CN110705854A - Driving level evaluation method and system - Google Patents

Driving level evaluation method and system Download PDF

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CN110705854A
CN110705854A CN201910893483.5A CN201910893483A CN110705854A CN 110705854 A CN110705854 A CN 110705854A CN 201910893483 A CN201910893483 A CN 201910893483A CN 110705854 A CN110705854 A CN 110705854A
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高洪波
李智军
刘康
储晓丽
郝正源
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University of Science and Technology of China USTC
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Abstract

The invention discloses a driving level evaluation method and a driving level evaluation system, which belong to the technical field of intelligent driving. When the real-time driving data of a driver with unknown level is acquired, a corresponding level evaluation cloud picture is constructed and compared with the cloud pictures with various driving levels, so that the real driving level of the driver with unknown level can be determined. The scheme can process uncertainty of the driving level and evaluate the driving level of the driver.

Description

Driving level evaluation method and system
Technical Field
The invention relates to the technical field of intelligent driving, in particular to a driving level evaluation method and system.
Background
The intelligent driving technology relates to various disciplines such as information engineering, control science and engineering, computer science, mechanical engineering, mathematical science, life science and the like, and is an important mark for measuring the national scientific research strength and the industrial level. The intelligent driving changes the traditional vehicle driving mode fundamentally and frees the driver from a 'vehicle-road-person' closed loop system. The method controls the vehicle to run by utilizing advanced electronic and information technology, so that the conventional, lasting and fatigue operation in the driving activity is automatically completed, and people only do high-level objective operation, thereby greatly improving the efficiency and the safety of a traffic system and having wide application prospect. Meanwhile, the research of the intelligent driving technology can greatly enhance the core competitiveness of China in the aspect of automobile active safety systems, and has great strategic significance for improving the independent innovation capacity of automobile electronic products and automobile industries in China.
However, due to different driving levels, drivers with different driving levels have different road resource occupation conditions and different congestion influences, so that the driving levels of the drivers need to be evaluated to provide a basis for researching the relationship between the road right and the urban congestion, and the problem of urban traffic congestion is solved to the greatest extent.
Disclosure of Invention
The invention aims to overcome the defects of the prior art, so as to evaluate the driving level of a driver and solve the problem of urban traffic jam to the greatest extent.
In order to achieve the above object, the present invention employs a driving level assessment method, comprising the steps of:
s100, classifying the driving states of the driver, and setting evaluation indexes of various driving states;
s200, obtaining an evaluation value of each driving state based on an expert evaluation method;
s300, inputting the evaluation value of each driving state under the same driving level into a reverse cloud generator, and calculating the characterization number of the cloud model in each driving state;
s400, inputting the characterization number of the cloud model in each driving state under the same driving level into a forward cloud generator, obtaining the quantitative position of the cloud droplets of the driving level in a number domain space and the certainty degree of a qualitative concept, and constructing an evaluation cloud picture of the driving level;
s500, repeatedly executing the steps S200-S400 to obtain evaluation cloud pictures corresponding to different driving levels;
s600, acquiring real-time driving data of a driver to be evaluated, executing the steps S200-S400, and acquiring a horizontal evaluation cloud picture of the driver to be evaluated;
s700, comparing the level evaluation cloud picture of the driver to be evaluated with the evaluation cloud pictures corresponding to different driving levels, and determining the driving level of the driver to be evaluated.
Further, the inputting the evaluation value of each driving state under the same driving level into the inverse cloud generator, and calculating the characterization number of the cloud model in each driving state comprises:
acquiring evaluation values of N drivers with the same driving state at the same driving level as cloud droplets corresponding to the driving state;
respectively calculating the mean value of the cloud droplets, the variance of the cloud droplets and the entropy of the cloud droplets according to the cloud droplets corresponding to the driving state;
calculating the super entropy of the cloud droplets according to the variance of the cloud droplets and the entropy of the cloud droplets;
and taking the mean value of the cloud droplets, the entropy of the cloud droplets and the super entropy of the cloud droplets as the characterization numbers of the cloud model corresponding to the driving state.
Further, the step of inputting the characterization number of the cloud model in each driving state under the same driving level into the forward cloud generator to obtain the quantitative position of the cloud droplets of the driving level in the number domain space and the certainty degree of the qualitative concept, and constructing the evaluation cloud picture of the driving level comprises the following steps:
s401, establishing a comprehensive cloud model A (E) for evaluating the driving level according to the characterization number of the cloud model in each driving state under the same driving levelx,En,He) The characterization numbers of the comprehensive cloud model are respectively expected values ExEntropy EnAnd entropy He；
S402, according to the expected value ExEntropy EnAnd entropy HeAnd a given cloud drop number N, to obtain an average value ExSign, signTolerance of HeNormal random number of
Figure BDA0002209515460000021
And a mean value of EnStandard deviation of
Figure BDA0002209515460000031
The normal random number x of (a);
s403, calculating
Figure BDA0002209515460000032
Let x be a specific quantization value of the qualitative concept and let y be the certainty of x;
s404, repeatedly executing the steps S402-S403 until N cloud droplets are generated;
s405, outputting the quantitative positions of the cloud droplets of N drivers at the same level in a number domain space and the certainty degree (x, y) of a qualitative concept;
s406, obtaining the quantitative position of the cloud droplets of the driving level in the number domain space and the certainty degree of the qualitative concept, and constructing an evaluation cloud picture of the driving level.
