CN108108766B - Driving behavior identification method and system based on multi-sensor data fusion - Google Patents
Driving behavior identification method and system based on multi-sensor data fusion Download PDFInfo
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Abstract
A driving behavior recognition method and system based on multi-sensor data fusion are characterized in that OBD sensing data acceleration data of a vehicle are obtained through an OBD sensing information collector and an acceleration sensor which are installed on the vehicle, feature information in the OBD sensing data acceleration data is classified through two machine learning models, classification confidence and classification accuracy of each classification result in the two models are independently calculated, the classification results in the two models are weighted and fused according to the classification confidence and the classification accuracy of the classification results, and finally the classification result with the largest weighted fusion value is used as a recognition result of the driving behavior. The invention can comprehensively consider various sensing data at the same time through the data fusion technology, thereby having higher identification precision and being more beneficial to evaluating the driving behavior of the vehicle. Moreover, most of sensing data required by identification can be directly obtained through the bus of the electric vehicle, and only an acceleration sensor needs to be additionally arranged on the vehicle, so that the method is low in cost and suitable for popularization.
Description
Technical Field
The invention relates to the field of sensing data processing, in particular to a technology for identifying driving behaviors based on multi-sensor data.
Background
The driving behavior habit of drivers has been widely paid attention as an important reference for judging the safety condition of vehicles used by drivers and evaluating users in the automobile leasing and automobile insurance industries. The driving behavior-based recognition technology can reflect the specific driving habits of the driver in the driving process from one side, and further rank the driving risk of the driver according to the driving habits.
At present, the existing driving behavior recognition technology is generally only limited to fuel locomotives, and the research on the driving behavior recognition technology of electric automobiles is less. Also, currently, research on driving behavior recognition technology is mainly directed only to two directions: based on an analysis of the driving behaviour of the driver, or based on an analysis of the driving behaviour of the vehicle.
The former, such as Chiyomi Miyajima, Yoshihiro Nishiwaki and Koji Ozawa, models the actuation of the accelerator brake pedal by different drivers, and proposes a hybrid gaussian algorithm by which the driving behavior of the driver is analyzed with an accuracy of approximately 80%.
Some scholars want to use the latter to reflect the driving condition of the vehicle through the sensing signal of the smart phone and recognize the driving behavior. Under the thought, Liweijian and the like can identify whether the vehicle has an accident or not through the change degree of the acceleration sensor in the mobile phone.
Although both of the above-described research directions can classify driving behaviors, they have a common problem in that recognition accuracy is not high.
Considering that the number of sensors that can be mounted on an electric vehicle is large, a considerable number of driving data during driving behavior can be obtained actually by various sensors mounted on the vehicle. This means that the identification system input information source is sufficient, which with the use of calendars will make it possible to greatly improve the accuracy of the identification of the driving behaviour. However, the driving behavior recognition technology based on vehicle sensing data is almost blank at present.
Therefore, a driving behavior recognition technology based on sensor data is urgently needed to improve the recognition accuracy of the driving behavior of the electric vehicle.
Disclosure of Invention
In order to solve the defects in the prior art, the invention aims to provide a driving behavior identification method and system based on multi-sensor data fusion.
Firstly, in order to achieve the above purpose, a driving behavior recognition method based on multi-sensor data fusion is provided, which comprises the following steps:
firstly, acquiring OBD (On Board Diagnostics) sensing information and acceleration information of a vehicle, preprocessing the OBD sensing information and the acceleration information, and acquiring OBD sensing data and acceleration data of the vehicle;
secondly, respectively extracting characteristic information of the OBD sensing data and characteristic information of the acceleration data;
thirdly, inputting the characteristic information of the OBD sensing data into a first machine learning model trained in advance for classification, and calculating a first classification confidence h1 and a first classification accuracy W1 of each classification result according to the classification data of the first machine learning model; inputting the characteristic information of the acceleration data into a second pre-trained machine learning model and a pre-trained learning model thereof for classification, and calculating a second classification confidence h2 and a second classification accuracy W2 of each classification result according to the classification data of the second machine learning model;
and fourthly, according to the first classification confidence h1, the first classification accuracy W1, the second classification confidence h2 and the second classification accuracy W2 obtained in the third step, performing weighted fusion on each classification result of the first machine learning model and the second machine learning model according to a formula W1 x h1+ W2 x h2, and outputting a classification result with the maximum weighted fusion value, namely a recognition result of the driving behavior.
Specifically, in the above method, in the first step, the pretreatment is a normalization treatment. The denoising processing adopts a wavelet denoising method.
Specifically, in the above method, in the second step, the feature information of the OBD sensing data includes a mean value, a variance, a maximum value, and a minimum value of the OBD sensing information; the characteristic information of the acceleration data is the time domain fluctuation characteristic and the stability of the acceleration data.
