CN109711691A - A kind of driving style evaluation method based on entropy weight model of fuzzy synthetic evaluation - Google Patents
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- 238000011156 evaluation Methods 0.000 title claims abstract description 81
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- 238000012512 characterization method Methods 0.000 claims description 64
- 239000011159 matrix material Substances 0.000 claims description 49
- 239000013598 vector Substances 0.000 claims description 9
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Abstract
The invention discloses a kind of driving style evaluation methods based on entropy weight model of fuzzy synthetic evaluation, the present invention carrys out the weight of Calculation Estimation parameter with the value of utility of comentropy reflection data itself, making the determination of weight has certain theoretical foundation, comprehensive weight is determined using the method that entropy weight and subjective weight combine simultaneously, subjective preferences and objective attribute are taken into account, keep result more rationally reliable；The present invention constructs the Driving Scene under different driving cycles on driving simulator, more tally with the actual situation in driving situation, the evaluation of driver's driving style is easier to obtain accurate conclusion；The present invention establishes the evaluation method of driver's driving style based on entropy weight model of fuzzy synthetic evaluation, can accurately distinguish the driving style of driver, can provide certain theories integration for the exploitation of personalized driving assistance system.
Description
Technical field
The invention belongs to mechanical transport fields, and in particular to a kind of driving style based on entropy weight model of fuzzy synthetic evaluation
Evaluation method.
Background technique
The universal trip for making people of automobile is more convenient, has also brought many puzzlements, therefore automobile vendor is
Vehicle is assembled with the automobile electronic systems such as advanced driving assistance system, ACC, and to improve drive safety and comfort, reduction is driven
The driver workload for the person of sailing.But multiplicity that Chinese terrain is complex, different drivers has different drive under different driving cycles
Sailing lattice also have different judgements to the danger level of driving situation.Driving assistance system based on the design of different driving styles
Equal automobile electronic systems can be improved the validity and consumers' acceptable degree of system, while can also preferably control driver couple
The dependence of system.It therefore is to improve the automobile electronic systems such as driving assistance system to have to effective differentiation of driver's driving style
The necessary condition of effect property.
Know through consulting literatures, currently, the evaluation majority of driver's driving style is confined under single driving cycle, and uses
The method of subjective assessment, influence of the evaluation result vulnerable to subjective factor, causes evaluation result deviation occur.
In the patent of Publication No. 106570560A, disclose a kind of based on standardization driving behavior and phase space reconfiguration
Driving style quantitative evaluation method, only evaluated in the method with driving style of the single operating condition to driver,
The operating condition situation of change that will appear in actual moving process is not met.
Summary of the invention
The purpose of the present invention is to overcome the above shortcomings and to provide a kind of driving wind based on entropy weight model of fuzzy synthetic evaluation
Lattice evaluation method, the evaluation method are carried out experiment by constructing different driving cycles on driving simulator, are determined relevant
Characterization parameter, and reflect that the value of utility of data itself carrys out the weight of Calculation Estimation parameter with comentropy, make weight determines tool
Have certain theoretical foundation, while comprehensive weight determined using the method that entropy weight and subjective weight combine, take into account subjective preferences with
Objective attribute keeps result more rationally reliable.
In order to achieve the above object, the present invention the following steps are included:
Step 1 constructs the Driving Scene under different operating conditions on driving simulator, and divides driving cycle type
Class；
Step 2 is tested in the Driving Scene of the different operating conditions constructed in step 1, and installs sensor acquisition additional
The state parameter of vehicle；
Step 3 determines the characterization parameter of driver's driving style according to the state parameter that sensor acquires；
Step 4 determines the objective weight of each characterization parameter using Information Entropy
Step 5 determines the subjective weight W ' of each index parameter using analytic hierarchy process (AHP)；
Step 6, calculate characterization parameter comprehensive weight WB；
Step 7, calculate each driving cycle type comprehensive weight WX；
Step 8 determines the degree of membership evaluations matrix of each characterization parameter under different bend types；
Step 9 establishes entropy weight model of fuzzy synthetic evaluation；
Step 10 determines that driver's driving style is evaluated according to entropy weight model of fuzzy synthetic evaluation.
Different operating conditions include urban road operating condition, backroad operating condition, highway operating condition, mountain road operating condition and town and country
Road condition.
The characterization parameter of driver's driving style includes opposite speed, the lengthwise position of vehicle, the lateral position of vehicle, side
To disk corner angular speed, the steering angular velocity of bend, acceleration, anxious plus/minus speed number and number of overtaking other vehicles.
