CN112660140A - Driving style classification method and device based on machine learning and electronic equipment - Google Patents
Driving style classification method and device based on machine learning and electronic equipment Download PDFInfo
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Abstract
The embodiment of the application provides a driving style classification method and device based on machine learning and electronic equipment. The method is used for classifying the driving style of a driver and comprises the following steps: carrying out correlation analysis on the selected driving style characteristic parameters of the driver to obtain correlation coefficients among the driving style characteristic parameters; removing redundant driving style characteristic parameters in a driving behavior data sample obtained in advance according to the correlation coefficient and a preset correlation coefficient threshold; and classifying the driving style of the driver based on a machine learning model according to the driving behavior data sample without the redundant driving style characteristic parameters. Therefore, the driving habit of a driver can be effectively and pertinently guided, and the purpose of enhancing the fuel economy of the mining truck is achieved.
Description
Technical Field
The present disclosure relates to the field of driver assistance technologies, and in particular, to a method and an apparatus for classifying driving styles based on machine learning, and an electronic device.
Background
In the driving process of the mining truck, a reckimic driver can frequently and greatly step on an accelerator pedal or a brake pedal, the mining truck is more oil-consuming in driving, and the fuel economy is poor; a mild driver can slightly step on an accelerator pedal or a brake pedal, so that the mining truck is more fuel-saving when running, and the fuel economy is better. Therefore, the behavior characteristics of the driver in driving the mining truck are completely reflected in the aspects of the input of the driver to the mining truck and the response of the mining truck in the driving process of the mining truck, namely the driving style of the driver can have great influence on the fuel economy of the mining truck, and therefore, the method has important significance in accurately and effectively classifying the driving style of the driver.
Disclosure of Invention
The present application aims to provide a method, an apparatus and an electronic device for classifying driving styles based on machine learning, so as to solve or alleviate the problems in the prior art.
In order to achieve the above purpose, the present application provides the following technical solutions:
the application provides a driving style classification method based on machine learning, which is used for classifying the driving style of a driver and comprises the following steps: carrying out correlation analysis on the selected driving style characteristic parameters of the driver to obtain correlation coefficients among the driving style characteristic parameters; removing redundant driving style characteristic parameters in a driving behavior data sample obtained in advance according to the correlation coefficient and a preset correlation coefficient threshold; and classifying the driving style of the driver based on a machine learning model according to the driving behavior data sample without the redundant driving style characteristic parameters.
Optionally, in any embodiment of the present application, the performing correlation analysis on the driving style characteristic parameters of the driver selected to obtain a correlation coefficient between the driving style characteristic parameters specifically includes: and carrying out relevance analysis on the selected driving style characteristic parameters based on a preset relevance analysis model to obtain a correlation coefficient between the driving style characteristic parameters.
Optionally, in any embodiment of the application, the removing, according to the correlation coefficient and a preset correlation coefficient threshold, redundant driving style characteristic parameters in a driving behavior data sample obtained in advance specifically includes: and comparing the correlation coefficient with a preset correlation coefficient threshold, and removing redundant driving style characteristic parameters in the driving behavior data sample obtained in advance according to the comparison result.
Optionally, in any embodiment of the application, the classifying the driving style of the driver based on a machine learning model according to the driving behavior data sample without the redundant driving style characteristic parameters specifically includes: and classifying the driving style of the driver based on a preset clustering algorithm model according to the driving behavior data sample without the redundant driving style characteristic parameters.
Optionally, in any embodiment of the present application, the driving behavior data sample includes: driving behavior data samples under a heavy load operation state and driving behavior data samples under a no-load operation state; correspondingly, the step of classifying the driving style of the driver based on a preset clustering algorithm model according to the driving behavior data sample without the redundant driving style characteristic parameters comprises the following steps: respectively fitting the driving behavior data samples with the redundant driving style characteristic parameters removed under the heavy-load operation state and the driving behavior data samples with the redundant driving style characteristic parameters removed under the no-load operation state based on a K-means clustering algorithm model, and respectively determining the classified number of the driving styles under the heavy-load operation state and the no-load operation state; and classifying the driving styles of the driver in the heavy load operation state and the no-load operation state based on a K-means clustering algorithm model according to the classified number of the driving styles in the heavy load operation state and the no-load operation state respectively.
The embodiment of the present application further provides a driving style classification device based on machine learning, which is used for classifying the driving style of a driver, and includes: the correlation analysis unit is configured to perform correlation analysis on the selected driving style characteristic parameters of the driver to obtain correlation coefficients among the driving style characteristic parameters; the redundant parameter removing unit is configured to remove redundant driving style characteristic parameters in a driving behavior data sample obtained in advance according to the correlation coefficient; and the driving style classification unit is configured to classify the driving style of the driver according to the driving behavior data sample without the redundant driving style characteristic parameters based on the machine learning model.
Optionally, in any embodiment of the application, the correlation analysis unit is further configured to perform correlation analysis on the selected driving style characteristic parameters based on a preset correlation analysis model to obtain a correlation coefficient between the driving style characteristic parameters.
