CN102662320A - Car-following simulation method based on fuzzy mathematics - Google Patents
Car-following simulation method based on fuzzy mathematics Download PDFInfo
- Publication number
- CN102662320A CN102662320A CN2012100558432A CN201210055843A CN102662320A CN 102662320 A CN102662320 A CN 102662320A CN 2012100558432 A CN2012100558432 A CN 2012100558432A CN 201210055843 A CN201210055843 A CN 201210055843A CN 102662320 A CN102662320 A CN 102662320A
- Authority
- CN
- China
- Prior art keywords
- vehicle
- model
- car
- speed
- driver
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Landscapes
- Traffic Control Systems (AREA)
Abstract
The invention discloses a car-following simulation method based on fuzzy mathematics in the technical filed of motor vehicle driving behavior simulation. The method comprises the steps of: firstly, drawing a speed-time curve and a speed-displacement curve of a leading car and a following car respectively; acquiring driving character parameters of the following car and moving state parameters of the two cars one after the other; and then, substituting the character parameters and the moving state parameters of the following car into a fuzzy inference system, deriving a vehicle motion control rule and simulating car driving. By taking into consideration feature differences of drivers and different characteristics of vehicles, the method can better reflect drivers' decision making process, better simulate drivers' driving behavior, and reproduce real traffic scenes more effectively.
Description
Technical field
The invention belongs to motor vehicle driving Behavior modeling technical field, relate in particular to a kind of vehicle follow gallop analogy method based on fuzzy mathematics.
Background technology
Restriction along with development of modern science and technology and land resource; Countries in the world enlarge road network scale from main dependence gradually solves growing transport need and transfers to new and high technology and transform existing road traffic system and management system thereof, thereby reaches the traffic capacity and the service quality that increases substantially transportation network.Traffic simulation has overcome that traffic system field experiment cost is big, the shortcoming of performance difficulty, for urban transportation construction and research provide good test platform.The traffic Modeling Method puts forward for the requirement that adapts to urban traffic control under the new situation just.
Set up an analogue system that can reflect real conditions as far as possible; A realistic model that matches must be arranged; The model of setting up will be convenient to analogue system and simulate the various actual traffic behaviors that realize in the road network realistically, like the sailing of vehicle, lane changing with speeding on, overtake other vehicles go, the various situations of change such as control of intersection signal lamp; In addition; In order to make analogue system reach the performance of traffic programme, evaluation; Also require the foundation of model will be convenient to the dynamic perfromance that analogue system can be reacted overall road network at any time; And can write down current various states of arbitrary entity in the road network and relation to each other, so that obtain various statistical parameters.So the method for building up of Traffic Flow Simulation Models has just become one of core content of traffic simulation behavioral study.
In the process of moving, vehicle movement receives the influence of its front truck, and the driver hopes must keep certain safe distance with front truck again on the other hand with the expectation speed on the one hand.But this influence is asymmetric, and back car can not produce same influence to front truck.What describe this relation is with speeding model.The present invention is exactly that the research and utilization theory of fuzzy mathematics is set up the method with the model of speeding.
Describing method with the model of speeding is " ancient " problem of traffic flow research field, as far back as nineteen fifty ReuschelA.The travel condition of vehicle in formation just begins one's study.Over more than 50 year; Chinese scholars has been carried out number of research projects to vehicle follow gallop model description method; Delivered numerous achievements in research, the reaction model of stimulation, safe distance model, a psychological physiology model and a cellular Automation Model have more typically been arranged in the theoretical model.
1. constant is with speeding model
The model of speeding of following the earliest is meant that the time headway of front and back two cars keeps 2 seconds strokes.Through the relation between flow, speed and the density three that research model reflected, find that this model is a constant flow model, promptly flow is a certain value, can not change with speed, variable density.Therefore, the whole state procedure of using this model to describe traffic flow is inappropriate.
2. stimulate a reaction with speeding model
In road traffic, there is certain mutual relationship between the vehicle of front and back.In the system of this people's one car one tunnel, the driver is in a kind of positive state all the time, and his psychology and sense organ are all movable ceaselessly.At first being perception, then is decision, is action at last.Stimulate a reaction with the ultimate principle of the model of speeding to be: the driver attempts to be consistent with the front truck driving behavior, as long as the stimulation of front truck is promptly arranged, back car will be made a response to this.This model focuses on describes in the driving environment various stimulations to the influence of driving behavior,
The restriction, retardance and the transitivity that stimulate a reaction model clearly to reflect vehicle follow gallop to go.And stimulating a reaction model form simple, the achievement in research as early stage has the meaning of starting.But there is following shortcoming in this model, less now use:
(1) no matter how far the front and back car is at a distance of for model hypothesis, all has the influence relation.But in fact the vehicle follow gallop transitivity of going has scope, and obviously, the behavior of front truck will can be by the perception of back car institute outside sighting distance.In addition, when spacing is very big, the driver thinks that model also will lose efficacy when the behavior of front truck does not constitute any threat to oneself, and at this moment vehicle will be in freestream conditions;
(2) when the front and back two car speed of a motor vehicle are identical, the headstock that model allows two cars be apart from can infinitely reducing until being zero, and do not introduce safe distance notion;
(3) because a large amount of research and test all are in the traffic behavior of low velocity and stop-go, to carry out; Can not reflect well general with speed on into; And with speeding on to being highly susceptible to along with transportation condition and traffic circulation state variation; So the versatility of model is poor, model parameter m and 1 has multiple combination version, has dispute;
(4) hypothesis traffic character is even in the model, and all type of vehicle are all adopted identical parameter with all tracks.