In step 4, determining the objective weight of each characterization parameter using Information Entropy, the specific method is as follows:
The first step, construction m are evaluated driver, the judgment matrix R of n characterization parameter,
R=(rij)m×n
Wherein, rijThe measured value of j-th of characterization parameter of driver, i=1,2 ..., m are evaluated for i-th；J=1,
2,…,n；
Judgment matrix R is normalized second step, obtains normalizing matrix B, the element of B are as follows:
Wherein, max (ri)、min(ri) it is respectively maximum value and minimum of the same driver under different characterization parameters
Value；
Third step determines the entropy H of characterization parameterj,
Wherein,I=1,2 ..., m；J=1,2 ..., n；0≤Hj≤1；
Work as fijWhen=0, then define
4th step, according to entropy HjDetermine the objective weight of each characterization parameter
Wherein, ∑ HiFor all entropy summations, i=1,2 ..., m；J=1,2 ... n.
In step 5, determining the subjective weight of each index parameter using analytic hierarchy process (AHP), the specific method is as follows:
The first step selects 1-9 scale to establish judgment matrix Q；
Second step carries out consistency check to judgment matrix, wherein coincident indicator CI and consistency ration CR, calculates public
Formula are as follows:
Wherein, λmaxIt is the maximum eigenvalue of judgment matrix；N is judgment matrix order, i.e. the number of characterization parameter；RI is
Aver-age Random Consistency Index；
If CR < 0.1, then it is assumed that the judgment matrix passes through consistency check；
Third step, with the subjective weight W ' for obtaining each characterization parameter with method；
Row vector arithmetic mean of instantaneous value after the n column vector of judgment matrix Q is normalized, approximation are used as weight vectors, it may be assumed that
W '=(ω 'i)1×n
Wherein, qijFor the i-th row jth column element,For the sum of jth column element, i=1,2 ..., n.
In step 6, calculate characterization parameter comprehensive weight WBThe specific method is as follows:
According to objective weightThe comprehensive weight W of each characterization parameter is obtained with subjective weight W 'B:
WB=(ωi)1×n
Wherein, i=1,2 ..., n.
In step 8, determining the degree of membership evaluations matrix of each characterization parameter under different bend types, the specific method is as follows:
Construct a set T:T={ t1,t2,…,t8, element tjIndicate the evaluation index of different characteristic parameter, wherein j
=1~8, the ratio value of total amount of data is accounted for as evaluation index t using the data volume for being greater than characterization parameter weighted averagej, adopt
It uses normal distribution as Fuzzy Distribution, obtains the degree of membership evaluations matrix of each characterization parameter under different driving cycle types:
AX=(AXi)1×5
Wherein, AXiX is taken for driving cycle typeiWhen the position the m driver degree of membership under 8 kinds of characterization parameters respectively, i=
1~5；Subordinated-degree matrix under 5 kinds of driving cycle types forms AX；Matrix AXiMiddle elementMiddle i is driving cycle type volume
Number i=1~5；J is characterization parameter number j=1~8, and k is driver's number k=1~m.
In step 9, the method for establishing entropy weight model of fuzzy synthetic evaluation is as follows:
The model of fuzzy synthetic evaluation of driver's driving style evaluation is the conjunction of comprehensive weight W and degree of membership evaluations matrix R
At operation, i.e., according to the degree of membership evaluations matrix A of each characterization parameter under different driving cycle typesXi, 8 kinds of characterization parameters synthesis
Weight WBAnd the comprehensive weight W of 5 kinds of driving cycle typesX, obtain driving style Comprehensis pertaining A (t) square of m drivers
Battle array, the comprehensive evaluation index as driver:
Compared with prior art, the present invention carrys out the power of Calculation Estimation parameter with the value of utility of comentropy reflection data itself
Weight makes the determination of weight have certain theoretical foundation, while determining synthetic weights using the method that entropy weight and subjective weight combine
Weight, takes into account subjective preferences and objective attribute, keeps result more rationally reliable；The present invention is constructed on driving simulator and is not gone together
Sail the Driving Scene under operating condition, more tally with the actual situation in driving situation, the evaluation of driver's driving style is easier to obtain
Accurate conclusion；The present invention establishes the evaluation method of driver's driving style based on entropy weight model of fuzzy synthetic evaluation, energy
Enough driving styles for accurately distinguishing driver can provide certain theoretical branch for the exploitation of personalized driving assistance system
It holds.