Optionally, in any embodiment of the application, the redundant parameter removing unit is further configured to compare the correlation coefficient with a preset correlation coefficient threshold, and remove redundant driving style characteristic parameters in a driving behavior data sample obtained in advance according to a comparison result.
Optionally, in any embodiment of the application, the driving style classification unit is further configured to classify the driving style of the driver based on a preset clustering algorithm model according to the driving behavior data sample without the redundant driving style characteristic parameters.
An embodiment of the present application further provides an electronic device, including: a memory, a processor, and a program stored in the memory and executable on the processor, the processor when executing the program implementing a machine learning based driving style classification method as described in any of the embodiments above.
Compared with the closest prior art, the technical scheme of the embodiment of the application has the following beneficial effects:
the technical scheme provided by the embodiment of the application is used for classifying the driving style of the driver, performing correlation analysis on the driving style characteristic parameters of the selected driver, removing redundant driving style characteristic parameters in the driving behavior data sample according to the correlation analysis result, and further classifying the driving style of the driver according to the driving behavior data sample without the redundant driving style characteristic parameters based on the machine learning model. Therefore, the driving habit of a driver can be effectively and pertinently guided, and the purpose of enhancing the fuel economy of the mining truck is achieved.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this application, illustrate embodiments of the application and, together with the description, serve to explain the application and are not intended to limit the application. Wherein:
fig. 1 is a schematic flow diagram of a method for machine learning based driving style classification provided in accordance with some embodiments of the present application;
fig. 2 is a correlation coefficient thermodynamic diagram between driving behavior characteristic parameters of a mining truck under heavy-duty operating conditions, provided in accordance with some embodiments of the present application;
fig. 3 is a correlation coefficient thermodynamic diagram between driving behavior characteristic parameters of a mining truck in an unloaded operating state, provided in accordance with some embodiments of the present application;
FIG. 4 is a diagram of accelerator pedal travel for a mining truck in a heavy duty operating condition and an unloaded operating condition;
FIG. 5 is a graph of the travel speed of a mining truck in a heavy load operating condition and an empty load operating condition;
fig. 6 is a schematic flowchart of step S103 in a method for classifying driving styles based on machine learning according to some embodiments of the present application;
FIG. 7 is a schematic illustration of a determination of a number of classifications of driving styles of a mining truck under heavy duty operating conditions using elbow rules, provided in accordance with some embodiments of the present application;
FIG. 8 is a schematic illustration of a determination of a number of classifications of driving styles of a mining truck under an empty operating condition using elbow rules, provided in accordance with some embodiments of the present application;
fig. 9 is a schematic structural diagram of a machine learning-based driving style classification apparatus according to some embodiments of the present application;
fig. 10 is a schematic structural diagram of a driving style classification unit provided according to some embodiments of the present application;
FIG. 11 is a schematic structural diagram of an electronic device provided in accordance with some embodiments of the present application;
fig. 12 is a hardware block diagram of an electronic device provided in accordance with some embodiments of the present application.
Detailed Description
The present application will be described in detail below with reference to the embodiments with reference to the attached drawings. The various examples are provided by way of explanation of the application and are not limiting of the application. In fact, it will be apparent to those skilled in the art that modifications and variations can be made in the present application without departing from the scope or spirit of the application. For instance, features illustrated or described as part of one embodiment, can be used with another embodiment to yield a still further embodiment. It is therefore intended that the present application cover the modifications and variations of this invention provided they come within the scope of the appended claims and their equivalents.
Firstly, in the embodiment of the application, the driving styles of drivers of the trucks for strip mines are classified, and the driving behavior data samples are obtained by respectively acquiring the heavy-load operation state and the no-load operation state when a plurality of drivers drive the trucks, wherein the data acquired by the trucks for mines in the process of transporting stripped rocks to the dump site unloading point at the loading point each time is the driving behavior data sample in the heavy-load operation state, and the data acquired by the trucks in the process of returning to the loading point at the dump site unloading point at the no-load original road each time is the driving behavior data sample in the no-load operation state. It should be understood that the above description is only exemplary, and the embodiments of the present application do not limit the present invention.
Exemplary method
Fig. 1 is a schematic flow diagram of a method for machine learning based driving style classification provided in accordance with some embodiments of the present application; as shown in fig. 1, the method is used for classifying the driving style of a driver, and comprises the following steps:
step S101, carrying out relevance analysis on the driving style characteristic parameters of the driver and selected to obtain a correlation coefficient among the driving style characteristic parameters;
in the embodiment of the application, in order to classify and identify the driving style of the driver of the truck for the strip mine, firstly, characteristic parameters capable of representing the driving style of the driver are determined. Typically, statistical values (maximum value, average value, standard deviation) of an accelerator pedal stroke, an accelerator pedal angular velocity, a speed of the mining truck, a longitudinal acceleration, and the like are selected as the driving style characteristic parameters. As shown in the following table 1,
TABLE 1
In the embodiment of the present application, when multiple collinearity (multicollinearity) exists between the driving style characteristic parameters, the weight occupied by the related driving style characteristic parameters in the euclidean distance (euclidian distance) calculation is higher, and the influence on the accuracy of the driving style classification is larger. Therefore, correlation analysis (correlation analysis) is required for the driving style characteristic parameters of the driver. It should be understood that the above description is only exemplary, and the embodiments of the present application do not limit the present invention.
