CN106202698B - A kind of construction method of vehicle-mounted sensing net node intelligent movable pilot model - Google Patents
A kind of construction method of vehicle-mounted sensing net node intelligent movable pilot model Download PDFInfo
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Abstract
The invention discloses a kind of construction methods of vehicle-mounted sensing net node intelligent movable pilot model, this method is based on intelligent Driver Model, train tracing model in intelligent Driver Model is judged, by the relative velocity in vehicle moving process, relative position, Time Dependent degree, the influence factor collection of space dependency degree and 5 class uncertainty index of road surface dependency degree as node motion cloud model, intelligent Driver Model is introduced using the uncertain index of cloud model description, relevant parameter in intelligent Driver Model is modified, characterize node motion strategy and movement law.
Description
Technical field
The present invention relates to vehicle-mounted sensing net node mobility model technical field, in particular to a kind of vehicle-mounted sensing net node moves
The construction method of dynamic intelligent Driver Model.
Background technique
As the typical case of technology of Internet of things, vehicle-mounted Sensor Network, which has become, to be improved road quality and first develops skill
Art.Since urban road vehicle flowrate is larger, constituting, there is certain density, stable vehicle-mounted Sensor Network to be possibly realized.Node motion
Strategy is that vehicle-mounted Sensor Network carries out the basis of data distribution and critical issue, vehicle-mounted sensing net node mobility model describe node
Shift strategy and feature.
The building of mobility model plays the role of extremely important in the development and application of vehicle-mounted Sensor Network.Existing movement
What model characterized is the driving behavior of a kind of " intelligence ", seldom considers the factor for influencing node motion, is moved departing from node
Dynamic actual conditions.The movement of vehicle node is influenced by driver and running environment factor, and these factors have multiplicity
Property, complexity, ambiguity, randomness and correlation, if the qualitative analysis to match with actual conditions can not be done to these factors
With quantitative description, mobility model just can not truly describe the shift strategy of node.Therefore, it is necessary to construct to meet vehicle-mounted Sensor Network
The mobility model of self-characteristic.
Current vehicle and leading vehicle speed difference and spacing are the exact values obtained by model in intelligent Driver Model, are not had
There is the influence of the otherness and extraneous factor that consider different driver's perception；Away from being also in model when desired speed and safe follow the bus
Emulation initial stage is just set as fixed value, does not account for the random variability of driving environment.
The Clouds theory that newly-developed gets up has the characteristics that comprehensive ambiguity and randomness are integrated.Pass through the expectation of cloud
The randomness of ambiguity and degree of membership is fully integratible into together by three value, entropy and super entropy numerical characteristics, completes qualitative point of concept
The conversion of analysis and quantitative description.Therefore, Clouds theory is introduced into the foundation of vehicle-mounted node motion model, becomes the vehicle-mounted sensing of research
The preferred method of net.
Summary of the invention
The object of the present invention is to provide a kind of vehicle-mounted sensing net node intelligent movable pilot model construction methods, should
Cloud model is completed qualitative division and the quantitative description of node motion influence factor by method, and then is constructed based on the vehicle-mounted of cloud model
Mobility model is modified the parameter in intelligent Driver Model, expands the descriptive power of intelligent Driver Model, more really
Ground characterizes node motion strategy and movement law.
In order to realize that above-mentioned task, the present invention are achieved using following technical solution:
A kind of construction method of vehicle-mounted sensing net node intelligent movable pilot model, which is characterized in that this method is with intelligence
Based on energy pilot model, train tracing model in intelligent Driver Model is judged, during vehicle-mounted net node motion
Relative velocity, relative position, Time Dependent degree, space dependency degree and 5 class uncertainty index of road surface dependency degree as node
The influence factor collection of mobile cloud model introduces intelligent Driver Model using the uncertain index of cloud model description, to intelligence
Relevant parameter in pilot model is modified, and characterizes node motion strategy and movement law.
According to the present invention, train tracing model in intelligent Driver Model is judged, to improve Acceleration Formula are as follows:
In formula:
The peak acceleration of a expression vehicle；
The present speed of v expression vehicle；
v0Indicate desired speed of the vehicle under freestream conditions；
The speed difference of Δ v expression rear car and front truck；
s*(the expectation safe spacing of v, Δ v) expression current vehicle and preceding guide-car；
sTIndicate judge with speed on as threshold value, take 125m；
δ is indicated for adjusting acceleration behavior, and as δ → ∞, i.e. the acceleration a that keeps constant of vehicle is reached until speed
v0, usually control δ ∈ [1,5]；
Work as s > sTAnd s*When < 0, vehicle freedom of entry acceleration mode；
As s < sTOr s*When > 0, vehicle is in train tracing model.
