CN113635906A - Driving style identification method and device based on local time series extraction algorithm - Google Patents
Driving style identification method and device based on local time series extraction algorithm Download PDFInfo
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- CN113635906A CN113635906A CN202111005909.2A CN202111005909A CN113635906A CN 113635906 A CN113635906 A CN 113635906A CN 202111005909 A CN202111005909 A CN 202111005909A CN 113635906 A CN113635906 A CN 113635906A
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W40/00—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models
- B60W40/08—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models related to drivers or passengers
- B60W40/09—Driving style or behaviour
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W2540/00—Input parameters relating to occupants
- B60W2540/30—Driving style
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
- Y02D10/00—Energy efficient computing, e.g. low power processors, power management or thermal management
Abstract
The invention provides a driving style identification method and a device based on a local time series extraction algorithm, wherein the method comprises the following steps: determining at least two driving style sample sets with different driving styles; dividing each driving style sample set in the at least two driving style sample sets into a plurality of initial time subsequences by adopting a preset time sequence division method; extracting a plurality of target time subsequences from the plurality of initial time subsequences by adopting a preset local time sequence extraction algorithm; and constructing a similarity recognition model according to the target time subsequences, and recognizing the driving style of the driving data to be recognized according to the similarity recognition model. The invention improves the timeliness and the robustness of the driving style identification method.
Description
Technical Field
The invention relates to the technical field of autonomous driving, in particular to a driving style identification method and device based on a local time series extraction algorithm.
Background
The driving style is the embodiment of the individual driving of the driver, and the research of the driving style comprises the content of a plurality of aspects such as the concentration degree of the attention of the driver, the subjective demand of the driver on the motion state of the vehicle and the like. Since the driving style of a person involves complexity and uncertainty of the person, learning the driving style involves a relatively wide range of relevant contents, including the reaction mechanism of the driver to the traffic environment, the age, mind, driving experience, and the like of the driver. As a new evaluation index, the driving style can enable the driving behavior to be integrally explained, so that the early warning or the forced execution action sent by the vehicle meets the will of the driver, and the recognition degree and the utilization rate of the driver to the automobile system are improved.
Existing research techniques mainly include machine learning based methods. However, the method based on machine learning has the following defects: (1) the method based on machine learning needs to construct a neural network to identify the driving style, and the neural network has a complex structure, so that the identification timeliness of the driving style is reduced; (2) the method based on machine learning needs a large amount of driving data training samples, so that the identification process of the driving style is very time-consuming, and further the effectiveness is further reduced; (3) due to the high dimensionality of the driving data and the complex relationship between variables, the existing machine learning method cannot effectively process multivariable driving data, and the robustness of the machine learning method is poor.
Disclosure of Invention
In view of the above, it is necessary to provide a driving style identification method and device based on a local time series extraction algorithm, so as to solve the technical problems of poor timeliness and robustness of the driving style identification method in the prior art.
In order to solve the technical problem, the invention provides a driving style identification method based on a local time series extraction algorithm, which comprises the following steps:
determining at least two driving style sample sets with different driving styles;
dividing each driving style sample set in the at least two driving style sample sets into a plurality of initial time subsequences by adopting a preset time sequence division method;
extracting a plurality of target time subsequences from the plurality of initial time subsequences by adopting a preset local time sequence extraction algorithm;
and constructing a similarity recognition model according to the target time subsequences, and recognizing the driving style of the driving data to be recognized according to the similarity recognition model.
In a possible implementation manner, the preset local time series extraction algorithm is a shapelets extraction algorithm.
In a possible implementation manner, the preset time series segmentation method is a sliding window method.
In one possible implementation, the determining at least two driving style sample sets of which the driving styles are different includes:
acquiring an initial driving data set;
reducing the dimension of the initial driving data set by adopting a preset dimension reduction algorithm to generate a driving data set to be processed;
and clustering the data to be processed by adopting a clustering algorithm to generate the at least two driving style sample sets.