In the embodiment of the application, the correlation coefficient is used for representing the correlation size among the selected driving style characteristic parameters, and whether redundancy exists among different driving style characteristic parameters is determined through calculation of the correlation coefficient. It should be understood that the above description is only exemplary, and the embodiments of the present application do not limit the present invention.
In some optional embodiments, the performing correlation analysis on the driving style characteristic parameters of the driver and selected to obtain a correlation coefficient between the driving style characteristic parameters specifically includes: and carrying out relevance analysis on the selected driving style characteristic parameters based on a preset relevance analysis model to obtain a correlation coefficient between the driving style characteristic parameters. It should be understood that the above description is only exemplary, and the embodiments of the present application do not limit the present invention.
In the embodiment of the present application, the correlation analysis model is a calculation model of a Pearson correlation coefficient, and is defined as shown in the following formula (1);
wherein r represents a correlation coefficient, x and y represent two different driving style characteristic parameters respectively, and xi、yiRespectively represent the values of the driving style characteristic parameters,
respectively, represent driving style characteristic parameter averages. It should be understood that the above description is only exemplary, and the embodiments of the present application do not limit the present invention.
Fig. 2 is a correlation coefficient thermodynamic diagram between driving behavior characteristic parameters of a mining truck under heavy-duty operating conditions, provided in accordance with some embodiments of the present application; fig. 3 is a correlation coefficient thermodynamic diagram between driving behavior characteristic parameters of a mining truck in an unloaded operating state, provided in accordance with some embodiments of the present application; as shown in fig. 2 and 3, the linear correlation degree between different driving style characteristic parameters can be clarified by the Pearson correlation coefficient between different driving style characteristic parameters calculated by the correlation analysis model. The range of the Pearson correlation coefficient is (-1, 1), and the larger the absolute value of the Pearson correlation coefficient is, the stronger the correlation between two different driving style characteristic parameters is; the closer the absolute value of the elsen correlation coefficient is to 0, the weaker the correlation between two different driving style characteristic parameters is. It should be understood that the above description is only exemplary, and the embodiments of the present application do not limit the present invention.
Step S102, removing redundant driving style characteristic parameters in a driving behavior data sample obtained in advance according to the correlation coefficient and a preset correlation coefficient threshold;
in the embodiment of the present application, when the pearson correlation coefficient is in the range (0.6, 0.8), it is considered that there is a strong correlation between two different driving style characteristic parameters; for example, the pearson correlation coefficient of each driving style characteristic parameter of the mining truck in the heavy-load operation state is less than 0.8, which indicates that each driving style characteristic parameter has strong independence; the Pearson correlation coefficient of the angular speed average value (wx3_ mean) and the angular speed standard deviation (wx3_ std) of the mining truck under the no-load operation state is 0.94, which shows that the angular speed average value (wx3_ mean) and the angular speed standard deviation (wx3_ std) have extremely strong positive correlation under the no-load operation state.
In some optional embodiments, the removing, according to the correlation coefficient and a preset correlation coefficient threshold, redundant driving style characteristic parameters in a driving behavior data sample obtained in advance specifically includes: and comparing the correlation coefficient with a preset correlation coefficient threshold, and removing redundant driving style characteristic parameters in the driving behavior data sample obtained in advance according to the comparison result. It should be understood that the above description is only exemplary, and the embodiments of the present application do not limit the present invention.
In the embodiment of the application, the pearson correlation coefficient between different driving style characteristic parameters calculated by the correlation analysis model is compared with a preset correlation coefficient threshold value, so that the correlation degree between the different driving style characteristic parameters is determined, and two driving style characteristic parameters which are extremely strong in correlation indicate that the two driving style characteristic parameters are redundant data, and one of the two driving style characteristic parameters needs to be removed. For example, the pearson correlation coefficient of the angular velocity average value (wx3_ mean) and the angular velocity standard deviation (wx3_ std) of the mining truck under the no-load operation state is 0.94, which indicates that the angular velocity average value (wx3_ mean) and the angular velocity standard deviation (wx3_ std) have strong positive correlation under the no-load operation state, the angular velocity average value (wx3_ mean) in the driving behavior data sample can be eliminated, and the angular velocity standard deviation (wx3_ std) is reserved. Table 2 shows the driving style characteristic parameters of the mining truck obtained according to the correlation coefficient thermodynamic diagrams of fig. 2 and 3 after removing redundancy in the heavy load operation state and the no load operation state, where table 2 is as follows:
TABLE 2
Table 3 is a driving behavior data sample obtained after redundant driving style characteristic parameters are removed based on the correlation coefficient thermodynamic diagrams of fig. 2 and 3 in the heavy-load operation state of the mining truck; table 3 is as follows:
TABLE 3
Table 4 is a driving behavior data sample obtained after removing the redundant driving style characteristic parameters based on the correlation coefficient thermodynamic diagrams of fig. 2 and 3 in the heavy-load operation state of the mining truck; table 4 is as follows:
TABLE 4
And S103, classifying the driving style of the driver based on a machine learning model according to the driving behavior data sample without the redundant driving style characteristic parameters.