Further, the comparing the level evaluation cloud picture of the driver to be evaluated with the evaluation cloud pictures corresponding to the different driving levels to determine the driving level of the driver to be evaluated includes:
calculating the similarity between the level evaluation cloud picture of the driver to be evaluated and the evaluation cloud pictures corresponding to different driving levels;
and taking the driving level corresponding to the evaluation cloud picture with the maximum similarity as the driving level of the driver to be evaluated.
Further, the driving state of the driver comprises a violation condition, a driving direction and a long-term state;
the evaluation indexes of the violation conditions comprise the passing conditions specified by traffic lights, the running conditions at specified speed and the running conditions at specified lanes;
the evaluation indexes of the running direction comprise a lateral deviation condition and a direction deviation condition;
the evaluation indexes of the long-term state comprise driving emotion stability, running speed judgment capability, emergency handling response capability and time and space judgment capability of different driving environments.
Further, the cloud models corresponding to the violation condition, the driving direction and the long-term state are respectively A1＝(E1x,E1n,H1e)，A2＝(E2x,E2n,H2e) And A3＝(E3x,E3n,H3e) The establishing of the comprehensive cloud model for evaluating the driving level according to the characterization number of the cloud model in each driving state under the same driving level comprises the following steps:
suppose E1x≤E2xIf, if
Figure BDA0002209515460000033
Then compute cloud model a' ═ a1∪A2＝A2；
If it is not
Figure BDA0002209515460000034
Then a new cloud model a' ═ a is computed1∪A2＝A1；
By new cloud models A' and A3Of the current driving level, resulting in an integrated cloud model a ═ (E) for the driving behavior assessment of the driver at the current driving levelx,En,He)。
In another aspect, a driving level evaluation system is employed, including: the cloud model evaluation system comprises a classification module, an evaluation value calculation module, a cloud model calculation module, a first evaluation cloud picture construction module, a second evaluation cloud picture construction module and a comparison module;
the classification module is used for classifying the driving states of drivers with different driving levels and setting evaluation indexes of various driving states;
the evaluation value calculation module is used for obtaining a weight coefficient of the influence degree of the evaluation index on the driving state based on an expert evaluation method and obtaining an evaluation value of each driving state;
the cloud model calculation module is used for inputting the evaluation value of each driving state under the same driving level into the reverse cloud generator and calculating the characterization number of the cloud model in each driving state;
the first evaluation cloud picture construction module is used for inputting the characterization number of the cloud model in each driving state under the same driving level into the forward cloud generator, obtaining the quantitative position of cloud droplets of the driving level in a number domain space and the certainty degree of a qualitative concept, and constructing the evaluation cloud picture of the driving level so as to obtain the evaluation cloud pictures corresponding to different driving levels;
the second evaluation cloud picture construction module is used for acquiring real-time driving data of the driver to be evaluated and acquiring a level evaluation cloud picture of the driver to be evaluated;
the comparison module is used for comparing the level evaluation cloud picture of the driver to be evaluated with the evaluation cloud pictures corresponding to different driving levels to determine the driving level of the driver to be evaluated.
Further, the cloud model computing module comprises a cloud droplet obtaining unit, a parameter computing unit, a super-entropy computing unit and a cloud model computing unit;
the cloud droplet acquisition unit is used for acquiring the evaluation values of N drivers with the same driving level in the same driving state as the cloud droplets corresponding to the driving state;
the parameter calculation unit is used for respectively calculating the mean value of the cloud droplets, the variance of the cloud droplets and the entropy of the cloud droplets according to the cloud droplets corresponding to the driving state;
the super-entropy calculation unit is used for calculating the super-entropy of the cloud droplets according to the variance of the cloud droplets and the entropy of the cloud droplets;
and the cloud model computing unit is used for constructing a cloud model by taking the mean value of the cloud droplets, the entropy of the cloud droplets and the super entropy of the cloud droplets as the representation numbers of the cloud model corresponding to the driving state.