Detailed description of the invention
Fig. 1 is flow chart of the invention；
Fig. 2 is the flow chart that entropy weight model of fuzzy synthetic evaluation is established in the present invention.
Specific embodiment
The present invention will be further described with reference to the accompanying drawing.
Referring to Fig. 1, the present invention the following steps are included:
Step 1: it requires to construct the Driving Scene under different operating conditions on driving simulator according to evaluation method, and to row
Operating condition type is sailed to classify；
Step 2: the scene constructed by step 1 is tested, and installs the correlation of necessary sensor acquisition vehicle additional
State parameter；
Step 3: the vehicle status parameters acquired from step 2 determine the characterization parameter of driver's driving style；
Step 4: the comprehensive weight of each parameter in step 3 is determined using entropy weight model of fuzzy synthetic evaluation, determines driver
The evaluation method of driving style.
Driving Scene under different driving cycles includes urban road operating condition, backroad operating condition, highway operating condition, mountain
Area's road condition, urban and suburban roads operating condition；
Driving simulator installs the correlated condition parameter of necessary sensor acquisition vehicle additional；
The characterization parameter of driver's driving style include: opposite speed, the lengthwise position of vehicle, vehicle lateral position,
Steering wheel angle angular speed, the steering angular velocity of bend, acceleration, anxious plus/minus speed number, number of overtaking other vehicles；
Referring to fig. 2, in step 4, the comprehensive weight of each parameter in step 3 is determined using entropy weight model of fuzzy synthetic evaluation
The specific method is as follows:
S1: Information Entropy determines the objective weight of each characterization parameter:
S1.1: construction m are evaluated driver, the judgment matrix R of n characterization parameter:
R=(rij)m×n: (1)
Wherein rijFor be evaluated for i-th driver j-th of characterization parameter measured value (i=1,2 ..., m；J=1,
2,…,n)。
S1.2: judgment matrix R is normalized, and obtains normalizing matrix B, the element of B are as follows:
In formula: max (ri)、min(ri) it is respectively maximum value and minimum of the same driver under different characterization parameters
Value.
S1.3: the entropy H of characterization parameter is determinedj:
In formula:0≤Hj≤1.Work as fij=0, then it defines
S1.4: according to entropy HjDetermine the objective weight of each characterization parameter
S2: analytic hierarchy process (AHP) determines the subjective weight of each index parameter；
S2.1: 1-9 scale is selected to establish judgment matrix Q；
2 1~9 Scale Method table of table
S2.2: consistency check is carried out to judgment matrix, wherein coincident indicator CI and consistency ration CR, calculation formula
Are as follows:
In formula: λmaxIt is the maximum eigenvalue of judgment matrix；N is judgment matrix order, i.e. the number of characterization parameter；RI is
Aver-age Random Consistency Index can table look-up.If CR < 0.1, then it is assumed that the judgment matrix passes through consistency check.
S2.3: with the subjective weight W ' for obtaining each characterization parameter with method:
Row vector arithmetic mean of instantaneous value after the n column vector of judgment matrix Q is normalized, approximation are used as weight vectors, i.e.,
W '=(ω 'i)1×n (8)
S3: calculate characterization parameter comprehensive weight WB:
Resulting objective weight is calculated according to steps 1 and 2Subjective weight W ' obtains the comprehensive weight W of each characterization parameterB:
WB=(ωi)1×n (10)
S4: according to the formula in S1, S2, S3 calculate each driving cycle type comprehensive weight WX。
S5: the degree of membership evaluations matrix of each characterization parameter under different bend types is determined:
Construct a set T:T={ t1,t2,…,t8, element tj(j=1~8) indicate that the evaluation of different characteristic parameter refers to
Mark.The present invention uses the data volume greater than characterization parameter weighted average to account for the ratio value of total amount of data as evaluation index tj,
Using normal distribution as Fuzzy Distribution, specific formula be see the table below.
3 normal fuzzy distribution of table
The degree of membership evaluations matrix of each characterization parameter under different driving cycle types is obtained according to table 3:
AX=(AXi)1×5 (11)
In formula, AXiX is taken for driving cycle typeiThe position m driver when (i=1~5) is respectively under 8 kinds of characterization parameters
Degree of membership；Subordinated-degree matrix under 5 kinds of driving cycle types forms AX；Matrix AXiMiddle elementUpper and lower mark: i be traveling
Operating condition type number (i=1~5)；J is that characterization parameter numbers (j=1~8), and k is that driver numbers (k=1~m).