In the embodiment of the application, in the actual transportation operation of the truck for the open-pit mine, under the condition that the driving style of a driver is unknown, data in driving behavior data samples with redundant driving style characteristic parameters removed are divided into different clusters through unsupervised cluster analysis (unsupervised clustering analysis), so that the similarity of the samples in each cluster is larger than that of the samples in other clusters, and then the results are transmitted to supervised machine learning models such as regression or classification to classify the driving style of the driver. It should be understood that the above description is only exemplary, and the embodiments of the present application do not limit the present invention.
In some optional embodiments, the classifying the driving style of the driver based on a machine learning model according to the driving behavior data sample without the redundant driving style characteristic parameters specifically includes: and classifying the driving style of the driver based on a preset clustering algorithm model according to the driving behavior data sample without the redundant driving style characteristic parameters. It should be understood that the above description is only exemplary, and the embodiments of the present application do not limit the present invention.
In the embodiment of the application, the number of the driving style clustering centers is determined by utilizing an elbow rule according to the driving behavior data sample without redundant driving style characteristic parameters, and then the driving style of the driver of the mining truck is classified based on a preset clustering algorithm model. It should be understood that the above description is only exemplary, and the embodiments of the present application do not limit the present invention.
In the embodiment of the present application, the clustering algorithm model that can be adopted includes: a K-means Clustering algorithm model Based on distance Clustering, a hierarchical Clustering algorithm model, a fuzzy Clustering algorithm model, a Spatial Clustering algorithm model Based on Density (e.g., a Density-Based Clustering method with Noise (DBSCAN)). It should be understood that the above description is only exemplary, and the embodiments of the present application do not limit the present invention.
In some optional embodiments, the driving behavior data samples comprise: driving behavior data samples under heavy load operation state, and driving behavior data samples under no load operation state. It should be understood that the above description is only exemplary, and the embodiments of the present application do not limit the present invention.
In the embodiment of the application, a hardware part for acquiring data of the mining truck mainly comprises 1 ARM microcontroller (model STM32F103), 2 inertial navigation sensors (model WTGARRS 2), 1 SD memory card, a vehicle-mounted direct-current power supply, a protective shell and the like. It should be understood that the above description is only exemplary, and the embodiments of the present application do not limit the present invention.
In the embodiment of the application, in order to acquire driving behavior data of a driver of a mining truck in a heavy-load operation state and an idle-load operation state in real time, an inertial navigation sensor and an advanced RISC machines (advanced RISC machines) microcontroller are mounted on the mining truck, and data of an accelerator pedal stroke, an angular velocity of the accelerator pedal, a speed and a longitudinal acceleration of the mining truck, a gradient of a driving surface of the mining truck, a position and the like when the driver drives the mining truck are acquired and stored at a data sampling frequency of 2 hertz. Table 5 is a table of parameters collected for a sensor having a mileage of about 650 km for 11 drivers in actual transportation operation based on the same mining truck, i.e., an experimental road, as follows:
TABLE 5
In the embodiment of the application, due to the existence of factors such as GPS signal shielding or other electromagnetic interference, the sensor may output wrong and invalid data, and in order to avoid the influence of driving behavior data samples on the learning result of the machine learning algorithm, the data collected by the sensor needs to be processed (for example, data extraction, data deletion, and the like) before performing cluster analysis. Thereby, the accuracy of machine learning is improved. It should be understood that the above description is only exemplary, and the embodiments of the present application do not limit the present invention.
In the embodiment of the application, the inertial navigation sensors are two, and one is defined as a sensor No. 1, and the other is defined as a sensor No. 2. The No. 1 sensor is mainly used for collecting data of the travel and the angular speed of an accelerator pedal of the mining truck, and the No. 2 sensor is mainly used for collecting data of the speed, the acceleration, the position and the running surface slope of the mining truck. The inertial navigation sensor is a ten-axis inertial navigation sensor, modules such as a high-precision gyroscope, an accelerometer, a GPS and the like are integrated in the ten-axis inertial navigation sensor to form a GPS-IMU combined navigation unit, and the inertial navigation sensor has the advantages of high precision, low cost, low power consumption and small size, and can accurately measure parameters such as acceleration, speed, GPS precision (namely position precision when the No. 2 sensor collects data of the position of the mining truck), angular speed and the like of the mining truck. Wherein, the performance parameters of the ten-axis inertial navigation sensor are shown in the following table 6:
TABLE 6
In the embodiment of the application, the No. 1 sensor is firmly installed on the back surface of an accelerator pedal of the mining truck along the X-axis direction, and the No. 2 sensor is firmly installed in the horizontal position (or approximate horizontal position) in a cab along the Y-axis direction. It should be understood that the above description is only exemplary, and the embodiments of the present application do not limit the present invention.