The relevant parameter modification method are as follows:
Step 1, computation model provide " intelligence " data to the activation degree of each index classification, that is, are subordinate to angle value, by being subordinate to
Category degree maximum value determines classification belonging to data；
Step 2 is generated the water dust drop (x of maximum membership degree value by Y condition cloud generatori,umax), wherein i=1,2,
3 ... n, umaxIndicate maximum membership degree value, xiIndicate the output valve of cloud model；
Step 3, to xiCarry out mathematic(al) meanX is then through the modified parameter value of cloud model, wherein Y item
Part cloud generator algorithmic formula is
In formula, Ey、En、HeIt is the cloud numerical characteristic classified belonging to achievement data, xi
Indicate the output valve of cloud model, RnFor the mark for generating random number, yiIndicate normal distribution random number, uiIndicate degree of membership.
The modified parameter value of the cloud model is as follows:
(1) relative velocity Δ v, relative position Δ s are corrected
The relative velocity of cloud model description, relative position index are introduced into intelligent Driver Model, to accurate in model
Speed difference and spacing are modified, and comply with driver's perception；
Relative velocity is divided by psychology-behavioural habits of comprehensive driver in conjunction with cloud model about conception division theory
5 subsets: it is fast very much, it is fast, it approaches, it is slow, slow very much, correspond to { Δ v1、Δv2、Δv3、Δv4、Δv5}；Sentence
The corresponding qualitativing concept classification of " intelligence " data that disconnected model provides, completes quantitative data to the conversion of qualitativing concept, this meets
Classification of the driver to relative velocity, relative position judgement, driver is often also to feel relative velocity, and perception obtains
It is " fast ", still " slow ", " close ", " slow ", " slow very much "；Same degree of membership is generated by cloud generator again
Water dust is equivalent to driver and is sensuously consistent to these data values；
Using the mathematical mean of these water dusts as the correction value of index, by the modified relative velocity Δ v of cloud model, relatively
Position Δ s index introduces intelligent Driver Model, modified acceleration and desired distance equation are as follows:
Wherein, Δ v, Δ s are through the revised value of cloud model, and a indicates the peak acceleration of vehicle, s0What is indicated is peace
Full distance, b indicate deceleration, and T indicates the reaction time of driver；
(2) reaction time is corrected
By Time Dependent degree τ index introduce intelligent Driver Model, compensation driver from perception peripheral information variation up to
The consumed time is given a response, Time Dependent angle value is corrected through cloud model, and reflection is modified to vehicle acceleration, is corrected
Acceleration value formula it is as follows:
Wherein, τ is indicated through the revised value of cloud model；
(3) desired speed is corrected
Space dependency degree k is introduced, using the visibility influenced by weather condition as intermediate variable, quantization rain, snow and dense fog pair
The influence of driver's sighting distance and psychology, is modified the desired speed of vehicle, formula are as follows:
vk=kv0
Wherein, vkIndicate the desired speed under different line of sight conditions；
v0Indicate the desired speed under ideal line of sight conditions；
K indicates modified according to the space dependency degree obtained under different visibility conditions by cloud model；
(4) away from amendment when safe follow the bus
Introduce road surface dependency degree l, using coefficient of road adhesion as intermediate variable, quantization rain, snow and ice severe weather conditions and
The different road surfaces Micro influence mobile to vehicle node, characterizes vehicle node available degree of dependence in road surface in brake deceleration
Size, away from being modified when to the safe follow the bus of vehicle, formula are as follows:
Tl=T0/l
Wherein, TlAway from T when indicating the safe follow the bus of different pavement conditions0Indicate the i.e. dry cypress of normal road conditions
Away from l expression passes through the modified road obtained according to different pavement conditions of cloud model when the desired Safety follow the bus of oil/concrete road surface
Face dependency degree.