Specifically, in the above method, the time-domain fluctuation characteristic and the stability of the acceleration data are calculated by using an MMA (multiple finite analysis) method; the MMA method comprises the following specific steps:
step M1, calculating K pieces of acceleration data xkThe profile signal sequence Y (i) of the acceleration sequence is formed, K is more than or equal to 1 and less than or equal to K:
wherein K represents the acceleration data sequence x1...xKThe length of (a) of (b),<x>representing a set of acceleration sequences intercepted in a fixed time window, i representing the ith data in said profile signal sequence, xkRepresenting the kth group of acceleration sequences; step M2, dividing the contour signal sequence Y (i) into NsInt (N/s) contiguous and non-overlapping subintervals Vj(j＝1，2，...Ns) (ii) a Wherein s represents a scale parameter and N represents the number of data in a set of contour signal sequences Y (i);
step M3, calculating each subinterval V through least square estimation algorithmj(j＝1，2，...Ns) Each subinterval V is calculated according to the following formulaj(j＝1，2，...Ns) Variance G of2(s，v)：
Wherein v represents the sequence number of the subinterval; s represents a scale parameter representing the length of the subsequence;
yv(i) is an n-th order (n is a positive integer range) fitting polynomial for each subinterval:
yv(i)＝aj0+aj1i+...ajn-1in-1+ajnin，n＝1，2...
wherein n represents the regression order of the fitting polynomial (n ═ 1, 2, 3 …); a isjnA coefficient representing each order obtained by the above method;
step M4, calculating each subinterval Vj(j＝1，2，...Ns) Of order q of a ripple function
Wherein q represents the fluctuation order (q is typically set to [ -5, 5 ]](ii) a When q is a positive value, analysis on large fluctuation of data is indicated; when q is a negative value, analysis of small fluctuation of data is indicated);
step M5, calculating the Hurst surface function h (q, s), obtaining the time domain fluctuation characteristic and the stability of the acceleration data, wherein,
wherein Ri represents a fitting window Ri(i is 1, 2, 3, …, i is a range of positive integers) where the fitting window is introduced as a moving fitting window, F (q, s)RiAnd sRiRespectively corresponding values under the fitting serial port Ri; Δ F (q, s)RiRepresenting the variation of F (q, s) values under different windows; Δ sRiRepresenting the amount of change in the s-value for different windows.
Furthermore, in order to fully utilize the data, in the step M2 of the above method, the head and tail ends of the sequence formed by the contour signals y (i) are respectively determined according to NsStep cutting of the sub-interval of the equivalent to int (N/s) to obtain 2NsA plurality of contiguous and non-overlapping subintervals;
then to the 2NsAnd calculating the time domain fluctuation characteristic and the stability of the acceleration data in the subintervals according to the steps from M3 to M5.
Specifically, in the above method, in the third step, the first machine learning model is a random forest classifier;
the first machine learning model is obtained by training the following steps:
step A1, creating a labeled training sample set { (O) of OBD sensing information1，y1)，(O2，y2)，…，(Ok，yk) Where k is the sample number, k is 2 or more, OkRepresents the kth OBD sensory information training sample, ykA label value representing a kth training sample; each label in the labeled training sample set at least comprises 1 OBD sensing information training sample; carrying out noise reduction and normalization processing on the OBD sensing information training sample to obtain OBD training sample sensing data of the vehicle;
step A2, extracting characteristic information of the OBD training sample sensing data;
step A3, inputting the characteristic information of the OBD training sample sensing data into the random forest classifier, and repeatedly extracting L times from the characteristic information of the OBD training sample sensing data through a self-help (bootstrap) resampling technology by the random forest classifier to obtain a sample set, wherein L is the total number of samples put into the classifier; then randomly selecting m training sample attribute values to form a new sample set, wherein m is a set parameter and is generally greater than 1/3 of attribute number, so as to obtain a decision tree, and each decision tree obtains a classification result;
step A4, the proportion h of the number of the decision trees of each classification result of the random forest classifier to the total number of all the decision trees1(p | x) as the classification data, a first classification confidence h1 ═ h of each classification result is obtained1(p|x)；
The first classification accuracy W1 of each classification result is
Wherein N isacc1Number, N, of labeled training sample sets representing OBD sensory information that are correctly classifiedp1The total number of samples in the labeled training sample set representing the OBD sensory information.
Similarly, in the third step of the above method, the second machine learning model is a random forest classifier with the same parameters as the first machine learning model;
the second machine learning model is obtained by training the following steps:
step B1, creating a labeled training sample set of acceleration information { (x)1，y1)，(x2，y2)，…，(xk，yk) Where k is the sample number, k is 2 or more, xkRepresents the kth acceleration information training sample, ykA label value representing a kth training sample; each label in the labeled training sample set at least comprises 1 acceleration information training sample; carrying out noise reduction processing and normalization processing on the acceleration information training sample to obtain the acceleration training sample sensing data of the vehicle;
step B2; extracting characteristic information of the acceleration training sample sensing data;
step B3, inputting the characteristic information of the acceleration training sample sensing data into the random forest classifier, and repeatedly extracting L times from the characteristic information of the acceleration training sample sensing data through a self-help (bootstrap) resampling technology by the random forest classifier to obtain a sample set, wherein L is the total number of samples; then randomly selecting m training sample attribute values to form a new sample set, wherein m is a set parameter and is generally greater than 1/3 of attribute number, so as to obtain a decision tree, and each decision tree obtains a classification result;
step B4, the proportion h of the number of the decision trees of each classification result of the random forest classifier to the total number of all the decision trees2(p | x) as the classification data, a second classification confidence h2 ═ h of each classification result is obtained2(p|x)；
In the second machine learning model, the second classification accuracy W2 of each classification result is
Where N isacc2Number of correctly classified samples, N, representing acceleration informationp2Representing the total number of test samples.