In the embodiment of the application, when data collected by the sensor is extracted, the data collected by the sensor No. 1 and the sensor No. 2 are respectively and independently stored in the SD card, the serial number identification and the time of the sensor are used as marks, a sensor data fusion program is developed based on Python language, the data collected by the sensor No. 1 and the sensor No. 2 are spliced at the same moment, and a finished driving behavior data sample is provided. It should be understood that the above description is only exemplary, and the embodiments of the present application do not limit the present invention.
In the embodiment of the application, because the driving style classification and identification are established in the dynamic transportation operation process of the mining truck, when data of the sensor is deleted, data with the speed of 0 in the data collected by the sensor is removed (the speed of 0 represents that the mining truck is in a static state); setting a threshold value of the operation running speed of the mining truck in consideration of errors caused by road bumping when the mining truck runs, if the speed of the mining truck exceeds 45km/h, determining the mining truck as abnormal data, and eliminating data with the speed being more than 45km/h in data collected by a sensor; since the longitudinal acceleration of the mining truck is limited by the deadweight and the load of the mining truck, the acceleration of the mining truck generally does not exceed 0.55m/s, considering that the deadweight and the load of the mining truck are combined to be about 230 tons2Therefore, acceleration abnormal values in the data collected by the sensor are eliminated. It should be understood that the above description is only exemplary, and the embodiments of the present application do not limit the present invention.
FIG. 4 is a diagram of accelerator pedal travel for a mining truck in a heavy duty operating condition and an unloaded operating condition; FIG. 5 is a graph of the travel speed of a mining truck in a heavy load operating condition and an empty load operating condition; wherein, load represents the heavy load operation state, and unload represents the no load operation state. As shown in fig. 4 and 5, the travel and speed of the accelerator pedal of the mining truck have a large difference between the heavy-load operation state and the no-load operation state, so that the data acquired during the process of transporting and peeling rocks from each loading point to the dump site unloading point is the driving behavior data sample in the heavy-load operation state, and the data acquired during the process of returning the dump site unloading point to the loading point in the no-load original way is the driving behavior data sample in the no-load operation state. The data of 11 drivers collected by the sensors are divided into 111 driving behavior data under the heavy-load operation state and 108 driving behavior data under the no-load operation state. It should be understood that the above description is only exemplary, and the embodiments of the present application do not limit the present invention.
Fig. 6 is a schematic flowchart of step S103 in a method for classifying driving styles based on machine learning according to some embodiments of the present application; as shown in fig. 6, the classifying the driving style of the driver based on a preset clustering algorithm model according to the driving behavior data sample without the redundant driving style characteristic parameters includes:
step S113, based on a K-means clustering algorithm model, respectively fitting the driving behavior data samples with redundant driving style characteristic parameters removed under the heavy-load operation state and the driving behavior data samples with redundant driving style characteristic parameters removed under the no-load operation state, and respectively determining the classification number of the driving styles under the heavy-load operation state and the no-load operation state;
in the embodiment of the application, the driving behavior data samples are fitted based on a K-means (K-means) clustering algorithm model, so that the operating efficiency and the accuracy of the classified number of the driving styles can be effectively improved. It should be understood that the above description is only exemplary, and the embodiments of the present application do not limit the present invention.
In the embodiment of the application, the K-means clustering algorithm uses the error square sum in the cluster as a target function for clustering, the error square sum in the cluster of sample data of the same driving style is small, the similarity degree is high and is distributed to the same cluster, the error square sum in the clusters of different driving styles is large, and the similarity degree is low and is distributed to different clusters. It should be understood that the above description is only exemplary, and the embodiments of the present application do not limit the present invention.
In the embodiment of the application, the driving behavior data sample is XiIndicating that the driving behavior data sample T contains data collected by n sensors, namely Xi＝{xi1、xi2、……、xinAnd aggregating the data acquired by the n sensors into k classes (namely the classification quantity of the driving styles is k classes), wherein k is a natural number, namely the number of the clustering centers is k, and c is used for the data1、c2、……ckAnd (4) showing. Wherein, the calculation model of the clustering center is shown as the following formula (2):
wherein j is (1, k), and j is a natural number;
n represents the number of sensors for acquiring data of the mining truck;
u represents the number of centers in each class.
The calculation model of the error criterion function is shown in equation (3) below:
where J represents an error criterion function. It should be understood that the above description is only exemplary, and the embodiments of the present application do not limit the present invention.
In the embodiment of the application, based on a K-means clustering algorithm model, after the driving behavior data samples in a heavy load operation state and a no-load operation state are respectively fitted, the elbow rule is utilized to determine the classification data of the driving style. FIG. 7 is a schematic illustration of a determination of a number of classifications of driving styles of a mining truck under heavy duty operating conditions using elbow rules, provided in accordance with some embodiments of the present application; FIG. 8 is a schematic illustration of a determination of a number of classifications of driving styles of a mining truck under an empty operating condition using elbow rules, provided in accordance with some embodiments of the present application; as shown in fig. 7 and 8, when the number of the clustering centers of the mining truck is 3 in the heavy load operation state and the no load operation state, the square of the error in the cluster and the descending speed are obviously changed and then slowly descend, so that the number of the clustering centers of the driving style of the mining truck in the heavy load operation state and the no load operation state is 3. Namely, the number of the driving style classifications of the mining truck in the heavy-load operation state is 3, and the number of the driving style classifications in the no-load operation state is 3. It should be understood that the above description is only exemplary, and the embodiments of the present application do not limit the present invention.