Further, the first evaluation cloud picture construction module comprises a comprehensive cloud model construction unit, a normal random number calculation unit, a quantitative and qualitative unit, a certainty factor calculation unit and an evaluation cloud picture construction unit;
the comprehensive cloud model construction unit is used for constructing the characterization number of the cloud model in each driving state according to the same driving levelEstablishing a comprehensive cloud model a ═ (E) for evaluating the driving levelx,En,He) The characterization numbers of the comprehensive cloud model are respectively expected values ExEntropy EnAnd entropy He；
A normal random number calculation unit for calculating a normal random number according to the expected value ExEntropy EnAnd entropy HeAnd a given cloud drop number N to obtain a normal random number
Figure BDA0002209515460000051
Normal random number
Figure BDA0002209515460000052
Has a mean value of ExStandard deviation of HeAnd a normal random number x whose mean value is EnStandard deviation of
Figure BDA0002209515460000053
The quantitative and qualitative unit is used for calculatingLet x be a specific quantization value of the qualitative concept and let y be the certainty of x;
the certainty factor calculation unit is used for outputting the quantitative positions of the cloud drips of the N drivers at the same level in the number domain space and the certainty factors (x, y) of the qualitative concepts when the N cloud drips are generated;
the evaluation cloud picture construction unit is used for constructing the evaluation cloud picture of the driving level according to the quantitative position of the cloud drops of the driving level in the number domain space and the certainty degree of the qualitative concept.
Further, the comparison module comprises a similarity calculation unit and a comparison unit;
the similarity calculation unit is used for calculating the similarity between the level evaluation cloud picture of the driver to be evaluated and the evaluation cloud pictures corresponding to different driving levels;
the comparison unit is used for comparing the calculated similarity, and taking the driving level corresponding to the evaluation cloud picture with the maximum similarity as the driving level of the driver to be evaluated.
Compared with the prior art, the invention has the following technical effects: the driving states of drivers with different driving levels are classified, the cloud model of each driving state is calculated by using the reverse cloud generator, and the evaluation cloud picture corresponding to the driving level is constructed according to the cloud models of various driving states under the same driving level. When the real-time driving data of a driver with unknown level is acquired, a corresponding level evaluation cloud picture is constructed and compared with the cloud pictures with various driving levels, so that the real driving level of the driver with unknown level can be determined. The scheme can process uncertainty of the driving level and evaluate the driving level of the driver.
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The following detailed description of embodiments of the invention refers to the accompanying drawings in which:
FIG. 1 is a schematic flow diagram of a driving level assessment method;
FIG. 2 is a diagram of an intelligent vehicle driving behavior assessment architecture;
FIG. 3 is a schematic view of driving state characterizing parameters;
fig. 4 is a flowchart of a driving level evaluation system.
Detailed Description
To further illustrate the features of the present invention, refer to the following detailed description of the invention and the accompanying drawings. The drawings are for reference and illustration purposes only and are not intended to limit the scope of the present disclosure.
As shown in fig. 1, the present embodiment discloses a driving level evaluation method, including the following steps S100 to S700:
s100, classifying the driving states of the driver, and setting evaluation indexes of various driving states;
s200, obtaining an evaluation value of each driving state based on an expert evaluation method;
s300, inputting the evaluation value of each driving state under the same driving level into a reverse cloud generator, and calculating the characterization number of the cloud model in each driving state;
s400, inputting the characterization number of the cloud model in each driving state under the same driving level into a forward cloud generator, obtaining the quantitative position of the cloud droplets of the driving level in a number domain space and the certainty degree of a qualitative concept, and constructing an evaluation cloud picture of the driving level;
s500, repeatedly executing the steps S200-S400 to obtain evaluation cloud pictures corresponding to different driving levels;
s600, acquiring real-time driving data of a driver to be evaluated, executing the steps S200-S400, and acquiring a horizontal evaluation cloud picture of the driver to be evaluated;
s700, comparing the level evaluation cloud picture of the driver to be evaluated with the evaluation cloud pictures corresponding to different driving levels, and determining the driving level of the driver to be evaluated.
The driving level of the driver includes a normal driving level, a low driving level (vegetable bird), a high driving level (car racing driver), and the like, as shown in fig. 2, in this embodiment, by recording a video of a large number of roads when the actual vehicle is running, and by researching the driving actions and psychological characteristics analysis of different drivers, the driving status of the driver can be classified into: violation condition, driving orientation and long-term status. The evaluation indexes of each type of driving state are respectively as follows:
(1) violation condition: the traffic regulation compliance of the driver in the driving process is represented and classified into non-violation, general violation and serious violation. The index reflects the driver's ability to master rules and regulations and driver's ability to be quality. The traffic violation conditions of different drivers during driving can be obtained through the assistance of the electronic eyes at the intersection, the vehicle-mounted camera and the traffic police department. The evaluation index includes: (1-1) specifying the passing condition according to a traffic signal lamp; (1-2) driving at a prescribed speed; and (1-3) driving according to the specified lane.