3. safe distance is with speeding model
The safe distance model is also claimed crashproof model; Propose by Kometani and Sasaki at first; Be intended to seek one specific for speeding distance; Even make when the front truck driver takes a beyond thought operation, as long as back car and preceding following distance just can not bump greater than this specific distance of speeding of following.
The Gipps that in the traffic microcosmic Simulation, is widely used is exactly a kind of safe distance model with the model of speeding.This model is exactly that it can react vehicle more really and in fleet, walks to stop behavior by a major reason that extensively adopts, and simultaneously, this Model parameter can be carried out verification easily through the data of actual observation on the road.
Although model has been considered total reaction time and braking distance, owing to do not consider minimum following distance (the minimum vehicle headway when stationary vehicle is motionless obtains through the observation jam density), so the jam density that calculates is still bigger than normal.And by the theoretical traffic capacity of this Model Calculation much smaller than the actual maximum volume of traffic that measures.But because desired parameters is few, calculate simply, the safe distance model still has a wide range of applications in Computer Simulation, like the SISTM of Department for Transport, all uses this class model among the VARSIM of the U.S. and carries out emulation.Although this model can draw the result that can make us accepting, still have many problems to wait to solve, for example, there are a certain distance in hypothesis and the actual conditions avoiding colliding; In actual travel, the driver does not go according to safe distance under many circumstances.As many that can see the place ahead preceding guide-cars, or consider influences such as other vehicle and front signal lamp, the driver thinks in the time of can in time making a response to these changing factors that he just can not keep a safe distance.Therefore; When utilize based on safe distance vehicle follow gallop model when carrying out Traffic Capacity Analysis; The theoretical traffic capacity of calculating is much smaller than the actual maximum volume of traffic that measures; Optimum velocity when reaching the traffic capacity is lower than actual value, and can not reflect the relation of the traffic capacity and design speed and free flow density.Such as a design speed is the track of 120km/h, surface friction coefficient y=0.7, and the optimum velocity that the theoretical traffic capacity of utilizing the Leutzbach Model Calculation to obtain is 1424/hour/track pcu/h/In, reach the traffic capacity is 37.71km/h; And in U.S. HCM2000, this grade road corresponding traffic capacity should be 2400/hour/track pcu/h/In, optimum velocity is 85.71km/h.
4. a psychological physiology model
Psychology one physiology model is also claimed reaction model, is sensation and the reaction that embodies the people with a series of threshold values and desired distance, and these boundary values delimited different codomains, and in different codomains, there is different influence relations in back car with front truck.The MISSION model that the Wiedemann of Germany Karlsruhe university set up in 1974 is the model that gos deep into, meets most driving behavior in this class model the most.Six the value AXs of model through on Δ X/AV plane, ABX, SDX, SDV, CLDV, OPDV is divided into five zones with the vehicle follow gallop state, that is: the district of freely going, break away from the front truck district, approach the front truck district, sail the district with speeding on, brake the district of escaping disaster.In zones of different, adopt different model to calculate next state constantly of vehicle.
Driver's driving behavior is a complex process that receives factor affecting such as environment, physiology, psychology, can not regard a kind of accurate mechanical process as.Taken into full account of influence and the restriction of multiple factor in the psychology one physiology model, on modeling method, more approached actual conditions, also more can describe most driving behavior exactly driving behavior.But different drivers is different to the sensation of speed difference and variable in distance with evaluation, therefore causes this model to be difficult to carry out verification.The parameter of this class model is more simultaneously, and the mutual relationship between submodel is complicated, and all compares difficulty for the investigation and the observation of various threshold values.
5. cellular Automation Model
Cellular Automation Model is called the particulate hopping model again, is applicable to that simulation has the spontaneous phenomenon of discreteness and randomness, has been used to many fields, at present like biology, physics, computer science and sociology etc.This model is applied to traffic simulation, is equipped with parallel computer, microscopic characteristics that can the large-scale simulation road network.Model is divided into 75 meters long unit one by one with the street, and each unit comprises a car or is sky.Each car carries very limited relevant information, wherein the most important thing is speed, and its span is to the integer the maximal rate from zero.The motion of vehicle is to jump to another unit with discrete way from a unit.