And S123, classifying the driving styles of the driver in the heavy load operation state and the driver in the no-load operation state based on a K-means clustering algorithm model according to the classified number of the driving styles in the heavy load operation state and the no-load operation state respectively.
In the embodiment of the application, the mining truck carries out cluster analysis on the driving style of a driver based on a K-means clustering algorithm model in a heavy load operation state and an idle load operation state, and unsupervised cluster analysis is respectively carried out on driving behavior data in the heavy load operation state and the idle load operation state by setting the number of clustering centers to be 3, the maximum iteration frequency to be 100 and the like, so that the driving style of the driver in the heavy load operation state and the idle load operation state is classified. Table 7 shows unsupervised cluster analysis results of the mining truck in the heavy-duty operation state; table 8 shows unsupervised cluster analysis results of the mining truck in the heavy-duty operation state; as can be seen from table 7, in the no-load operation state of the mining truck, the Cluster center of the driving style characteristic parameters related to the accelerator pedal stroke, the accelerator pedal angular velocity, the mining truck speed and the like in Cluster2 is the largest, the Cluster center of the driving style characteristic parameters related to the accelerator pedal stroke, the accelerator pedal angular velocity, the mining truck speed and the like in Cluster0 is the smallest, the distribution of different driving style characteristic parameters conforms to the law, that is, the median and the upper quartile of the accelerator pedal stroke conforming to the aggressive driving style are larger than those of the normal type and the mild type, and more oil pedal strokes of the mild type driving style are distributed at the low level. Therefore, the driving style of the mining truck driver in the no-load operation state can be divided into three categories: normal (Cluster0), mild (Cluster1), aggressive (Cluster 2). In the same manner, as can be seen from table 8, in the heavy-load operation state of the mining truck, the distribution rule of the characteristic parameters related to the angular velocity of the accelerator pedal and the velocity of the mining truck is relatively obvious, and the driving style of the driver of the mining truck in the heavy-load operation state is classified into three categories according to the driving style characteristic parameters related to the angular velocity of the accelerator pedal and the velocity of the mining truck (i.e., statistical values (maximum value, average value, standard deviation) of the stroke of the accelerator pedal, the angular velocity of the accelerator pedal, the velocity, the longitudinal acceleration, and the like): normal (Cluster2), mild (Cluster0), aggressive (Cluster 1).
It should be understood that the above description is only exemplary, and the embodiments of the present application do not limit the present invention.
TABLE 7
TABLE 8
In the embodiment of the application, the driving style of the driver is classified according to the driving behavior data sample without the redundant driving style characteristic parameters based on the machine learning model by performing correlation analysis on the selected driving style characteristic parameters of the driver and removing the redundant driving style characteristic parameters from the driving behavior data sample according to the correlation analysis result. Therefore, the driving habit of a driver can be effectively and pertinently guided, and the purpose of enhancing the fuel economy of the mining truck is achieved. It should be understood that the above description is only exemplary, and the embodiments of the present application do not limit the present invention.
Exemplary devices
Fig. 9 is a schematic structural diagram of a machine learning-based driving style classification apparatus according to some embodiments of the present application; as shown in fig. 9, the apparatus for classifying a driving style of a driver includes: a correlation analysis unit 901 configured to perform correlation analysis on the selected driving style characteristic parameters of the driver to obtain a correlation coefficient between the driving style characteristic parameters; a redundant parameter removing unit 902 configured to remove redundant driving style characteristic parameters in a driving behavior data sample obtained in advance according to the correlation coefficient; a driving style classification unit 903 configured to classify the driving style of the driver according to the driving behavior data sample without the redundant driving style characteristic parameters based on a machine learning model. It should be understood that the above description is only exemplary, and the embodiments of the present application do not limit the present invention.
In some optional embodiments, the correlation analysis unit 901 is further configured to perform correlation analysis on the selected driving style characteristic parameters based on a preset correlation analysis model to obtain a correlation coefficient between the driving style characteristic parameters. It should be understood that the above description is only exemplary, and the embodiments of the present application do not limit the present invention.
In some optional embodiments, the redundant parameter removing unit 902 is further configured to compare the correlation coefficient with a preset correlation coefficient threshold, and remove redundant driving style characteristic parameters in a driving behavior data sample obtained in advance according to a comparison result. It should be understood that the above description is only exemplary, and the embodiments of the present application do not limit the present invention.
In some optional embodiments, the driving classification unit 903 is further configured to classify the driving style of the driver based on a preset clustering algorithm model according to the driving behavior data sample without the redundant driving style characteristic parameters. It should be understood that the above description is only exemplary, and the embodiments of the present application do not limit the present invention.
In some optional embodiments, the driving behavior data samples comprise: driving behavior data samples under heavy load operation state, and driving behavior data samples under no load operation state. It should be understood that the above description is only exemplary, and the embodiments of the present application do not limit the present invention.