(2) Driving direction: the display shows the habitual relative position of the vehicle and the center of the road during driving, and is classified into right-left or left-right running, and left-right shaking running. The index reflects the driving habit of the driver, and the driver should drive in the middle of the road if the driving habit is good. And detecting the left lane line and the right lane line of the current lane of the intelligent vehicle on the current driving road by using a lane line detection method or a road detection method through the road information acquired by the actual vehicle information acquisition system.
As shown in fig. 3, the current driving direction and the center deviation degree of the intelligent vehicle are evaluated by using two characterization parameters of lateral deviation and direction deviation, and the specific calculation formula is as follows: lateral deviation-right lateral deviation-left lateral deviation; the azimuth offset is right azimuth offset-left azimuth offset. The evaluation index includes: (2-1) a lateral deviation condition; (2-2) an azimuth deviation condition.
(3) And (3) long-term state: the degree of retention of the driving state of the driver during continuous driving is represented by good retention, normal retention, and poor retention. The index reflects the degree of driving proficiency and the change in the psychological state while driving. The data can be analyzed through psychological tests, interviews and vehicle monitoring records to obtain the state conditions of different drivers after long-term driving. The evaluation index includes: (3-1) driving emotional stability; (3-2) operation speed judgment capability; (3-3) handling emergency response capabilities; and (3-4) space-time judgment capability of different driving environments.
It should be noted that each evaluation index set in the present embodiment is not connected to each other. Each refined evaluation index has a corresponding and direct qualitative protocol according to a specific evaluation standard.
It should be understood that the principles of the present embodiment are not limited to the above-mentioned proposed driving state index classifications and their evaluation indexes, and may include all reasonable index classifications and evaluation indexes for analyzing the driving state.
Further, the step S200: based on an expert evaluation method, in obtaining the evaluation value of each driving state, taking the violation condition in the driving state as an example, the weight coefficient w of each driving indexiIs defined as:
Figure BDA0002209515460000081
wherein, based on synthesisAnalyzing the importance of each index by mechanism, and determining the quality of each evaluation index in the violation condition as Mi. Setting an ideal value for the index value of the evaluation targetCalculating an index value of x ═ x1,x2,x3) And obtaining the evaluation value of the traffic violation in the driving state of the jth driver according to the Euclidean distance from the ideal value. The specific evaluation formula is as follows:
Figure BDA0002209515460000083
j is 1,2, N, which represents the j-th driver of the N drivers with the same driving level, i is 1,2,3 which represents the traffic condition of the evaluation index (1-1) of the violation condition according to the traffic signal light regulation; (1-2) driving at a prescribed speed; and (1-3) driving according to the specified lane. Each evaluation index "quality" MiAnd ideal value
Figure BDA0002209515460000084
It may be set based on an expert evaluation method.
Similarly, taking the violation in the driving state as an example, the step S300: inputting the evaluation value of each driving state under the same driving level into a reverse cloud generator, and calculating the characterization number of the cloud model in each driving state, wherein the method specifically comprises the following steps:
obtaining evaluation values Y of N drivers in the same driving state at the same driving level1＝(Y11,Y12,...,Y1NB), as the cloud droplet corresponding to the driving state;
calculating the cloud drop average value, wherein the formula is as follows:
Figure BDA0002209515460000091
calculating cloud drop variance, and the formula is as follows:
Figure BDA0002209515460000092
calculating the entropy of the cloud drop, and the formula is as follows:
Figure BDA0002209515460000093
calculating the super entropy of the cloud drop, wherein the formula is as follows:
Figure BDA0002209515460000094
outputting digital characteristics A of cloud droplets1＝(E1x,E1n,H1e)。
According to the same way, the digital characteristics of the cloud model of the driving direction and the long-term state in the driving state are obtained, and A is respectively2＝(E2x,E2n,H2e) And A3＝(E3x,E3n,H3e)。
Further, the step S400: inputting the characterization number of the cloud model in each driving state under the same driving level into a forward cloud generator, obtaining the quantitative position of cloud droplets of the driving level in a number domain space and the certainty degree of a qualitative concept, and constructing an evaluation cloud picture of the driving level, wherein the evaluation cloud picture comprises the following steps S401-S406:
s401, establishing a comprehensive cloud model A (E) for evaluating the driving level according to the characterization number of the cloud model in each driving state under the same driving levelx,En,He) The characterization numbers of the comprehensive cloud model are respectively expected values ExEntropy EnAnd entropy He；
S402, according to the expected value ExEntropy EnAnd entropy HeAnd a given cloud drop number N, to obtain an average value ExStandard deviation of HeNormal random number of
Figure BDA0002209515460000095
And a mean value of EnStandard deviation of
Figure BDA0002209515460000096
The normal random number x of (a);
s403, calculating
Figure BDA0002209515460000097
Let x be a specific quantization value of the qualitative concept and let y be the certainty of x;
s404, repeatedly executing the steps S402-S403 until N cloud droplets are generated;
s405, outputting the quantitative positions of the cloud droplets of N drivers at the same level in a number domain space and the certainty degree (x, y) of a qualitative concept;
s406, obtaining the quantitative position of the cloud droplets of the driving level in the number domain space and the certainty degree of the qualitative concept, and constructing an evaluation cloud picture of the driving level.
