CN106997689A - V2P collision free methods based on crossing - Google Patents
V2P collision free methods based on crossing Download PDFInfo
- Publication number
- CN106997689A CN106997689A CN201710329646.8A CN201710329646A CN106997689A CN 106997689 A CN106997689 A CN 106997689A CN 201710329646 A CN201710329646 A CN 201710329646A CN 106997689 A CN106997689 A CN 106997689A
- Authority
- CN
- China
- Prior art keywords
- pedestrian
- traffic
- driver
- crossing
- vehicle
- 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.)
- Granted
Links
Classifications
-
- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/16—Anti-collision systems
- G08G1/166—Anti-collision systems for active traffic, e.g. moving vehicles, pedestrians, bikes
Abstract
The invention discloses a kind of V2P collision free methods based on crossing, it includes：First, the participant for determining game is vehicle and pedestrian, and participant point is come, and defines their current set of strategies；2nd, crossing vehicle and the street crossing delay of pedestrian are asked for respectively and risk pays and sets up V2P betting models；3rd, traffic rewarding and punishing are added on the basis of delay, risk are paid and pays the rate that gives way for improving vehicle；4th, SEIR viral transmission models are improved, the psychology of IEVRI model analysis traffic participants is set up, it was demonstrated that the necessity of traffic rewarding and punishing is introduced.The present invention has considered flow of the people, vehicle flowrate in traffic flow, distance, speed of people's car from decision-making area to conflict area, the relative gesture of unit batch street pedestrian's quantity, add traffic rewarding and punishing factor, V2P betting models are set up, traffic efficiency has been ensured while improving traffic safety.
Description
Technical field
The present invention relates to technical field of intelligent traffic, and in particular to a kind of V2P collision free methods based on crossing.
Background technology
World Health Organization's whole world road safety status report, has 1,240,000 people to die from traffic accident, 50,000,000 people wound every year
Residual, wherein 22% is pedestrian, about 20% traffic accident occurs in the cross road mouthful.Because China human mortality is numerous, traffic system occurs
Serious people's car mixes row phenomenon, even more increases vehicle and the traffic conflict of pedestrian, with a large scale should for 4G LTE technologies
With, and smart mobile phone popularization, realize people's car mutual using existing network technology, reduce traffic accident, kept away than traditional
Hit method more actively, effectively.
In order to solve vehicle and pedestrian (V2P, Vehicle to Pedestrian) collision problem, Bayesian network, hidden horse
Er Kefu models, SVMs scheduling algorithm model are used vehicle and the research of pedestrian movement extensively.For example some scholars use shellfish
One model of this network struction of leaf, obtains the priori point of Model Parameter from the mass data of the cross road mouthful vehicle pass-through situation
Whether cloth, know that pedestrian jaywalks according to the state estimation driver of vehicle, or left side is when driver will turn to
No someone passes through crossing.Some scholars propose a motion planning model for the vehicle of turning, including pedestrian is intended to
Detection model, three submodels of gap detection model and wagon control, can detect the appropriate gap between pedestrian, and select
Optimum speed is distributed to control vehicle to pass through gap.The process of vehicle and street pedestrian's information exchange is also a gambling process,
Domestic Many researchers are setting up driver behavior model using repeated game without signal-controlled intersection, give vehicle and reasonably lead to
Row decision-making, but repeated game increases risk of collision and traffic efficiency is low, also it has been proposed that driver is non-with street pedestrian
Time delay and risk income are take into account in the Dynamic Game Model of cooperation, game but is not quantified.
Therefore, it is necessary to develop a kind of V2P collision free methods based on crossing.
The content of the invention
It is an object of the invention to provide a kind of V2P collision free methods based on crossing, to ensure the safety of road traffic.
V2P collision free methods of the present invention based on crossing, comprise the following steps：
Step 1: the participant for determining game is vehicle and pedestrian, the current set of strategies of pedestrian and vehicle is defined, and will be driven
The person of sailing and street pedestrian's classification；
Step 2: observation crossing vehicle, pedestrian density, distance of people's car from decision-making area to conflict area, speed, speed and
The quantity of unit batch street pedestrian, solves V2P and takes the delay payment of the different strategies that pass through and risk to pay at crossing respectively,
The total revenue of both sides is calculated, V2P betting models is set up, tries to achieve both optimal current strategy combinations；
Paid Step 3: introducing traffic rewarding and punishing, reward the driver that pedestrian is avoided in crossing active parking, punishment and pedestrian
The driver scrambled for roads, recalculates the total revenue of both sides, sets up improved V2P betting models, tries to achieve both optimal current plans
Slightly combine；
Step 4: setting up IEVRI models, the psychology of traffic participant is analyzed, it was demonstrated that introduce the necessity of traffic rewarding and punishing.