Specifically, in the method of the present invention, in the first step, the OBD sensor information includes vehicle speed, motor torque, output current information of the motor, and motor power.
Secondly, in order to achieve the purpose, a driving behavior recognition system based on multi-sensor data fusion is also provided, and the driving behavior recognition system comprises an acceleration sensor and an OBD sensing information collector which are arranged on a vehicle, and a server; acceleration sensor with OBD sensing information collector all with the server is connected:
the acceleration sensor is used for acquiring acceleration information of the vehicle and uploading the acceleration information to the server;
the OBD sensing information collector is used for obtaining OBD sensing information of the vehicle and uploading the OBD sensing information to the server; the server is used for receiving the OBD sensing information and the acceleration information, and obtaining OBD sensing data and acceleration data of the vehicle after normalization processing is carried out on the OBD sensing information and the acceleration information; respectively extracting characteristic information of the OBD sensing data and characteristic information of the acceleration data; classifying the characteristic information of the OBD sensing data to obtain a first classification confidence h1 and a first classification accuracy W1; classifying the characteristic information of the acceleration data to obtain a second classification confidence h2 and a second classification accuracy W2; and finally, performing weighted fusion on each classification result according to a formula W1 h1+ W2 h2, calculating to obtain a classification result with the maximum weighted fusion value, namely a driving behavior recognition result, and outputting the recognition result.
Specifically, in the system, the acceleration sensor is a six-axis accelerometer.
Further, in the system, the server is a vehicle-mounted server or a remote server;
when the server selects a remote server, the driving behavior recognition system based on the multi-sensor data fusion also comprises a storage module and a data transceiver module;
the storage module is simultaneously connected with the acceleration sensor and the OBD sensing information collector, and is used for storing the acceleration information of the vehicle acquired by the acceleration sensor and the OBD sensing information of the vehicle acquired by the OBD sensing information collector and outputting the acceleration information and the OBD sensing information to the data transceiver module;
the data transceiver module is connected with the storage module and the remote server at the same time, and the data transceiver module is used for uploading the acceleration information and the OBD sensing information of the vehicle stored by the storage module to the remote server.
Advantageous effects
According to the invention, OBD sensing data acceleration data of a vehicle are obtained through an OBD sensing information collector and an acceleration sensor which are installed on the vehicle, characteristic information in the OBD sensing data acceleration data is classified through two machine learning models, the classification confidence coefficient and the classification accuracy of each classification result in the two models are independently calculated, the classification results in the two models are weighted and fused according to the classification confidence coefficient and the classification accuracy of each classification result, and finally the classification result with the maximum weighted fusion value is used as a recognition result of driving behaviors. The invention can comprehensively consider various sensing data at the same time through the data fusion technology, thereby having higher identification precision and being more beneficial to evaluating the driving behavior of the vehicle. Moreover, most of sensing data required by identification can be directly obtained through the bus of the electric vehicle, and only an acceleration sensor needs to be additionally arranged on the vehicle, so that the method is low in cost and suitable for popularization.
Further, the present invention calculates the temporal fluctuation characteristic and stability of the acceleration data by an MMA (multiple fractional analysis) method. In particular, the sequence of contour signals Y (i) can be divided into N sequences at the beginning and the endsAnd (2) cutting the step size of the sub-interval of the equivalent to the int (N/s) to obtain 2Ns continuous and non-overlapping sub-intervals so as to improve the utilization rate of data and further improve the calculation precision. Of course, there are other methods for the processing of the acceleration signal, such as wavelet packet energy and SVD (singular value decomposition). But considering the device transmission cost problem, since these two methods for time domain require a large amount of time domain data, it obviously requires higher sampling cost. In the present invention, for cost, the acquisition frequency of the acceleration signal is 10 points per second, and each set of data has 50 points. The amount of such data limits the wavelet packet energy and the processing effect of SVD, unlike for the MMA algorithm. The MMA algorithm can efficiently extract the data valid information through the fluctuation and stability of the signal. Comparative experiments also prove that the MMA method has higher accuracy.
Further, the invention takes a random forest classifier as a machine learning model. There are many kinds of machine learning models, and in order to embody the different classification effects of different classifiers on each sample, the invention introduces the confidence h (p | x). The machine learning model may generally select random forests, naive Bayes and libsvm. For the three classifiers, a naive Bayes classifier is simpler, belongs to an early classifier, and has no advantage for the classification with excessive categories; the libsvm is required to be more suitable for the selection of parametersHigh. Because a plurality of decision trees are established in the random forest classification process, each decision tree obtains a classification result, and the classification result of each decision tree is voted to be determined as a final classification result. Therefore, the classification data can be directly the proportion h (p | x) of the number of decision trees of each classification result to the total number of all decision trees, and the classification correct proportion of each classification result can be used as the classification data
As the classification accuracy. Through comparison experiments, the random forest has higher accuracy compared with other two forests, does not need too many parameter selections, and is more suitable for the method.