Fig. 10 is a schematic structural diagram of a driving style classification unit provided according to some embodiments of the present application; as shown in fig. 10, the driving style classification unit 903 includes: the classified number determining subunit 913 is configured to perform fitting on the driving behavior data samples from which the redundant driving style characteristic parameters are removed in the heavy load operation state and the driving behavior data samples from which the redundant driving style characteristic parameters are removed in the no-load operation state based on the K-means clustering computation model, and determine the classified number of the driving styles in the heavy load operation state and the no-load operation state respectively; the style classification subunit 923 is configured to classify the driving styles of the driver in the heavy load operation state and the driver in the no-load operation state based on a K-means clustering algorithm model according to the classification number of the driving styles in the heavy load operation state and the no-load operation state, respectively. It should be understood that the above description is only exemplary, and the embodiments of the present application do not limit the present invention.
The driving style classification device based on machine learning provided by the embodiment of the application can realize each process in the driving style classification method based on machine learning, and achieve the same functions and effects, which are not repeated one by one.
Exemplary device
FIG. 11 is a schematic structural diagram of an electronic device provided in accordance with some embodiments of the present application; as shown in fig. 11, the electronic apparatus includes:
one or more processors 1101;
a computer readable medium, which may be configured to store one or more programs 1102 that when executed by the one or more processors, perform the steps of: carrying out correlation analysis on the selected driving style characteristic parameters of the driver to obtain correlation coefficients among the driving style characteristic parameters; removing redundant driving style characteristic parameters in a driving behavior data sample obtained in advance according to the correlation coefficient and a preset correlation coefficient threshold; and classifying the driving style of the driver according to the driving behavior data sample without the redundant driving style characteristic parameters based on a machine learning model. It should be understood that the above description is only exemplary, and the embodiments of the present application do not limit the present invention.
FIG. 12 is a hardware block diagram of an electronic device provided in accordance with some embodiments of the present application; as shown in fig. 12, the hardware structure of the electronic device may include: a processor 1201, a communication interface 1202, a computer readable medium 1203, and a communication bus 1204;
the processor 1201, the communication interface 1202, and the computer readable medium 1203 complete communication with each other through the communication bus 1204;
alternatively, the communication interface 1202 may be an interface of a communication module, such as an interface of a GSM module;
the processor 1201 may be specifically configured to: carrying out correlation analysis on the selected driving style characteristic parameters of the driver to obtain correlation coefficients among the driving style characteristic parameters; removing redundant driving style characteristic parameters in a driving behavior data sample obtained in advance according to the correlation coefficient and a preset correlation coefficient threshold; and classifying the driving style of the driver according to the driving behavior data sample without the redundant driving style characteristic parameters based on a machine learning model. It should be understood that the above description is only exemplary, and the embodiments of the present application do not limit the present invention.
The Processor 1201 may be a general-purpose Processor including a Central Processing Unit (CPU), a Network Processor (NP), and the like, and may also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf programmable gate array (FPGA) or other programmable logic device, a discrete gate or transistor logic device, or a discrete hardware component. The various methods, steps, and logic blocks disclosed in the embodiments of the present application may be implemented or performed. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The electronic device of the embodiments of the present application exists in various forms, including but not limited to:
(1) a mobile communication device: such devices are characterized by mobile communications capabilities and are primarily targeted at providing voice, data communications. Such terminals include: smart phones (e.g., IPhone), multimedia phones, functional phones, and low-end phones, etc.
(2) Ultra mobile personal computer device: the equipment belongs to the category of personal computers, has calculation and processing functions and generally has the characteristic of mobile internet access. Such terminals include: PDA, MID, and UMPC devices, etc., such as Ipad.
(3) A portable entertainment device: such devices can display and play multimedia content. This type of device comprises: audio and video players (e.g., iPod), handheld game players, electronic books, and smart toys and portable car navigation devices.
(4) A server: the device for providing the computing service comprises a processor, a hard disk, a memory, a system bus and the like, and the server is similar to a general computer architecture, but has higher requirements on processing capacity, stability, reliability, safety, expandability, manageability and the like because of the need of providing high-reliability service.
(5) And other electronic devices with data interaction functions.
It should be noted that, according to the implementation requirement, each component/step described in the embodiment of the present application may be divided into more components/steps, or two or more components/steps or partial operations of the components/steps may be combined into a new component/step to achieve the purpose of the embodiment of the present application.
The above-described methods according to embodiments of the present application may be implemented in hardware, firmware, or as software or computer code storable in a recording medium such as a CD ROM, a RAM, a floppy disk, a hard disk, or a magneto-optical disk, or as computer code originally stored in a remote recording medium or a non-transitory machine storage medium and to be stored in a local recording medium downloaded through a network, so that the methods described herein may be stored in such software processes on a recording medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware such as an ASIC or FPGA. It will be appreciated that the computer, processor, microprocessor controller, or programmable hardware includes memory components (e.g., RAM, ROM, flash memory, etc.) that can store or receive software or computer code that, when accessed and executed by the computer, processor, or hardware, implements the machine learning-based driving style classification methods described herein. Further, when a general-purpose computer accesses code for implementing the methods illustrated herein, execution of the code transforms the general-purpose computer into a special-purpose computer for performing the methods illustrated herein.
