle driving segment data based on the idle state, which is divided into complete kinematic segments of the time period from when the vehicle's accelerator is pressed to when the accelerator is released. This division method can achieve more accurate driving style analysis.

[0100] The above idle state refers to the engine running without load, when the engine is separated from the transmission system and the accelerator pedal is completely released, the engine only runs to overcome its own resistance and does not output work to the outside. In the idle state, the engine runs to maintain its lowest stable speed, not to propel the vehicle forward.

[0101] The vehicle driving adjustment method provided in the embodiment of the present application may be executed by the vehicle driving adjustment device 200. In the embodiment of the present application, the vehicle driving adjustment device 200 executing the vehicle driving adjustment method is taken as an example to illustrate the vehicle driving adjustment device 200 provided in the embodiment of the present application.

[0102] See also Figure 2 , is a schematic diagram of the structure of a vehicle driving adjustment device 200 provided in an embodiment of the present application. Figure 2 As shown, the vehicle driving adjustment device 200 includes: The acquisition module 201 is used to acquire vehicle driving data, and the vehicle driving data is collected by vehicle-mounted sensors; The division module 202 is used to divide the vehicle driving data into a plurality of vehicle driving segment data; each vehicle driving segment data corresponds to the vehicle driving data within a vehicle driving time period; A selection module 203 is used to select longitudinal feature parameters corresponding to the longitudinal driving style and transverse feature parameters corresponding to the transverse driving style according to the plurality of vehicle driving segment data; the longitudinal driving style represents the style characteristics of the driver controlling the vehicle to move longitudinally, and the transverse driving style represents the style characteristics of the driver controlling the vehicle to move transversely; A determination module 204 is used to determine a target longitudinal driving style level according to the longitudinal characteristic parameters; and to determine a target lateral driving style level according to the lateral characteristic parameters; The adjustment module 205 is used to adjust the vehicle power mode according to the target longitudinal driving style level; and adjust the vehicle steering mode according to the target lateral driving style level.

[0103] In some implementations, the determination module 204 may be specifically configured to: The principal component analysis method is used to reduce the dimension of the longitudinal characteristic parameters and the transverse characteristic parameters to obtain the longitudinal dimension reduction characteristic parameters and the transverse dimension reduction characteristic parameters; The target longitudinal driving style level is determined based on the longitudinal dimension reduction feature parameters; the target lateral driving style level is determined based on the lateral dimension reduction feature parameters.

[0104] In some implementations, the determination module 204 may be specifically configured to: All longitudinal characteristic parameters are classified by using a clustering algorithm, and the longitudinal driving style level of each vehicle driving segment data is determined according to the clustering result; according to the longitudinal driving style level of each vehicle driving segment data, the target longitudinal driving style level of the vehicle driving data is determined; All lateral characteristic parameters are classified by using a clustering algorithm, and the lateral driving style level of each vehicle driving segment data is determined according to the clustering result; according to the lateral driving style level of each vehicle driving segment data, the target lateral driving style level of the vehicle driving data is determined.

[0105] In some implementations, the determination module 204 may be specifically configured to: The longitudinal driving style levels of all vehicle driving segment data are counted, and the longitudinal driving style level with the highest number is determined as the target longitudinal driving style level; Determining a target lateral driving style level of the vehicle driving data according to the lateral driving style level of each vehicle driving segment data includes: The lateral driving style levels of all vehicle driving segment data are counted, and the lateral driving style level with the highest proportion in the number is determined as the target lateral driving style level.

[0106] In some embodiments, the vehicle driving data includes the vehicle's average speed, maximum speed, speed standard deviation, average acceleration, maximum acceleration, acceleration standard deviation, average accelerator pedal opening, accelerator pedal opening standard deviation, average brake pedal opening, brake pedal opening standard deviation, average lateral acceleration, maximum lateral acceleration, lateral acceleration standard deviation, average steering wheel angular acceleration, and steering wheel angular acceleration standard deviation; The selection module 203 may be specifically used for: The average speed, maximum speed, speed standard deviation, average acceleration, maximum acceleration, acceleration standard deviation, average accelerator pedal opening, accelerator pedal opening standard deviation, average brake pedal opening and brake pedal opening standard deviation of multiple vehicle driving segment data are selected as longitudinal characteristic parameters; The average speed, maximum speed, speed standard deviation, average lateral acceleration, maximum lateral acceleration, lateral acceleration standard deviation, average steering wheel angular acceleration and steering wheel angular acceleration standard deviation of multiple vehicle driving segment data are selected as lateral characteristic parameters.