Particularly, in the structure of the device, as the CAN bus of the electric automobile is provided with most of OBD sensing data required by the invention, the invention CAN directly acquire the information through the 0BD sensing information acquisition device. In addition, because the data on the CAN bus is updated in real time along with the running condition of the vehicle, the invention is more sensitive to the driving condition of the vehicle and has higher precision compared with the prior information obtained by an intermediate sensor or a data processing unit. Particularly, after various types of sensing information of the vehicle is obtained, the method also comprises preprocessing the information so as to extract characteristic information of the information. The characteristic information only retains relatively sensitive data characteristics, so that the deviation of the identification accuracy caused by the sensitivity of the sensor can be effectively corrected, and the identification accuracy of the invention is further improved. By increasing the number of training samples, the method can effectively inhibit overfitting and further improve the identification precision. The remote server can greatly improve the computing capability of the system and is more convenient for the extension and the update of the training samples.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention.
Drawings
The accompanying drawings are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the principles of the invention and not to limit the invention. In the drawings:
FIG. 1 is a flow chart of a driving behavior recognition method based on multi-sensor data fusion according to the present invention;
FIG. 2 is a block diagram of a driving behavior recognition system based on multi-sensor data fusion in accordance with the present invention;
fig. 3 is a schematic flow chart of processing training samples or sensing data in the server according to the present invention.
Detailed Description
The preferred embodiments of the present invention will be described in conjunction with the accompanying drawings, and it will be understood that they are described herein for the purpose of illustration and explanation and not limitation.
Fig. 1 is a flow chart of a driving behavior recognition method based on multi-sensor data fusion according to the invention, which comprises the following steps:
the method comprises the steps that firstly, an On Board Diagnostics (OBD) of a vehicle is obtained, wherein OBD sensing information is actually data interaction is realized through a CAN (controller area network) bus of an electric vehicle, and some data under the running state of the electric vehicle are obtained;
secondly, respectively extracting characteristic information of the OBD sensing data and characteristic information of the acceleration data;
thirdly, inputting the characteristic information of the OBD sensing data into a first machine learning model trained in advance for classification, and calculating a first classification confidence h1 and a first classification accuracy W1 of each classification result according to the classification data of the first machine learning model; inputting the characteristic information of the acceleration data into a second pre-trained machine learning model and a pre-trained learning model thereof for classification, and calculating a second classification confidence h2 and a second classification accuracy W2 of each classification result according to the classification data of the second machine learning model; (for different machine learning models, there is a difference in the specific calculation mode of the classification confidence coefficient: taking Bayes model as an example, the posterior probability can be used as the classification confidence coefficient, and for the classification accuracy, the accuracy of different classifiers can be effectively represented, namely the classification ability of the classifier for the data sample)
And fourthly, according to the first classification confidence h1, the first classification accuracy W1, the second classification confidence h2 and the second classification accuracy W2 obtained in the third step, performing weighted fusion on each classification result of the first machine learning model and the second machine learning model according to a formula W1 x h1+ W2 x h2, and outputting a classification result with the maximum weighted fusion value, namely a recognition result of the driving behavior.
Specifically, in the above method, in the first step, the pretreatment is a normalization treatment. The denoising processing adopts a wavelet denoising method.
Specifically, in the above method, in the second step, the feature information of the OBD sensing data includes a mean value, a variance, a maximum value, and a minimum value of the OBD sensing information; the characteristic information of the acceleration data is the time domain fluctuation characteristic and the stability of the acceleration data.
Specifically, in the above method, the time-domain fluctuation characteristic and the stability of the acceleration data are calculated by using an MMA (multiple finite analysis) method; the MMA algorithm is a method of processing time domain signals. In effect, the standard multiple analysis algorithm incorporates a scaling factor. The fluctuation characteristics of the obtained signal and the stability of the signal can be analyzed through an MMA algorithm analysis. The result of this algorithm is a Hurst surface describing the amplitude of variation of the Hurst index in the case of fluctuations with different parameters q (fluctuation order) and s (scale) [7 ]. However, in practice, the method has not been used for feature extraction, and the method is adopted herein, and the Hurst exponential sequence thereof is selected as the extracted feature. The MMA method comprises the following specific steps:
step M1, calculating K pieces of acceleration data xkThe profile signal sequence Y (i) of the acceleration sequence is formed, K is more than or equal to 1 and less than or equal to K:
wherein K represents the acceleration data sequence x1...xKThe length of (a) of (b),<x>represents that the data collected during the period is intercepted by a fixed time window (for example, a time window of 5 seconds is selected in the embodiment) to be used as a group of acceleration sequences, i represents the ith data in the profile signal sequence, and xkRepresenting the kth group of acceleration sequences;
step M2, dividing the contour signal sequence Y (i) into NsInt (N/s) contiguous and non-overlapping subintervals Vj(j＝1，2，...Ns) (ii) a Wherein s represents a scale parameter and N represents the number of data in a set of contour signal sequences Y (i);
step M3, calculating each subinterval V through least square estimation algorithmj(j＝1，2，...Ns) Each subinterval V is calculated according to the following formulaj(j＝1，2，...Ns) Variance G of2(s，v)：
Wherein v represents the sequence number of the subinterval; s represents a scale parameter representing the length of the subsequence; y isv(i) Denotes each subinterval Vj(j＝1，2，...Ns) The fitting polynomial of order n (in this embodiment, the order n may be 5, and n may be a value within a positive integer range):
yv(i)＝aj0+aj1i+...ajn-1in-1+ajnin，n＝1，2...