Specifically, step S401: establishing a comprehensive cloud model for evaluating the driving level according to the characterization number of the cloud model in each driving state under the same driving level, specifically comprising the following steps:
suppose E1x≤E2xIf, if
Figure BDA0002209515460000101
Then compute cloud model a' ═ a1∪A2＝A2；
If it is not
Figure BDA0002209515460000102
Then a new cloud model a' ═ a is computed1∪A2＝A1；
By new cloud models A' and A3Of the current driving level, resulting in an integrated cloud model a ═ (E) for the driving behavior assessment of the driver at the current driving levelx,En,He)。
Suppose E1x≤E2xIf | E1x-E2x|＜|3(E1n+E2n) I, | then new cloud model a '═ E'x,E′n,H′e) The three characterizing parameters of (a) can be calculated according to the following formula:
Figure BDA0002209515460000103
if | E1x-E2x|≥|3(E1n+E2n) If the new cloud model A' is A1And A2Two cloud model representations, i.e. ifThen A ═ A1∪A2＝A2. On the contrary, if
Figure BDA0002209515460000105
Then A ═ A1∪A2＝A1。
By cloud models A' and A3To obtain a comprehensive cloud model a ═ (E) for assessment of driving behavior of birdsx,En,He)。
Further, the step S700: comparing the level evaluation cloud picture of the driver to be evaluated with the evaluation cloud pictures corresponding to the different driving levels, and determining the driving level of the driver to be evaluated, wherein the step of determining the driving level of the driver to be evaluated comprises the following steps:
calculating the similarity between the level evaluation cloud picture of the driver to be evaluated and the evaluation cloud pictures corresponding to different driving levels;
and taking the driving level corresponding to the evaluation cloud picture with the maximum similarity as the driving level of the driver to be evaluated.
Specifically, real-time driving data of a driver with unknown level is recorded, average values of different evaluation indexes in different driving states are obtained, and an evaluation cloud picture X of the driver with unknown level is described*. Will evaluate cloud image X*Respectively associated with the evaluation cloud picture A*、B*And C*A comparison is made, wherein cloud image A is evaluated*、B*And C*Indicating a low driving level, a normal driving level and a high driving level, respectively. If the cloud image X is evaluated*And evaluating cloud image A*Similarity greater thanAnd evaluating cloud picture B*And C*If the similarity is not equal, the driving level of the driver can be judged to be a low driving level; if the cloud image X is evaluated*And evaluating cloud picture B*Similarity is greater than and evaluated cloud picture A*And C*If the driving level of the driver is the normal driving level, judging that the driving level of the driver is the normal driving level; similarly, if cloud X is evaluated*And evaluating cloud chart C*Similarity is greater than and evaluated cloud picture A*And B*The driving level of the driver can be judged to be a high driving level.
According to the method, a large amount of experimental data are analyzed through the cloud model, the uncertainty conversion between the qualitative mode and the quantitative mode in the driving behavior judging process is effectively achieved, the intelligent vehicle has the same driving habit as a driver, the uncertainty of the driving level can be processed, the driving modes of the drivers with different driving levels can be simulated, and the problems encountered in real-time traffic and the processing method can be more truly researched. Meanwhile, the relationship between the right of way and urban traffic jam can be researched through the occupation condition of drivers with different driving levels on road resources and the influence of the drivers on the jam, so that the problem of urban traffic jam is solved to the maximum extent.