In one possible implementation, the initial driving data set includes multi-dimensional initial driving data for a plurality of drivers; the preset dimension reduction algorithm is a principal component analysis method; the step of reducing the dimension of the initial driving data set by adopting a preset dimension reduction algorithm to generate a driving data set to be processed comprises the following steps:
constructing an initial driving data matrix according to the initial driving data set, wherein the number of rows of the initial driving data matrix is equal to the dimension of the multi-dimensional initial driving data, and the number of columns of the initial driving data matrix is equal to the number of the drivers;
zero-averaging each row of the initial driving data matrix to generate a zero-average matrix;
calculating a covariance matrix of the zero mean matrix;
calculating a plurality of eigenvalues of the covariance matrix and a plurality of eigenvectors corresponding to the eigenvalues one by one;
arranging the plurality of eigenvectors into an alternative matrix according to the sequence of the plurality of eigenvalues from big to small;
and selecting a first threshold row from the candidate matrix to generate the driving data set to be processed.
In one possible implementation, after the generating the at least two driving style sample sets, the method further includes:
acquiring a driving data verification set;
verifying the accuracy of the at least two driving style sample sets by the driving data verification set.
In a possible implementation manner, the constructing a similarity recognition model according to the plurality of target time subsequences includes:
symbolizing the target time subsequences by adopting a symbol set approximation algorithm to generate a plurality of target character strings;
calculating a plurality of TF-IDF weight vectors of the character strings, and generating the similarity recognition model according to the TF-IDF weight vectors.
In a possible implementation manner, the recognizing, according to the similarity recognition model, a driving style of the driving data to be recognized includes:
symbolizing the driving data to be recognized by adopting a symbol set approximation algorithm to generate a plurality of driving character strings;
calculating the frequency of each driving character string in the plurality of driving character strings to generate a frequency vector;
calculating a plurality of cosine similarity values of the frequency vector and the plurality of TF-IDF weight vectors;
determining a maximum cosine similarity value in the cosine similarity values, and determining a most similar target time subsequence corresponding to the maximum cosine similarity value, wherein the driving style of the driving style sample set corresponding to the most similar target time subsequence is the driving style of the driving data to be identified.
In one possible implementation, the tokenizing the plurality of target time subsequences using a symbol set approximation algorithm to generate a plurality of target character strings includes:
normalizing the plurality of target time subsequences to generate a plurality of normalized target time subsequences; the mean of the plurality of normalized target subsequences is 0 and the standard deviation is 1;
performing dimensionality reduction on the multiple standardized target subsequences based on a piecewise accumulation approximation method to generate multiple dimensionality reduction subsequences;
and representing the plurality of dimension reduction subsequences by characters to generate the plurality of character strings.
On the other hand, the invention also provides a driving style identification device based on the local time series extraction algorithm, which comprises the following steps:
the system comprises a sample set determining unit, a driving style determining unit and a driving style determining unit, wherein the sample set determining unit is used for determining at least two driving style sample sets with different driving styles;
the sample set segmentation unit is used for segmenting each driving style sample set in the at least two driving style sample sets into a plurality of initial time subsequences by adopting a preset time sequence segmentation method;
a local time sequence extraction unit, configured to extract a plurality of target time subsequences from the plurality of initial time subsequences by using a preset local time sequence extraction algorithm;
and the driving style identification unit is used for constructing a similarity identification model according to the target time subsequences, identifying the driving data to be identified according to the similarity identification model, and identifying the driving style of the driving data to be identified.
The beneficial effects of adopting the above embodiment are: according to the driving style identification method based on the local time series extraction algorithm, the driving style of the driving data to be identified is identified by constructing the similarity identification model without establishing a neural network model, so that the identification model is light, the speed of constructing the similarity identification model is increased, and the timeliness of identifying the driving style of the driving data to be identified is improved. Furthermore, a plurality of target time subsequences are extracted from at least two driving style sample sets by sequentially adopting a preset time sequence segmentation method and a preset local time sequence extraction algorithm, and dimension reduction can be realized on the driving style sample data in the driving style sample sets, so that the speed of constructing a similarity recognition model is further improved, and the timeliness of the driving style recognition of the driving data to be recognized is further improved. Furthermore, the dimension reduction of the driving style sample data in the driving style sample set is realized by sequentially adopting a preset time sequence segmentation method and a preset local time sequence extraction algorithm, so that redundant sequences and noises in the driving style sample data are reduced, the complex relation between high dimension and variables can be processed, and the robustness of the driving style identification method is improved.
