CN107585164A - A kind of method and device for the driver that classifies - Google Patents
A kind of method and device for the driver that classifies Download PDFInfo
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Abstract
The invention discloses a kind of method and device for the driver that classifies, belong to driving behavior analysis technical field.Methods described includes：Obtain the sample data sets of M driver；The sample data sets of each driver in the M driver, calculate the Euclidean distance matrix between the driving behavior sample data of any two driver in the M driver；Euclidean distance matrix between the driving behavior sample data of any two driver in the M driver, the M driver is clustered at least one driver and clustered.Described device includes：Acquisition module, computing module and determining module.The present invention can realize to classify to driver.
Description
Technical field
The present invention relates to driving behavior analysis technical field, more particularly to a kind of method and device for the driver that classifies.
Background technology
Large number of (China's automobile driver sum is more than 200,000,000 people) of automobile driver, due to each driver
The educating of receiving, the makings of residing environment and individual, individual character, health degree are different, and the driving of each driver is practised
Used is all different.Therefore in driving behavior data analysis process, distinguishing the custom of the driving behavior between different drivers is
It is no similar, and it is very important that substantial amounts of driver is accurately carried out into classification.However, but driver is not entered at present
The method of row classification, therefore how driver is classified, it is current urgent problem.
The content of the invention
In order to solve relevant issues, the invention provides a kind of method and device for the driver that classifies.The technical scheme
It is as follows：
In a first aspect, the invention provides a kind of method for the driver that classifies, methods described includes：
The sample data sets of M driver are obtained, the sample data sets of driver include gathering in N number of unit interval
Driving behavior sample data, the travel speed of the driving behavior sample data including the driver-operated vehicle, car
Transversal displacement in road, operating range in the unit interval, gas pedal depth in the unit interval, brake pedal enters in the unit interval
Depth, steering wheel angle angular speed, transverse acceleration, vehicle angular speed, running distance and leading vehicle distance in the unit interval, before described
Car distance is the distance between the vehicle and front truck, and the front truck is traveling in same track before the vehicle and from institute
The nearest automobile of vehicle is stated, M and N are respectively the integer of size 1；
The sample data sets of each driver in the M driver, calculate appointing in the M driver
Euclidean distance between the driving behavior sample data of two drivers of anticipating, the Euclidean distance are used to reflect described two driving
The similar degree of driving habit of member；
Euclidean distance between the driving behavior sample data of any two driver in the M driver,
The M driver is clustered at least one driver's cluster.
Optionally, the sample data sets of each driver in the M driver, the M are calculated
Euclidean distance between the driving behavior sample data of any two driver in driver, including：
Travel speed, acceleration and the leading vehicle distance included according to the driving behavior sample data of each driver,
The sample data sets of each driver are divided into multiple first sets respectively；
According to first set corresponding to each driver, the eigenmatrix of calculating each driver, driver
Eigenmatrix be used to react the driving habit of the driver；
According to the eigenmatrix of each driver, between the driving behavior sample data for calculating any two driver
Euclidean distance.
Optionally, the driving behavior sample data according to each driver includes travel speed and front truck away from
From, the sample data sets of each driver are divided into multiple first sets respectively, including：
The travel speed that each driving behavior sample data included according to the sample data sets of the first driver includes
The running time needed for the leading vehicle distance has been travelled with the leading vehicle distance calculating vehicle, has obtained each driving behavior sample
Running time corresponding to notebook data, the first driver are any driver in the M driver；
Running time is divided into less than or equal to the driving behavior sample data of default very first time threshold value and follows driving
Set, running time is more than default very first time threshold value and drawn less than the driving behavior sample data for presetting the second time threshold
Assign to limited drive to gather, the driving behavior sample data that running time is more than or equal to default 3rd time threshold is divided into
Freely drive set.
Optionally, the first set according to corresponding to each driver, the feature of each driver is calculated
Matrix, including：
The travel speed that each driving behavior sample data that the first set according to corresponding to kid includes includes,
The driving behavior sample data that first set corresponding to the kid includes is divided into multiple second sets, positioned at same
The travel speed in each driving behavior sample data in one second set is located at same speed interval, the kid
For any driver in the M driver；
Driving behavior sample data in each second set, calculate multiple corresponding to each second set drive
Sail behavioural characteristic value；
By driving behavior value corresponding to each second set, feature square corresponding to the kid is formed
Battle array.
Optionally, the driving behavior sample data in each second set of the basis, each second set is calculated
Corresponding multiple driving behavior values, including：
Transversal displacement in the track that any driving behavior sample data in second set includes, in the unit interval
Operating range, gas pedal depth, brake pedal depth, steering wheel angle angular speed, transverse direction in the unit interval in the unit interval
Acceleration and vehicle angular speed, calculate multiple characteristic values corresponding to the driving behavior sample data, the multiple characteristic value bag
Include the track lateral shift value and the first ratio in the unit interval between operating range, brake in the unit interval
The second ratio between pedal depth and the unit interval length, gas pedal depth and the unit in the unit interval
The 4th ratio between the 3rd ratio, the transverse acceleration and the vehicle angular speed and the direction between time span
Disk corner angular speed；
First ratio corresponding to each driving behavior sample data included to the second set is ranked up, from sequence
Selection comes the first ratio of target location as a driving behavior value in the first sequence of ratio values afterwards；
Second ratio corresponding to each driving behavior sample data included to the second set is ranked up, from sequence
Selection comes the second ratio of target location as a driving behavior value in the second sequence of ratio values afterwards；
3rd ratio corresponding to each driving behavior sample data included to the second set is ranked up, from sequence
Selection comes the 3rd ratio of target location as a driving behavior value in the 3rd sequence of ratio values afterwards；
4th ratio corresponding to each driving behavior sample data included to the second set is ranked up, from sequence
Selection comes the 4th ratio of target location as a driving behavior value in the 4th sequence of ratio values afterwards；
Steering wheel angle angular speed corresponding to each driving behavior sample data included to the second set is arranged
Sequence, the steering wheel angle angular speed for selecting to come target location from the steering wheel angle angular speed sequence after sequence is as one
Driving behavior value.