As shown in fig. 4, the present embodiment discloses a driving level evaluation system including: the cloud model evaluation system comprises a classification module 10, an evaluation value calculation module 20, a cloud model calculation module 30, a first evaluation cloud picture construction module 40, a second evaluation cloud picture construction module 50 and a comparison module 60;
the classification module 10 is used for classifying the driving states of the drivers with different driving levels and setting evaluation indexes of various driving states;
the evaluation value calculation module 20 is configured to obtain a weight coefficient of an influence degree of an evaluation index on a driving state based on an expert evaluation method, and obtain an evaluation value of each driving state;
the cloud model calculation module 30 is configured to input the evaluation value of each driving state in the same driving level into the reverse cloud generator, and calculate the characterization number of the cloud model in each driving state;
the first evaluation cloud picture construction module 40 is configured to input the characterization number of the cloud model in each driving state under the same driving level into the forward cloud generator, obtain the quantitative position and the certainty degree of the qualitative concept of the cloud droplets of the driving level in the number domain space, and construct an evaluation cloud picture of the driving level, so as to obtain evaluation cloud pictures corresponding to different driving levels;
the second evaluation cloud picture construction module 50 is used for acquiring real-time driving data of a driver to be evaluated and acquiring a level evaluation cloud picture of the driver to be evaluated;
the comparison module 60 is configured to compare the level evaluation cloud chart of the driver to be evaluated with the evaluation cloud charts corresponding to the different driving levels, and determine the driving level of the driver to be evaluated.
Specifically, the cloud model calculation module 30 includes a cloud droplet acquisition unit, a parameter calculation unit, a super-entropy calculation unit, and a cloud model calculation unit;
the cloud droplet acquisition unit is used for acquiring the evaluation values of N drivers with the same driving level in the same driving state as the cloud droplets corresponding to the driving state;
the parameter calculation unit is used for respectively calculating the mean value of the cloud droplets, the variance of the cloud droplets and the entropy of the cloud droplets according to the cloud droplets corresponding to the driving state;
the super-entropy calculation unit is used for calculating the super-entropy of the cloud droplets according to the variance of the cloud droplets and the entropy of the cloud droplets;
and the cloud model computing unit is used for constructing a cloud model by taking the mean value of the cloud droplets, the entropy of the cloud droplets and the super entropy of the cloud droplets as the representation numbers of the cloud model corresponding to the driving state.
Specifically, the first evaluation cloud picture constructing module 40 includes a comprehensive cloud model constructing unit, a normal random number calculating unit, a quantitative and qualitative unit, a certainty factor calculating unit, and an evaluation cloud picture constructing unit;
the comprehensive cloud model building unit is used for building a comprehensive cloud model A (E) for evaluating the driving level according to the characterization number of the cloud model in each driving state under the same driving levelx,En,He) The characterization numbers of the comprehensive cloud model are respectively expected values ExEntropy EnAnd entropy He；
A normal random number calculation unit for calculating a normal random number according to the expected value ExEntropy EnAnd entropy HeAnd a given cloud drop number N to obtain a normal random number
Figure BDA0002209515460000121
Normal random number
Figure BDA0002209515460000122
Has a mean value of ExStandard deviation of HeAnd a normal random number x whose mean value is EnStandard deviation of
Figure BDA0002209515460000123
The quantitative and qualitative unit is used for calculating
Figure BDA0002209515460000124
Let x be a specific quantization value of the qualitative concept and let y be the certainty of x;
the certainty factor calculation unit is used for outputting the quantitative positions of the cloud drips of the N drivers at the same level in the number domain space and the certainty factors (x, y) of the qualitative concepts when the N cloud drips are generated;
the evaluation cloud picture construction unit is used for constructing the evaluation cloud picture of the driving level according to the quantitative position of the cloud drops of the driving level in the number domain space and the certainty degree of the qualitative concept.
Specifically, the comparison module 60 includes a similarity calculation unit and a comparison unit;
the similarity calculation unit is used for calculating the similarity between the level evaluation cloud picture of the driver to be evaluated and the evaluation cloud pictures corresponding to different driving levels;
the comparison unit is used for comparing the calculated similarity, and taking the driving level corresponding to the evaluation cloud picture with the maximum similarity as the driving level of the driver to be evaluated.
It should be noted that, the similarity calculation process in this embodiment is as follows:
through a root mean square error formula, N cloud drops generated by the level evaluation cloud picture of the driver to be evaluated according to time sequence are respectively compared with N cloud drops generated by the evaluation cloud pictures corresponding to different driving levels according to time sequence, and the calculation formula is as follows:
Figure BDA0002209515460000131
wherein λ is1And λ2Error coefficients representing the quantitative location x and the certainty y of the qualitative concept in the domain space, respectively; n represents the number of cloud droplets; r ═ 1,2,3 denote low driving level, normal driving level, and advanced driving level, respectively; p represents the driving level to be evaluated.
If RSME1If the value is minimum, the driving level to be evaluated belongs to a low driving level; if RSME2If the value is minimum, the driving level to be evaluated belongs to the normal driving level; if RSME3The value is minimum, and the driving level to be evaluated belongs to the advanced driving level.