Initial cellular automaton single track model set up in 1996, produced the cellular Automation Model of multilane, multi-vehicle-type in 1999 again.Because calculate simply, and adopt the parallel processing technology, such model calculation speed is very fast, can be used for the traffic running of large-scale simulation road network or be used for traffic forecast.Model has merged the advantage of simulation model of microscopic when pursuing operation efficiency.Test through on Germany and U.S.'s highway and city road network reflects that the conclusion that cellular Automation Model obtains tallies with the actual situation in macro-scope.But there is bigger gap in the vehicle follow gallop rule in the model with true driving behavior after all, lacks intuitive.And description and research for traffic details such as overtaking other vehicles, conflux in the model are all more coarse.
Generally speaking, the existing model description method of speeding of following mainly contains following problem:
1. fail to take into full account driver's oneself factor from the psychology angle;
2. fail to take all factors into consideration the factor that influences the vehicle follow gallop behavior;
3. some traditional vehicle follow gallop model; Often only consider driver-vehicle and road three of living in or study isolated the coming of driver, vehicle and road traffic environment of living in, and selection and the realization of ignoring the driving behavior pattern are synergistic effects between people, machine, road, the environment;
4. traditional vehicle follow gallop model is difficult to embody the uncertainty and the inconsistency of a series of psychology such as driver's sensation, understanding, judgement, decision, physiological activity
Summary of the invention
To having deficiencies such as not considering the driver psychology factor in the existing vehicle follow gallop research of mentioning in the above-mentioned background technology, the present invention proposes a kind of vehicle follow gallop analogy method based on fuzzy mathematics.
Technical scheme of the present invention is that a kind of vehicle follow gallop analogy method based on fuzzy mathematics is characterized in that this method may further comprise the steps:
Step 1: draw leading vehicle respectively and with speeding the speed time curve and the speed displacement curve of vehicle;
Step 2: obtain characteristic parameter with the vehicle of speeding by step 1;
Step 3: will bring fuzzy inference system into speed vehicle characteristics parameter and motion state parameters, and draw the vehicle movement control law, simulating vehicle goes.
Said is vehicle relative velocity and time headway ratio with the state parameter of speeding.
Said computing formula with the state parameter of speeding is:
DV＝V_LeadingVehicle-V_SubjectVehicle
Wherein:
DV is the vehicle relative velocity;
V_LeadingVehicle is the speed of leading vehicle;
V_SubjectVehicle is the speed with the vehicle of speeding.
The computing formula of said time headway ratio is:
DSSD＝DS/sd
Wherein:
DSSD is the time headway ratio;
DS is a time headway;
Sd is the expectation time headway.
The computing formula of said time headway DS is:
DS＝DX/V_SubjectVehicle
Wherein:
DX is meant two car relative distances.
Utilization of the present invention based on fuzzy mathematics describe with speed on into method; Analyze vehicle driver's information process and character trait; On the basis of having described based on driver's speed, distance judgement Car following model; The Fuzzy Mathematics method is set up the fuzzy logic control model of vehicle follow gallop driving behavior.This method has been considered the different characteristic of driver's feature difference and vehicle; The decision process that can better reflect the driver; Better drive simulating person's driving behavior makes simulation result more near the actual traffic behavior, the real traffic scene of more effective reproduction.
Description of drawings
Fig. 1 is a synoptic diagram of the present invention.
Embodiment
Below in conjunction with accompanying drawing, preferred embodiment is elaborated.Should be emphasized that following explanation only is exemplary, rather than in order to limit scope of the present invention and application thereof.
Set up an analogue system that can reflect real conditions as far as possible; A realistic model that matches must be arranged; The model of setting up will be simulated the various actual traffic behaviors that realize in the road network as far as possible realistically, so the accuracy of behavior model just seems particularly important.And present still can not meeting this requirement with the model description method of speeding.Exist deterministic contact between guide-car's stimulation and back the reaction before existing research is thought in the vehicle follow gallop process, promptly obviously exist certain cause-effect relationship between the action of front and back car with car.But under the reality, the reaction that the driver has done other driver's action possibly not be based on a deterministic man-to-man relation, and is based on a series of driving criterions next by driver's experience accumulation.The mode that these criterions are used maybe be different because of driver's difference, even also can be different with the difference of condition for same driver.These criterions are not strict and be based on the natural languages basis.For example, if preceding guide-car is slowed down, then should slow down with car in the back; If or the spacing of two cars then afterwards should be slowed down with car and follow following distance etc. to increase less than " safe spacing ".Some language inference forms are more suitable in analyzing with fuzzy logic and approximate resoning form like this.Fuzzy mathematics theory and logic allow to handle with mathematical method the deduction problem of subjective judgement.