Second aspect, the invention provides a kind of device for the driver that classifies, described device includes：
Acquisition module, for obtaining the sample data sets of M driver, the sample data sets of driver are including N number of
The driving behavior sample data of collection, the driving behavior sample data include the driver-operated vehicle in unit interval
Travel speed, transversal displacement in track, operating range in the unit interval, in the unit interval when gas pedal depth, unit
Interior brake pedal depth, steering wheel angle angular speed, transverse acceleration, vehicle angular speed, in the unit interval running distance and
Leading vehicle distance, the leading vehicle distance are the distance between the vehicle and front truck, and the front truck is traveling in same track in institute
State before vehicle and the automobile nearest from the vehicle, M and N are respectively the integer of size 1；
Computing module, for the sample data sets of each driver in the M driver, calculate the M
Euclidean distance between the driving behavior sample data of any two driver in individual driver, the Euclidean distance are used for anti-
Reflect the similar degree of the driving habit of described two drivers；
Determining module, for any two driver in the M driver driving behavior sample data it
Between Euclidean distance, the M driver is clustered at least one driver and clustered.
Optionally, the computing module includes：
Division unit, for included according to the driving behavior sample data of each driver travel speed, accelerate
Degree and leading vehicle distance, the sample data sets of each driver are divided into multiple first sets respectively；
First computing unit, for the first set according to corresponding to each driver, calculate each driver
Eigenmatrix, the eigenmatrix of driver is used for the driving habit for reacting the driver；
Second computing unit, for the eigenmatrix according to each driver, calculate between any two driver
Euclidean distance.
Optionally, the division unit includes：
First computation subunit, for each driving behavior sample included according to the sample data sets of the first driver
Travel speed and leading vehicle distance the calculating vehicle that data include have travelled the running time needed for the leading vehicle distance, obtain
Running time corresponding to each driving behavior sample data, the first driver are any driving in the M driver
Member；
First division subelement, for running time to be less than or equal to the driving behavior sample of default very first time threshold value
Data, which are divided into, follows driving to gather, and running time is more than into default very first time threshold value and less than default second time threshold
Driving behavior sample data is divided into the limited driving for driving set, running time being more than or equal to default 3rd time threshold
Behavior sample data, which are divided into, freely drives set.
Optionally, first computing unit includes：
Second division subelement, each driving behavior sample included for the first set according to corresponding to kid
The travel speed that data include, the driving behavior sample data that first set corresponding to the kid includes is divided into
Multiple second sets, the travel speed in each driving behavior sample data in same second set are located at same speed
Section, the kid are any driver in the M driver；
Second computation subunit, for the driving behavior sample data in each second set, calculate described each
Multiple driving behavior values corresponding to second set；
Subelement is formed, for driving behavior value, composition described second corresponding to each second set to be driven
Eigenmatrix corresponding to the person of sailing.
Optionally, second computation subunit performs the driving behavior sample number in each second set of basis
According to, the operation of multiple driving behavior values corresponding to each second set is calculated, including：
Transversal displacement in the track that any driving behavior sample data in second set includes, in the unit interval
Operating range, gas pedal depth, brake pedal depth, steering wheel angle angular speed, transverse direction in the unit interval in the unit interval
Acceleration and vehicle angular speed, calculate multiple characteristic values corresponding to the driving behavior sample data, the multiple characteristic value bag
Include the track lateral shift value and the first ratio in the unit interval between operating range, brake in the unit interval
The second ratio between pedal depth and the unit interval length, gas pedal depth and the unit in the unit interval
The 4th ratio between the 3rd ratio, the transverse acceleration and the vehicle angular speed and the direction between time span
Disk corner angular speed；
First ratio corresponding to each driving behavior sample data included to the second set is ranked up, from sequence
Selection comes the first ratio of target location as a driving behavior value in the first sequence of ratio values afterwards；
Second ratio corresponding to each driving behavior sample data included to the second set is ranked up, from sequence
Selection comes the second ratio of target location as a driving behavior value in the second sequence of ratio values afterwards；
3rd ratio corresponding to each driving behavior sample data included to the second set is ranked up, from sequence
Selection comes the 3rd ratio of target location as a driving behavior value in the 3rd sequence of ratio values afterwards；
4th ratio corresponding to each driving behavior sample data included to the second set is ranked up, from sequence
Selection comes the 4th ratio of target location as a driving behavior value in the 4th sequence of ratio values afterwards；
Steering wheel angle angular speed corresponding to each driving behavior sample data included to the second set is arranged
Sequence, the steering wheel angle angular speed for selecting to come target location from the steering wheel angle angular speed sequence after sequence is as one
Driving behavior value.
The beneficial effect of technical scheme provided by the invention is：
By obtaining the sample data sets of M driver, the sample number of each driver in the M driver
According to set, the Euclidean distance between any two driver in the M driver is calculated, appointing in the M driver
Euclidean distance between two drivers of meaning, at least one driver's cluster is determined, driver is divided so as to realize
Class.
Brief description of the drawings
Fig. 1 is a kind of method flow diagram for classification driver that the embodiment of the present invention 1 provides；
Fig. 2 is a kind of method flow diagram for classification driver that the embodiment of the present invention 2 provides；
Fig. 3 is a kind of square law device structural representation for classification driver that the embodiment of the present invention 3 provides；
Fig. 4 is a kind of square law device structural representation for classification driver that the embodiment of the present invention 4 provides.
Embodiment
To make the object, technical solutions and advantages of the present invention clearer, below in conjunction with accompanying drawing to embodiment party of the present invention
Formula is described in further detail.
Embodiment 1
Referring to Fig. 1, the embodiments of the invention provide a kind of method for the driver that classifies, including：
Step 101：Obtain the sample data sets of M driver.
Wherein, the sample data sets of driver include the driving behavior sample data of collection in N number of unit interval, and this is driven
Sail travel speed of the behavior sample data including driver-operated vehicle, transversal displacement, traveling in the unit interval in track
Distance, gas pedal depth in the unit interval, brake pedal depth, steering wheel angle angular speed in the unit interval, laterally accelerate
Degree, vehicle angular speed, running distance and leading vehicle distance in the unit interval, the leading vehicle distance be between the vehicle and front truck away from
From being traveling in the same track of front truck before the vehicle and the automobile nearest from the vehicle, M and N are respectively the whole of size 1
Number.
Step 102：The sample data sets of each driver in the M driver, calculate in the M driver
Any two driver driving behavior sample data between Euclidean distance.
Wherein, Euclidean distance is used to reflect the similar degree of the driving habit of two drivers.
Step 103：The Euclidean distance between any two driver in the M driver, by the M driver
It is clustered at least one driver's cluster.