Compared with prior art, vehicle-mounted sensing net node intelligent movable pilot model construction method of the invention is brought
Beneficial effect be: relative velocity, relative position, Time Dependent degree, space are introduced on the basis of intelligent Driver Model
5 category feature index of dependency degree and road surface dependency degree, fully considered the individual character of driver, road environment, traffic environment and
Influence of the climatic environmental factor to vehicle node mobility, and by cloud model fully considered characteristic index value divide,
Randomness and ambiguity in concept conversion process.Intelligent Driver Model based on cloud model theoretically can exact representation
Node motion strategy and movement law.
Specific embodiment
Applicant studies vehicle-mounted sensing net node shift strategy using cloud model, the uncertain index that cloud model is described
Intelligent Driver Model is introduced, relevant parameter is modified, the difference that different drivers reflect different driving environments is depicted
The opposite sex is commented to construct the mobility model of the characterization true movement law of node and strategy for vehicle-mounted net network communication link performance
Estimate and true node motion track is provided.
The present embodiment provides a kind of construction method of vehicle-mounted sensing net node intelligent movable pilot model, in intelligent driving
The relative velocity Δ v, relative position Δ s, Time Dependent degree τ, space dependence of cloud model description are introduced on the basis of member's model
It spends k and road surface dependency degree l5 class does not know index, relevant parameter is modified, different drivers are depicted to different driving
The otherness of environment reflection, makes mobility model more meet true movement law.
1, by relative velocity, relative position, Time Dependent degree, space dependency degree and the road during vehicle-mounted net node motion
Dependency degree 5 class uncertainty index in face introduces intelligent Driver Model.The cloud numerical characteristic of each index has been calculated, has utilized
Cloud model completes the conversion between uncertain index qualitative analysis and quantitative description, solve during node motion ambiguity and
Stochastic problems are modified the parameter in mobility model.
2, the amendment of moving condition is judged to train tracing model in intelligent Driver Model, to improve acceleration public affairs
Formula are as follows:
In formula:
A: the peak acceleration of vehicle is indicated；
V: the present speed of vehicle is indicated；
v0: indicate desired speed of the vehicle under freestream conditions；
Δ v: the speed difference of rear car and front truck is indicated；
s*(v, Δ v): indicate the expectation safe spacing of current vehicle and preceding guide-car；
sT: indicate judge with speed on as threshold value, take 125m；
δ is indicated for adjusting acceleration behavior, and as δ → ∞, i.e. the acceleration a that keeps constant of vehicle is reached until speed
v0, usually control δ ∈ [1,5]；
Work as s > sTAnd s*When < 0, vehicle freedom of entry acceleration mode；As s < sTOr s*When > 0, vehicle is in train tracing model.
By judging moving condition, when current guide-car is rapidly directed away from, driver think front be it is safe, thus
Using free acceleration.The judgement of moving condition, it will the average speed for improving the node when traffic density is smaller, to be promoted
Operational efficiency, increase node between connection probability.
3, to the modified method of relevant parameter are as follows:
Step 1, computation model provide " intelligence " data to the activation degree of each index classification, that is, are subordinate to angle value, by being subordinate to
Category degree maximum value determines classification belonging to data；
Step 2 is generated the water dust drop (x of maximum membership degree value by Y condition cloud generatori,umax) (wherein i=1,2,
3 ... n), umaxIndicate maximum membership degree value, xiIndicate the output valve of cloud model；
Step 3, to xiCarry out mathematic(al) meanX is then through the modified parameter value of cloud model, wherein Y condition
Cloud generator algorithmic formula are as follows:
In formula, Ey、En、HeIt is the cloud numerical characteristic classified belonging to achievement data, xiTable
Show the output valve of cloud model, RnFor the mark for generating random number, yiIndicate normal distribution random number, uiIndicate degree of membership；
4, as follows through the modified uncertain parameters of cloud model:
(1) relative velocity Δ v, relative position Δ s are corrected
The relative velocity of cloud model description, relative position index are introduced into intelligent Driver Model, to accurate in model
Speed difference and spacing are modified, and comply with driver's perception.
Relative velocity Δ v is the difference of current vehicle and leading vehicle speed, sees following formula:
Δ v=vF-vL
Wherein:
vF: it indicates current vehicle speed degree (km/h)；
vL: indicate leading vehicle speed (km/h).