Technical scheme of the present invention is:
1. analyze the driver information processing procedure based on people's psychological activity:
The cognitive process of obtaining, process, store, use relevant information, mental process that employing physiological psychology, cognitive psychology further investigation people stimulate to external world and the behavior of making, and carry out abstract to this process chain.
2. experimental data collection:
Analyzing used data obtains through using laboratory vehicle.Communication apparatus, detecting devices, recording unit etc. all are housed on laboratory vehicle; Can distinguish the speed time curve and the speed displacement curve that write down two cars simultaneously; Obtain the different moment, different transportation condition thus down with relative distance, relative velocity and the back car acceleration etc. of two cars of speeding, different moment backs are with position and speed and the acceleration statistical value of car with respect to laboratory vehicle.At driver-operated simultaneously, inquiry driver's various sensations are noted, and are used to analyze driver's course of reaction.
3. the judgement of research vehicle follow gallop state:
On the basis of a large amount of actual observation traffic flow datas, analyse in depth relation with speed running qualities of a wagon and time headway, propose to utilize the relative velocity absolute value to judge vehicle running state quantitatively with the rule that time headway changes.Wither through data and to look into the foundation with the driver information processing procedure, choose the vehicle follow gallop characteristic variable, judge the vehicle follow gallop state.
4. set up vehicle follow gallop model fuzzy inference system:
Fuzzy Mathematics is theoretical, utilizes the fuzzy logic control model that the decision process of the person of sailing is described, and sets up with speeding the model fuzzy inference system.Promptly be divided into n grade to each fuzzy set, be relative to each other between the fuzzy set, set the operation rule of fuzzy reasoning mechanism then
5. the verification of model:
Target is to make the check data difference of emulation output result and actual measurement minimum.
1. based on people's psychological activity, analyze the driver information processing procedure:
Going of vehicle realized by the pilot control appropriate authority. the driver is making decision, and takes certain operation behavior as slowing down, quicken or when overtaking other vehicles, can receiving the influence of various factors.In fact the process of driver's steering vehicle, the just information process of system.In driving procedure, driver's character trait is to the also important influence effect of output of behavior.For the defeated people of same information, the people of different character traits can make different behavior reactions.Concerning the driver, his mood, physical qualification, degree of fatigue, and disease, drug effect etc. all have confidential relation with system.These factors also can produce negative consequence to aspects such as the information processing of system and car engine reactions, both can produce positive role.Driver's handling characteristic is non-linear.Handling characteristic not only is decided by the condition of human pilot itself, and interacts relevant with environmental baseline.When these conditions are all more satisfactory, just can improve traffic usefulness, ensure traffic safety, otherwise, will produce a contrary effect.
Driver's course of reaction comprises 4 stages: sensation stage, understanding stage, judgement stage, execute phase.These 4 needed times in stage are called the reaction time.Driver's brake reaction time for example; It comprises the reflection interval of accepting to stimulate the back brain; Pin moves on to replacing time of brake pedal from accelerator pedal, steps on brake pedal and transmits the delay time at stop to the acting braking of detent, and three's summation is a brake reaction time.Different drivers has different physiological characteristics and psychological characteristic, thereby has the different reaction time.
The speed of reaction velocity is relevant to the familiarity of environment, the age of driving experience, driver, sex, makings etc. with the driver.According to relevant research, driver's reaction velocity of being familiar with environment is just fast, otherwise just slow; The fresh driver reacts slow because the driving experience is few; Middle age driver reaction, driver's reaction velocity of being familiar with environment is just fast, otherwise just slow; The fresh driver reacts slow because the driving experience is few; Middle age, the driver reacted than comparatively fast, and old driver reacts slow; Women driver is slower than male sex's reaction velocity of of the same age, identical driving experience.
From driver's angle, all hope during vehicle ' and front truck keeps a desired distance, promptly at the uniform velocity follow front truck when going with the comfortable safe headstock of front truck under, same speed, its desired distance of the people of different driving behavior characteristics is difference also.In order accurately to reflect this difference, we are divided into the driver plain edition, guard 3 types of type, impulsive styles.On bases to a large amount of investigation of different driving behavior parameters (comprise mainly that desired distance under the different speed of a motor vehicle is measured, the measurement etc. of acceleration-deceleration under the different condition), handle through certain statistical analysis technique, determine plain edition driver's behavior parameter.For conservative type and impulsive style driver, its behavior parameter is on medium-sized basis, and is general unsteady at random in-5%～-10% and 5%～10% interval.