In embodiments of the present invention, the sample data sets of M driver are obtained, it is each in the M driver
The sample data sets of driver, the Euclidean distance between any two driver in the M driver is calculated, according to the M
The Euclidean distance between any two driver in individual driver, at least one driver's cluster is determined, so as to realize
Driver is classified.In addition, the Euclidean distance between any two driver in the M driver is gathered
Class, it so need not can in advance preset classification.
Embodiment 2
Referring to Fig. 2, the embodiments of the invention provide a kind of method for the driver that classifies, including：
Step 201：Obtain the sample data sets of M driver.
Wherein, the sample data sets of driver include the driving behavior sample data of collection in N number of unit interval, and this is driven
Sail travel speed of the behavior sample data including driver-operated vehicle, transversal displacement, traveling in the unit interval in track
Distance, gas pedal depth in the unit interval, brake pedal depth, steering wheel angle angular speed in the unit interval, laterally accelerate
Degree, vehicle angular speed, running distance and leading vehicle distance in the unit interval, the leading vehicle distance be between the vehicle and front truck away from
From front truck is that automobile before the vehicle and nearest from the vehicle is travelled in same track with the vehicle, and M and N are respectively
The integer of size 1.
The sample data sets of driver can be obtained by driving simulator, or by setting at least one on vehicle
Individual sensor obtains the sample data sets of driver.
For driving simulator obtain driver sample data sets by way of, when actually realizing, Ke Yirang
Any one driver drives the driving simulator.During the driver drives the driving simulator, every a unit
Time just reads the driver from the drive simulation and drives the travel speed of vehicle, acceleration, transversal displacement, list in track
Operating range, gas pedal depth, brake pedal depth, steering wheel angle angle speed in the unit interval in the unit interval in the time of position
Degree, transverse acceleration, vehicle angular speed, the data such as running distance and leading vehicle distance in the unit interval, when can also obtain current
Between, turn signal state and the video data to driver shooting etc..The data that will finally be read from the driving simulator
Form a driving behavior sample data of the driver within the current one time.N number of unit interval is gathered in a manner described
Interior driving behavior sample data, N number of driving behavior sample data is formed to the sample data sets of the driver.
For by way of setting the sample data sets that at least one sensor obtains driver on vehicle, in reality
When border is realized, it can set and accelerate in vehicle in location settings sensors such as steering wheel for vehicle, gas pedal and brake pedals
Spend sensor and radar is set in the headstock position of vehicle, a driver then can be allowed to drive the vehicle.In the driving
Member drives the process of vehicle traveling, every a unit interval from being arranged on steering wheel for vehicle, gas pedal and brake pedal
On sensor obtain steering wheel angle angular speed, gas pedal depth and brake pedal enters in the unit interval in the unit interval
It is deep；According to horizontal in the travel speed of the data acquisition of acceleration transducer output driver driving vehicle, acceleration, track
Offset, operating range, transverse acceleration, vehicle angular speed and running distance in the unit interval in the unit interval, and pass through
Radar positioned at headstock obtains leading vehicle distance.Current time can also be obtained, turn signal state and to driver shooting
Video data etc..Finally braked by the steering wheel angle angular speed of acquisition, gas pedal depth in the unit interval, in the unit interval
Pedal depth, travel speed, acceleration, transversal displacement, operating range, transverse acceleration, vehicle in the unit interval in track
Angular speed, the data such as running distance and leading vehicle distance form one within the current one time of the driver and driven in the unit interval
Sail behavior sample data.The driving behavior sample data in N number of unit interval is gathered in a manner described, by N number of driving behavior
Sample data forms the sample data sets of the driver.
Wherein, N can be with integer values such as values 1000,2000,3000.M can be with the integer values such as 100,2000,10000.
Step 202：The travel speed and leading vehicle distance included according to the driving behavior sample data of each driver, respectively
The sample data sets of each driver are divided into multiple first sets.
Wherein, first set includes following driving set, limited driving set and freely drives set.This step can lead to
Following two steps are crossed to realize, including：
2021：The traveling that each driving behavior sample data included according to the sample data sets of the first driver includes
Speed, acceleration and leading vehicle distance calculating vehicle have travelled the running time needed for the leading vehicle distance, obtain each drive and go
For running time corresponding to sample data, the first driver is any driver in M driver.
For any one driving behavior sample data in the sample data sets of the first driver, according to the driving behavior
The travel speed and leading vehicle distance that sample data includes, calculate vehicle and travelled running time required for the leading vehicle distance,
Using the running time as running time corresponding to the sample driving behavior sample data.
, can be by above-mentioned for other each driving behavior sample datas in the sample data sets of the first driver
Mode calculates running time corresponding to other each driving behavior sample datas.
2022：Running time is divided into less than or equal to the driving behavior sample data of default very first time threshold value and followed
Set is driven, running time is more than default very first time threshold value and less than the driving behavior sample number of default second time threshold
Gather according to limited drive is divided into, the driving behavior sample data that running time is more than or equal to default 3rd time threshold is drawn
Assign to and freely drive set.
For example, it is assumed that default very first time threshold value is 6 seconds, it is 12 seconds to preset the second time threshold, then running time is small
Being divided into or equal to the driving behavior sample data of 6 seconds follows driving to gather, and running time is more than 6 seconds and less than 12 seconds
Driving behavior sample data is divided into limited drive and gathered, and running time is more than or equal to the driving behavior sample of 12 seconds
Data, which are divided into, freely drives set.
Step 203：What each driving behavior sample data that the first set according to corresponding to kid includes included
Travel speed, the driving behavior sample data that first set corresponding to the kid includes is divided into multiple second collection
Close.
Wherein, the travel speed in each driving behavior sample data in same second set is located at same speed
Section, kid are any driver in M driver.
Multiple speed intervals can be set in advance, for each driving behavior sample data in first set, it is determined that often
Speed interval where the travel speed that individual driving behavior sample data includes, by including travel speed be located at same speed area
Between driving behavior sample data be divided into same second set.
Step 204：Driving behavior sample data in each second set according to corresponding to kid, calculate each
Multiple driving behavior values corresponding to second set.
Driving behavior value is used for the driving behavior custom for describing driver.
This step can be realized by the following steps, including：
2041：The car included for each second set, any driving behavior sample data in the second set
Transversal displacement in road, operating range in the unit interval, gas pedal depth in the unit interval, brake pedal enters in the unit interval
Depth, steering wheel angle angular speed, transverse acceleration and vehicle angular speed, calculate multiple spies corresponding to the driving behavior sample data
Value indicative.