Relative velocity is divided by psychology-behavioural habits of comprehensive driver in conjunction with cloud model about conception division theory
5 subsets: it is fast very much, it is fast, it approaches, it is slow, slow very much, correspond to { Δ v1、Δv2、Δv3、Δv4、Δv5}.It adopts
Relative velocity index is acquired with test run method.
Researcher explains in detail related notion to driver before on-test；It is mentioned at intervals of two minutes to driver in test
It asks, according to the subjective judgement of driver, records the qualitative classification of two vehicle relative velocity of current time.With relative velocity for " close "
For the judgement of concept, all Δ v ∈ Δ v are extracted3Data, formed and be by two vehicle relative velocities before and after driver subjective judgement
Data acquisition system { the Δ v of " close "3}；The numerical characteristic of data set is generated using backward cloud generatorIt utilizes
Several water dusts of Normal Cloud Generator generation normal cloud modelWhereinIt is that " close " is general for relative velocity
The data of thought, uiThusIt is under the jurisdiction of the determination degree of the cloud, i.e. degree of membership.Using the mathematical mean of these water dusts as referring to
Target correction value.
Relative velocity is that the cloud numerical characteristic of " close " is (- 1.8,1.1,0.22), and showing that relative velocity is -1.8 is to drive
The most suitable value of the person's of sailing subjective judgement " close " concept is the most intensive place of water dust value；Entropy is 1.1 expressions " close "
The dispersion degree of the water dust of this qualitativing concept and the measurement of qualitativing concept model；Super entropy be 0.22 by entropy randomness
It codetermines with ambiguity, is embodied by the thickness of water dust.Current vehicle driver exists it can be seen from the degree of membership cloud atlas generated
Before this vehicle speed is slightly less than when guide-car's speed, it is believed that opposite speed is " close ", this meets vehicle and follows leading vehicle traveling
Behavioural habits.
The conversion of other qualitativing concepts of relative velocity to quantitative concept is also obtained by same method.Relative velocity index is each
Classification: the cloud model numerical characteristic (E of " fast very much "x,En,He)ΔV1=(11.2,1.9,0.21)；The cloud model number of " fast "
Word feature (Ex,En,He)ΔV2=(6.5,2.1,0.24)；The cloud model numerical characteristic of " close "Cloud model numerical characteristic (the E of " slow "x,En,He)ΔV4=(- 8.3,
2.9,0.29)；Cloud model numerical characteristic (the E of " slow very much "x,En,He)ΔV5=(- 16.6,2.4,0.32).
The difference of position of the relative position Δ s between current vehicle and preceding guide-car, is shown in following formula:
Δ s=xL-xF-L
Wherein: xLIndicate leading truck position (m)；xFIt indicates to work as front vehicle position (m)；Guide-car's length of wagon before L is indicated.
In conjunction with driver's perception and behavioural habits, relative position is divided into 5 subsets: very little, it is smaller, appropriately, compared with
Greatly, very greatly }, correspond to { Δ s1、Δs2、Δs3、Δs4、Δs5}.Relative position index is under the jurisdiction of the cloud number of each qualitativing concept
Feature is also obtained by test run method, with relative velocity achievement data acquisition methods.According to known sample data, occurred by reverse cloud
Device generates relative position and is under the jurisdiction of the cloud model numerical characteristic of each qualitativing concept, then generates water dust by Normal Cloud Generator.By this
Correction value of the mathematical mean of a little water dusts as index.
Relative position index is respectively classified:
Cloud model numerical characteristic (the E of " very little "x,En,He)ΔS1=(7.7,2.2,0.62)；
Cloud model numerical characteristic (the E of " smaller "x,En,He)ΔS2=(15.3,3.7,0.83)；
The cloud model numerical characteristic of " appropriate "
Cloud model numerical characteristic (the E of " larger "x,En,He)ΔS4=(33.4,4.1,0.71)；
Cloud model numerical characteristic (the E of " very big "x,En,He)ΔS5=(42.3,3.4,0.63).
The modified relative velocity Δ v of cloud model, relative position Δ s index are introduced into intelligent Driver Model, it is modified to add
Speed and desired spacing are shown in formula:
Wherein, Δ v, Δ s are through the revised value of cloud model, and a indicates the peak acceleration of vehicle, s0What is indicated is peace
Full distance, b indicate deceleration, and T indicates the reaction time of driver.