Further, in the step one, the set of strategies of the pedestrian is defined as P={ wait, pass through }；The plan of the vehicle
Slightly collection is defined as V={ wait, pass through }；The type of the pedestrian is divided into Pc={ the old, weak, sick, disabled, pregnant and young, other }；It is described to drive
The level of security that the type of member usually drives to be accustomed to according to driver is divided into Vc={ safety-type, GENERAL TYPE, dangerous type }.
Further, in the step 2, the total revenue such as following table of the both sides calculated：
Wherein, Dp *D is paid for pedestrian delayspUsing the result after min-max standardizations, Dv *It is delayed for driver
Pay DvUsing the result after min-max standardizations, Sp *S is paid for pedestrian's riskpUsing min-max standardizations
Result afterwards, Sv *S is paid for driver's riskvUsing the result after min-max standardizations, the weight that α delays are paid, β
The weight paid for risk, β=1- α.
Further, the pedestrian delays pay DpFor：
In formula：ρp1、ρp2To come and go the respective pedestrian's flow in side in road；T' for vehicles traverse pedestrian safety pass through between
Gap；W is a lane width；vpFor the speed of street pedestrian；vvFor the speed of vehicle.
Further, driver's delay pays DvFor：
In formula：ρvnFor nth bar lane flow amount；tnFor the safety clearance time of nth bar track pedestrians travel's vehicle；tsFor
Pedestrian makes a decision the time；tlThe time passed through for length of wagon.
Further, solve pedestrian's risk and pay SpS is paid with driver's riskvMethod：
If pedestrian is a in the acceleration of decision pointpIf driver is a in the acceleration of decision pointv, driver to collision domain
Distance be respectively lv, the distance of pedestrian to collision domain is respectively lp, both using this state by time difference of collision domain as：
If T<1s, then regard the risk of both sides as infinity, vehicle preferentially must give way to pedestrian；
Define pedestrian's risk and pay SpFor：
Define driver's risk and pay SvFor：
In formula：δvFor threat degree of the traffic accident to driver's life；M is vehicle mass；npFor unit batch street pedestrian's
Quantity；δpFor threat degree of the traffic accident to pedestrian's life；B(vv,np) it is that speed is relative with unit batch street pedestrian's quantity
Gesture, its value is as follows：
Further, in the step 3, it is piecewise function to introduce traffic rewarding and punishing and pay, and the piecewise function is：
In formula：vvThe speed of vehicle is represented, punishment is added and pays, the total revenue function of driver is：
In formula：Dp *D is paid for pedestrian delayspUsing the result after min-max standardizations, Dv *It is delayed for driver
Pay DvUsing the result after min-max standardizations, Sp *S is paid for pedestrian's riskpUsing min-max standardizations
Result afterwards, Sv *S is paid for driver's riskvUsing the result after min-max standardizations, γ represents traffic rewarding and punishing
Weight, the weight that α delays are paid, β is the weight that risk is paid, β=1- α, and α>β,α>γ, vehicle selection passes through F (vv) take
On the occasion of otherwise taking negative value.
Further, in the step 4, I is know-nothing in the IEVRI models, and representative is not affected by traffic violation person's influence
People；E is lurker, represents and not yet decides whether to imitate the people of traffic violation person by violator's influence；V is violator, is represented
Imitate the people of traffic violation person；R is recuperator, represents the people for having received and temporarily no longer having been broken the rules after traffic punishment； I(t)、E
(t), V (t), R (t) represent the total quantity of this four classes people of t respectively, meet below equation：
I (t)+E (t)+V (t)+R (t)=1.
Further, in the IEVRI models know-nothing once being contacted with lurker necessarily as lurker can
Can, a lurker has a times of infection ability, then t I will turn into E with aE probability, and E will turn into R with δ probability,
V is turned into b probability, V will turn into R with c probability, and R turns into I with σ probability, and minority R may ignore traffic punishment, withProbability turn into V, the dynamics positive correlation that c is punished with traffic to a certain extent, punishment is bigger, and c is bigger；
Try to achieve the equalization point of IEVRI models, it was demonstrated that c plays vital work to the stabilization of whole traffic system and differentiation
With introducing after traffic punishment, the rate violating the regulations of traffic participant will greatly reduce.
Beneficial effects of the present invention：Pedestrian is primarily based on, time delay, risk of the vehicle at crossing, which are paid, sets up V2P game moulds
Type, gives people's car rational priority pass decision-making, and improved V2P is set up in the rewarding and punishing payment of introducing driver on this basis
Betting model, examples comparative analysis, it was demonstrated that improved V2P betting models add the rate that gives way of vehicle, are more beneficial for road
Traffic safety.In addition, traditional viral transmission model is improved in the present invention sets up new IEVRI model analysis traffic participation
The psychology of person, it was demonstrated that the necessity of punishment on contravention of regulation.