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In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a schematic flow chart of an embodiment of a driving style identification method based on a local time series extraction algorithm according to the present invention;
FIG. 2 is a schematic flow chart of one embodiment of S101 of FIG. 1;
FIG. 3 is a flowchart illustrating an embodiment of S202 in FIG. 2 according to the present invention;
FIG. 4 is a schematic flow chart of an embodiment of the present invention after step S203;
FIG. 5 is a flowchart illustrating an embodiment of constructing a similarity recognition model in S104 of FIG. 1 according to the present invention;
FIG. 6 is a flowchart illustrating an embodiment of S501 in FIG. 5;
FIG. 7 is a structural diagram illustrating an embodiment of step S601 of the present invention;
fig. 8 is a schematic flow chart illustrating an embodiment of identifying the driving style of the driving data to be identified according to the similarity identification model in S104 in fig. 1;
FIG. 9 is a schematic structural diagram of an embodiment of a driving style recognition apparatus based on a local time series extraction algorithm according to the present invention;
fig. 10 is a schematic structural diagram of an embodiment of an electronic device provided in the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention. It is to be understood that the described embodiments are merely exemplary of the invention, and not restrictive of the full scope of the invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any inventive step, are within the scope of the present invention.
In the description of the embodiments of the present application, "a plurality" means two or more unless otherwise specified. "and/or" describes the association relationship of the associated objects, meaning that three relationships may exist, for example: a and/or B, may represent: a exists alone, A and B exist simultaneously, and B exists alone.
Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the invention. The appearances of the phrase in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. It is explicitly and implicitly understood by one skilled in the art that the embodiments described herein can be combined with other embodiments.
The invention provides a driving style identification method and a driving style identification device based on a local time series extraction algorithm, which are respectively explained below.
Fig. 1 is a schematic flow chart of an embodiment of a driving style identification method based on a local time series extraction algorithm provided by the present invention, as shown in fig. 1, the driving style identification method based on the local time series extraction algorithm includes:
s101, determining at least two driving style sample sets with different driving styles;
s102, dividing each driving style sample set of at least two driving style sample sets into a plurality of initial time subsequences by adopting a preset time sequence dividing method;
s103, extracting a plurality of target time subsequences from the initial time subsequences by adopting a preset local time sequence extraction algorithm;
and S104, constructing a similarity recognition model according to the target time subsequences, and recognizing the driving style of the driving data to be recognized according to the similarity recognition model.
Compared with the prior art, the driving style identification method based on the local time series extraction algorithm provided by the embodiment of the invention identifies the driving style of the driving data to be identified by constructing the similarity identification model without establishing a neural network model, so that the identification model is light in weight, the speed of constructing the similarity identification model is increased, and the timeliness of identifying the driving style of the driving data to be identified is improved. Furthermore, a plurality of target time subsequences are extracted from at least two driving style sample sets by sequentially adopting a preset time sequence segmentation method and a preset local time sequence extraction algorithm, so that the dimension reduction of the driving style sample data in the driving style sample sets can be realized, the speed of constructing a similarity recognition model is further improved, and the timeliness of the driving style recognition of the driving data to be recognized is further improved. Furthermore, the preset time sequence segmentation method and the preset local time sequence extraction algorithm are adopted in sequence to achieve dimension reduction on the driving style sample data in the driving style sample set, redundant sequences and noise in the driving style sample data are reduced, the complex relation between high dimension and variables can be processed, and the robustness of the driving style identification method is improved.
In some embodiments of the present invention, the driving styles may include conservative and aggressive styles, and the at least two driving style sample sets in step S101 may include conservative sample sets and aggressive sample sets.
In some other embodiments of the present invention, the driving styles may include conservative, intermediate and aggressive styles, and the at least two driving style sample sets in step S101 may include conservative, intermediate and aggressive sample sets.
It should be understood that: the driving style may include more types, and may be adjusted according to actual conditions, which is not described herein.
In some embodiments of the present invention, the time series segmentation method preset in step S102 is a sliding window method, and specifically, the segmentation of the driving style sample set is realized by sliding a sliding window along a time direction, where parameters of the sliding window include a window length and a sliding step length, the window length is used to represent a data extraction range of the sliding window, and the sliding step length is used to represent a length of each sliding of the sliding window.