S6: entropy weight model of fuzzy synthetic evaluation is established:
The model of fuzzy synthetic evaluation of driver's driving style evaluation is the conjunction of comprehensive weight W and degree of membership evaluations matrix R
At operation, i.e., according to the degree of membership evaluations matrix A of each characterization parameter under different driving cycle typesXi, 8 kinds of characterization parameters synthesis
Weight WBAnd the comprehensive weight W of 5 kinds of driving cycle typesX, the driving style Comprehensis pertaining A of available m driver
(t) matrix, the comprehensive evaluation index as driver:
S7: driver's driving style evaluation method is determined
Driver is divided into cautious style, radical by the comprehensive evaluation index that resulting driver's driving style is calculated according to S6
Type and mild three classes.Conservative, the few driver for anxious plus/minus speed occur, overtaking other vehicles too cautious when driving is defined as with caution
Type (comprehensive evaluation index is 0≤A (t)≤0.3)；It is fast, super to will appear anxious plus/minus when more careful, situation allows when will drive
The driver of vehicle is defined as mild (comprehensive evaluation index is 0.3≤A (t)≤0.7)；It is more radical when will be driven when will drive
Crudity, the anxious plus/minus speed for having more number in driving process, number of overtaking other vehicles driver be defined as radical type (overall merit refer to
Number is 0.7≤A (t)≤1).
Claims (8)
1. a kind of driving style evaluation method based on entropy weight model of fuzzy synthetic evaluation, which comprises the following steps:
Step 1 constructs the Driving Scene under different operating conditions on driving simulator, and classifies to driving cycle type；
Step 2 is tested in the Driving Scene of the different operating conditions constructed in step 1, and installs sensor acquisition vehicle additional
State parameter；
Step 3 determines the characterization parameter of driver's driving style according to the state parameter that sensor acquires；
Step 4 determines the objective weight W of each characterization parameter using Information Entropyj *；
Step 5 determines the subjective weight W ' of each index parameter using analytic hierarchy process (AHP)；
Step 6, calculate characterization parameter comprehensive weight WB；
Step 7, calculate each driving cycle type comprehensive weight WX；
Step 8 determines the degree of membership evaluations matrix of each characterization parameter under different bend types；
Step 9 establishes entropy weight model of fuzzy synthetic evaluation；
Step 10 determines that driver's driving style is evaluated according to entropy weight model of fuzzy synthetic evaluation.
2. a kind of driving style evaluation method based on entropy weight model of fuzzy synthetic evaluation according to claim 1, special
Sign is that different operating conditions include urban road operating condition, backroad operating condition, highway operating condition, mountain road operating condition and town and country
Road condition.
3. a kind of driving style evaluation method based on entropy weight model of fuzzy synthetic evaluation according to claim 1, special
Sign is that the characterization parameter of driver's driving style includes opposite speed, the lengthwise position of vehicle, the lateral position of vehicle, side
To disk corner angular speed, the steering angular velocity of bend, acceleration, anxious plus/minus speed number and number of overtaking other vehicles.
4. a kind of driving style evaluation method based on entropy weight model of fuzzy synthetic evaluation according to claim 1, special
Sign is, in step 4, determining the objective weight of each characterization parameter using Information Entropy, the specific method is as follows:
The first step, construction m are evaluated driver, the judgment matrix R of n characterization parameter,
R=(rij)m×n
Wherein, rijThe measured value of j-th of characterization parameter of driver, i=1,2 ..., m are evaluated for i-th；J=1,2 ...,
n；
Judgment matrix R is normalized second step, obtains normalizing matrix B, the element of B are as follows:
Wherein, max (ri)、min(ri) it is respectively maximum value and minimum value of the same driver under different characterization parameters；
Third step determines the entropy H of characterization parameterj,
Wherein,I=1,2 ..., m；J=1,2 ..., n；0≤Hj≤1；
Work as fijWhen=0, then define
4th step, according to entropy HjDetermine the objective weight W of each characterization parameterj *,
Wherein, ∑ HiFor all entropy summations, i=1,2 ..., m；J=1,2 ... n.