Brief description of the drawings
Fig. 1 is schematic diagram of the invention；
Fig. 2 is IEVRI model framework charts of the invention；
Fig. 3 is R of the invention0>When 1, the evolving trend of traffic system；
Fig. 4 is R of the invention0<When 1, the evolving trend of traffic system.
Embodiment
The invention will be further described with reference to the accompanying drawings and examples.
V2P collision free methods of the present invention based on crossing, what emphasis was solved is crossing street pedestrian and vehicle
Collision problem, its main thought is flow of the people, the vehicle flowrate first in traffic flow, and it is double to try to achieve V2P using interspersed theory
The delay of side is paid, and then considers distance, speed of people's car from decision-making area to conflict area, speed, proposed unit's batch street crossing
The concept of the relative gesture of pedestrian's quantity, the risk for trying to achieve both sides is paid, and then setting up V2P betting models, to give both sides rational
Traffic efficiency.But in order to further improve avoidance rate of the vehicle to pedestrian, traffic rewarding and punishing factor is added, new V2P games are set up
Model, and SEIR viral transmission models are improved, set up the psychology of IEVRI model analysis traffic participants, it was demonstrated that introduce traffic prize
The necessity penalized.
V2P collision free methods of the present invention based on crossing, comprise the following steps：
Step 1: the participant for determining game is vehicle and pedestrian, the current set of strategies of pedestrian and vehicle is defined, and will be driven
The person of sailing and street pedestrian's classification.
Step 2: observation crossing vehicle, pedestrian density, distance of people's car from decision-making area to conflict area, speed, speed and
The quantity of unit batch street pedestrian, solves V2P and takes the delay payment of the different strategies that pass through and risk to pay at crossing respectively,
The total revenue of both sides is calculated, V2P betting models is set up, tries to achieve both optimal current strategy combinations.
Paid Step 3: introducing traffic rewarding and punishing, reward the driver that pedestrian is avoided in crossing active parking, punishment and pedestrian
The driver scrambled for roads, recalculates the total revenue of both sides, sets up improved V2P betting models, tries to achieve both optimal current plans
Slightly combine.
Step 4: setting up IEVRI models, the psychology of traffic participant is analyzed, it was demonstrated that introduce the necessity of traffic rewarding and punishing.
The V2P collision free methods of the present invention based on crossing are described in detail below：
The present invention is by passing through the microcosmic analysis of street crossing behavior to V2P, and the participant of game is set to pedestrian and vehicle, pedestrian
Set of strategies be set to P={ wait, pass through }, the set of strategies of driver is set to V={ wait, pass through }.In view of street pedestrian's
Feature, the type of pedestrian is divided into Pc={ the old, weak, sick, disabled, pregnant and young, other }, the type of driver then can according to usually they drive
The level of security of custom is divided into Vc={ safety-type, GENERAL TYPE, dangerous type }, is given for different types of driver and pedestrian
Different current strategies.
The present invention initially sets up the V2P betting models for paying and being paid with risk based on delay, and embodiments thereof is as follows：
Step I, the delay payment for calculating V2P：
Delay pays the interspersed theory of main application to analyze, and when pedestrians travel's vehicle clearance, the time income of acquisition is car
Waiting delay it is as follows：
In formula：ρp1、ρp2To come and go the respective pedestrian's flow (people/s) in side in road, t' is the safety of vehicles traverse pedestrian
Crossing gap (s), w is a lane width (m), vpFor the speed (m/s) of street pedestrian, 1m/s~1.45m/s, v are takenvFor
The speed of vehicle.
The road in n bars track, as the waiting delay D that the time income that vehicles traverse pedestrian gap is obtained is pedestrianvIt is as follows：
In formula：ρvnFor nth bar lane flow amount (car/s)；tnFor nth bar track pedestrians travel's vehicle safety clearance when
Between (s)；tsFor pedestrian's resolution time (s), 2.0s is taken；tlThe time passed through for length of wagon, typically take standard vehicle 0.72s.
Step II, the risk payment for calculating V2P：
If pedestrian is a in the acceleration of decision pointp, speed is vpIf driver is a in the acceleration of decision pointv, speed
For vv, the distance of driver to collision domain is respectively lv, the distance of pedestrian to collision domain is respectively lp, both are passed through with this state
The time difference of collision domain is：
If T<1s, then regard the risk of both sides as infinity, vehicle preferentially must give way to pedestrian.Define V2P risk of collision
Pay off function be：
In formula：M is vehicle mass (kg)；SvPaid for the risk of driver；SpPaid for the risk of pedestrian；δvFor traffic accident
To the threat degree of driver's life, 0.8 is taken；npFor the quantity (people) of unit batch street pedestrian；δvPedestrian is given birth to for traffic accident
The threat degree of life, takes 1；B(vv,np) it is the speed gesture relative with unit batch street pedestrian's quantity, value is as follows：
Step III, the pay off function based on delay and risk set up the total revenue of both sides, participate in shown in table 1.