According to the sliding window method in the embodiment, each driving style sample set can be quickly divided into a plurality of initial time subsequences, and the recognition speed of the driving style is further improved.
In one embodiment of the present invention, the window length of the sliding window is 60 seconds, and the sliding step size is 30 seconds, that is: two adjacent sliding windows have an overlapping area, and through the arrangement, data can be prevented from being missed, and the reliability of the sliding window method is improved.
In some embodiments of the present invention, the local time series extraction algorithm preset in step S203 is a shapelets extraction algorithm.
Because the shape extraction algorithm has the characteristic of strong interpretability, compared with the prior art, the interpretability of the recognition result can be improved by setting the preset local time sequence extraction algorithm as the shape extraction algorithm. Namely: it is possible to explain why the driving data to be recognized is classified into specific driving styles for the driver's understanding and reception.
In some embodiments of the present invention, as shown in fig. 2, step S101 includes:
s201, acquiring an initial driving data set;
s202, reducing the dimension of the initial driving data set by adopting a preset dimension reduction algorithm to generate a driving data set to be processed;
and S203, clustering the data to be processed by adopting a clustering algorithm to generate at least two driving style sample sets.
According to the embodiment of the invention, the dimension of the driving style sample data in the driving style sample set can be reduced by adopting the preset dimension reduction algorithm to carry out dimension reduction on the initial driving data set, the speed of obtaining at least two driving style sample sets is further improved, and the timeliness of identifying the driving style of the driving data to be identified can be further improved. Furthermore, the dimension reduction is realized on the driving style sample data in the driving style sample set, and the redundant sequence and noise in the driving style sample data can be further reduced, so that the embodiment of the invention can process the driving data set with high dimension and complex relation between variables, and further improve the robustness of the driving style identification method.
In some embodiments of the present invention, the initial driving data set in step S201 may be obtained by a plurality of sensors, for example: steering wheel sensors, brake sensors, throttle sensors, etc.
And the initial driving data set may include data generated by the vehicle at the time of actual use or the vehicle at the time of testing.
It should be understood that the clustering algorithm in step S203 includes clustering centroids equal to the number of at least two driving style sample sets. Namely: each cluster centroid corresponds to a set of driving style samples.
In some embodiments of the present invention, the dimension reduction algorithm preset in step S202 may be any one of a principal component analysis method, a linear discriminant analysis method, a local linear embedding method, and a laplacian feature map.
In a preferred embodiment of the present invention, in order to increase the dimension reduction speed, the preset dimension reduction algorithm is a principal component analysis method.
In a specific embodiment of the invention, the initial driving data set comprises multi-dimensional initial driving data of a plurality of drivers; as shown in fig. 3, step S202 includes:
s301, constructing an initial driving data matrix according to the initial driving data set; the number of rows of the initial driving data matrix is equal to the dimension of the multi-dimensional initial driving data, and the number of columns of the initial driving data matrix is equal to the number of the plurality of drivers;
s302, carrying out zero-mean value treatment on each row of the initial driving data matrix to generate a zero-mean value matrix;
s303, calculating a covariance matrix of the zero-mean matrix;
s304, calculating a plurality of eigenvalues of the covariance matrix and a plurality of eigenvectors corresponding to the eigenvalues one by one;
s305, arranging a plurality of eigenvectors into a candidate matrix according to the sequence of the eigenvalues from big to small;
s306, selecting a first threshold row from the alternative matrix, and generating a driving data set to be processed.
In some embodiments of the present invention, the specific process of zero averaging in step S302 is: the average of each initial driving data in each row of the initial driving data matrix is subtracted from the average of that row.
It should be understood that: the first threshold is less than the dimensionality of the multi-dimensional initial driving data.
In order to ensure the reliability of at least two driving style sample sets, in some embodiments of the present invention, as shown in fig. 4, after step S203, the method further includes:
s401, acquiring a driving data verification set;
s402, verifying the accuracy of at least two driving style sample sets through the driving data verification set.
It should be understood that: when the accuracy of at least two driving style sample sets is verified to be greater than the threshold accuracy through the driving data verification set, step S102 is executed to ensure the identification accuracy of the constructed similarity identification model.
In some embodiments of the present invention, the driving data verification set of step S401 may be obtained by way of a questionnaire.