(2) reaction time is corrected
By Time Dependent degree τ index introduce intelligent Driver Model, compensation driver from perception peripheral information variation up to
It responds the consumed time.Utilize the otherness and particularity in cloud model processing driver's personal feature, deadline
The division of dependency degree index qualitative to quantitative concept.Time Dependent degree τ is divided into 5 subsets, and it is fast very much, it is fast, just
Often, slow, slow very much, correspond to { τ1、τ2、τ3、τ4、τ5, classification standard are as follows:
" fast many τ1" it is 0.3~0.7s；
" fast some τ2" it is 0.7~0.9s；
" normal τ3" it is 0.9~1.2s；
" slow some τ4" it is 1.2~1.8s；
" slow many τ5" it is 1.8~2.5s.
The range that Time Dependent degree is under the jurisdiction of each subset has up-and-down boundary, shaped like A [Xmin,Xmax], cloud number
Feature may be expressed as:
In formula: Xmin、XmaxThe respectively lower bound of index and the upper bound；K is constant, can be adjusted according to the fuzziness of variable
It is whole.
Present invention determine that Time Dependent degree index cloud model parameter it is as follows:
Time Dependent degree index is respectively classified:
The cloud model numerical characteristic of " fast very much "
The cloud model numerical characteristic of " fast "
The cloud model numerical characteristic of " normal "
The cloud model numerical characteristic of " slow "
The cloud model numerical characteristic of " slow very much "
By three numerical characteristic (E that cloud is calculatedx, En, He), using Normal Cloud Generator, generate normal cloud model
Several water dust drop (τi,ui).Wherein τiFor the measured data of Time Dependent degree, uiτ thusiIt is under the jurisdiction of the qualitativing concept really
Determine degree, i.e. degree of membership.
Time Dependent angle value is corrected through cloud model, and reflection is modified to vehicle acceleration, and modified acceleration value is public
Formula:
Wherein, τ is indicated through the revised value of cloud model.
(3) desired speed is corrected
Space dependency degree k is introduced, using the visibility influenced by weather condition as intermediate variable, quantization rain, snow and dense fog pair
The influence of driver's sighting distance and psychology, is modified the desired speed of vehicle, by following formula:
vk=kv0
Wherein:
vkIndicate the desired speed under different line of sight conditions；
v0Indicate the desired speed under ideal line of sight conditions；
K indicates modified according to the space dependency degree obtained under different visibility conditions by cloud model.
Specific value classification standard:
Space dependency degree value k1Visibility range V is 1000~10000m, and corresponding space dependency degree value is
0.90~0.98；
Space dependency degree value k2Visibility range V is 500~1000m, and corresponding space dependency degree value is 0.80
~0.90；
Space dependency degree value k3Visibility range V be 200~500m, corresponding space dependency degree value be 0.70~
0.80；
Space dependency degree value k4Visibility range V be 50~200m, corresponding space dependency degree value be 0.60~
0.75；
Space dependency degree value k5Visibility range V is less than 50m, corresponding space dependency degree value is 0.4~
0.65。
Calculation criterion using cloud model numerical characteristic is:
Obtain the cloud model numerical characteristic of 5 classification standards of space dependency degree.
Space dependency degree k index is respectively classified:
Space dependency degree k1Cloud model numerical characteristic
Space dependency degree k2Cloud model numerical characteristic
Space dependency degree k3Cloud model numerical characteristic
Space dependency degree k4Cloud model numerical characteristic
Space dependency degree k5Cloud model numerical characteristic
(4) away from amendment when safe follow the bus
Road surface dependency degree l is introduced, using coefficient of road adhesion as intermediate variable, the severe weather conditions such as quantization rain, snow and ice
And different road surfaces characterize the available dependence journey in vehicle node road surface in brake deceleration to the Micro influence of vehicle node movement
The size of degree.
It can be verified by the formula of braking distance, road surface can be supplied to the maximum deceleration of vehicle with coefficient of road adhesion
Reduction and reduce, safe distance needed for vehicle braking is longer.The attachment coefficient on different road surfaces is classified are as follows: ice road surface l1Attachment
Coefficient section is 0.10~0.30；Avenge road surface l2Attachment coefficient section is 0.22~0.45；Soil surface l3Attachment coefficient section is
0.40~0.65；Wet pitch/concrete road surface l4Attachment coefficient section is 0.60~0.85；Dry pitch/concrete road surface l5Attachment
Coefficient section is 0.80~0.98.