[0107] In some embodiments, the longitudinal driving style level is divided into three categories, including longitudinal aggressive type, longitudinal general type and longitudinal robust type, and the lateral driving style level is divided into three categories, including lateral aggressive type, lateral general type and lateral robust type.

[0108] In some implementations, the partitioning module 202 may be specifically configured to: Obtain the start time of each idle state during vehicle driving; The vehicle driving data is divided into a plurality of vehicle driving segment data by taking the start time of each idle state as a time node.

[0109] Since the vehicle driving adjustment device 200 adopts all the technical solutions of the vehicle driving adjustment method of the above-mentioned embodiment, it at least has all the beneficial effects brought by the technical solutions of the above-mentioned embodiment, which will not be described in detail here.

[0110] Figure 3 A schematic diagram of the hardware structure of an electronic device provided in an embodiment of the present application.

[0111] The electronic device may include a processor 301 and a memory 302 storing computer program instructions.

[0112] Specifically, the processor 301 may include a central processing unit (CPU), or an application specific integrated circuit (ASIC), or may be configured to implement one or more integrated circuits of the embodiments of the present application.

[0113] The memory 302 may include a large capacity memory for data or instructions. By way of example and not limitation, the memory 302 may include a hard disk drive (HDD), a floppy disk drive, a flash memory, an optical disk, a magneto-optical disk, a magnetic tape, or a universal serial bus (USB) drive or a combination of two or more of these. In appropriate cases, the memory 302 may include a removable or non-removable (or fixed) medium. In appropriate cases, the memory 302 may be inside or outside the integrated gateway disaster recovery device. In a specific embodiment, the memory 302 is a non-volatile solid-state memory.

[0114] In some embodiments, the memory 302 may include a read-only memory (ROM), a random access memory (RAM), a magnetic disk storage media device, an optical storage media device, a flash memory device, an electrical, optical or other physical / tangible memory storage device. Thus, in general, the memory includes one or more tangible (non-transitory) computer-readable storage media (e.g., a memory device) encoded with software including computer-executable instructions, and when the software is executed (e.g., by one or more processors), it is operable to perform the operations described with reference to the method according to an aspect of the present disclosure.

[0115] The processor 301 implements any one of the vehicle driving adjustment methods in the above embodiments by reading and executing computer program instructions stored in the memory 302 .

[0116] In one example, the electronic device may further include a communication interface 303 and a bus 310. Figure 3 As shown, the processor 301, the memory 302, and the communication interface 303 are connected via a bus 310 and communicate with each other.

[0117] The communication interface 303 is mainly used to implement communication between various modules, devices, units and / or equipment in the embodiments of the present application.

[0118] Bus 310 includes hardware, software or both, and couples the components of online data traffic billing equipment to each other. For example, but not limitation, the bus may include accelerated graphics port (AGP) or other graphics bus, enhanced industrial standard architecture (EISA) bus, front-side bus (FSB), hypertransport (HT) interconnection, industrial standard architecture (ISA) bus, infinite bandwidth interconnection, low pin count (LPC) bus, memory bus, micro channel architecture (MCA) bus, peripheral component interconnect (PCI) bus, PCI-Express (PCI-X) bus, serial advanced technology attachment (SATA) bus, video electronics standard association local (VLB) bus or other suitable bus or two or more of these combinations. Where appropriate, bus 310 may include one or more buses. Although the present application embodiment describes and shows a specific bus, the present application considers any suitable bus or interconnection.

[0119] The electronic device can execute the vehicle driving adjustment method in the embodiment of the present application, thereby realizing the combination Figure 1 and Figure 2 A vehicle driving adjustment method and device are described.

[0120] In addition, in combination with the vehicle driving adjustment method in the above embodiment, the embodiment of the present application can provide a computer storage medium for implementation. The computer storage medium stores computer program instructions; when the computer program instructions are executed by the processor, any one of the vehicle driving adjustment methods in the above embodiment is implemented.

[0121] It should be clear that the present application is not limited to the specific configuration and processing described above and shown in the figures. For the sake of simplicity, a detailed description of the known method is omitted here. In the above embodiments, several specific steps are described and shown as examples. However, the method process of the present application is not limited to the specific steps described and shown, and those skilled in the art can make various changes, modifications and additions, or change the order between the steps after understanding the spirit of the present application.