In some embodiments of the present invention, as shown in fig. 5, the constructing the similarity recognition model according to the plurality of target time subsequences in step S104 includes:
s501, symbolizing a plurality of target time subsequences by adopting a symbol aggregation Approximation (SAX) algorithm to generate a plurality of target character strings;
s502, calculating a plurality of TF-IDF weight vectors of a plurality of character strings, and generating a similarity recognition model according to the TF-IDF weight vectors.
According to the embodiment of the invention, the SAX algorithm is adopted to symbolize the target time subsequences to generate the target character strings, so that the dimensionality of the target time subsequences can be reduced, and the timeliness of the driving style identification of the driving data to be identified is further improved. Furthermore, the dimension reduction is realized on the plurality of target time subsequences, and redundant sequences and noise in the plurality of target subsequences can be reduced, so that the embodiment of the invention can process a driving data set with high dimension and complex relation between variables, and further improve the robustness of the driving style identification method based on the local time sequence extraction algorithm.
In some embodiments of the present invention, as shown in fig. 6, step S501 includes:
s601, standardizing the plurality of target time subsequences to generate a plurality of standardized target time subsequences; the mean of the plurality of normalized target subsequences is 0 and the standard deviation is 1;
s602, performing dimensionality reduction on the plurality of standardized target subsequences based on a piecewise accumulation approximation method to generate a plurality of dimensionality reduction subsequences;
s603, the plurality of dimension reduction subsequences are expressed by characters to generate a plurality of character strings.
In an embodiment of the present invention, as shown in fig. 7, an abscissa X represents a sample point of the target time subsequence data, an ordinate Y represents a corresponding value of the normalized target time subsequence sample point, in fig. 7, 4 horizontal dotted lines are used to perform equal probability division on the target time subsequence, 5 intervals are counted, and corresponding characters A, B, C, D, E are respectively assigned to each interval, a real curve in the diagram is one normalized target subsequence in the plurality of normalized target subsequences, and a short horizontal line is a plurality of dimension-reduced subsequences after dimension reduction, and after steps S601-S603, a character string corresponding to one target time subsequence is "CDEEEDCB".
In some embodiments of the present invention, as shown in fig. 8, the identifying the driving style of the driving data to be identified according to the similarity recognition model in step S104 includes:
s801, symbolizing driving data to be identified by adopting a symbol set approximation algorithm to generate a plurality of driving character strings;
s802, calculating the frequency of each driving character string in a plurality of driving character strings, and generating a frequency vector;
s803, calculating a plurality of cosine similarity values of the frequency vector and a plurality of TF-IDF weight vectors;
s804, determining the maximum cosine similarity value in the cosine similarity values, and determining the most similar target time subsequence corresponding to the maximum cosine similarity value, wherein the driving style of the driving style sample set corresponding to the most similar target time subsequence is the driving style of the driving data to be identified.
It should be understood that: the step S801 is the same as the step S501 in the process of converting characters, and is not described herein again.
In order to better implement the driving style identification method based on the local time series extraction algorithm in the embodiment of the present invention, on the basis of the driving style identification method based on the local time series extraction algorithm, as shown in fig. 9, correspondingly, an embodiment of the present invention further provides a driving style identification device 900 based on the local time series extraction algorithm, including:
a sample set determining unit 901, configured to determine at least two driving style sample sets with different driving styles;
a sample set segmentation unit 902, configured to segment each driving style sample set of at least two driving style sample sets into a plurality of initial time subsequences by using a preset time sequence segmentation method;
a local time sequence extraction unit 903, configured to extract multiple target time subsequences from multiple initial time subsequences by using a preset local time sequence extraction algorithm;
and the driving style recognition unit 904 is configured to construct a similarity recognition model according to the plurality of target time subsequences, recognize the driving data to be recognized according to the similarity recognition model, and recognize the driving style of the driving data to be recognized.
The driving style recognition apparatus 900 based on the local time series extraction algorithm provided in the above embodiment may implement the technical scheme described in the above driving style recognition method based on the local time series extraction algorithm, and the specific implementation principle of each module or unit may refer to the corresponding content in the above driving style recognition method based on the local time series extraction algorithm, which is not described herein again.