wherein n represents the regression order of the fitting polynomial (n ═ 1, 2, 3 …); a isjnCoefficients representing each order of the general solution (as will be understood by those skilled in the art, this n-th order fitting polynomial is obtained by a toolbox, and a fitting polynomial can be obtained by knowing the subintervals and setting n);
step M4, calculating each subinterval Vj(j＝1，2，...Ns) Of order q of a ripple function
Where q represents the fluctuation order (q is an artificially set parameter, typically set to [ -5, 5 [)]When q is a positive value, it means that large fluctuation of data is analyzed, and when q is a negative value, it means that small fluctuation of data is analyzed);
step M5, calculating the Hurst surface function h (q, s), obtaining the time domain fluctuation characteristic and the stability of the acceleration data, wherein,
wherein Ri represents a fitting window Ri(i is 1, 2, 3, …, in this embodiment, i to 5 may be selected, and the value range of i includes the whole range of positive integers), where Ri is introduced as a fitting window of the movement, and s value with quasi-continuous variation may be introduced into the operation of the q-order fluctuation function F (q, s), F (q, s)RiAnd sRiRespectively representing a q-order fluctuation function F (q, s) under a Ri fitting windowRiThe corresponding value and the value corresponding to the scale parameter s; Δ F (q, s)RiRepresenting the variation of F (q, s) values under different windows; Δ sRiRepresenting the amount of change in the s-value for different windows.
In the MMA method, the selection of a scale value s is crucial, and a moving fitting window is adopted in the MMA, and the quasi-continuous changing s value in the window is substituted into the F (q, s) operation. Assume a fitting window Ri (i ═ 1, 2.., n), and hRi corresponds to the value of h calculated in Ri. Then, the value of h can be calculated for each fitting window, h(s) { h ═ hR1，hR2，...，hRnFor a fixed value of q, a quasi-continuously varying value of h (q) can be obtained over a range of s. Repeating the above operations for different q values can obtain a generalized Hurst surface.
The h (q, s) obtained by the above method also has a certain physical significance: h epsilon (0, 0.5) indicates that the time domain signal has certain inverse persistence, h epsilon (0.5, 1) indicates that the signal is uncorrelated noise, h epsilon (0.5, 1) indicates that the signal is persistent, h epsilon (1.5) indicates that the object does brownian motion, and h > 2 indicates that the object is black noise.
Furthermore, in order to fully utilize the data, in the step M2 of the above method, the head and tail ends of the sequence formed by the contour signals y (i) are respectively determined according to NsStep cutting of the sub-interval of the equivalent to int (N/s) to obtain 2NsA plurality of contiguous and non-overlapping subintervals;
then to the 2NsAnd calculating the time domain fluctuation characteristic and the stability of the acceleration data in the subintervals according to the steps from M3 to M5.
Specifically, in the above method, in the third step, the first machine learning model is a random forest classifier. Random forests are decision tree-based integrated methods that can be used for classification, regression, and other ensemble learning. The minimum decision unit of the random forest is a random decision tree, a plurality of decision trees are constructed during training, a plurality of output classification result modes are obtained according to the decision trees, and finally, the decision trees perform comprehensive voting to determine an output result. In the embodiment, a plurality of decision trees are established by utilizing a random forest, each decision tree obtains a classification result, the classification result of each decision tree is voted, and the class with the highest vote number is used as the final classification result;
the first machine learning model is obtained by training the following steps:
step A1, creating a labeled training sample set { (O) of OBD sensing information1，y1)，(O2，y2)，…，(Ok，yk) Where k is the sample number, k is 2 or more, OkRepresents the kth OBD sensory information training sample, ykA label value representing a kth training sample; each label in the labeled training sample set at least comprises 1 OBD sensing information training sample; carrying out normalization processing on the OBD sensing information training sample to obtain the OBD training sample sensing data of the vehicle;
step A2, extracting characteristic information of the OBD training sample sensing data;
step A3, inputting the characteristic information of the OBD training sample sensing data into the random forest classifier, and repeatedly extracting L times from the characteristic information of the OBD training sample sensing data through a self-help (bootstrap) resampling technology by the random forest classifier to obtain a sample set, wherein L is the total number of samples put into the classifier; then randomly selecting m training sample attribute values to form a new sample set, wherein m is a set parameter and is generally greater than 1/3 of attribute number, so as to obtain a decision tree, and each decision tree obtains a classification result;
step A4, the proportion h of the number of the decision trees of each classification result of the random forest classifier to the total number of all the decision trees1(p | x) as the classification data, a first classification confidence h1 ═ h of each classification result is obtained1(p|x)；
The first classification accuracy W1 of each classification result is
Wherein N isacc1Number, N, of labeled training sample sets representing OBD sensory information that are correctly classifiedp1The total number of samples in the labeled training sample set representing the OBD sensory information.