[0122] The functional blocks shown in the structural block diagram described above can be implemented as hardware, software, firmware or a combination thereof. When implemented in hardware, it can be, for example, an electronic circuit, an application-specific integrated circuit (ASIC), appropriate firmware, a plug-in, a function card, etc. When implemented in software, the elements of the present application are programs or code segments used to perform the required tasks. The program or code segment can be stored in a machine-readable medium, or transmitted on a transmission medium or a communication link by a data signal carried in a carrier. "Machine-readable medium" may include any medium capable of storing or transmitting information. Examples of machine-readable media include electronic circuits, semiconductor memory devices, ROMs, flash memories, erasable ROMs (EROMs), floppy disks, CD-ROMs, optical disks, hard disks, optical fiber media, radio frequency (RF) links, etc. The code segment can be downloaded via a computer network such as the Internet, an intranet, etc.

[0123] It should also be noted that the exemplary embodiments mentioned in this application describe some methods or systems based on a series of steps or devices. However, this application is not limited to the order of the above steps, that is, the steps can be performed in the order mentioned in the embodiment, or in a different order from the embodiment, or several steps can be performed simultaneously.

[0124] Aspects of the present disclosure are described above with reference to the flowcharts and / or block diagrams of the methods, devices (systems) and computer program products according to the embodiments of the present disclosure. It should be understood that each box in the flowchart and / or block diagram and the combination of each box in the flowchart and / or block diagram can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device to produce a machine so that these instructions executed by the processor of the computer or other programmable data processing device enable the implementation of the functions / actions specified in one or more boxes of the flowchart and / or block diagram. Such a processor can be, but is not limited to, a general-purpose processor, a special-purpose processor, a special application processor, or a field programmable logic circuit. It can also be understood that each box in the block diagram and / or flowchart and the combination of boxes in the block diagram and / or flowchart can also be implemented by dedicated hardware that performs the specified function or action, or can be implemented by a combination of dedicated hardware and computer instructions.

[0125] The above is only a specific implementation of the present application. Those skilled in the art can clearly understand that for the convenience and simplicity of description, the specific working processes of the systems, modules and units described above can refer to the corresponding processes in the aforementioned method embodiments, and will not be repeated here. It should be understood that the protection scope of the present application is not limited to this. Any technician familiar with the technical field can easily think of various equivalent modifications or replacements within the technical scope disclosed in this application, and these modifications or replacements should be included in the protection scope of this application.

Claims

1. A vehicle driving adjustment method, characterized in that: The following steps are involved: Acquiring vehicle driving data, wherein the vehicle driving data is collected by a vehicle-mounted sensor; Dividing the vehicle driving data into a plurality of vehicle driving segment data; each of the vehicle driving segment data corresponds to the vehicle driving data within a vehicle driving time period; Selecting, according to the plurality of vehicle driving segment data, a longitudinal feature parameter corresponding to a longitudinal driving style and a lateral feature parameter corresponding to a lateral driving style; the longitudinal driving style represents a style feature of a driver manipulating a vehicle to move longitudinally, and the lateral driving style represents a style feature of a driver manipulating a vehicle to move laterally; determining a target longitudinal driving style level according to the longitudinal characteristic parameters; determining a target lateral driving style level according to the lateral characteristic parameters; According to the target longitudinal driving style level, a vehicle power mode is adjusted; according to the target lateral driving style level, a vehicle steering mode is adjusted.

2. The vehicle driving adjustment method according to claim 1, characterized in that: determining a target longitudinal driving style level according to the longitudinal characteristic parameters; Determining a target lateral driving style level according to the lateral characteristic parameters includes: The longitudinal characteristic parameters and the transverse characteristic parameters are subjected to dimensionality reduction processing by using a principal component analysis method to obtain longitudinal dimensionality reduction characteristic parameters and transverse dimensionality reduction characteristic parameters; The target longitudinal driving style level is determined according to the longitudinal dimension reduction feature parameters; and the target lateral driving style level is determined according to the lateral dimension reduction feature parameters.