As shown in fig. 10, the present invention further provides an electronic device 1000 accordingly. The electronic device 1000 includes a processor 1001, a memory 1002, and a display 1003. Fig. 10 shows only a portion of the electronic device 1000, but it is to be understood that not all of the shown components are required and that more or fewer components may alternatively be implemented.
The storage 1002 may be an internal storage unit of the electronic device 1000 in some embodiments, such as a hard disk or a memory of the electronic device 1000. The memory 1002 may also be an external storage device of the electronic device 1000 in other embodiments, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), and the like, provided on the electronic device 1000.
Further, the memory 1002 may also include both internal and external storage units of the electronic device 1000. The memory 1002 is used for storing application software and various data for installing the electronic device 1000.
The processor 1001 may be a Central Processing Unit (CPU), microprocessor or other data Processing chip in some embodiments, and is used to run program code stored in the memory 1002 or process data, such as the driving style identification method based on the local time series extraction algorithm in the present invention.
The display 1003 may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, an OLED (Organic Light-Emitting Diode) touch panel, or the like in some embodiments. The display 1003 is used to display information at the electronic device 1000 and to display a visual user interface. The components 1001 and 1003 of the electronic device 1000 communicate with each other via a system bus.
In one embodiment, when the processor 1001 executes a driving style recognition program based on a local time series extraction algorithm in the memory 1002, the following steps may be implemented:
determining at least two driving style sample sets with different driving styles;
dividing each driving style sample set in at least two driving style sample sets into a plurality of initial time subsequences by adopting a preset time sequence division method;
extracting a plurality of target time subsequences from the plurality of initial time subsequences by adopting a preset local time sequence extraction algorithm;
and constructing a similarity recognition model according to the target time subsequences, and recognizing the driving style of the driving data to be recognized according to the similarity recognition model.
It should be understood that: the processor 1001, when executing the driving style identification program based on the local time series extraction algorithm in the memory 1002, may also implement other functions in addition to the above functions, which may be specifically referred to the description of the corresponding method embodiment above.
Further, the type of the electronic device 1000 is not particularly limited in the embodiment of the present invention, and the electronic device 1000 may be a portable electronic device such as a mobile phone, a tablet computer, a Personal Digital Assistant (PDA), a wearable device, and a laptop computer (laptop). Exemplary embodiments of portable electronic devices include, but are not limited to, portable electronic devices that carry IOS, android, microsoft, or other operating systems. The portable electronic device may also be other portable electronic devices such as laptop computers (laptop) with touch sensitive surfaces (e.g., touch panels), etc. It should also be understood that in other embodiments of the present invention, the electronic device 1000 may not be a portable electronic device, but may be a desktop computer having a touch-sensitive surface (e.g., a touch panel).
Accordingly, the present application also provides a computer-readable storage medium, which is used for storing a computer-readable program or instruction, and when the program or instruction is executed by a processor, the method steps or functions provided by the above method embodiments can be implemented.
Those skilled in the art will appreciate that all or part of the flow of the method implementing the above embodiments may be implemented by a computer program instructing associated hardware, and the driving style identification program based on the local time series extraction algorithm may be stored in a computer-readable storage medium. The computer readable storage medium is a magnetic disk, an optical disk, a read-only memory or a random access memory.
The driving style identification method and device based on the local time series extraction algorithm provided by the invention are described in detail, a specific example is applied in the text to explain the principle and the implementation mode of the invention, and the description of the embodiment is only used for helping to understand the method and the core idea of the invention; meanwhile, for those skilled in the art, according to the idea of the present invention, there may be variations in the specific embodiments and the application scope, and in summary, the content of the present specification should not be construed as a limitation to the present invention.
Claims (10)
1. A driving style identification method based on a local time series extraction algorithm is characterized by comprising the following steps:
determining at least two driving style sample sets with different driving styles;
dividing each driving style sample set in the at least two driving style sample sets into a plurality of initial time subsequences by adopting a preset time sequence division method;
extracting a plurality of target time subsequences from the plurality of initial time subsequences by adopting a preset local time sequence extraction algorithm;
and constructing a similarity recognition model according to the target time subsequences, and recognizing the driving style of the driving data to be recognized according to the similarity recognition model.
2. The driving style recognition method based on the local time series extraction algorithm according to claim 1, wherein the preset local time series extraction algorithm is a shapelets extraction algorithm.