Wherein, the plurality of characteristic value include the track lateral shift value and in the unit interval between operating range first
Ratio, the second ratio in the unit interval between brake pedal depth and the unit interval length, throttle in the unit interval
The 4th ratio between the 3rd ratio, the transverse acceleration and the vehicle angular speed between pedal depth and the unit interval length
Value and direction disk corner angular speed.
2042：First ratio corresponding to each driving behavior sample data included to the second set is ranked up, from
Selection comes the first ratio of target location as a driving behavior value in the first sequence of ratio values after sequence.
Between the driving behavior sample data total number that target location can be default ratio and the second set includes
Product.
For example, it is assumed that default ratio is 0.85, the driving behavior sample data total number that the second set includes is
200, target location 170.So the second set includes the first ratio corresponding to 200 driving behavior sample datas, to this
200 the first ratios are ranked up, and select to come the first ratio conduct of the 170th position from the first sequence of ratio values after sequence
One driving behavior value.
2043：Second ratio corresponding to each driving behavior sample data included to the second set is ranked up, from
Selection comes the second ratio of target location as a driving behavior value in the second sequence of ratio values after sequence.
2044：3rd ratio corresponding to each driving behavior sample data included to the second set is ranked up, from
Selection comes the 3rd ratio of target location as a driving behavior value in the 3rd sequence of ratio values after sequence.
2045：4th ratio corresponding to each driving behavior sample data included to the second set is ranked up, from
Selection comes the 4th ratio of target location as a driving behavior value in the 4th sequence of ratio values after sequence.
2046：Steering wheel angle angular speed corresponding to each driving behavior sample data included to the second set is carried out
Sequence, the steering wheel angle angular speed for selecting to come target location from the steering wheel angle angular speed sequence after sequence is as one
Individual driving behavior value.
Driving behavior value corresponding to second set includes five.Kid corresponds to multiple second sets, by upper
Step 2041 is stated to five driving behavior values corresponding to each second set can be obtained the step of 2046.
Step 205：By driving behavior value corresponding to each second set, feature square corresponding to kid is formed
Battle array.
Each corresponding five driving behavior values of second set.It is assumed that corresponding four second sets of kid, then
5x4 eigenmatrix can be formed.
For other each drivers in the M driver by above-mentioned kid in the way of to calculate other every
The eigenmatrix of individual driver.The eigenmatrix of driver can react the driving habit of driver.
Step 206：According to the eigenmatrix of each driver, the driving behavior sample data of calculating any two driver
Between Euclidean distance.
Euclidean distance between the driving behavior sample data of two drivers is used for the driving for reflecting two drivers
It is accustomed to similar degree.
This step can be the eigenmatrix of each driver to be formed into a whole matrix, the whole matrix is often gone
A corresponding driver, the row include the eigenmatrix of driver corresponding to the row.Between any two driver is calculated
During Euclidean distance, row corresponding to two drivers is read from whole matrix, according to corresponding to two drivers of reading
Row calculates the Euclidean distance between two drivers by the default algorithm for calculating Euclidean distance.
For any row in whole matrix, it is assumed that the eigenmatrix of driver is corresponding to the rowThen
A line corresponding to the driver can be [a1, b1, c1, a2, b2, c2, a3, b3, c3] in whole matrix.
Wherein, the algorithm for calculating Euclidean distance can be euclidean (Euclidean distance)
Step 207：Euclidean between the driving behavior sample data of any two driver in the M driver
Distance, the M driver is clustered at least one driver and clustered.
This step can include following two steps, be respectively：
2071：Euclidean between the driving behavior sample data of any two driver in the M driver away from
From structure MxM-1 Euclidean matrixes, often going in the M rows in the Euclidean matrix corresponds to a driver, each column pair in M-1 row
A driver is answered, the Euclidean matrix includes the Euclidean distance between any two driver.
2072：The Euclidean distance of a line corresponding to each driver in the Euclidean matrix is read, according to the driver couple
The Euclidean distance of a line answered, plane point coordinates corresponding to the driver is calculated by default algorithm.
Preset algorithm can be MDS (Multidimensional Scaling, Multidimensional Scaling) algorithm.
2073：According to plane point coordinates corresponding to each driver, by default kmeans algorithms by the M driver
It is clustered into predetermined number driver cluster.
Multiple predetermined numbers, respectively a1, a2 ... an can be included, n is default integer.Kmeans algorithms can export
Silhouette coefficient corresponding to a1 driver cluster and a1, export a2 driver cluster and a2 corresponding to silhouette coefficient ...
Export silhouette coefficient corresponding to an driver's cluster and an.Silhouette coefficient is to assess the effect of cluster, and its value is bigger, table
Classifying quality corresponding to showing is better.Then predetermined number a corresponding to selecting largest contours coefficient, a driver of output is gathered
Class clusters as final driver.
Due to obtaining multiple first sets in step 203, above-mentioned steps 203 to 207 are performed to each first set
Operation obtains driver's cluster.
Step 208：Clustered according to obtained driver, the whole matrix formed with reference to M driver, utilize default point
Class tree algorithm obtains driver's disaggregated model.
Driver's disaggregated model includes multiple driving behavior sample data scopes corresponding to each driver cluster, the plurality of
Driving behavior sample data scope is respectively gas pedal depth scope, brake pedal depth model in the unit interval in the unit interval
Enclose, steering wheel angle angular velocity range, transverse acceleration scope and steering wheel angle angular velocity range etc..
In the present embodiment, after execution of step 207, if getting the sample data set of the M+1 driver
Close；Determine the driving behavior sample where each driving behavior sample data in the sample data sets of the M+1 driver
Data area, driver's cluster according to where the driving behavior sample data scope of determination determines the M+1 driver.
, can be according to the driving of the driver of same cluster because the driving habit of the driver positioned at same cluster is similar
It is accustomed to providing service to the driver positioned at the cluster.For example, it is assumed that the driving habit of each driver of the cluster be drive compared with
For ferociousness, so prompting letter can be sent to driver when each driver of the cluster is travelled in relatively hazardous section
Breath, the prompt message are used to prompt the driver to take care traveling.
In embodiments of the present invention, the sample data sets of M driver are obtained, it is each in the M driver
The sample data sets of driver, the Euclidean distance between any two driver in the M driver is calculated, according to the M
The Euclidean distance between any two driver in individual driver, using Kmeans clustering methods, preset classification number be 2,3,
4th, 5,6,7,8,9,10 class, the silhouette coefficient of different classifications number is then calculated, it is target to choose silhouette coefficient highest classification number
Classification number, classifies so as to realize to driver.