2. collection experimental data:
Through using laboratory vehicle to obtain required data.Communication apparatus, detecting devices, recording unit etc. all are housed on laboratory vehicle; Can distinguish the speed time curve and the speed displacement curve that write down two cars simultaneously; Obtain the different moment, different transportation condition thus down with relative distance, relative velocity and the back car acceleration etc. of two cars of speeding, different moment backs are with position and speed and the acceleration statistical value of car with respect to laboratory vehicle.At driver-operated simultaneously, inquiry driver's various sensations are noted, and are used to analyze driver's course of reaction.
3. the judgement of research vehicle follow gallop state:
In with the model decision process of speeding, two important parameters are arranged: " vehicle relative velocity (DV) " and " time headway ratio (DSSD) "." vehicle relative velocity (DV) " and " time headway ratio (DSSD) " is two basic variablees with the model of speeding, and the acceleration-deceleration of vehicle is with relative velocity and the relative distance decision of basis with front truck, and is as shown in Figure 1.
Variable 1: vehicle relative velocity (DV)
DV＝V_LeadingVehicle-V_SubjectVehicle (1)
Wherein:
V_LeadingVehicle is the speed of leading vehicle;
V_SubjectVehicle is the speed with the vehicle of speeding.
When calculating relative velocity, have more than the speed of considering the car in front and the difference between this car, but several the cars in the place ahead that can see the driver are considered all to come in.
Variable 2: time headway ratio (DSSD)
DSSD＝DS/sd (2)
Wherein:
DS is a time headway
Sd is the expectation time headway, and the personal feature behavior of reflection target driver is for the driver of colony accord with normal distribution normally;
DS＝DX/V_SubjectVehicle (3)
Wherein:
DX is meant two car relative distances.
4. set up vehicle follow gallop model fuzzy inference system:
Through analyzing driver's information process, in the pattern based on driver's speed, distance judgement, driver's decision process can be described through the fuzzy logic control model.In the system that the front and back two cars is formed, import the distance in two workshops, the speed of back car, the speed of front truck, according to the fuzzy logic control rule, the driving behavior of exporting the driver is promptly quickened, deceleration or speed is constant.With two factors as decision variable: vehicle relative velocity and time headway ratio (actual time headway and expectation time headway ratio); Each variable is divided into 5 fuzzy subsets; Adopt Triangleshape grade of membership function, use fuzzy control rule and draw the vehicle control law, the control vehicle '.
Table 1 fuzzy set
Typical can be described below with the principle of speeding based on fuzzy logic: if the time headway ratio is too big, relative velocity is too fast simultaneously, and so, driver's reaction is " acceleration ".Above-mentioned fuzzy control collection has 5 * 5=25 control law, according to measured data and corresponding subordinate function type (generally getting triangular function), can obtain the subordinate function figure of these fuzzy sets.To each summit using gravity-center method of the figure that obtains at last, promptly the independent variable with each point multiply by functional value, addition then, draw at last and greater than zero for quickening, less than zero for slowing down, the size of numeral is represented the degree of acceleration and deceleration.
5. the verification of model:
The verification of model is divided into qualitative and quantitative two kinds.Qualitative verification is carried out verification to following three characteristics: local stability, reaction asymmetry, target and skew property.For quantitative verification, comprise average velocity, flow of speed, acceleration, following distance and the macroscopic aspect of microcosmic point etc.
6. use
FLOWSIM (Fuzzy Logic based motorWay traffic Simulation Model) is based on the cover microscopic traffic simulation software that fuzzy mathematics theory is set up.It is one can the true reappearance dynamic traffic the Stochastic Traffic simulation software.The kernel model of this software be exactly utilize that method among the present invention sets up based on fuzzy deduction system car-following model, can 2 peacekeepings, 3 dimensions dynamically show various simulation processes and result.
From nineteen ninety-five, the exploitation troop of FLOWSIM gathers and has used millions of data various theoretical models have been carried out checking repeatedly, comprises microcosmic and macroscopic view checking.The result is very desirable.
FLOWSIM can be applicable to various road network: urban network, highway, ordinary highway, the main line of communication or mix; Can simulate different traffic controls: signalized intersections, unsignalized intersection (give way or stop), ring road control, rotary island etc. are arranged; Can be used for the analysis and research of traffic prediction scheme, as: road closed, traffic hazard etc. are to the influence of road traffic; Can simulate VMS and go up of the influence of the message of demonstration traffic behavior; Can the analog variable speed(-)limit sign to the influence of traffic flow; Or the like.Can provide and surpass 20 kinds statistics output document: flow, speed, hourage, track density, the distribution of spaces of vehicles time, conflict time distribute or the like.
In a word, FLOWSIM can be widely used in the roading scheme relatively, traffic organization, Managed Solution evaluation, and traffic control system is optimized timing.Also be widely used in scientific research simultaneously, for example: the system stability analysis of novel intelligent transportation system, for around, traffic, Environmental Impact and society, evaluation of economic benefit etc.