3. The driving style recognition method based on the local time series extraction algorithm as claimed in claim 1, wherein the preset time series segmentation method is a sliding window method.
4. The driving style identification method based on the local time series extraction algorithm according to claim 1, wherein the determining at least two driving style sample sets with different driving styles comprises:
acquiring an initial driving data set;
reducing the dimension of the initial driving data set by adopting a preset dimension reduction algorithm to generate a driving data set to be processed;
and clustering the data to be processed by adopting a clustering algorithm to generate the at least two driving style sample sets.
5. The driving style recognition method based on the local time series extraction algorithm as claimed in claim 4, wherein the initial driving data set comprises multi-dimensional initial driving data of a plurality of drivers; the preset dimension reduction algorithm is a principal component analysis method; the step of reducing the dimension of the initial driving data set by adopting a preset dimension reduction algorithm to generate a driving data set to be processed comprises the following steps:
constructing an initial driving data matrix according to the initial driving data set, wherein the number of rows of the initial driving data matrix is equal to the dimension of the multi-dimensional initial driving data, and the number of columns of the initial driving data matrix is equal to the number of the drivers;
zero-averaging each row of the initial driving data matrix to generate a zero-average matrix;
calculating a covariance matrix of the zero mean matrix;
calculating a plurality of eigenvalues of the covariance matrix and a plurality of eigenvectors corresponding to the eigenvalues one by one;
arranging the plurality of eigenvectors into a candidate matrix according to the sequence of the plurality of eigenvalues from big to small;
and selecting a first threshold row from the candidate matrix to generate the driving data set to be processed.
6. The driving style identification method based on the local time series extraction algorithm according to claim 4, further comprising, after the generating the at least two driving style sample sets:
acquiring a driving data verification set;
verifying the accuracy of the at least two driving style sample sets by the driving data verification set.
7. The driving style recognition method based on the local time series extraction algorithm according to claim 1, wherein the constructing of the similarity recognition model according to the plurality of target time subsequences comprises:
symbolizing the target time subsequences by adopting a symbol set approximation algorithm to generate a plurality of target character strings;
calculating a plurality of TF-IDF weight vectors of the character strings, and generating the similarity recognition model according to the TF-IDF weight vectors.
8. The driving style recognition method based on the local time series extraction algorithm as claimed in claim 7, wherein the recognizing the driving style of the driving data to be recognized according to the similarity recognition model comprises:
symbolizing the driving data to be recognized by adopting a symbol set approximation algorithm to generate a plurality of driving character strings;
calculating the frequency of each driving character string in the plurality of driving character strings to generate a frequency vector;
calculating a plurality of cosine similarity values of the frequency vector and the plurality of TF-IDF weight vectors;
determining a maximum cosine similarity value in the cosine similarity values, and determining a most similar target time subsequence corresponding to the maximum cosine similarity value, wherein the driving style of the driving style sample set corresponding to the most similar target time subsequence is the driving style of the driving data to be identified.
9. The driving style recognition method based on the local time series extraction algorithm as claimed in claim 7, wherein the symbolizing the plurality of target time subsequences by using a symbol set approximation algorithm to generate a plurality of target character strings comprises:
normalizing the plurality of target time subsequences to generate a plurality of normalized target time subsequences; the mean of the plurality of normalized target subsequences is 0 and the standard deviation is 1;
performing dimensionality reduction on the multiple standardized target subsequences based on a piecewise accumulation approximation method to generate multiple dimensionality reduction subsequences;
and representing the plurality of dimension reduction subsequences by characters to generate the plurality of character strings.
10. A driving style recognition device based on a local time series extraction algorithm is characterized by comprising:
the system comprises a sample set determining unit, a driving style determining unit and a driving style determining unit, wherein the sample set determining unit is used for determining at least two driving style sample sets with different driving styles;
the sample set segmentation unit is used for segmenting each driving style sample set in the at least two driving style sample sets into a plurality of initial time subsequences by adopting a preset time sequence segmentation method;
a local time sequence extraction unit, configured to extract a plurality of target time subsequences from the plurality of initial time subsequences by using a preset local time sequence extraction algorithm;
and the driving style identification unit is used for constructing a similarity identification model according to the target time subsequences, identifying the driving data to be identified according to the similarity identification model, and identifying the driving style of the driving data to be identified.
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