The total revenue of table 1V2P both sides
The result of V2P games is reasonably current tactful in order to give people's car, therefore does not consider that both sides are all wait in table 1
Strategy, wherein, Dp *D is paid for pedestrian delayspUsing the result after min-max standardizations, Dv *It is delayed for driver
Pay DvUsing the result after min-max standardizations, Sp *S is paid for pedestrian's riskpUsing min-max standardizations
Result afterwards, Sv *S is paid for driver's riskvUsing the result after min-max standardizations, Dp *、Dv *、Sv *、 Sp *Take
It is worth for (0,1)；The weight that α delays are paid；β is the weight that risk is paid；When pedestrian's type is that the old,weak,sick and disabled children is pregnant, drive
Member's type safety rank from high to low, α difference values 0.6,0.7,0.8, when pedestrian's type other when, driver's type safety
From high to low, α distinguishes value 0.7,0.8,0.9, β=1- α to rank.
Paid present invention introduces traffic rewarding and punishing and improve above-mentioned betting model to improve avoidance rate of the vehicle to pedestrian, it is real
Apply mode as follows：
It is piecewise function that step I, introducing traffic rewarding and punishing, which are paid,：
In formula：vvRepresent speed of the driver in decision region.
Step II, addition punishment are paid, and the total revenue function for redefining driver is：
In formula, γ represents the weight of traffic rewarding and punishing, γ=0.3, and α>β,α>γ, vehicle selection passes through F (vv) take on the occasion of,
Otherwise negative value is taken.
Step III：Model comparative analysis：Example calculation, as a result as shown in accompanying drawing table 2,3, table 2 is based on delay and risk
The pedestrian that calculates of V2P betting models and vehicle optimum combination, table 3 is adds the V2P betting models after traffic rewarding and punishing are paid
(overstriking font is the current strategies of V2P in table for the pedestrian of (the V2P betting models after improving) calculating and the optimum combination of vehicle
Optimum combination).Comparative analysis, it is clear that the V2P betting models introduced after traffic rewarding and punishing can improve the rate that gives way of vehicle, more
The safety of road traffic can be ensured.
V2P payoff of the table 2 based on delay and risk
Table 3 introduces the V2P payoffs that traffic rewarding and punishing are paid
The present invention improves SEIR models and sets up IEVRI models, analyzes the psychology of traffic participant, it was demonstrated that punishment on contravention of regulation must
I (Innocent) is know-nothing in the property wanted, model, represents the people for being not affected by traffic violation person's influence, E (Exposed) is latent
Fu Zhe.Represent and not yet decided whether to imitate the people of traffic violation person by violator's influence, V (Violator) violator represents effect
The people of imitative traffic violation person, R (recovered) is recuperator, represents and has received what is temporarily no longer broken the rules after traffic punishment
People, I (t), E (t), V (t), R (t) represent the total quantity of this four classes people of t respectively, have
I (t)+E (t)+V (t)+R (t)=1 (11).
Specific implementation step is as follows：
Step I, set up IEVRI models：IEVRI models are as shown in Figure 1：Know-nothing in IEVRI models once with
Lurker's contact just necessarily turns into the possibility of lurker, and a lurker has a times of infection ability, then t I will be with aE
Probability turn into E, E will with δ probability turn into R, with b probability turn into V, V by with c probability turn into R, R with σ probability into
For I, and minority R may ignore traffic punishment, withProbability turn into V, c to a certain extent with traffic punish dynamics just
Correlation, punishment is bigger, and c is bigger.
Step II, the equalization point for trying to achieve model：IEVRI models according to Fig. 2 set up equation below group：
Initial value：
D=(I, E, V, R) | I, E, V, R >=0 and I+E+V+R=1 } (13)
The right-hand member for making equation (12) is 0, obtains the equalization point P (I of equation*,E*,S*,R*)：
From formula (13) and formula (14), orderWhen, equalization point P exists in D, and local progressive steady
It is fixed, R0>When 1, equalization point is P (1,0,0,0), R0The final evolving trend of whole system is determined, claims R0For traffic violation row
For transmission threshold, itself and δ, b, a are related.
Step III, numerical simulation analysis：From the foregoing, it will be observed that R0The scope and situation of motoring offence propagation are directly affected,
Analyze δ, b, a, influence overall to system c.Invent by the way of numerical simulation come the evolving trend that analysis system is final,
Take I (0)=0.4, E (0)=0.1, S (0)=0.1, R (0)=0.4.
(1) a=0.6, b=0.6, c=0.2 are taken, δ=0.3, δ=0.2,Emulation
As a result as shown in (A) in Fig. 3, increase penalty factor c=0.8, lurker then change under the pressure of punishment the factor b=0.4, δ=
0.5, simulation result is as shown in (B) in Fig. 3.