At present, the FLOWSIM simulation software is by in a lot of actual traffic organization optimization of being applied in of the success project.Like Changhong bridge, the Guan Yuanqiao of Beijing, the traffic organization Optimization Project of building outer street, institute's bridge and information bridge; Yimeng Road, Linyi City, Shandong Province coordinating control of traffic signals and emulation project; Hangzhou road traffic signal " father and son's lamp " traffic assessment item; Freeway in Henan Province emergency preplan simulation evaluation project etc.
The above; Be merely the preferable embodiment of the present invention, but protection scope of the present invention is not limited thereto, any technician who is familiar with the present technique field is in the technical scope that the present invention discloses; The variation that can expect easily or replacement all should be encompassed within protection scope of the present invention.Therefore, protection scope of the present invention should be as the criterion with the protection domain of claim.
Claims (5)
1. vehicle follow gallop analogy method based on fuzzy mathematics is characterized in that this method may further comprise the steps:
Step 1: draw leading vehicle respectively and with speeding the speed time curve and the speed displacement curve of vehicle;
Step 2: obtain characteristic parameter with the vehicle of speeding by step 1;
Step 3: will bring fuzzy inference system into speed vehicle characteristics parameter and motion state parameters, and draw the vehicle movement control law, simulating vehicle goes.
2. a kind of vehicle follow gallop analogy method based on fuzzy mathematics according to claim 1 is characterized in that said is vehicle relative velocity and time headway ratio with the state parameter of speeding.
3. a kind of vehicle follow gallop analogy method based on fuzzy mathematics according to claim 2 is characterized in that said computing formula with the state parameter of speeding is:
DV＝V_LeadingVehicle-V_SubjectVehicle
Wherein:
DV is the vehicle relative velocity;
V_LeadingVehicle is the speed of leading vehicle;
V_SubjectVehicle is the speed with the vehicle of speeding.
4. a kind of vehicle follow gallop analogy method based on fuzzy mathematics according to claim 3 is characterized in that the computing formula of said time headway ratio is:
DSSD＝DS/sd
Wherein:
DSSD is the time headway ratio;
DS is a time headway;
Sd is the expectation time headway.
5. a kind of vehicle follow gallop analogy method based on fuzzy mathematics according to claim 4 is characterized in that the computing formula of said time headway DS is:
DS＝DX/V_SubjectVehicle
Wherein:
DX is meant two car relative distances.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN2012100558432A CN102662320A (en) | 2012-03-05 | 2012-03-05 | Car-following simulation method based on fuzzy mathematics |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN2012100558432A CN102662320A (en) | 2012-03-05 | 2012-03-05 | Car-following simulation method based on fuzzy mathematics |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN102662320A true CN102662320A (en) | 2012-09-12 |
Family
ID=46771826
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN2012100558432A Pending CN102662320A (en) | 2012-03-05 | 2012-03-05 | Car-following simulation method based on fuzzy mathematics |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN102662320A (en) |
Cited By (17)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103101559A (en) * | 2013-02-16 | 2013-05-15 | 同济大学 | Full-speed field train interval real-time control method based on car-following behavior quality evaluation |
| CN105679092A (en) * | 2016-01-29 | 2016-06-15 | 深圳市美好幸福生活安全系统有限公司 | Driving behavior analysis system and method |
| CN105912814A (en) * | 2016-05-05 | 2016-08-31 | 苏州京坤达汽车电子科技有限公司 | Lane change decision model of intelligent drive vehicle |
| CN106355880A (en) * | 2016-10-09 | 2017-01-25 | 东南大学 | Unmanned vehicle control parameter calibrating method for vehicle-following safety |
| CN107145936A (en) * | 2017-04-22 | 2017-09-08 | 大连理工大学 | A kind of vehicle following-model method for building up based on intensified learning |
| CN108415245A (en) * | 2018-01-26 | 2018-08-17 | 华南理工大学 | The fault tolerant control method of autonomous fleet operations under the conditions of a kind of heterogeneous car networking |
| CN109859465A (en) * | 2019-01-09 | 2019-06-07 | 吉林大学 | The longitudinally controlled parameter adjustment system of automatic Pilot based on traffic flow step response |
| CN110033617A (en) * | 2019-04-19 | 2019-07-19 | 中国汽车工程研究院股份有限公司 | A kind of train tracing model assessment system and method towards natural driving data |
| CN110126641A (en) * | 2019-05-21 | 2019-08-16 | 河南科技学院 | A kind of electric car straight line is with the method for speeding and with the system of speeding |
| CN110570049A (en) * | 2019-09-19 | 2019-12-13 | 西南交通大学 | expressway mixed traffic flow convergence collaborative optimization bottom layer control method |
| CN110750877A (en) * | 2019-09-27 | 2020-02-04 | 西安理工大学 | Method for predicting car following behavior under Apollo platform |
| CN110851995A (en) * | 2019-11-27 | 2020-02-28 | 河北工业大学 | Hybrid traffic flow car following model and simulation method |
| CN111968372A (en) * | 2020-08-25 | 2020-11-20 | 重庆大学 | Multi-vehicle type mixed traffic following behavior simulation method considering subjective factors |
| CN112193245A (en) * | 2020-09-24 | 2021-01-08 | 同济大学 | Deep learning following prediction method considering driver fuzzy perception |
| CN112373482A (en) * | 2020-11-23 | 2021-02-19 | 浙江天行健智能科技有限公司 | Driving habit modeling method based on driving simulator |
| CN113066282A (en) * | 2021-02-26 | 2021-07-02 | 北京航空航天大学合肥创新研究院(北京航空航天大学合肥研究生院) | Vehicle-following coupling relation modeling method and system in mixed-line environment |
| CN113928314A (en) * | 2021-11-17 | 2022-01-14 | 吉林大学 | Automatic driving vehicle following control method considering front and rear vehicles under ice and snow road surface condition |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1719354A (en) * | 2005-05-08 | 2006-01-11 | 上海交通大学 | Acceleration control method of vehicle follow gallop sports |
| CN1876461A (en) * | 2006-07-06 | 2006-12-13 | 上海交通大学 | Method for controlling speed difference-distance when following a driving vehicle |
| WO2007033735A1 (en) * | 2005-09-16 | 2007-03-29 | Bayerische Motoren Werke Aktiengesellschaft | Inter-vehicle-distance-related cruise control system |
-
2012
- 2012-03-05 CN CN2012100558432A patent/CN102662320A/en active Pending
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1719354A (en) * | 2005-05-08 | 2006-01-11 | 上海交通大学 | Acceleration control method of vehicle follow gallop sports |
| WO2007033735A1 (en) * | 2005-09-16 | 2007-03-29 | Bayerische Motoren Werke Aktiengesellschaft | Inter-vehicle-distance-related cruise control system |
| CN1876461A (en) * | 2006-07-06 | 2006-12-13 | 上海交通大学 | Method for controlling speed difference-distance when following a driving vehicle |
Non-Patent Citations (1)
| Title |
|---|
| 贾顺平等: "《车辆跟驰模拟中的驾驶行为与模糊逻辑控制》", 《北方交通大学学报》 * |
Cited By (27)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103101559A (en) * | 2013-02-16 | 2013-05-15 | 同济大学 | Full-speed field train interval real-time control method based on car-following behavior quality evaluation |
| CN103101559B (en) * | 2013-02-16 | 2015-06-03 | 同济大学 | Full-speed field train interval real-time control method based on car-following behavior quality evaluation |
| CN105679092A (en) * | 2016-01-29 | 2016-06-15 | 深圳市美好幸福生活安全系统有限公司 | Driving behavior analysis system and method |
| CN105912814A (en) * | 2016-05-05 | 2016-08-31 | 苏州京坤达汽车电子科技有限公司 | Lane change decision model of intelligent drive vehicle |
| CN106355880A (en) * | 2016-10-09 | 2017-01-25 | 东南大学 | Unmanned vehicle control parameter calibrating method for vehicle-following safety |
| CN106355880B (en) * | 2016-10-09 | 2018-08-21 | 东南大学 | A kind of automatic driving vehicle control parameter scaling method towards with vehicle safety |
| CN107145936A (en) * | 2017-04-22 | 2017-09-08 | 大连理工大学 | A kind of vehicle following-model method for building up based on intensified learning |
| CN108415245A (en) * | 2018-01-26 | 2018-08-17 | 华南理工大学 | The fault tolerant control method of autonomous fleet operations under the conditions of a kind of heterogeneous car networking |
| CN108415245B (en) * | 2018-01-26 | 2019-05-14 | 华南理工大学 | The fault tolerant control method of autonomous fleet operations under the conditions of a kind of heterogeneous car networking |
| CN109859465B (en) * | 2019-01-09 | 2021-05-11 | 吉林大学 | Automatic driving longitudinal control parameter adjusting system based on traffic flow dynamic characteristics |
| CN109859465A (en) * | 2019-01-09 | 2019-06-07 | 吉林大学 | The longitudinally controlled parameter adjustment system of automatic Pilot based on traffic flow step response |
| CN110033617A (en) * | 2019-04-19 | 2019-07-19 | 中国汽车工程研究院股份有限公司 | A kind of train tracing model assessment system and method towards natural driving data |
| CN110126641A (en) * | 2019-05-21 | 2019-08-16 | 河南科技学院 | A kind of electric car straight line is with the method for speeding and with the system of speeding |
| CN110126641B (en) * | 2019-05-21 | 2022-10-04 | 河南科技学院 | Linear following method and system for electric automobile |
| CN110570049A (en) * | 2019-09-19 | 2019-12-13 | 西南交通大学 | expressway mixed traffic flow convergence collaborative optimization bottom layer control method |
| CN110570049B (en) * | 2019-09-19 | 2022-04-29 | 西南交通大学 | Expressway mixed traffic flow convergence collaborative optimization bottom layer control method |
| CN110750877A (en) * | 2019-09-27 | 2020-02-04 | 西安理工大学 | Method for predicting car following behavior under Apollo platform |
| CN110750877B (en) * | 2019-09-27 | 2024-05-03 | 西安理工大学 | Vehicle following behavior prediction method under Apollo platform |
| CN110851995A (en) * | 2019-11-27 | 2020-02-28 | 河北工业大学 | Hybrid traffic flow car following model and simulation method |
| CN110851995B (en) * | 2019-11-27 | 2023-06-06 | 河北工业大学 | Mixed traffic flow following system and simulation method |
| CN111968372B (en) * | 2020-08-25 | 2022-07-22 | 重庆大学 | Multi-vehicle type mixed traffic following behavior simulation method considering subjective factors |
| CN111968372A (en) * | 2020-08-25 | 2020-11-20 | 重庆大学 | Multi-vehicle type mixed traffic following behavior simulation method considering subjective factors |
| CN112193245A (en) * | 2020-09-24 | 2021-01-08 | 同济大学 | Deep learning following prediction method considering driver fuzzy perception |
| CN112373482A (en) * | 2020-11-23 | 2021-02-19 | 浙江天行健智能科技有限公司 | Driving habit modeling method based on driving simulator |
| CN113066282A (en) * | 2021-02-26 | 2021-07-02 | 北京航空航天大学合肥创新研究院(北京航空航天大学合肥研究生院) | Vehicle-following coupling relation modeling method and system in mixed-line environment |
| CN113928314A (en) * | 2021-11-17 | 2022-01-14 | 吉林大学 | Automatic driving vehicle following control method considering front and rear vehicles under ice and snow road surface condition |
| CN113928314B (en) * | 2021-11-17 | 2023-11-10 | 吉林大学 | Automatic driving vehicle following control method considering front and rear vehicles under ice and snow road surface condition |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN102662320A (en) | Car-following simulation method based on fuzzy mathematics | |
| Meng et al. | An improved cellular automata model for heterogeneous work zone traffic | |
| Magana et al. | GAFU: Using a gamification tool to save fuel | |
| Lu et al. | A cellular automaton simulation model for pedestrian and vehicle interaction behaviors at unsignalized mid-block crosswalks | |
| Li et al. | Using cellular automata to investigate pedestrian conflicts with vehicles in crosswalk at signalized intersection | |
| Xie et al. | SIV-DSS: Smart in-vehicle decision support system for driving at signalized intersections with V2I communication | |
| CN105047057A (en) | Highway network macroscopic traffic flow simulation method with consideration of multiple driver styles and lane selection preferences | |
| Karimi et al. | Investigation into passing behavior at passing zones to validate and extend the use of driving simulators in two-lane roads safety analysis | |
| CN102682155A (en) | Network analysis micro-simulation system for urban road traffic | |
| Wang et al. | Effect of human factors on driver behavior | |
| Zhang et al. | Modeling mixed traffic flow at crosswalks in microsimulations using cellular automata | |
| Hassana et al. | Factors affecting overtaking behaviour on single carriageway road: case study at Jalan Kluang-Kulai | |
| Pi et al. | Automotive platoon energy-saving: A review | |
| Hua et al. | Modeling and simulation of approaching behaviors to signalized intersections based on risk quantification | |
| Wang et al. | Lane-changing model with dynamic consideration of driver's propensity | |
| Wang et al. | Estimating rear-end accident probabilities with different driving tendencies at signalized intersections in China | |
| Chen et al. | Two-dimensional following lane-changing (2DF-LC): A framework for dynamic decision-making and rapid behavior planning | |
| Wang et al. | Driver’s lane selection model based on phase-field coupling and multiplayer dynamic game with incomplete information | |
| Elhenawy et al. | Modeling the perception reaction time and deceleration level for different surface conditions using machine learning techniques | |
| Jiang et al. | Research of vehicle flow based on cellular automaton in different safety parameters | |
| Xie et al. | Capturing driving behavior Heterogeneity based on trajectory data | |
| Li et al. | Multiple criteria decision-making for lane-change model | |
| Mubasher et al. | Modeling of individual differences in car-following behaviour of drivers | |
| Khan et al. | Macroscopic traffic characterization based on driver memory and traffic stimuli | |
| Ištoka Otković et al. | Analysis of the influence of car-following input parameters on the modelled travelling time |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| C12 | Rejection of a patent application after its publication | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20120912 |