It should be noted that, the conventional driving level assessment method avoids qualitative indexes of randomness, ambiguity and statistics of assessment, so that the result of driving level assessment is inaccurate. In the embodiment, a conversion relation from a fixed characteristic to a quantitative characteristic is established by utilizing a forward cloud generator and a reverse cloud generator in a cloud model based on a cloud model theory, subjective judgment and objective data are fused, the subjective influence of artificially determining driving level evaluation is avoided, the evaluation problem contains qualitative indexes which are difficult to count, fuzzy and random, and the credibility and the scientificity of the driving level evaluation are greatly improved.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that fall within the spirit and principle of the present invention are intended to be included therein.

Claims (10)

1. A driving level evaluation method characterized by comprising:
s100, classifying the driving states of the driver, and setting evaluation indexes of various driving states;
s200, obtaining an evaluation value of each driving state based on an expert evaluation method;
s300, inputting the evaluation value of each driving state under the same driving level into a reverse cloud generator, and calculating the characterization number of the cloud model in each driving state;
s400, inputting the characterization number of the cloud model in each driving state under the same driving level into a forward cloud generator, obtaining the quantitative position of the cloud droplets of the driving level in a number domain space and the certainty degree of a qualitative concept, and constructing an evaluation cloud picture of the driving level;
s500, repeatedly executing the steps S200-S400 to obtain evaluation cloud pictures corresponding to different driving levels;
s600, acquiring real-time driving data of a driver to be evaluated, executing the steps S200-S400, and acquiring a horizontal evaluation cloud picture of the driver to be evaluated;
s700, comparing the level evaluation cloud picture of the driver to be evaluated with the evaluation cloud pictures corresponding to different driving levels, and determining the driving level of the driver to be evaluated.
2. The driving level assessment method according to claim 1, wherein the inputting of the assessment value for each driving state at the same driving level into the inverse cloud generator, and the calculating of the characterization number of the cloud model in each driving state comprises:
acquiring evaluation values of N drivers with the same driving state at the same driving level as cloud droplets corresponding to the driving state;
respectively calculating the mean value of the cloud droplets, the variance of the cloud droplets and the entropy of the cloud droplets according to the cloud droplets corresponding to the driving state;
calculating the super entropy of the cloud droplets according to the variance of the cloud droplets and the entropy of the cloud droplets;
and taking the mean value of the cloud droplets, the entropy of the cloud droplets and the super entropy of the cloud droplets as the characterization numbers of the cloud model corresponding to the driving state.
3. The driving level assessment method according to claim 2, wherein the inputting the characterization number of the cloud model in each driving state of the same driving level into the forward cloud generator to obtain the quantitative position and the certainty degree of the qualitative concept of the cloud droplet of the driving level in the number domain space, and constructing the assessment cloud map of the driving level comprises:
s401, establishing a comprehensive cloud model A (E) for evaluating the driving level according to the characterization number of the cloud model in each driving state under the same driving levelx,En,He) The characterization numbers of the comprehensive cloud model are respectively expected values ExEntropy EnAnd entropy He；
S402, according to the expected value ExEntropy EnAnd entropy HeAnd a given cloud drop number N, to obtain an average value ExStandard deviation of HeNormal random number of
Figure FDA0002209515450000021
And a mean value of EnStandard deviation of
Figure FDA0002209515450000022
The normal random number x of (a);
s403, calculatingLet x be a specific quantization value of the qualitative concept and let y be the certainty of x;
s404, repeatedly executing the steps S402-S403 until N cloud droplets are generated;
s405, outputting the quantitative positions of the cloud droplets of N drivers at the same level in a number domain space and the certainty degree (x, y) of a qualitative concept;
s406, obtaining the quantitative position of the cloud droplets of the driving level in the number domain space and the certainty degree of the qualitative concept, and constructing an evaluation cloud picture of the driving level.
4. The driving level assessment method according to claim 1, wherein the comparing the level assessment cloud of the driver to be assessed with the assessment clouds corresponding to the different driving levels to determine the driving level of the driver to be assessed comprises:
calculating the similarity between the level evaluation cloud picture of the driver to be evaluated and the evaluation cloud pictures corresponding to different driving levels;
and taking the driving level corresponding to the evaluation cloud picture with the maximum similarity as the driving level of the driver to be evaluated.
5. The driving level assessment method of claim 3, wherein the driving status of the driver includes a violation, a driving direction, and a long-term status;
the evaluation indexes of the violation conditions comprise the passing conditions specified by traffic lights, the running conditions at specified speed and the running conditions at specified lanes;
the evaluation indexes of the running direction comprise a lateral deviation condition and a direction deviation condition;
the evaluation indexes of the long-term state comprise driving emotion stability, running speed judgment capability, emergency handling response capability and time and space judgment capability of different driving environments.