Following is embodiment of the present disclosure, can be used for performing embodiments of the present disclosure.It is real for disclosure device
The details not disclosed in example is applied, refer to embodiments of the present disclosure.
Embodiment 3
Referring to Fig. 3, the embodiments of the invention provide a kind of device 300 for the driver that classifies, described device 300 includes：
Acquisition module 301, for obtaining the sample data sets of M driver, the sample data sets of driver include N
The driving behavior sample data of collection, the driving behavior sample data include the driver-operated car in the individual unit interval
Travel speed, acceleration, transversal displacement in track, operating range in the unit interval, gas pedal is entered in the unit interval
Brake pedal depth, steering wheel angle angular speed, transverse acceleration, vehicle angular speed, unit interval expert in the deep, unit interval
Car distance and leading vehicle distance, the leading vehicle distance are the distance between the vehicle and front truck, and the front truck is in same track
For traveling before the vehicle and the automobile nearest from the vehicle, M and N are respectively the integer of size 1；
Computing module 302, for the sample data sets of each driver in the M driver, calculate institute
The Euclidean distance between the driving behavior sample data of any two driver in M driver is stated, the Euclidean distance is used
In the similar degree of the driving habit of the described two drivers of reflection；
Determining module 303, will for the Euclidean distance between any two driver in the M driver
The M driver is clustered at least one driver's cluster.
Optionally, the computing module 302 includes：
Division unit, for the travel speed and front truck included according to the driving behavior sample data of each driver
Distance, the sample data sets of each driver are divided into multiple first sets respectively；
First computing unit, for the first set according to corresponding to each driver, calculate each driver
Eigenmatrix, the eigenmatrix of driver is used for the driving habit for reacting the driver；
Second computing unit, for the eigenmatrix according to each driver, calculate driving for any two driver
Sail the Euclidean distance between behavior sample data.
Optionally, the division unit includes：
First computation subunit, for each driving behavior sample included according to the sample data sets of the first driver
When travel speed, acceleration and the leading vehicle distance calculating vehicle that data include have travelled the traveling needed for the leading vehicle distance
Between, running time corresponding to each driving behavior sample data is obtained, the first driver is appointing in the M driver
One driver；
First division subelement, for running time to be less than or equal to the driving behavior sample of default very first time threshold value
Data, which are divided into, follows driving to gather, and running time is more than into default very first time threshold value and less than default second time threshold
Driving behavior sample data is divided into the limited driving for driving set, running time being more than or equal to default 3rd time threshold
Behavior sample data, which are divided into, freely drives set.
Optionally, first computing unit includes：
Second division subelement, each driving behavior sample included for the first set according to corresponding to kid
The travel speed that data include, the driving behavior sample data that first set corresponding to the kid includes is divided into
Multiple second sets, the travel speed in each driving behavior sample data in same second set are located at same speed
Section, the kid are any driver in the M driver；
Second computation subunit, for the driving behavior sample data in each second set, calculate described each
Multiple driving behavior values corresponding to second set；
Subelement is formed, for driving behavior value, composition described second corresponding to each second set to be driven
Eigenmatrix corresponding to the person of sailing.
Optionally, second computation subunit performs the driving behavior sample number in each second set of basis
According to, the operation of multiple driving behavior values corresponding to each second set is calculated, including：
Transversal displacement in the track that any driving behavior sample data in second set includes, in the unit interval
Operating range, gas pedal depth, brake pedal depth, steering wheel angle angular speed, transverse direction in the unit interval in the unit interval
Acceleration and vehicle angular speed, calculate multiple characteristic values corresponding to the driving behavior sample data, the multiple characteristic value bag
Include the track lateral shift value and the first ratio in the unit interval between operating range, brake in the unit interval
The second ratio between pedal depth and the unit interval length, gas pedal depth and the unit in the unit interval
The 4th ratio between the 3rd ratio, the transverse acceleration and the vehicle angular speed and the direction between time span
Disk corner angular speed；
First ratio corresponding to each driving behavior sample data included to the second set is ranked up, from sequence
Selection comes the first ratio of target location as a driving behavior value in the first sequence of ratio values afterwards；
Second ratio corresponding to each driving behavior sample data included to the second set is ranked up, from sequence
Selection comes the second ratio of target location as a driving behavior value in the second sequence of ratio values afterwards；
3rd ratio corresponding to each driving behavior sample data included to the second set is ranked up, from sequence
Selection comes the 3rd ratio of target location as a driving behavior value in the 3rd sequence of ratio values afterwards；
4th ratio corresponding to each driving behavior sample data included to the second set is ranked up, from sequence
Selection comes the 4th ratio of target location as a driving behavior value in the 4th sequence of ratio values afterwards；
Steering wheel angle angular speed corresponding to each driving behavior sample data included to the second set is arranged
Sequence, the steering wheel angle angular speed for selecting to come target location from the steering wheel angle angular speed sequence after sequence is as one
Driving behavior value.
In embodiments of the present invention, by obtaining the sample data sets of M driver, according in the M driver
The sample data sets of each driver, calculate the Euclidean distance between any two driver in the M driver, according to
Euclidean distance between the driving behavior sample data of any two driver in the M driver, determines at least one drive
The person of sailing is clustered, and driver is classified so as to realize.
On the device in above-described embodiment, wherein modules perform the concrete mode of operation in relevant this method
Embodiment in be described in detail, explanation will be not set forth in detail herein.
Fig. 4 is a kind of block diagram of the device 400 of classification driver according to an exemplary embodiment.For example, device
400 can be mobile phone, computer, digital broadcast terminal, messaging devices, game console, tablet device, and medical treatment is set
It is standby, body-building equipment, personal digital assistant etc..
Reference picture 4, device 400 can include following one or more assemblies：Processing component 402, memory 404, power supply
Component 406, multimedia groupware 408, audio-frequency assembly 410, the interface 412 of input/output (I/O), sensor cluster 414, and
Communication component 416.
The integrated operation of the usual control device 400 of processing component 402, such as communicated with display, call, data, phase
The operation that machine operates and record operation is associated.Processing component 402 can refer to including one or more processors 420 to perform
Order, to complete all or part of step of above-mentioned method.In addition, processing component 402 can include one or more modules, just
Interaction between processing component 402 and other assemblies.For example, processing component 402 can include multi-media module, it is more to facilitate
Interaction between media component 408 and processing component 402.