Often there is some difference for coefficient of road adhesion value under same pavement state, by road surface dependency degree value range
It is equal to coefficient of road adhesion value, this information content is high, uncertainty is big concept is converted to cloud model number
The quantitative concept of characteristic present.
Road surface dependency degree index is respectively classified:
Road surface dependency degree l1Cloud model numerical characteristic
Road surface dependency degree l2Cloud model numerical characteristic
Road surface dependency degree l3Cloud model numerical characteristic
Road surface dependency degree l4Cloud model numerical characteristic
Road surface dependency degree l5Cloud model numerical characteristic
Away from being modified when to the safe follow the bus of vehicle, formula are as follows:
Tl=T0/l
Wherein:
Tl: when indicating the safe follow the bus of different pavement conditions away from；
T0: when indicating the desired Safety follow the bus of normal road conditions (dry pitch/concrete road surface) away from；
L: expression passes through the modified road surface dependency degree obtained according to different pavement conditions of cloud model.
Application example 1:
In vehicle-mounted Sensor Network, the 5 class uncertainty indexs that the intelligent Driver Model based on cloud model introduces can pass through dress
The sensor being loaded on vehicle obtains.The relative velocity of front and back vehicle, relative position can pass through laser radar, radar range finder, sound
Range sensor etc. obtains；Time Dependent degree can be obtained according to driver's age, psychologic status；The intermediate quantity of space dependency degree
Visibility can be obtained by visibility meter；The intermediate quantity coefficient of road adhesion of road surface dependency degree can be combined by vehicle GPS data
Vehicle dynamic model real-time estimation obtains.In the emulation experiment of the intelligent Driver Model based on cloud model, by it is all kinds of not
The actual value of certainty index is modified through cloud model, keeps all kinds of indexs uncertain in covering for truly maximum magnitude
Property information.
By taking relative velocity Δ v as an example, actual value Δ v=-4.4km/h, cloud model makeover process is as follows:
Step 1. calculates Δ v=-4.4km/h to the activation degree of each classification of relative velocity by X condition cloud model, that is, is subordinate to
Belong to angle value, classification judges the classification of the affiliated relative velocity of Δ v=-4.4km/h where degree of membership maximum value.It is computed u (Δ
v3)=0.7782, i.e. Δ v ∈ U (III).Specific calculated result is u (Δ v1)=0, u (Δ v2)=0, u (Δ v3)=0.7782, u
(Δv4)=0.4733, u (Δ v5)=0.
Step 2. generates degree of membership u (Δ v by Y condition cloud model310 water dusts of)=0.7782, after mathematic(al) mean
It is revised out
X condition cloud model and Y condition cloud model have been used in above-mentioned calculating, be related in the calculating process of cloud model with
The generation of machine number.For identical input numerical value, the calculated result of homogeneous does not have some differences, and cloud is generated in step 2
The number of drop can be adjusted according to model practical application.Therefore, cloud model reasoning process has the uncertain and extensive of essence
Applicability.
Relative position, Time Dependent degree, road surface dependency degree numerical value cloud model makeover process and relative velocity value revision
Process is identical.Space dependency degree numerical value calculates that corresponding space relies on angle value by the ratio of intermediate quantity visibility location, then
It is modified by cloud model.