Similarly, in the third step of the above method, the second machine learning model is a random forest classifier with the same parameters as the first machine learning model;
the second machine learning model is obtained by training the following steps:
step B1, creating a labeled training sample set of acceleration information { (x)1，y1)，(x2，y2)，…，(xk，yk) Where k is the sample number, k is 2 or more, xkRepresents the kth acceleration information training sample, ykA label value representing a kth training sample; each label in the labeled training sample set at least comprises 1 acceleration information training sample; carrying out normalization processing on the acceleration information training sample to obtain an acceleration training sample of the vehicleThe present sensing data;
step B2; extracting characteristic information of the acceleration training sample sensing data;
step B3, inputting the characteristic information of the acceleration training sample sensing data into the random forest classifier, and repeatedly extracting L times from the characteristic information of the acceleration training sample sensing data through a self-help (bootstrap) resampling technology by the random forest classifier to obtain a sample set, wherein L is the total number of samples; then randomly selecting m training sample attribute values to form a new sample set, wherein m is a set parameter and is generally greater than 1/3 of attribute number, so as to obtain a decision tree, and each decision tree obtains a classification result; here exactly in line with the classifier step employed in step 6;
step B4, the proportion h of the number of the decision trees of each classification result of the random forest classifier to the total number of all the decision trees2(p | x) as the classification data, a second classification confidence h2 ═ h of each classification result is obtained2(p|x)；
In the second machine learning model, the second classification accuracy W2 of each classification result is
Where N isacc2Number of correctly classified samples, N, representing acceleration informationp2Representing the total number of test samples.
Specifically, in the method of the present invention, in the first step, the OBD sensor information includes vehicle speed, motor torque, output current information of the motor, and motor power.
To better plan the driving behavior, the model built here is mainly used to identify the following driving behaviors: accelerating, decelerating, turning left, turning right, changing left lane, changing right lane, and driving normally.
Secondly, referring to fig. 2, in order to achieve the above object, a driving behavior recognition system based on multi-sensor data fusion is further provided, which includes an acceleration sensor and an OBD sensing information collector mounted on a vehicle, and further includes a server; acceleration sensor with OBD sensing information collector all with the server is connected:
the acceleration sensor is used for acquiring acceleration information of the vehicle and uploading the acceleration information to the server;
the OBD sensing information collector is used for obtaining OBD sensing information of the vehicle and uploading the OBD sensing information to the server; the server is used for receiving the OBD sensing information and the acceleration information, and obtaining OBD sensing data and acceleration data of the vehicle after denoising and normalizing the OBD sensing information and the acceleration information; respectively extracting characteristic information of the OBD sensing data and characteristic information of the acceleration data; classifying the characteristic information of the OBD sensing data to obtain a first classification confidence h1 and a first classification accuracy W1; classifying the characteristic information of the acceleration data to obtain a second classification confidence h2 and a second classification accuracy W2; and finally, performing weighted fusion on each classification result according to a formula W1 h1+ W2 h2, calculating to obtain a classification result with the maximum weighted fusion value, namely a driving behavior recognition result, and outputting the recognition result.
The system only needs to be provided with an accelerometer and an OBD acquisition instrument on the electric automobile. The OBD information is provided by the electric automobile, the accuracy rate of the OBD information is quite reliable, the needed cost is low, and the OBD information is suitable for mass production. The result obtained by the method can be used as a basis for judging the safe driving condition of the driver, and provides a scientific basis for providing differentiated products and services for automobile leasing and vehicle insurance industries.
Furthermore, different signal data are acquired through each sensor, characteristics of the signal data are extracted and used for training of a machine learning model, different sensor data have different sensitivities to different driving behaviors, and then information of the sensors is fused through the information fusion method, so that the identification accuracy can be effectively improved. The driving behavior of the driver can be effectively distinguished. By adding the data enhancement processing step, the amount of training sample data is increased, and overfitting is inhibited.
Specifically, in the system, the acceleration sensor is a six-axis accelerometer in the MEMS acquisition system, and the acceleration information acquired by the acceleration sensor includes acceleration information in three directions of X, Y, and Z axes.
Further, in the system, the server is a vehicle-mounted server or a remote server;
when the server selects a remote server, the driving behavior recognition system based on the multi-sensor data fusion also comprises a storage module and a data transceiver module;
the storage module is simultaneously connected with the acceleration sensor and the OBD sensing information collector, and is used for storing the acceleration information of the vehicle acquired by the acceleration sensor and the OBD sensing information of the vehicle acquired by the OBD sensing information collector and outputting the acceleration information and the OBD sensing information to the data transceiver module;
the data transceiver module is connected with the storage module and the remote server at the same time, and the data transceiver module is used for uploading the acceleration information and the OBD sensing information of the vehicle stored by the storage module to the remote server.
The method applies the machine learning method to the electric vehicle driving behavior identification for the first time, and analyzes the collected related sensor data through the server to obtain the correct driving behavior of the driver. The invention CAN directly analyze the vehicle CAN bus data, thereby avoiding increasing the number of sensors meaninglessly, effectively controlling the cost and obviously improving the identification result of the driving behavior. The result obtained by the method can be used as a basis for judging the safe driving condition of the driver, and provides a scientific basis for providing differentiated products and services for automobile leasing and vehicle insurance industries.
By adopting the method for identifying the driving behaviors of the electric vehicle based on the information fusion, higher accuracy can be obtained. Reference is made to the following comparative data:
1) the characteristic value of the acceleration information is extracted through an MMA algorithm, a classifier is trained through a random forest algorithm, and the obtained classification result is as follows
TABLE 1 acceleration information recognition results based on MMA algorithm (based on random forest)
2) By carrying out the feature extraction on the OBD information data and training a classifier by adopting a random forest algorithm, the obtained classification result is as follows:
TABLE 2 recognition results based on ODB information (based on random forest)
3) The classification result obtained by the information fusion algorithm is as follows:
TABLE 3 identification results based on information fusion method (based on random forest)
Through comparing the three experiments, when the driving behavior is analyzed by independently using the acceleration information and the OBD information, although the driving behavior can be identified to a certain degree, the accuracy of the result is not high, and the accuracy is greatly improved by adopting the information fusion method, so that the information fusion method has considerable feasibility.