Those of ordinary skill in the art will appreciate that the various illustrative elements and method steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the particular application of the solution and the constraints involved. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the embodiments of the present application.
It should be noted that, in the present specification, each embodiment is described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, for the apparatus and system embodiments, since they are substantially similar to the method embodiments, they are described in a relatively simple manner, and reference may be made to some of the descriptions of the method embodiments for related points. The above-described embodiments of the apparatus and system are merely illustrative, and elements not shown as separate may or may not be physically separate, and elements not shown as unit hints may or may not be physical elements, may be located in one place, or may be distributed across multiple network elements. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment. One of ordinary skill in the art can understand and implement it without inventive effort.
The above embodiments are only used for illustrating the embodiments of the present application, and not for limiting the embodiments of the present application, and those skilled in the relevant art can make various changes and modifications without departing from the spirit and scope of the embodiments of the present application, so that all equivalent technical solutions also belong to the scope of the embodiments of the present application, and the scope of the embodiments of the present application should be defined by the claims.
Claims (10)
1. A driving style classification method based on machine learning, for classifying a driving style of a driver, comprising:
carrying out correlation analysis on the selected driving style characteristic parameters of the driver to obtain correlation coefficients among the driving style characteristic parameters;
removing redundant driving style characteristic parameters in a driving behavior data sample obtained in advance according to the correlation coefficient and a preset correlation coefficient threshold;
and classifying the driving style of the driver based on a machine learning model according to the driving behavior data sample without the redundant driving style characteristic parameters.
2. The method according to claim 1, wherein the correlation analysis is performed on the selected driving style characteristic parameters of the driver to obtain correlation coefficients between the driving style characteristic parameters, specifically: and carrying out relevance analysis on the selected driving style characteristic parameters based on a preset relevance analysis model to obtain a correlation coefficient between the driving style characteristic parameters.
3. The method according to claim 1, wherein the removing of the redundant driving style characteristic parameters from the pre-obtained driving behavior data sample according to the correlation coefficient and a preset correlation coefficient threshold specifically comprises: and comparing the correlation coefficient with a preset correlation coefficient threshold, and removing redundant driving style characteristic parameters in the driving behavior data sample obtained in advance according to the comparison result.
4. The method according to any one of claims 1 to 3, wherein the driving style of the driver is classified based on a machine learning model according to the driving behavior data sample without redundant driving style characteristic parameters, specifically: and classifying the driving style of the driver based on a preset clustering algorithm model according to the driving behavior data sample without the redundant driving style characteristic parameters.
5. The method of claim 4, wherein the driving behavior data samples comprise: driving behavior data samples under a heavy load operation state and driving behavior data samples under a no-load operation state;
in a corresponding manner, the first and second optical fibers are,
the method for classifying the driving style of the driver based on a preset clustering algorithm model according to the driving behavior data sample without the redundant driving style characteristic parameters comprises the following steps:
respectively fitting the driving behavior data samples with the redundant driving style characteristic parameters removed under the heavy-load operation state and the driving behavior data samples with the redundant driving style characteristic parameters removed under the no-load operation state based on a K-means clustering algorithm model, and respectively determining the classified number of the driving styles under the heavy-load operation state and the no-load operation state;
and classifying the driving styles of the driver in the heavy load operation state and the no-load operation state based on a K-means clustering algorithm model according to the classified number of the driving styles in the heavy load operation state and the no-load operation state respectively.
6. A machine learning-based driving style classification apparatus for classifying a driving style of a driver, comprising:
the correlation analysis unit is configured to perform correlation analysis on the selected driving style characteristic parameters of the driver to obtain correlation coefficients among the driving style characteristic parameters;
the redundant parameter removing unit is configured to remove redundant driving style characteristic parameters in a driving behavior data sample obtained in advance according to the correlation coefficient;
and the driving style classification unit is configured to classify the driving style of the driver according to the driving behavior data sample without the redundant driving style characteristic parameters based on the machine learning model.
7. The apparatus according to claim 6, wherein the correlation analysis unit is further configured to perform correlation analysis on the selected driving style characteristic parameters based on a preset correlation analysis model to obtain correlation coefficients between the driving style characteristic parameters.
8. The device according to claim 6, wherein the redundant parameter removing unit is further configured to compare the correlation coefficient with a preset correlation coefficient threshold, and remove redundant driving style characteristic parameters in the driving behavior data sample obtained in advance according to the comparison result.
9. The apparatus according to any one of claims 6 to 8, wherein the driving style classification unit is further configured to classify the driving style of the driver based on a preset clustering algorithm model according to the driving behavior data sample without redundant driving style characteristic parameters.
10. An electronic device, comprising: a memory, a processor, and a program stored in the memory and executable on the processor, the processor when executing the program implementing a machine learning based driving style classification method according to any one of claims 1-5.
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