Contrast (A) and (B) in accompanying drawing 3, R0>When 1, equalization point is P (1,0,0,0), increases penalty factor c, violator
0 can be leveled off to faster, system can integrally tend towards stability faster, develop into owner and be in compliance with traffic rules.
(2) a=0.8, b=0.4, c=0.2 are taken, δ=0.2, δ=0.2,Emulation
As a result as shown in accompanying drawing 4 (A), increased penalty factor c=0.8, lurker then changes factor b=0.2 under the pressure of punishment, δ=0.4,
Shown in simulation result such as accompanying drawing 4 (B).
(A) and (B), R in comparison diagram 40<When 1, equalization point increases penalty factor c, violator can be faster in D
The violator leveled off in stable state, and system is stable in less state, and the people observed traffic rules and regulations is stable more
State.
As can be seen here, c plays vital effect to the stabilization of whole traffic system and differentiation, draws in traffic rules
Enter appropriate punishment on contravention of regulation and be able to ensure that more drivers observe traffic rules and regulations, motor vehicle is handed over by no traffic lights
During the cross road mouthful, the behavior scrambled for roads with pedestrian should also include traffic punishment.
No signal crossing is influenceed into the current factor quantifications of V2P in the present invention, V2P current decision-making carried out using game
Instruct, be then introduced into the rate that gives way that the rewarding and punishing factor improves vehicle into game, finally improve the model analysis of SEIR viral transmissions and hand over
The psychology of logical participant, it was demonstrated that introduce the necessity that rewarding and punishing are paid.
Claims (9)
1. a kind of V2P collision free methods based on crossing, it is characterised in that comprise the following steps：
Step 1: the participant for determining game is vehicle and pedestrian, the current set of strategies of pedestrian and vehicle is defined, and by driver
With street pedestrian's classification；
Step 2: observation crossing vehicle, pedestrian density, distance, speed, speed and unit of people's car from decision-making area to conflict area
The quantity of batch street pedestrian, solves V2P and takes the delay payment of the different strategies that pass through and risk to pay at crossing, calculate respectively
The total revenue of both sides, sets up V2P betting models, tries to achieve both optimal current strategy combinations；
Paid Step 3: introducing traffic rewarding and punishing, reward the driver that pedestrian is avoided in crossing active parking, punishment is scrambled for roads with pedestrian
Driver, recalculate the total revenue of both sides, set up improved V2P betting models, try to achieve both optimal current tactful group
Close；
Step 4: setting up IEVRI models, the psychology of traffic participant is analyzed, it was demonstrated that introduce the necessity of traffic rewarding and punishing.
2. the V2P collision free methods according to claim 1 based on crossing, it is characterised in that：In the step one, institute
The set of strategies for stating pedestrian is defined as P={ wait, pass through }；The set of strategies of the vehicle is defined as V={ wait, pass through }；It is described
The type of pedestrian is divided into Pc={ the old, weak, sick, disabled, pregnant and young, other }；The type of the driver usually drives to be accustomed to according to driver
Level of security be divided into Vc={ safety-type, GENERAL TYPE, dangerous type }.
3. the V2P collision free methods according to claim 1 or 2 based on crossing, it is characterised in that：The step 2
In, the total revenue such as following table of the both sides calculated：
Wherein, Dp *D is paid for pedestrian delayspUsing the result after min-max standardizations, Dv *D is paid for driver's delayv
Using the result after min-max standardizations, Sp *S is paid for pedestrian's riskpUsing the knot after min-max standardizations
Really, Sv *S is paid for driver's riskvUsing the result after min-max standardizations, the weight that α delays are paid, β is risk
The weight of payment, β=1- α.
4. the V2P collision free methods according to claim 3 based on crossing, it is characterised in that：The pedestrian delays branch
Pay DpFor：
In formula：ρp1、ρp2To come and go the respective pedestrian's flow in side in road；T' is the safe crossing gap of vehicles traverse pedestrian；W is
One lane width；vpFor the speed of street pedestrian；vvFor the speed of vehicle.
5. the V2P collision free methods according to claim 4 based on crossing, it is characterised in that：Driver's delay
Pay DvFor：
In formula：ρvnFor nth bar lane flow amount；tnFor the safety clearance time of nth bar track pedestrians travel's vehicle；tsFor pedestrian
The resolution time；tlThe time passed through for length of wagon.