Memory 404 is configured as storing various types of data to support the operation in device 400.These data are shown
Example includes the instruction of any application program or method for being operated on device 400, contact data, telephone book data, disappears
Breath, picture, video etc..Memory 404 can be by any kind of volatibility or non-volatile memory device or their group
Close and realize, as static RAM (SRAM), Electrically Erasable Read Only Memory (EEPROM) are erasable to compile
Journey read-only storage (EPROM), programmable read only memory (PROM), read-only storage (ROM), magnetic memory, flash
Device, disk or CD.
Power supply module 406 provides electric power for the various assemblies of device 400.Power supply module 406 can include power management system
System, one or more power supplys, and other components associated with generating, managing and distributing electric power for device 400.
Multimedia groupware 408 is included in the screen of one output interface of offer between described device 400 and user.One
In a little embodiments, screen can include liquid crystal display (LCD) and touch panel (TP).If screen includes touch panel, screen
Curtain may be implemented as touch-screen, to receive the input signal from user.Touch panel includes one or more touch sensings
Device is with the gesture on sensing touch, slip and touch panel.The touch sensor can not only sensing touch or sliding action
Border, but also detect and touched or the related duration and pressure of slide with described.In certain embodiments, more matchmakers
Body component 408 includes a front camera and/or rear camera.When device 400 is in operator scheme, such as screening-mode or
During video mode, front camera and/or rear camera can receive outside multi-medium data.Each front camera and
Rear camera can be a fixed optical lens system or have focusing and optical zoom capabilities.
Audio-frequency assembly 410 is configured as output and/or input audio signal.For example, audio-frequency assembly 410 includes a Mike
Wind (MIC), when device 400 is in operator scheme, during such as call model, logging mode and speech recognition mode, microphone by with
It is set to reception external audio signal.The audio signal received can be further stored in memory 404 or via communication set
Part 416 is sent.In certain embodiments, audio-frequency assembly 410 also includes a loudspeaker, for exports audio signal.
I/O interfaces 412 provide interface between processing component 402 and peripheral interface module, and above-mentioned peripheral interface module can
To be keyboard, click wheel, button etc..These buttons may include but be not limited to：Home button, volume button, start button and lock
Determine button.
Sensor cluster 414 includes one or more sensors, and the state for providing various aspects for device 400 is commented
Estimate.For example, sensor cluster 414 can detect opening/closed mode of device 400, and the relative positioning of component, for example, it is described
Component is the display and keypad of device 400, and sensor cluster 414 can be with 400 1 components of detection means 400 or device
Position change, the existence or non-existence that user contacts with device 400, the orientation of device 400 or acceleration/deceleration and device 400
Temperature change.Sensor cluster 414 can include proximity transducer, be configured to detect in no any physical contact
The presence of neighbouring object.Sensor cluster 414 can also include optical sensor, such as CMOS or ccd image sensor, for into
As being used in application.In certain embodiments, the sensor cluster 414 can also include acceleration transducer, gyro sensors
Device, Magnetic Sensor, pressure sensor or temperature sensor.
Communication component 416 is configured to facilitate the communication of wired or wireless way between device 400 and other equipment.Device
400 can access the wireless network based on communication standard, such as WiFi, 2G or 3G, or combinations thereof.In an exemplary implementation
In example, communication component 416 receives broadcast singal or broadcast related information from external broadcasting management system via broadcast channel.
In one exemplary embodiment, the communication component 416 also includes near-field communication (NFC) module, to promote junction service.Example
Such as, in NFC module radio frequency identification (RFID) technology can be based on, Infrared Data Association (IrDA) technology, ultra wide band (UWB) technology,
Bluetooth (BT) technology and other technologies are realized.
In the exemplary embodiment, device 400 can be believed by one or more application specific integrated circuits (ASIC), numeral
Number processor (DSP), digital signal processing appts (DSPD), PLD (PLD), field programmable gate array
(FPGA), controller, microcontroller, microprocessor or other electronic components are realized, for performing the above method.
In the exemplary embodiment, a kind of non-transitorycomputer readable storage medium including instructing, example are additionally provided
Such as include the memory 404 of instruction, above-mentioned instruction can be performed to complete the above method by the processor 420 of device 400.For example,
The non-transitorycomputer readable storage medium can be ROM, random access memory (RAM), CD-ROM, tape, floppy disk
With optical data storage devices etc..
A kind of non-transitorycomputer readable storage medium, when the instruction in the storage medium is by the processing of device 400
When device performs so that device 400 is able to carry out a kind of method for the driver that classifies, and methods described includes：
The sample data sets of M driver are obtained, the sample data sets of driver include gathering in N number of unit interval
Driving behavior sample data, the travel speed of the driving behavior sample data including the driver-operated vehicle, car
Transversal displacement in road, operating range in the unit interval, gas pedal depth in the unit interval, brake pedal enters in the unit interval
Depth, steering wheel angle angular speed, transverse acceleration, vehicle angular speed, running distance and leading vehicle distance in the unit interval, before described
Car distance is the distance between the vehicle and front truck, and the front truck is traveling in same track before the vehicle and from institute
The nearest automobile of vehicle is stated, M and N are respectively the integer of size 1；
The sample data sets of each driver in the M driver, calculate appointing in the M driver
Euclidean distance between the driving behavior sample data of two drivers of anticipating, the Euclidean distance are used to reflect described two driving
The similar degree of driving habit of member；
Euclidean distance between the driving behavior sample data of any two driver in the M driver,
The M driver is clustered at least one driver's cluster.
Optionally, the sample data sets of each driver in the M driver, the M are calculated
Euclidean distance between the driving behavior sample data of any two driver in driver, including：
Travel speed, acceleration and the leading vehicle distance included according to the driving behavior sample data of each driver,
The sample data sets of each driver are divided into multiple first sets respectively；
According to first set corresponding to each driver, the eigenmatrix of calculating each driver, driver
Eigenmatrix be used to react the driving habit of the driver；
According to the eigenmatrix of each driver, between the driving behavior sample data for calculating any two driver
Euclidean distance.
Optionally, the driving behavior sample data according to each driver includes travel speed and front truck away from
From, the sample data sets of each driver are divided into multiple first sets respectively, including：
The travel speed that each driving behavior sample data included according to the sample data sets of the first driver includes
The running time needed for the leading vehicle distance has been travelled with the leading vehicle distance calculating vehicle, has obtained each driving behavior sample
Running time corresponding to notebook data, the first driver are any driver in the M driver；
Running time is divided into less than or equal to the driving behavior sample data of default very first time threshold value and follows driving
Set, running time is more than default very first time threshold value and drawn less than the driving behavior sample data for presetting the second time threshold
Assign to limited drive to gather, the driving behavior sample data that running time is more than or equal to default 3rd time threshold is divided into
Freely drive set.