Claims (2)
1. a kind of construction method of vehicle-mounted sensing net node intelligent movable pilot model, which is characterized in that this method is with intelligence
Based on pilot model, train tracing model in intelligent Driver Model is judged, during vehicle-mounted net node motion
Relative velocity, relative position, Time Dependent degree, space dependency degree and 5 class uncertainty index of road surface dependency degree are moved as node
The influence factor collection of dynamic cloud model introduces intelligent Driver Model using the uncertain index of cloud model description, drives to intelligence
Relevant parameter in the person's of sailing model is modified, and characterizes node motion strategy and movement law；
The relevant parameter modification method are as follows:
Step 1, computation model provide " intelligence " data to the activation degree of each index classification, that is, are subordinate to angle value, by degree of membership
Maximum value determines classification belonging to data；
Step 2 is generated the water dust drop (x of maximum membership degree value by Y condition cloud generatori,umax), wherein i=1,2,3 ... n,
umaxIndicate maximum membership degree value, xiIndicate the output valve of cloud model；
Step 3, to xiCarry out mathematic(al) meanX is then through the modified parameter value of cloud model, wherein Y condition cloud hair
Giving birth to device algorithmic formula isIn formula, Ey、En、HeIt is the cloud number classified belonging to achievement data
Feature, xiIndicate the output valve of cloud model, RnFor the mark for generating random number, yiIndicate normal distribution random number, uiExpression is subordinate to
Degree；
The modified parameter of the cloud model is as follows:
(1) relative velocity Δ v, relative position Δ s are corrected
The relative velocity of cloud model description, relative position index are introduced into intelligent Driver Model, to speed accurate in model
Difference and spacing are modified, and comply with driver's perception；
Relative velocity is divided into 5 in conjunction with cloud model about conception division theory by psychology-behavioural habits of comprehensive driver
Subset: it is fast very much, it is fast, it approaches, it is slow, slow very much, correspond to { Δ v1、Δv2、Δv3、Δv4、Δv5}；Judge mould
The corresponding qualitativing concept classification of " intelligence " data that type provides, completes quantitative data to the conversion of qualitativing concept, this meets driving
Classification of the member to relative velocity, relative position judgement, driver is often also to feel relative velocity, and perception, which obtains, is
" fast ", still " slow ", " close ", " slow ", " slow very much "；The cloud of same degree of membership is generated by cloud generator again
Drop, is equivalent to driver and is sensuously consistent to these data values；
Using the mathematical mean of these water dusts as the correction value of index, by the modified relative velocity Δ v of cloud model, relative position
Δ s index introduces intelligent Driver Model, modified acceleration and desired distance equation are as follows:
Wherein, Δ v, Δ s are through the revised value of cloud model, and a indicates the peak acceleration of vehicle, s0Indicate be safety away from
From b indicates deceleration, and T indicates the reaction time of driver；
(2) reaction time is corrected
Time Dependent degree τ index is introduced into intelligent Driver Model, compensation driver is from perception peripheral information variation up to making
Time consumed by responding, Time Dependent angle value are corrected through cloud model, and reflection is modified to vehicle acceleration, modified to add
Velocity amplitude formula is as follows:
Wherein, τ is indicated through the revised value of cloud model；
(3) desired speed is corrected
Space dependency degree k is introduced, using the visibility influenced by weather condition as intermediate variable, quantization rain, snow and dense fog are to driving
The influence of member's sighting distance and psychology, is modified the desired speed of vehicle, formula are as follows:
vk=kv0
Wherein, vkIndicate the desired speed under different line of sight conditions；
v0Indicate the desired speed under ideal line of sight conditions；
K indicates modified according to the space dependency degree obtained under different visibility conditions by cloud model；
(4) away from amendment when safe follow the bus
Road surface dependency degree l is introduced, using coefficient of road adhesion as intermediate variable, quantization rain, snow and ice severe weather conditions and difference
The road surface Micro influence mobile to vehicle node, characterize vehicle node in brake deceleration the available degree of dependence in road surface it is big
It is small, away from being modified when to the safe follow the bus of vehicle, formula are as follows:
Tl=T0/l
Wherein, TlAway from T when indicating the safe follow the bus of different pavement conditions0Indicate the i.e. dry pitch of normal road conditions/mixed
When the desired Safety follow the bus of solidifying soil surface away from, l indicate by the modified road surface obtained according to different pavement conditions of cloud model according to
Lai Du.
2. the method as described in claim 1, which is characterized in that described make to train tracing model in intelligent Driver Model is sentenced
It is disconnected, to improve Acceleration Formula are as follows:
In formula:
The peak acceleration of a expression vehicle；
The present speed of v expression vehicle；
v0Indicate desired speed of the vehicle under freestream conditions；
The speed difference of Δ v expression rear car and front truck；
s*(the expectation safe spacing of v, Δ v) expression current vehicle and preceding guide-car；
sTIndicate judge with speed on as threshold value, take 125m；
δ is indicated for adjusting acceleration behavior, and as δ → ∞, i.e. the acceleration a that keeps constant of vehicle reaches v until speed0, δ ∈
[1,5]；
Work as s > sTAnd s*When < 0, vehicle freedom of entry acceleration mode；
As s < sTOr s*When > 0, vehicle is in train tracing model.
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