The technical scheme of the invention has the advantages that:
the invention collects different sensor information in the driving process of the electric automobile through a plurality of sensors of the automobile, processes original data through an effective characteristic processing method, extracts the characteristics of signals, adopts a machine learning method to obtain a training model, classifies the collected data by using the training model, and simultaneously adopts an information fusion method to fuse the information of the plurality of sensors to obtain a final classification result. The invention can automatically identify different driving behaviors of the driver of the electric automobile, thereby judging the safe driving condition of the driver. The method provides a basis for providing differentiated products and services for the electric automobile leasing and vehicle insurance industries. Compared with the traditional machine learning method, the identification result has higher identification precision through information fusion, and the identification of the driving behaviors of the electric automobile is facilitated.
Those of ordinary skill in the art will understand that: although the present invention has been described in detail with reference to the foregoing embodiments, it will be apparent to those skilled in the art that changes may be made in the embodiments and/or equivalents thereof without departing from the spirit and scope of the invention. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. A driving behavior identification method based on multi-sensor data fusion is characterized by comprising the following steps:
the method comprises the steps of firstly, obtaining OBD sensing information and acceleration information of a vehicle, preprocessing the OBD sensing information and the acceleration information, and obtaining OBD sensing data and acceleration data of the vehicle;
secondly, respectively extracting characteristic information of the OBD sensing data and characteristic information of the acceleration data;
thirdly, inputting the characteristic information of the OBD sensing data into a first machine learning model trained in advance for classification, and calculating a first classification confidence h1 and a first classification accuracy W1 of each classification result according to the classification data of the first machine learning model; inputting the characteristic information of the acceleration data into a second machine learning model trained in advance for classification, and calculating a second classification confidence h2 and a second classification accuracy W2 of each classification result according to the classification data of the second machine learning model;
and fourthly, according to the first classification confidence h1, the first classification accuracy W1, the second classification confidence h2 and the second classification accuracy W2 obtained in the third step, performing weighted fusion on each classification result of the first machine learning model and the second machine learning model according to a formula W1 x h1+ W2 x h2, and outputting a classification result with the maximum weighted fusion value, namely a recognition result of the driving behavior.
2. The driving behavior recognition method based on multi-sensor data fusion of claim 1, wherein in the second step, the characteristic information of the OBD sensing data comprises a mean value, a variance, a maximum value and a minimum value of the OBD sensing information; the characteristic information of the acceleration data is the time domain fluctuation characteristic and the stability of the acceleration data.
3. The multi-sensor data fusion-based driving behavior recognition method according to claim 2, wherein the time-domain fluctuation characteristics and stability of the acceleration data are calculated by an MMA (multi scale multi fractional analysis) method; the MMA method comprises the following specific steps:
step M1, calculating K pieces of acceleration data xkThe profile signal sequence Y (i) of the acceleration sequence is formed, K is more than or equal to 1 and less than or equal to K:
wherein K represents the acceleration data sequence x1...xKThe length of (a) of (b),<x>representing a set of acceleration sequences intercepted in a fixed time window, i representing the ith data in said profile signal sequence, xkRepresenting the kth group of acceleration sequences;
step M2, dividing the contour signal sequence Y (i) into NsInt (N/s) contiguous and non-overlapping subintervals Vj(j＝1，2，...Ns)；Wherein s represents a scale parameter and N represents the number of data in a set of contour signal sequences Y (i);
step M3, calculating each subinterval V through least square estimation algorithmj(j＝1，2，...Ns) Each subinterval V is calculated according to the following formulaj(j＝1，2，...Ns) Variance G of2(s，v)：
Wherein v denotes the sequence number of the subinterval, s denotes the scale parameter,
yv(i) is an n-th order fitting polynomial for each subinterval:
yv(i)＝aj0+aj1i+...ajn-1in-1+ajnin，n＝1，2...
n represents the regression order of the fitting polynomial (n ═ 1, 2, 3 …); ajn represents a coefficient of each order obtained by the general method;
step M4, calculating each subinterval Vj(j＝1，2，...Ns) Of order q of a ripple function
Wherein q represents the fluctuation order;
step M5, calculating Hutst surface function h (q, s), obtaining the time domain fluctuation characteristic and stability of the acceleration data, wherein,
wherein R isiRepresents a fitting window Ri(i ═ 1, 2, 3, …, positive integer range); f (q, s)RiAnd sRiAre all the corresponding values under the Ri window, Δ F (q, s)RiRepresenting the variation of F (q, s) values under different windows; Δ sRiRepresenting the amount of change in the s-value for different windows.
4. As claimed in claim 3The driving behavior recognition method based on multi-sensor data fusion is characterized in that in the step M2, the head end and the tail end of the sequence formed by the contour signals y (i) are respectively in accordance with NsStep cutting of the sub-interval of the equivalent to int (N/s) to obtain 2NsA plurality of contiguous and non-overlapping subintervals; then to the 2NsAnd calculating the time domain fluctuation characteristic and the stability of the acceleration data in the subintervals according to the steps from M3 to M5.