Optionally, the first set according to corresponding to each driver, the feature of each driver is calculated
Matrix, including：
The travel speed that each driving behavior sample data that the first set according to corresponding to kid includes includes,
The driving behavior sample data that first set corresponding to the kid includes is divided into multiple second sets, positioned at same
The travel speed in each driving behavior sample data in one second set is located at same speed interval, the kid
For any driver in the M driver；
Driving behavior sample data in each second set, calculate multiple corresponding to each second set drive
Sail behavioural characteristic value；
By driving behavior value corresponding to each second set, feature square corresponding to the kid is formed
Battle array.
Optionally, the driving behavior sample data in each second set of the basis, each second set is calculated
Corresponding multiple driving behavior values, including：
Transversal displacement in the track that any driving behavior sample data in second set includes, in the unit interval
Operating range, gas pedal depth, brake pedal depth, steering wheel angle angular speed, transverse direction in the unit interval in the unit interval
Acceleration and vehicle angular speed, calculate multiple characteristic values corresponding to the driving behavior sample data, the multiple characteristic value bag
Include the track lateral shift value and the first ratio in the unit interval between operating range, brake in the unit interval
The second ratio between pedal depth and the unit interval length, gas pedal depth and the unit in the unit interval
The 4th ratio between the 3rd ratio, the transverse acceleration and the vehicle angular speed and the direction between time span
Disk corner angular speed；
First ratio corresponding to each driving behavior sample data included to the second set is ranked up, from sequence
Selection comes the first ratio of target location as a driving behavior value in the first sequence of ratio values afterwards；
Second ratio corresponding to each driving behavior sample data included to the second set is ranked up, from sequence
Selection comes the second ratio of target location as a driving behavior value in the second sequence of ratio values afterwards；
3rd ratio corresponding to each driving behavior sample data included to the second set is ranked up, from sequence
Selection comes the 3rd ratio of target location as a driving behavior value in the 3rd sequence of ratio values afterwards；
4th ratio corresponding to each driving behavior sample data included to the second set is ranked up, from sequence
Selection comes the 4th ratio of target location as a driving behavior value in the 4th sequence of ratio values afterwards；
Steering wheel angle angular speed corresponding to each driving behavior sample data included to the second set is arranged
Sequence, the steering wheel angle angular speed for selecting to come target location from the steering wheel angle angular speed sequence after sequence is as one
Driving behavior value.
In embodiments of the present invention, by obtaining the sample data sets of M driver, according in the M driver
The sample data sets of each driver, calculate the Euclidean distance between any two driver in the M driver, according to
The Euclidean distance between any two driver in the M driver, at least one driver's cluster is determined, so as to reality
Now driver is classified.
One of ordinary skill in the art will appreciate that hardware can be passed through by realizing all or part of step of above-described embodiment
To complete, by program the hardware of correlation can also be instructed to complete, described program can be stored in a kind of computer-readable
In storage medium, storage medium mentioned above can be read-only storage, disk or CD etc..
The foregoing is only presently preferred embodiments of the present invention, be not intended to limit the invention, it is all the present invention spirit and
Within principle, any modification, equivalent substitution and improvements made etc., it should be included in the scope of the protection.
Claims (10)
- A kind of 1. method for the driver that classifies, it is characterised in that methods described includes：The sample data sets of M driver are obtained, what the sample data sets of driver included gathering in N number of unit interval drives Sail behavior sample data, the driving behavior sample data includes the travel speed of the driver-operated vehicle, in track Transversal displacement, operating range in the unit interval, gas pedal depth in the unit interval, brake pedal depth in the unit interval, Steering wheel angle angular speed, transverse acceleration, vehicle angular speed, running distance and leading vehicle distance in the unit interval, the front truck Distance is the distance between the vehicle and front truck, and the front truck is to be travelled in same track before the vehicle and from described The nearest automobile of vehicle, M and N are respectively the integer of size 1；The sample data sets of each driver in the M driver, calculate any two in the M driver Euclidean distance between the driving behavior sample data of individual driver, the Euclidean distance are used to reflect described two drivers' The similar degree of driving habit；Euclidean distance between the driving behavior sample data of any two driver in the M driver, by institute State M driver and be clustered at least one driver's cluster.
- 2. the method as described in claim 1, it is characterised in that each driver's in the M driver Sample data sets, calculate the Euclidean between the driving behavior sample data of any two driver in the M driver Distance, including：Travel speed, acceleration and the leading vehicle distance included according to the driving behavior sample data of each driver, respectively The sample data sets of each driver are divided into multiple first sets；According to first set corresponding to each driver, the eigenmatrix of calculating each driver, the spy of driver Sign matrix is used for the driving habit for reacting the driver；According to the eigenmatrix of each driver, the Europe between the driving behavior sample data of any two driver is calculated Family name's distance.
- 3. method as claimed in claim 2, it is characterised in that the driving behavior sample number according to each driver According to including travel speed and leading vehicle distance, the sample data sets of each driver are divided into multiple first collection respectively Close, including：The travel speed that each driving behavior sample data included according to the sample data sets of the first driver includes is with before The car distance calculating vehicle has travelled the running time needed for the leading vehicle distance, obtains each driving behavior sample number According to corresponding running time, the first driver is any driver in the M driver；Running time is divided into less than or equal to the driving behavior sample data of default very first time threshold value follows driving to gather, Running time is more than default very first time threshold value and is divided into less than the driving behavior sample data for presetting the second time threshold Limited to drive set, the driving behavior sample data that running time is more than or equal to default 3rd time threshold is divided into freedom Drive set.
- 4. method as claimed in claim 2, it is characterised in that the first set according to corresponding to each driver, The eigenmatrix of each driver is calculated, including：The travel speed that each driving behavior sample data that the first set according to corresponding to kid includes includes, by institute The driving behavior sample data that stating first set corresponding to kid includes is divided into multiple second sets, positioned at same The travel speed in each driving behavior sample data in two set is located at same speed interval, and the kid is institute State any driver in M driver；Driving behavior sample data in each second set, calculate multiple driving rows corresponding to each second set It is characterized value；By driving behavior value corresponding to each second set, eigenmatrix corresponding to the kid is formed.