3. The vehicle driving adjustment method according to claim 1, characterized in that: determining a target longitudinal driving style level according to the longitudinal characteristic parameters; Determining a target lateral driving style level according to the lateral characteristic parameters includes: Classifying all the longitudinal characteristic parameters using a clustering algorithm, and determining the longitudinal driving style level of each of the vehicle driving segment data according to the clustering result; determining the target longitudinal driving style level of the vehicle driving data according to the longitudinal driving style level of each of the vehicle driving segment data; All the lateral feature parameters are classified using a clustering algorithm, and the lateral driving style level of each of the vehicle driving segment data is determined according to the clustering result; and the target lateral driving style level of the vehicle driving data is determined according to the lateral driving style level of each of the vehicle driving segment data.

4. The vehicle driving adjustment method according to claim 3, characterized in that: The step of determining the target longitudinal driving style level of the vehicle driving data according to the longitudinal driving style level of each of the vehicle driving segment data comprises: Counting the longitudinal driving style levels of all the vehicle driving segment data, and determining the longitudinal driving style level with the highest number as the target longitudinal driving style level; Determining the target lateral driving style level of the vehicle driving data according to the lateral driving style level of each of the vehicle driving segment data comprises: The lateral driving style levels of all the vehicle driving segment data are counted, and the lateral driving style level with the highest proportion in the number is determined as the target lateral driving style level.

5. The vehicle driving adjustment method according to any one of claims 1 to 4, characterized in that: The vehicle driving data includes the vehicle's average speed, maximum speed, speed standard deviation, average acceleration, maximum acceleration, acceleration standard deviation, average accelerator pedal opening, accelerator pedal opening standard deviation, average brake pedal opening, brake pedal opening standard deviation, average lateral acceleration, maximum lateral acceleration, lateral acceleration standard deviation, average steering wheel angular acceleration, and steering wheel angular acceleration standard deviation; The step of selecting the longitudinal characteristic parameter corresponding to the longitudinal driving style and the lateral characteristic parameter corresponding to the lateral driving style according to the plurality of vehicle driving segment data comprises the following steps: Selecting the average speed, maximum speed, speed standard deviation, average acceleration, maximum acceleration, acceleration standard deviation, average accelerator pedal opening, accelerator pedal opening standard deviation, average brake pedal opening and brake pedal opening standard deviation of the plurality of vehicle driving segment data as the longitudinal characteristic parameters; The average speed, maximum speed, speed standard deviation, average lateral acceleration, maximum lateral acceleration, lateral acceleration standard deviation, average steering wheel angular acceleration and steering wheel angular acceleration standard deviation of the multiple vehicle driving segment data are selected as the lateral characteristic parameters.

6. The vehicle driving adjustment method according to any one of claims 1 to 4, characterized in that: The longitudinal driving style level is divided into three categories, including longitudinal aggressive type, longitudinal general type and longitudinal stable type, and the lateral driving style level is divided into three categories, including lateral aggressive type, lateral general type and lateral stable type.

7. The vehicle driving adjustment method according to claim 1, characterized in that: The step of dividing the vehicle driving data into a plurality of vehicle driving segment data comprises the following steps: Obtain the start time of each idle state during vehicle driving; The vehicle driving data is divided into a plurality of vehicle driving segment data by taking the start time of each idle state as a time node.

8. A vehicle driving adjustment device, characterized in that: The device comprises: An acquisition module, used to acquire vehicle driving data, wherein the vehicle driving data is collected by a vehicle-mounted sensor; A division module, used for dividing the vehicle driving data into a plurality of vehicle driving segment data; each of the vehicle driving segment data corresponds to the vehicle driving data within a vehicle driving time period; a selection module, configured to select, according to the plurality of vehicle driving segment data, a longitudinal feature parameter corresponding to a longitudinal driving style and a lateral feature parameter corresponding to a lateral driving style; the longitudinal driving style represents a style feature of a driver manipulating a vehicle to move longitudinally, and the lateral driving style represents a style feature of a driver manipulating a vehicle to move laterally; a determination module, configured to determine a target longitudinal driving style level according to the longitudinal characteristic parameters; and determine a target lateral driving style level according to the lateral characteristic parameters; The adjustment module is used to adjust the vehicle power mode according to the target longitudinal driving style level; and adjust the vehicle steering mode according to the target lateral driving style level.

9. An electronic device, characterized in that: The method comprises a processor and a memory, wherein the memory stores a program or instruction that can be run on the processor, and when the program or instruction is executed by the processor, the steps of the vehicle driving adjustment method as claimed in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-executable instructions, and the computer-executable instructions are used to enable a computer to execute the vehicle driving adjustment method as described in any one of claims 1 to 7.

Images