6. the V2P collision free methods according to claim 5 based on crossing, it is characterised in that：Solve pedestrian's wind
Danger pays SpS is paid with driver's riskvMethod：
If pedestrian is a in the acceleration of decision pointpIf driver is a in the acceleration of decision pointv, driver to collision domain away from
From respectively lv, the distance of pedestrian to collision domain is respectively lp, both using this state by time difference of collision domain as：
If T<1s, then regard the risk of both sides as infinity, vehicle preferentially must give way to pedestrian；
Define pedestrian's risk and pay SpFor：
Define driver's risk and pay SvFor：
In formula：δvFor threat degree of the traffic accident to driver's life；M is vehicle mass；npFor the number of unit batch street pedestrian
Amount；δpFor threat degree of the traffic accident to pedestrian's life；B(vv,np) it is the speed gesture relative with unit batch street pedestrian's quantity,
Its value is as follows：
7. the V2P collision free methods based on crossing according to claim 1 or 2 or 4 or 5 or 6, it is characterised in that：Institute
State in step 3, it is piecewise function to introduce traffic rewarding and punishing and pay, and the piecewise function is：
In formula：vvThe speed of vehicle is represented, punishment is added and pays, the total revenue function of driver is：
In formula：Dp *D is paid for pedestrian delayspUsing the result after min-max standardizations, Dv *D is paid for driver's delayv
Using the result after min-max standardizations, Sp *S is paid for pedestrian's riskpUsing the knot after min-max standardizations
Really, Sv *S is paid for driver's riskvUsing the result after min-max standardizations, γ represents the weight of traffic rewarding and punishing, and α prolongs
The weight paid by mistake, β is the weight that risk is paid, β=1- α, and α>β,α>γ, vehicle selection passes through F (vv) take on the occasion of otherwise
Take negative value.
8. the V2P collision free methods according to claim 7 based on crossing, it is characterised in that：In the step 4, institute
It is know-nothing to state I in IEVRI models, represents the people for being not affected by traffic violation person's influence；E is lurker, is represented by violator's shadow
Sound not yet decides whether to imitate the people of traffic violation person；V is violator, represents the people for imitating traffic violation person；R is recuperator,
Represent the people for having received and temporarily no longer having been broken the rules after traffic punishment；I (t), E (t), V (t), R (t) represent this four class of t respectively
The total quantity of people, meets below equation：
I (t)+E (t)+V (t)+R (t)=1.
9. the V2P collision free methods according to claim 8 based on crossing, it is characterised in that：In the IEVRI models
One know-nothing necessarily turns into the possibility of lurker once being contacted with lurker, and a lurker has a times of infection ability, that
T I will turn into E with aE probability, and E will turn into R with δ probability, and V is turned into b probability, and V will turn into R with c probability,
R turns into I with σ probability, and minority R may ignore traffic punishment, withProbability turn into V, c to a certain extent with traffic
The dynamics positive correlation penalized, punishment is bigger, and c is bigger；
Try to achieve the equalization point of IEVRI models, it was demonstrated that c plays vital effect to the stabilization of whole traffic system and differentiation i.e.
Introduce after traffic punishment, the rate violating the regulations of traffic participant will greatly reduce.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710329646.8A CN106997689B (en) | 2017-05-11 | 2017-05-11 | V2P based on crossing avoids collision method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710329646.8A CN106997689B (en) | 2017-05-11 | 2017-05-11 | V2P based on crossing avoids collision method |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN106997689A true CN106997689A (en) | 2017-08-01 |
| CN106997689B CN106997689B (en) | 2019-08-27 |
Family
ID=59435258
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201710329646.8A Active CN106997689B (en) | 2017-05-11 | 2017-05-11 | V2P based on crossing avoids collision method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN106997689B (en) |
Cited By (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108491600A (en) * | 2018-03-12 | 2018-09-04 | 郑州大学 | A kind of vehicle movement emulation mode based on people's this autonomy traffic |
| US10235882B1 (en) | 2018-03-19 | 2019-03-19 | Derq Inc. | Early warning and collision avoidance |
| CN110111605A (en) * | 2019-06-12 | 2019-08-09 | 吉林大学 | Automatic driving vehicle entrance ring road based on dynamic game travels decision-making technique |