- 5. method as claimed in claim 4, it is characterised in that the driving behavior sample number in each second set of basis According to, multiple driving behavior values corresponding to each second set are calculated, including：Transversal displacement in the track that any driving behavior sample data in second set includes, travelled in the unit interval Distance, gas pedal depth in the unit interval, brake pedal depth, steering wheel angle angular speed in the unit interval, laterally accelerate Degree and vehicle angular speed, calculate multiple characteristic values corresponding to the driving behavior sample data, the multiple characteristic value includes institute State track lateral shift value and the first ratio in the unit interval between operating range, brake pedal in the unit interval The second ratio between depth and the unit interval length, gas pedal depth and the unit interval in the unit interval The 4th ratio and the steering wheel turn between the 3rd ratio, the transverse acceleration and the vehicle angular speed between length Angle angular speed；First ratio corresponding to each driving behavior sample data included to the second set is ranked up, after sequence Selection comes the first ratio of target location as a driving behavior value in first sequence of ratio values；Second ratio corresponding to each driving behavior sample data included to the second set is ranked up, after sequence Selection comes the second ratio of target location as a driving behavior value in second sequence of ratio values；3rd ratio corresponding to each driving behavior sample data included to the second set is ranked up, after sequence Selection comes the 3rd ratio of target location as a driving behavior value in 3rd sequence of ratio values；4th ratio corresponding to each driving behavior sample data included to the second set is ranked up, after sequence Selection comes the 4th ratio of target location as a driving behavior value in 4th sequence of ratio values；Steering wheel angle angular speed corresponding to each driving behavior sample data included to the second set is ranked up, from Selection comes the steering wheel angle angular speed of target location as a driving in steering wheel angle angular speed sequence after sequence Behavioural characteristic value.
- 6. a kind of device for the driver that classifies, it is characterised in that described device includes：Acquisition module, for obtaining the sample data sets of M driver, the sample data sets of driver include N number of unit The driving behavior sample data of collection, the driving behavior sample data include the row of the driver-operated vehicle in time Sail speed, transversal displacement in track, operating range in the unit interval, gas pedal depth in the unit interval, in the unit interval Brake pedal depth, steering wheel angle angular speed, transverse acceleration, vehicle angular speed, running distance and front truck in the unit interval Distance, the leading vehicle distance are the distance between the vehicle and front truck, and the front truck is to be travelled in same track in the car Before and the automobile nearest from the vehicle, M and N are respectively the integer of size 1；Computing module, for the sample data sets of each driver in the M driver, calculate the M and drive Euclidean distance between the driving behavior sample data of any two driver in the person of sailing, the Euclidean distance are used to reflect institute State the similar degree of the driving habit of two drivers；Determining module, between the driving behavior sample data for any two driver in the M driver Euclidean distance, the M driver is clustered at least one driver and clustered.
- 7. device as claimed in claim 6, it is characterised in that the computing module includes：Division unit, for included according to the driving behavior sample data of each driver travel speed, acceleration and Leading vehicle distance, the sample data sets of each driver are divided into multiple first sets respectively；First computing unit, for the first set according to corresponding to each driver, the spy of calculating each driver Matrix is levied, the eigenmatrix of driver is used for the driving habit for reacting the driver；Second computing unit, for the eigenmatrix according to each driver, the driving row of calculating any two driver Euclidean distance between sample data.
- 8. device as claimed in claim 7, it is characterised in that the division unit includes：First computation subunit, for each driving behavior sample data included according to the sample data sets of the first driver Including travel speed and leading vehicle distance calculate the vehicle and travel running time needed for the leading vehicle distance, obtain described in Running time corresponding to each driving behavior sample data, the first driver are any driver in the M driver；First division subelement, for running time to be less than or equal to the driving behavior sample data of default very first time threshold value It is divided into and follows driving to gather, running time is more than default very first time threshold value and less than the driving of default second time threshold Behavior sample data are divided into the limited driving behavior for driving set, running time being more than or equal to default 3rd time threshold Sample data, which is divided into, freely drives set.
- 9. device as claimed in claim 7, it is characterised in that first computing unit includes：Second division subelement, each driving behavior sample data included for the first set according to corresponding to kid Including travel speed, the driving behavior sample data that first set corresponding to the kid includes is divided into multiple Second set, the travel speed in each driving behavior sample data in same second set are located at same speed area Between, the kid is any driver in the M driver；Second computation subunit, for the driving behavior sample data in each second set, calculate described each second Multiple driving behavior values corresponding to set；Form subelement, for will each driving behavior value corresponding to second set, form the kid Corresponding eigenmatrix.
- 10. device as claimed in claim 9, it is characterised in that second computation subunit performs the basis each the Driving behavior sample data in two set, calculate the behaviour of multiple driving behavior values corresponding to each second set Make, including：Transversal displacement in the track that any driving behavior sample data in second set includes, travelled in the unit interval Distance, gas pedal depth in the unit interval, brake pedal depth, steering wheel angle angular speed in the unit interval, laterally accelerate Degree and vehicle angular speed, calculate multiple characteristic values corresponding to the driving behavior sample data, the multiple characteristic value includes institute State track lateral shift value and the first ratio in the unit interval between operating range, brake pedal in the unit interval The second ratio between depth and the unit interval length, gas pedal depth and the unit interval in the unit interval The 4th ratio and the steering wheel turn between the 3rd ratio, the transverse acceleration and the vehicle angular speed between length Angle angular speed；First ratio corresponding to each driving behavior sample data included to the second set is ranked up, after sequence Selection comes the first ratio of target location as a driving behavior value in first sequence of ratio values；Second ratio corresponding to each driving behavior sample data included to the second set is ranked up, after sequence Selection comes the second ratio of target location as a driving behavior value in second sequence of ratio values；3rd ratio corresponding to each driving behavior sample data included to the second set is ranked up, after sequence Selection comes the 3rd ratio of target location as a driving behavior value in 3rd sequence of ratio values；4th ratio corresponding to each driving behavior sample data included to the second set is ranked up, after sequence Selection comes the 4th ratio of target location as a driving behavior value in 4th sequence of ratio values；Steering wheel angle angular speed corresponding to each driving behavior sample data included to the second set is ranked up, from Selection comes the steering wheel angle angular speed of target location as a driving in steering wheel angle angular speed sequence after sequence Behavioural characteristic value.
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Inventor after: Zhang Weihan Inventor after: Wang Meng Inventor after: Mao Yan Inventor after: Guo Da Inventor after: Di Shengde Inventor before: Zhang Weihan Inventor before: Wang Meng Inventor before: Mao Yan Inventor before: Di Shengde |