5. a kind of driving style evaluation method based on entropy weight model of fuzzy synthetic evaluation according to claim 1, special
Sign is, in step 5, determining the subjective weight of each index parameter using analytic hierarchy process (AHP), the specific method is as follows:
The first step selects 1-9 scale to establish judgment matrix Q；
Second step carries out consistency check to judgment matrix, wherein coincident indicator CI and consistency ration CR, calculation formula
Are as follows:
Wherein, λmaxIt is the maximum eigenvalue of judgment matrix；N is judgment matrix order, i.e. the number of characterization parameter；RI is average
Random index；
If CR < 0.1, then it is assumed that the judgment matrix passes through consistency check；
Third step, with the subjective weight W ' for obtaining each characterization parameter with method；
Row vector arithmetic mean of instantaneous value after the n column vector of judgment matrix Q is normalized, approximation are used as weight vectors, it may be assumed that
W '=(ωi′)1×n
Wherein, qijFor the i-th row jth column element,For the sum of jth column element, i=1,2 ..., n.
6. a kind of driving style evaluation method based on entropy weight model of fuzzy synthetic evaluation according to claim 1, special
Sign is, in step 6, calculate characterization parameter comprehensive weight WBThe specific method is as follows:
According to objective weight Wj *The comprehensive weight W of each characterization parameter is obtained with subjective weight W 'B:
WB=(ωi)1×n
Wherein, i=1,2 ..., n.
7. a kind of driving style evaluation method based on entropy weight model of fuzzy synthetic evaluation according to claim 1, special
Sign is, in step 8, determining the degree of membership evaluations matrix of each characterization parameter under different bend types, the specific method is as follows:
Construct a set T:T={ t1,t2,…,t8, element tjIndicate the evaluation index of different characteristic parameter, wherein j=1~
8, the ratio value of total amount of data is accounted for as evaluation index t using the data volume for being greater than characterization parameter weighted averagej, using normal state
Distribution is used as Fuzzy Distribution, obtains the degree of membership evaluations matrix of each characterization parameter under different driving cycle types:
AX=(AXi)1×5
Wherein, AXiX is taken for driving cycle typeiWhen the position the m driver degree of membership under 8 kinds of characterization parameters respectively, i=1~
5；Subordinated-degree matrix under 5 kinds of driving cycle types forms AX；Matrix AXiMiddle elementMiddle i is driving cycle type number i
=1~5；J is characterization parameter number j=1~8, and k is driver's number k=1~m.
8. a kind of driving style evaluation method based on entropy weight model of fuzzy synthetic evaluation according to claim 1, special
Sign is, in step 9, the method for establishing entropy weight model of fuzzy synthetic evaluation is as follows:
The synthesis that the model of fuzzy synthetic evaluation of driver's driving style evaluation is comprehensive weight W and degree of membership evaluations matrix R is transported
It calculates, i.e., according to the degree of membership evaluations matrix A of each characterization parameter under different driving cycle typesXi, 8 kinds of characterization parameters comprehensive weight
WBAnd the comprehensive weight W of 5 kinds of driving cycle typesX, driving style Comprehensis pertaining A (t) matrix of m drivers is obtained, is made
For the comprehensive evaluation index of driver:
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| CN110412556A (en) * | 2019-07-31 | 2019-11-05 | 吉林大学 | A kind of vehicle running state appraisal procedure based on the frequency of overtaking other vehicles |
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| CN110412556A (en) * | 2019-07-31 | 2019-11-05 | 吉林大学 | A kind of vehicle running state appraisal procedure based on the frequency of overtaking other vehicles |
| CN110599011A (en) * | 2019-08-29 | 2019-12-20 | 广东省水利水电科学研究院 | Aqueduct safety evaluation method, system and storage medium based on improved AHP-FCE method |
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| CN113642832A (en) * | 2021-06-29 | 2021-11-12 | 桂林电子科技大学 | Method and system for evaluating driving behavior of commercial vehicle |
| CN113657716A (en) * | 2021-07-16 | 2021-11-16 | 长安大学 | Comprehensive evaluation method for safety of driver driving behavior based on entropy weight method |
| CN113657716B (en) * | 2021-07-16 | 2024-03-05 | 长安大学 | Comprehensive evaluation method for driving behavior safety of driver based on entropy weight method |
| CN113297685A (en) * | 2021-07-27 | 2021-08-24 | 中汽研（天津）汽车工程研究院有限公司 | Vehicle operation condition mode identification method |
| CN113867536A (en) * | 2021-10-12 | 2021-12-31 | 浙江数智交院科技股份有限公司 | Interaction mode determination method, data acquisition method and device and electronic equipment |
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