| CN110363986A (en) * | 2019-06-28 | 2019-10-22 | 江苏大学 | A kind of centralized merging area car speed optimization method based on the game of vehicle vehicle and driving potential field power |
| US11062606B2 (en) | 2018-05-10 | 2021-07-13 | Bastien Beauchamp | Method and system for vehicle-to-pedestrian collision avoidance |
| CN113470430A (en) * | 2021-06-22 | 2021-10-01 | 南京航空航天大学 | Early warning method for vehicle collision at non-signalized intersection based on steering intention prediction |
| CN113808394A (en) * | 2021-08-27 | 2021-12-17 | 东南大学 | Cross-street channel safety evaluation method based on risk combination mode |
| US11249184B2 (en) | 2019-05-07 | 2022-02-15 | The Charles Stark Draper Laboratory, Inc. | Autonomous collision avoidance through physical layer tracking |
| US11263896B2 (en) | 2020-04-06 | 2022-03-01 | B&H Licensing Inc. | Method and system for detecting jaywalking of vulnerable road users |
| CN114644018A (en) * | 2022-05-06 | 2022-06-21 | 重庆大学 | Game theory-based man-vehicle interaction decision planning method for automatic driving vehicle |
| US11443631B2 (en) | 2019-08-29 | 2022-09-13 | Derq Inc. | Enhanced onboard equipment |
| CN115273455A (en) * | 2022-05-13 | 2022-11-01 | 长安大学 | Design method for evaluating yield safety of motor vehicle and stop line |
| CN115662113A (en) * | 2022-09-30 | 2023-01-31 | 合肥工业大学 | Signalized intersection people-vehicle game conflict risk assessment and early warning method |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103249062A (en) * | 2013-01-24 | 2013-08-14 | 无锡南理工科技发展有限公司 | Repeated game-based converged ubiquitous network multi-terminal cooperation trust mechanism |
| JP2014225069A (en) * | 2013-05-15 | 2014-12-04 | スズキ株式会社 | Communication system between pedestrian and vehicle |
| JP2016184200A (en) * | 2015-03-25 | 2016-10-20 | 住友電気工業株式会社 | Pedestrian approach notification device, pedestrian approach notification system, computer program, and pedestrian approach notification method |
| CN106652556A (en) * | 2015-10-28 | 2017-05-10 | 中国移动通信集团公司 | Human-vehicle anti-collision method and apparatus |
-
2017
- 2017-05-11 CN CN201710329646.8A patent/CN106997689B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103249062A (en) * | 2013-01-24 | 2013-08-14 | 无锡南理工科技发展有限公司 | Repeated game-based converged ubiquitous network multi-terminal cooperation trust mechanism |
| JP2014225069A (en) * | 2013-05-15 | 2014-12-04 | スズキ株式会社 | Communication system between pedestrian and vehicle |
| JP2016184200A (en) * | 2015-03-25 | 2016-10-20 | 住友電気工業株式会社 | Pedestrian approach notification device, pedestrian approach notification system, computer program, and pedestrian approach notification method |
| CN106652556A (en) * | 2015-10-28 | 2017-05-10 | 中国移动通信集团公司 | Human-vehicle anti-collision method and apparatus |
Non-Patent Citations (4)
| Title |
|---|
| YORIYOSHI HASHIMOTO ET AL.: "A probabilistic model for the estimation of pedestrian crossing behavior at signalized intersections", 《2015 IEEE 18TH INTERNATIONAL CONFERENCE ON INTELLIGENT TRANSPORTATION SYSTEMS》 * |
| 刘博通: "驾驶员与过街行人非合作动态博弈模型研究", 《中国优秀硕士学位论文全文数据库工程科技Ⅱ辑》 * |
| 刘小明，王秀英: "基于重复博弈的无灯控交叉口驾驶员行为模型", 《中国公路学报》 * |
| 唐勍勍: "基于合作博弈的平面信号交叉口行人和机动车干扰研究", 《中国优秀硕士学位论文全文数据库工程科技Ⅱ辑》 * |
Cited By (28)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108491600A (en) * | 2018-03-12 | 2018-09-04 | 郑州大学 | A kind of vehicle movement emulation mode based on people's this autonomy traffic |
| CN108491600B (en) * | 2018-03-12 | 2022-04-05 | 郑州大学 | Vehicle motion simulation method based on autonomous human traffic |
| US11257371B2 (en) | 2018-03-19 | 2022-02-22 | Derq Inc. | Early warning and collision avoidance |
| US10235882B1 (en) | 2018-03-19 | 2019-03-19 | Derq Inc. | Early warning and collision avoidance |
| US11763678B2 (en) | 2018-03-19 | 2023-09-19 | Derq Inc. | Early warning and collision avoidance |
| US11749111B2 (en) | 2018-03-19 | 2023-09-05 | Derq Inc. | Early warning and collision avoidance |
| US10565880B2 (en) | 2018-03-19 | 2020-02-18 | Derq Inc. | Early warning and collision avoidance |
| US10854079B2 (en) | 2018-03-19 | 2020-12-01 | Derq Inc. | Early warning and collision avoidance |
| US10950130B2 (en) | 2018-03-19 | 2021-03-16 | Derq Inc. | Early warning and collision avoidance |
| US11276311B2 (en) | 2018-03-19 | 2022-03-15 | Derq Inc. | Early warning and collision avoidance |
| US11257370B2 (en) | 2018-03-19 | 2022-02-22 | Derq Inc. | Early warning and collision avoidance |
| US11062606B2 (en) | 2018-05-10 | 2021-07-13 | Bastien Beauchamp | Method and system for vehicle-to-pedestrian collision avoidance |
| US11249184B2 (en) | 2019-05-07 | 2022-02-15 | The Charles Stark Draper Laboratory, Inc. | Autonomous collision avoidance through physical layer tracking |
| CN110111605B (en) * | 2019-06-12 | 2021-08-31 | 吉林大学 | Automatic driving vehicle entrance and exit ramp driving decision method based on dynamic game |