6. The method of claim 5 wherein the cloud models for the violation, the driving direction, and the long-term condition are A1＝(E1x,E1n,H1e)，A2＝(E2x,E2n,H2e) And A3＝(E3x,E3n,H3e) The establishing of the comprehensive cloud model for evaluating the driving level according to the characterization number of the cloud model in each driving state under the same driving level comprises the following steps:
suppose E1x≤E2xIf, if
Figure FDA0002209515450000031
Then compute cloud model a' ═ a1∪A2＝A2；
If it is not
Figure FDA0002209515450000032
Then a new cloud model a' ═ a is computed1∪A2＝A1；
By new cloud models A' and A3Of the current driving level, resulting in an integrated cloud model a ═ (E) for the driving behavior assessment of the driver at the current driving levelx,En,He)。
7. A driving level evaluation system characterized by comprising: the cloud model evaluation system comprises a classification module, an evaluation value calculation module, a cloud model calculation module, a first evaluation cloud picture construction module, a second evaluation cloud picture construction module and a comparison module;
the classification module is used for classifying the driving states of drivers with different driving levels and setting evaluation indexes of various driving states;
the evaluation value calculation module is used for obtaining a weight coefficient of the influence degree of the evaluation index on the driving state based on an expert evaluation method and obtaining an evaluation value of each driving state;
the cloud model calculation module is used for inputting the evaluation value of each driving state under the same driving level into the reverse cloud generator and calculating the characterization number of the cloud model in each driving state;
the first evaluation cloud picture construction module is used for inputting the characterization number of the cloud model in each driving state under the same driving level into the forward cloud generator, obtaining the quantitative position of cloud droplets of the driving level in a number domain space and the certainty degree of a qualitative concept, and constructing the evaluation cloud picture of the driving level so as to obtain the evaluation cloud pictures corresponding to different driving levels;
the second evaluation cloud picture construction module is used for acquiring real-time driving data of the driver to be evaluated and acquiring a level evaluation cloud picture of the driver to be evaluated;
the comparison module is used for comparing the level evaluation cloud picture of the driver to be evaluated with the evaluation cloud pictures corresponding to different driving levels to determine the driving level of the driver to be evaluated.
8. The driving level assessment system according to claim 7, wherein the cloud model calculation module comprises a cloud droplet acquisition unit, a parameter calculation unit, a hyper-entropy calculation unit, and a cloud model calculation unit;
the cloud droplet acquisition unit is used for acquiring the evaluation values of N drivers with the same driving level in the same driving state as the cloud droplets corresponding to the driving state;
the parameter calculation unit is used for respectively calculating the mean value of the cloud droplets, the variance of the cloud droplets and the entropy of the cloud droplets according to the cloud droplets corresponding to the driving state;
the super-entropy calculation unit is used for calculating the super-entropy of the cloud droplets according to the variance of the cloud droplets and the entropy of the cloud droplets;
and the cloud model computing unit is used for constructing a cloud model by taking the mean value of the cloud droplets, the entropy of the cloud droplets and the super entropy of the cloud droplets as the representation numbers of the cloud model corresponding to the driving state.
9. The driving level evaluation system of claim 8, wherein the first evaluation cloud picture construction module comprises a comprehensive cloud model construction unit, a normal random number calculation unit, a quantitative qualitative unit, a certainty factor calculation unit, and an evaluation cloud picture construction unit;
the comprehensive cloud model building unit is used for building a comprehensive cloud model A (E) for evaluating the driving level according to the characterization number of the cloud model in each driving state under the same driving levelx,En,He) The characterization numbers of the comprehensive cloud model are respectively expected values ExEntropy EnAnd entropy He；
A normal random number calculation unit for calculating a normal random number according to the expected value ExEntropy EnAnd entropy HeAnd a given cloud drop number N to obtain a normal random number
Figure FDA0002209515450000041
Normal random number
Figure FDA0002209515450000042
Has a mean value of ExStandard deviation of HeAnd a normal random number x whose mean value is EnStandard deviation of
Figure FDA0002209515450000043
The quantitative and qualitative unit is used for calculating
Figure FDA0002209515450000044
Let x be a specific quantization value of the qualitative concept and let y be the certainty of x;
the certainty factor calculation unit is used for outputting the quantitative positions of the cloud drips of the N drivers at the same level in the number domain space and the certainty factors (x, y) of the qualitative concepts when the N cloud drips are generated;
the evaluation cloud picture construction unit is used for constructing the evaluation cloud picture of the driving level according to the quantitative position of the cloud drops of the driving level in the number domain space and the certainty degree of the qualitative concept.
10. The driving level evaluation system according to claim 1, wherein the comparison module includes a similarity calculation unit and a comparison unit;
the similarity calculation unit is used for calculating the similarity between the level evaluation cloud picture of the driver to be evaluated and the evaluation cloud pictures corresponding to different driving levels;
the comparison unit is used for comparing the calculated similarity, and taking the driving level corresponding to the evaluation cloud picture with the maximum similarity as the driving level of the driver to be evaluated.
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