5. The multi-sensor data fusion-based driving behavior recognition method of claim 1, wherein in the third step, the first machine learning model is a random forest classifier;
the first machine learning model is obtained by training the following steps:
step A1, creating a labeled training sample set { (O) of OBD sensing information1，y1)，(O2，y2)，…，(Ok，yk) Where k is the sample number, k is 2 or more, OkRepresents the kth OBD sensory information training sample, ykA label value representing a kth training sample; each label in the labeled training sample set at least comprises 1 OBD sensing information training sample; carrying out normalization processing on the OBD sensing information training sample to obtain the OBD training sample sensing data of the vehicle;
step A2, extracting characteristic information of the OBD training sample sensing data;
step A3, inputting the feature information of the OBD training sample sensing data into the random forest classifier, and repeatedly extracting L times from the feature information of the OBD training sample sensing data through a self-help resampling technology by the random forest classifier to obtain a sample set, wherein L is the total number of samples put into the classifier; then randomly selecting m training sample attribute values to form a new sample set, wherein m is a set parameter and is generally greater than 1/3 of attribute number, so as to obtain a decision tree, and each decision tree obtains a classification result;
step A4, randomizing the randomThe proportion h of the number of decision trees of each classification result of the forest classifier to the total number of all decision trees1(p | x) as the classification data, a first classification confidence h1 ═ h of each classification result is obtained1(p|x)；
The first classification accuracy W1 of each classification result is
Where N isacc1Number, N, of labeled training sample sets representing OBD sensory information that are correctly classifiedp1The total number of samples in the labeled training sample set representing the OBD sensory information.
6. The multi-sensor data fusion-based driving behavior recognition method according to claim 5, wherein in the third step, the second machine learning model is a random forest classifier with the same parameters as the first machine learning model;
the second machine learning model is obtained by training the following steps:
step B1, creating a labeled training sample set of acceleration information { (x)1，y1)，(x2，y2)，…，(xk，yk) Where k is the sample number, k is 2 or more, xkRepresents the kth acceleration information training sample, ykA label value representing a kth training sample; each label in the labeled training sample set at least comprises 1 acceleration information training sample; carrying out normalization processing on the acceleration information training sample to obtain the acceleration training sample sensing data of the vehicle;
step B2; extracting characteristic information of the acceleration training sample sensing data;
step B3, inputting the characteristic information of the acceleration training sample sensing data into the random forest classifier, and repeatedly extracting the characteristic information of the acceleration training sample sensing data for L times by the random forest classifier through a self-help resampling technology to obtain a sample set, wherein L is the total number of samples; then randomly selecting m training sample attribute values to form a new sample set, wherein m is a set parameter and is generally greater than 1/3 of attribute number, so as to obtain a decision tree, and each decision tree obtains a classification result;
step B4, the proportion h of the number of the decision trees of each classification result of the random forest classifier to the total number of all the decision trees2(p | x) as the classification data, a second classification confidence h2 ═ h of each classification result is obtained2(p|X)
In the second machine learning model, the second classification accuracy W2 of each classification result is
Where N isacc2Number of correctly classified samples, N, representing acceleration informationp2Representing the total number of test samples.
7. The driving behavior recognition method based on multi-sensor data fusion of claim 1, wherein in the first step, the OBD sensing information comprises vehicle speed, motor torque, output current information of the motor, and motor power.
8. A driving behavior recognition system based on multi-sensor data fusion is characterized by comprising an acceleration sensor and an OBD sensing information collector which are arranged on a vehicle, and a server; the acceleration sensor and the OBD sensing information collector are both connected with the server;
the acceleration sensor is used for acquiring acceleration information of the vehicle and uploading the acceleration information to the server;
the OBD sensing information collector is used for obtaining OBD sensing information of the vehicle and uploading the OBD sensing information to the server;
the server is used for receiving the OBD sensing information and the acceleration information, and obtaining OBD sensing data and acceleration data of the vehicle after normalization processing is carried out on the OBD sensing information and the acceleration information; respectively extracting characteristic information of the OBD sensing data and characteristic information of the acceleration data; classifying the characteristic information of the OBD sensing data to obtain a first classification confidence h1 and a first classification accuracy W1; classifying the characteristic information of the acceleration data to obtain a second classification confidence h2 and a second classification accuracy W2; and finally, performing weighted fusion on each classification result according to a formula W1 h1+ W2 h2, calculating to obtain a classification result with the maximum weighted fusion value, namely a driving behavior recognition result, and outputting the recognition result.
9. The multi-sensor data fusion-based driving behavior recognition system of claim 8, wherein the acceleration sensor is a six-axis accelerometer.
10. The multi-sensor data fusion-based driving behavior recognition system of claim 9, wherein the server is an onboard server or a remote server;
if the server is a remote server, the driving behavior recognition system based on the multi-sensor data fusion also comprises a storage module and a data transceiver module;
the storage module is simultaneously connected with the acceleration sensor and the OBD sensing information collector, and is used for storing the acceleration information of the vehicle acquired by the acceleration sensor and the OBD sensing information of the vehicle acquired by the OBD sensing information collector and outputting the acceleration information and the OBD sensing information to the data transceiver module;
the data transceiver module is connected with the storage module and the remote server at the same time, and the data transceiver module is used for uploading the acceleration information and the OBD sensing information of the vehicle stored by the storage module to the remote server.
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