| CN110111605A (en) * | 2019-06-12 | 2019-08-09 | 吉林大学 | Automatic driving vehicle entrance ring road based on dynamic game travels decision-making technique |
| CN110363986A (en) * | 2019-06-28 | 2019-10-22 | 江苏大学 | A kind of centralized merging area car speed optimization method based on the game of vehicle vehicle and driving potential field power |
| CN110363986B (en) * | 2019-06-28 | 2021-09-10 | 江苏大学 | Centralized confluence area vehicle speed optimization method |
| US11443631B2 (en) | 2019-08-29 | 2022-09-13 | Derq Inc. | Enhanced onboard equipment |
| US11688282B2 (en) | 2019-08-29 | 2023-06-27 | Derq Inc. | Enhanced onboard equipment |
| US11263896B2 (en) | 2020-04-06 | 2022-03-01 | B&H Licensing Inc. | Method and system for detecting jaywalking of vulnerable road users |
| CN113470430B (en) * | 2021-06-22 | 2022-07-15 | 南京航空航天大学 | Non-signalized intersection vehicle conflict early warning method based on steering intention prediction |
| CN113470430A (en) * | 2021-06-22 | 2021-10-01 | 南京航空航天大学 | Early warning method for vehicle collision at non-signalized intersection based on steering intention prediction |
| CN113808394A (en) * | 2021-08-27 | 2021-12-17 | 东南大学 | Cross-street channel safety evaluation method based on risk combination mode |
| CN114644018A (en) * | 2022-05-06 | 2022-06-21 | 重庆大学 | Game theory-based man-vehicle interaction decision planning method for automatic driving vehicle |
| CN115273455A (en) * | 2022-05-13 | 2022-11-01 | 长安大学 | Design method for evaluating yield safety of motor vehicle and stop line |
| CN115273455B (en) * | 2022-05-13 | 2023-06-16 | 长安大学 | Design method for evaluating yield safety and parking line of motor vehicle |
| CN115662113A (en) * | 2022-09-30 | 2023-01-31 | 合肥工业大学 | Signalized intersection people-vehicle game conflict risk assessment and early warning method |
| CN115662113B (en) * | 2022-09-30 | 2023-10-13 | 合肥工业大学 | Signal intersection man-vehicle game conflict risk assessment and early warning method |
Also Published As
| Publication number | Publication date |
|---|---|
| CN106997689B (en) | 2019-08-27 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN106997689A (en) | V2P collision free methods based on crossing | |
| Aghabayk et al. | Comparing heavy vehicle and passenger car lane-changing maneuvers on arterial roads and freeways | |
| CN105225500B (en) | A kind of traffic control aid decision-making method and device | |
| CN110298131A (en) | Automatic Pilot lane-change decision model method for building up under a kind of mixing driving environment | |
| CN105046987B (en) | A kind of road traffic Control of coordinated signals method based on intensified learning | |
| Paruchuri et al. | Multi agent simulation of unorganized traffic | |
| CN107161155A (en) | A kind of vehicle collaboration lane-change method and its system based on artificial neural network | |
| Jin et al. | Dynamic characteristics of traffic flow with consideration of pedestrians’ road-crossing behavior | |
| CN114093161B (en) | Pedestrian crossing safety evaluation method and signal lamp setting method | |
| CN107507430A (en) | A kind of urban road crossing traffic control method and system | |
| CN110009918A (en) | A kind of single-point intersection public transportation lane signal control optimization method | |
| Zeng et al. | Person-based adaptive priority signal control with connected-vehicle information | |
| Chen et al. | Cellular automata (CA) simulation of the interaction of vehicle flows and pedestrian crossings on urban low-grade uncontrolled roads | |
| Harwood et al. | Modelling the impact of platooning on motorway capacity | |
| CN116740945B (en) | Method and system for multi-vehicle collaborative grouping intersection of expressway confluence region in mixed running environment | |
| CN108491600B (en) | Vehicle motion simulation method based on autonomous human traffic | |
| Yang et al. | Impact of connected and autonomous vehicles on traffic efficiency and safety of an on-ramp | |
| Huang et al. | Simulation of pedestrian–vehicle interference in railway station drop-off area based on cellular automata | |
| Trinh et al. | Two-player Game-theory-based Analysis of Motorcycle Driver's Behavior at a Signalized Intersection | |
| Li et al. | Effect of interactions between vehicles and pedestrians on fuel consumption and emissions | |
| CN117075473A (en) | Multi-vehicle collaborative decision-making method in man-machine mixed driving environment | |
| Lan et al. | Empirical observations and formulations of tri-class traffic flow properties for design of traffic signals | |
| CN115909784A (en) | Multi-lane intelligent network vehicle confluence control method and control device | |
| Li et al. | The automated lane-changing model of intelligent vehicle highway systems | |
| Du et al. | Impacts of vehicle-to-infrastructure communication on traffic flows with mixed connected vehicles and human-driven vehicles |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |