CN106997689B - V2P based on crossing avoids collision method - Google Patents
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Abstract
The invention discloses a kind of V2P based on crossing to avoid collision method comprising: one, it determines that the participant of game is vehicle and pedestrian, and participant point is come, define their passage set of strategies；Two, V2P betting model is established in the street crossing delay and risk payment for seeking crossing vehicle and pedestrian respectively；Three, the rate that gives way that traffic rewarding and punishing payment improves vehicle is added on the basis of delay, risk payment；Four, SEIR viral transmission model is improved, the psychology of IEVRI model analysis traffic participant is established, it was demonstrated that introduces the necessity of traffic rewarding and punishing.The present invention has comprehensively considered flow of the people, vehicle flowrate in traffic flow, distance, speed of people's vehicle from decision area to conflict area, the opposite gesture of unit batch street pedestrian's quantity, add traffic rewarding and punishing factor, V2P betting model is established, has ensured traffic efficiency while improving traffic safety.
Description
Technical field
The present invention relates to field of intelligent transportation technology, and in particular to a kind of V2P based on crossing avoids collision method.
Background technique
World Health Organization's whole world road safety status report has 1,240,000 people to die of traffic accident, 50,000,000 people wound every year
Residual, wherein 22% is pedestrian, about 20% traffic accident occurs in cross road mouth.Since China human mortality is numerous, traffic system occurs
Serious people's vehicle mixes row phenomenon, even more increases vehicle and the traffic conflict of pedestrian, with the large-scale application of 4G LTE technology,
And smart phone is universal, realizes people-car interaction using existing network technology, traffic accident is reduced, than traditional collision avoidance side
Method more active, effectively.
In order to solve vehicle and pedestrian (V2P, Vehicle to Pedestrian) collision problem, Bayesian network, hidden horse
Er Kefu model, support vector machines scheduling algorithm model are used the research of vehicle and pedestrian movement extensively.Such as some scholars use shellfish
One model of this network struction of leaf obtains the priori point of Model Parameter from the mass data of cross road mouth vehicle pass-through situation
Whether cloth knows 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 vehicle control can detecte the appropriate gap between pedestrian, and select most
Good VELOCITY DISTRIBUTION passes through gap to control vehicle.The process of vehicle and street pedestrian's information exchange is also a gambling process, state
Interior Many researchers establish driver behavior model in no signal-controlled intersection using repeated game, and giving vehicle, reasonably passage is determined
Plan, but repeated game increases risk of collision and traffic efficiency is low, also it has been proposed that driver and the non-cooperation of street pedestrian
Dynamic Game Model, consider time delay and risk income in game but do not quantify.
Therefore, it is necessary to which developing a kind of V2P based on crossing avoids collision method.
Summary of the invention
The object of the present invention is to provide a kind of V2P based on crossing to avoid collision method, to ensure the safety of road traffic.
V2P of the present invention based on crossing avoids collision method, comprising the following steps:
Step 1: determining that the participant of game is vehicle and pedestrian, the passage set of strategies of pedestrian and vehicle are defined, and will drive
The person of sailing and street pedestrian's classification；
Step 2: observation crossing vehicle, pedestrian density, distance of people's vehicle from decision area to conflict area, speed, speed and
The quantity of unit batch street pedestrian solves V2P at crossing respectively and takes the delay payment and risk payment of different current strategies,
The total revenue for calculating both sides, establishes V2P betting model, acquires the optimal passage strategy combination of the two；
Step 3: introducing traffic rewarding and punishing payment, the driver for evacuation pedestrian of actively stopping at crossing, punishment and pedestrian are rewarded
The driver to scramble for roads recalculates the total revenue of both sides, establishes improved V2P betting model, acquires the optimal passage plan of the two
Slightly combine；
Step 4: establishing IEVRI model, the psychology of traffic participant is analyzed, it was demonstrated that introduce the necessity of traffic rewarding and punishing.
Further, in the step 1, the set of strategies of the pedestrian is defined as P={ waiting, pass through }；The plan of the vehicle
Slightly collection is defined as V={ waiting, pass through }；The type of the pedestrian is divided into Pc={ the old, weak, sick, disabled, pregnant and young, other }；The driver
Type according to driver usually drive habit security level be divided into Vc={ safety-type, GENERAL TYPE, dangerous type }.
Further, in the step 2, the total revenue of calculated both sides is as follows:
Wherein, Dp *D is paid for pedestrian delayspUsing after min-max standardization as a result, Dv *It is delayed branch for driver
Pay DvUsing after min-max standardization as a result, Sp *S is paid for pedestrian's riskpAfter min-max standardization
As a result, Sv *S is paid for driver's riskvUsing after min-max standardization as a result, the weight that α delay is paid, β is wind
The weight nearly paid, β=1- α.
Further, the pedestrian delays pay DpAre as follows:
In formula: ρp1、ρp2For the respective pedestrian's flow in side round-trip in road；T' is between the safety of vehicles traverse pedestrian is passed through
Gap；W is a lane width；vpFor the speed of street pedestrian；vvFor the speed of vehicle.
Further, the driver is delayed payment DvAre as follows:
In formula: ρvnFor nth lane flow amount；tnFor the safety clearance time of nth lane pedestrians travel's vehicle；tsFor
Pedestrian makes a decision the time；tlFor the time that length of wagon passes through, tjRepresent the safety clearance time of j-th strip lane pedestrians travel's vehicle.
Further, pedestrian's risk payment S is solvedpS 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, time difference that the two passes through collision domain with this state are as follows:
If T < 1s, the risk of both sides is regarded as infinity, vehicle must preferentially give way to pedestrian；
Define pedestrian's risk payment SpAre as follows:
Define driver's risk payment SvAre as follows:
In formula: δvIt is traffic accident to the threat degree of driver's life；M is vehicle mass；npFor unit batch street pedestrian's
Quantity；δpIt is traffic accident to the threat degree of pedestrian's life；B(vv,np) it is that speed is opposite with unit batch street pedestrian's quantity
Gesture, value are as follows:
Further, in the step 3, introducing traffic rewarding and punishing payment is piecewise function, the piecewise function are as follows:
In formula: vvIt indicates the speed of vehicle, punishment payment, the total revenue function of driver is added are as follows:
In formula: Dp *D is paid for pedestrian delayspUsing after min-max standardization as a result, Dv *It is delayed branch for driver
Pay DvUsing after min-max standardization as a result, Sp *S is paid for pedestrian's riskpAfter min-max standardization
As a result, Sv *S is paid for driver's riskvUsing after min-max standardization as a result, γ represents the weight of traffic rewarding and punishing, α
It is delayed the weight of payment, β is the weight of risk payment, β=1- α, and α > β, α > γ, and vehicle selection passes through F (vv) positive value is taken,
Otherwise negative value is taken.
Further, in the step 4, I is know-nothing in the IEVRI model, and representative is not affected by traffic violation person's influence
People；E is lurker, represents the people for being influenced not yet to decide whether to imitate traffic violation person by violator；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 broken the rules after traffic punishment；I(t),E
(t), V (t), R (t) respectively indicate the total quantity of t moment these fourth types people, meet following formula:
+ R (t)=1 I (t)+E (t)+V (t).
Further, in the IEVRI model know-nothing once being contacted with lurker necessarily as lurker can
Can, a lurker has a times of infection ability, then t moment I will become E with the probability of aE, E will become R with the probability of δ, with
The probability of b becomes V, and V will become R with the probability of c, and R becomes I with the probability of σ, and minority R may ignore traffic punishment, with's
Probability becomes V, and dynamics of the c to a certain extent with traffic punishment is positively correlated, and punishment is bigger, and c is bigger；
Acquire the equalization point of IEVRI model, it was demonstrated that c plays vital work to the stabilization of entire traffic system and differentiation
After introducing traffic punishment, the rate violating the regulations of traffic participant will greatly reduce.
Beneficial effects of the present invention: being primarily based on pedestrian, and time delay, risk of the vehicle at crossing, which are paid, establishes V2P game mould
Type gives people's vehicle reasonable priority pass decision, and the rewarding and punishing payment for introducing driver on this basis is established improved V2P and won
Play chess model, examples comparative analysis, it was demonstrated that improved V2P betting model increases the rate that gives way of vehicle, is more advantageous to road friendship
Logical safety.In addition, improving traditional viral transmission model foundation new IEVRI model analysis traffic participant in the present invention
Psychology, it was demonstrated that the necessity of punishment on contravention of regulation.
Detailed description of the invention
Fig. 1 is the principle of the present invention figure；
Fig. 2 is IEVRI model framework chart of the invention；
Fig. 3 is R of the invention0When > 1, the evolving trend of traffic system；
Fig. 4 is R of the invention0When < 1, the evolving trend of traffic system.
Specific embodiment
The invention will be further described with reference to the accompanying drawings and examples.
V2P of the present invention based on crossing avoids collision method, and what emphasis solved is crossing street pedestrian and vehicle
Collision problem, main thought is that it is bis- to acquire V2P using interspersed theory first according to flow of the people, the vehicle flowrate in traffic flow
Then the delay payment of side considers distance, speed of people's vehicle from decision area to conflict area, speed, the street crossing of proposed unit's batch
The concept of the opposite gesture of pedestrian's quantity acquires the risk payment of both sides, then sets up V2P betting model and give both sides and reasonably lead to
Line efficiency.But in order to further increase vehicle to the evacuation rate of pedestrian, traffic rewarding and punishing factor is added, establishes new V2P game mould
Type, and SEIR viral transmission model is improved, establish the psychology of IEVRI model analysis traffic participant, it was demonstrated that introduce traffic rewarding and punishing
Necessity.
V2P of the present invention based on crossing avoids collision method, comprising the following steps:
Step 1: determining that the participant of game is vehicle and pedestrian, the passage set of strategies of pedestrian and vehicle are defined, and will drive
The person of sailing and street pedestrian's classification.
Step 2: observation crossing vehicle, pedestrian density, distance of people's vehicle from decision area to conflict area, speed, speed and
The quantity of unit batch street pedestrian solves V2P at crossing respectively and takes the delay payment and risk payment of different current strategies,
The total revenue for calculating both sides, establishes V2P betting model, acquires the optimal passage strategy combination of the two.
Step 3: introducing traffic rewarding and punishing payment, the driver for evacuation pedestrian of actively stopping at crossing, punishment and pedestrian are rewarded
The driver to scramble for roads recalculates the total revenue of both sides, establishes improved V2P betting model, acquires the optimal passage plan of the two
Slightly combine.
Step 4: establishing IEVRI model, the psychology of traffic participant is analyzed, it was demonstrated that introduce the necessity of traffic rewarding and punishing.
Method is avoided collision to the V2P of the present invention based on crossing below to be described in detail:
For the present invention by passing through the microcosmic analysis of street crossing behavior to V2P, 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={ waiting, pass through }.In view of street pedestrian's
Feature, the type of pedestrian are divided into Pc={ the old, weak, sick, disabled, pregnant and young, other }, the type of driver then can according to usually they drive to practise
Used security level is divided into Vc={ safety-type, GENERAL TYPE, dangerous type }, gives difference for different types of driver and pedestrian
Current strategy.
The present invention initially sets up the V2P betting model based on delay payment and risk payment, and embodiments thereof are as follows:
Step I, the delay payment for calculating V2P:
Mainly application interts theory to analyze for delay payment, and when pedestrians travel's vehicle clearance, the time income of acquisition is vehicle
Waiting delay (i.e. pedestrian delays pay Dp) it is as follows:
In formula: ρp1、ρp2For the respective pedestrian's flow (people/s) in side round-trip in road, t' is the safety of vehicles traverse pedestrian
Crossing gap (s), w are a lane width (m), vpFor the speed (m/s) of street pedestrian, 1m/s~1.45m/s, v are takenvFor vehicle
Speed.
The road in n lane, when the waiting delay that the time income that vehicles traverse pedestrian gap obtains is pedestrian (drives
Member's delay payment Dv), it is as follows:
In formula: ρvnFor nth lane flow amount (vehicle/s)；tnFor nth lane pedestrians travel's vehicle safety clearance when
Between (s)；tsTime (s) is made a decision for pedestrian, takes 2.0s；tlFor the time that length of wagon passes through, standard vehicle 0.72s is generally taken.tjGeneration
The safety clearance time of table j-th strip lane pedestrians travel's vehicle.
Step II, the risk payment for calculating V2P:
If pedestrian is a in the acceleration of decision pointp, speed 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, the two passes through with this state
The time difference of collision domain are as follows:
If T < 1s, the risk of both sides is regarded as infinity, vehicle must preferentially give way to pedestrian.Define V2P risk of collision
Pay off function are as follows:
In formula: m is vehicle mass (kg)；SvIt is paid for the risk of driver；SpIt is paid 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；δvIt is traffic accident to pedestrian's life
Threat degree, take 1；B(vv,np) it is the speed gesture opposite with unit batch street pedestrian's quantity, value is as follows:
Step III, the pay off function based on delay and risk establish the total revenue of both sides, participate in shown in table 1.
The total revenue of 1 V2P both sides of table
V2P game the result is that in order to give people's vehicle reasonably current strategy, therefore do not consider that both sides are all to wait in table 1
Strategy, wherein Dp *D is paid for pedestrian delayspUsing after min-max standardization as a result, Dv *It is delayed branch for driver
Pay DvUsing after min-max standardization as a result, Sp *S is paid for pedestrian's riskpAfter min-max standardization
As a result, Sv *S is paid for driver's riskvUsing after min-max standardization as a result, Dp *、Dv *、Sv *、Sp *Value be
(0,1)；The weight of α delay payment；β is the weight of risk payment；When pedestrian's type is that the old,weak,sick and disabled children is pregnant, driver's type
From high to low, α distinguishes value 0.6 to security level, 0.7,0.8, when pedestrian's type other when, driver's type safety rank is from height
To low, α difference value 0.7,0.8,0.9, β=1- α.
Above-mentioned betting model is improved present invention introduces traffic rewarding and punishing payment to improve vehicle to the evacuation rate of pedestrian, in fact
It is as follows to apply mode:
Step I, introducing traffic rewarding and punishing payment are piecewise function:
In formula: vvIndicate driver in the speed of decision region.
Punishment payment is added in step II, redefines the total revenue function of driver are as follows:
In formula, γ represents the weight of traffic rewarding and punishing, γ=0.3, and α > β, α > γ, and vehicle selection passes through F (vv) take just
Value, otherwise takes negative value.
Step III: model comparative analysis: example calculation, as a result as shown in attached drawing table 2,3, table 2 is based on delay and risk
V2P betting model calculate pedestrian and vehicle optimum combination, table 3 be added traffic rewarding and punishing pay after V2P betting model
(overstriking font is the current strategy of V2P to the optimum combination for the pedestrian and vehicle that (i.e. improved V2P betting model) calculates in table
Optimum combination).Comparative analysis, it is clear that the V2P betting model after introducing traffic rewarding and punishing can be improved the rate that gives way of vehicle, more can
Enough ensure the safety of road traffic.
V2P payoff of the table 2 based on delay and risk
Table 3 introduces the V2P payoff of traffic rewarding and punishing payment
The present invention improve SEIR model foundation IEVRI model, analyze the psychology of traffic participant, it was demonstrated that punishment on contravention of regulation must
The property wanted, I (Innocent) is know-nothing in model, represents the people for being not affected by traffic violation person's influence, E (Exposed) is latent
Person.The people for being influenced not yet to decide whether to imitate traffic violation person by violator, V (Violator) violator are represented, representative imitates
The people of traffic violation person, R (recovered) are recuperator, represent the people for having received and temporarily no longer having broken the rules after traffic punishment, I
(t), E (t), V (t), R (t) respectively indicate the total quantity of t moment these fourth types people, have
+ R (t)=1 I (t)+E (t)+V (t) (11).
Specific implementation step is as follows:
Step I establishes IEVRI model: IEVRI model is as shown in Fig. 1: know-nothing in IEVRI model once with
Lurker's contact just necessarily becomes the possibility of lurker, and a lurker has a times of infection ability, then t moment I will be with aE's
Probability becomes E, and E will become R with the probability of δ, becomes V with the probability of b, and V will become R with the probability of c, and R becomes I with the probability of σ,
And minority R may ignore traffic punishment, withProbability become V, c to a certain extent with traffic punishment dynamics be positively correlated,
Punishment is bigger, and c is bigger.
Step II, the equalization point for acquiring model: the following equation group of IEVRI model foundation described according to fig. 2:
Initial value:
D=(I, E, V, R) | I, E, V, R >=0 and I+E+V+R=1 } (13)
The right end for enabling equation (12) is 0, obtains the equalization point P (I of equation*,E*,S*,R*):
By formula (13) and formula (14) it is found that enablingWhen, equalization point P exists in D, and part is progressive steady
It is fixed, R0When > 1, equalization point is P (1,0,0,0), R0It determines the final evolving trend of whole system, claims R0For traffic violation row
For transmission threshold, and δ, b, a are related.
Step III, numerical simulation analysis: from the foregoing, it will be observed that R0The range and situation of motoring offence propagation are directly affected,
Analyze δ, b, a, influence of the c to system totality.Invention, come the evolving trend that analysis system is final, is taken by the way of numerical simulation
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, increasing penalty factor c=0.8, lurker then changes factor b=0.4 under the pressure of punishment, δ=
0.5, simulation result is as shown in (B) in Fig. 3.
Compare (A) and (B) in attached drawing 3, R0When > 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 attached 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 attached drawing 4 (B).
(A) and (B), R in comparison diagram 40When < 1, equalization point increases penalty factor c, violator can be faster in D
Level off to stable state, and the violator in system stablizes the people to observe traffic rules and regulations in less state and stablizes more
State.
It can be seen that c plays a crucial role the stabilization of entire traffic system and differentiation, draw in traffic rules
Entering appropriate punishment on contravention of regulation can ensure that more drivers observe traffic rules and regulations, and motor vehicle is intersected by no traffic lights
When crossing, the behavior scrambled for roads with pedestrian should also be included in traffic punishment.
No signal crossing is influenced into the current factor quantification of V2P in the present invention, is carried out using passage decision of the game to V2P
Guidance, is then introduced into the rate that gives way that the rewarding and punishing factor improves vehicle into game, finally improves the model analysis of SEIR viral transmission and hands over
The psychology of logical participant, it was demonstrated that introduce the necessity of rewarding and punishing payment.
Claims (8)
1. a kind of V2P based on crossing avoids collision method, which comprises the following steps:
Step 1: determining that the participant of game is vehicle and pedestrian, the passage set of strategies of pedestrian and vehicle are defined, and by driver
Classify with street pedestrian；
Step 2: observation crossing vehicle, pedestrian density, distance, speed, speed and unit of people's vehicle from decision area to conflict area
The quantity of batch street pedestrian solves V2P at crossing respectively and takes the delay payment and risk payment of different current strategies, calculates
The total revenue of both sides establishes V2P betting model, acquires the optimal passage strategy combination of the two；
The total revenue of calculated both sides is as follows:
Wherein, Dp *D is paid for pedestrian delayspUsing after min-max standardization as a result, Dv *It is delayed payment D for driverv
Using after min-max standardization as a result, Sp *S is paid for pedestrian's riskpUsing the knot after min-max standardization
Fruit, Sv *S is paid for driver's riskvUsing after min-max standardization as a result, the weight that α delay is paid, β is risk
The weight of payment, β=1- α；
Step 3: introducing traffic rewarding and punishing payment, the driver for evacuation pedestrian of actively stopping at crossing is rewarded, punishment is scrambled for roads with pedestrian
Driver, recalculate the total revenue of both sides, establish improved V2P betting model, both acquire optimal current tactful group
It closes；
Step 4: establishing IEVRI model, the psychology of traffic participant is analyzed, it was demonstrated that introduce the necessity of traffic rewarding and punishing.
2. the V2P according to claim 1 based on crossing avoids collision method, it is characterised in that: in the step 1, institute
The set of strategies for stating pedestrian is defined as P={ waiting, pass through }；The set of strategies of the vehicle is defined as V={ waiting, 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
Security level be divided into Vc={ safety-type, GENERAL TYPE, dangerous type }.
3. the V2P according to claim 2 based on crossing avoids collision method, it is characterised in that: the pedestrian delays branch
Pay DpAre as follows:
In formula: ρp1、ρp2For the respective pedestrian's flow in side round-trip 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.
4. the V2P according to claim 3 based on crossing avoids collision method, it is characterised in that: driver's delay
Pay DvAre as follows:
In formula: ρvnFor nth lane flow amount；tnFor the safety clearance time of nth lane pedestrians travel's vehicle；tsFor pedestrian
Make a decision the time；tlThe time passed through for length of wagon；tjRepresent the safety clearance time of j-th strip lane pedestrians travel's vehicle.
5. the V2P according to claim 4 based on crossing avoids collision method, it is characterised in that: solve pedestrian's wind
Danger payment 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, time difference that the two passes through collision domain with this state are as follows:
If T < 1s, the risk of both sides is regarded as infinity, vehicle must preferentially give way to pedestrian；
Define pedestrian's risk payment SpAre as follows:
Define driver's risk payment SvAre as follows:
In formula: δvIt is traffic accident to the threat degree of driver's life；M is vehicle mass；npFor the number of unit batch street pedestrian
Amount；δpIt is traffic accident to the threat degree of pedestrian's life；B(vv,np) it is the speed gesture opposite with unit batch street pedestrian's quantity,
Its value is as follows:
6. the V2P according to any one of claims 1 to 5 based on crossing avoids collision method, it is characterised in that: the step
In three, introducing traffic rewarding and punishing payment is piecewise function, the piecewise function are as follows:
In formula: vvIt indicates the speed of vehicle, punishment payment, the total revenue function of driver is added are as follows:
In formula: Dp *D is paid for pedestrian delayspUsing after min-max standardization as a result, Dv *It is delayed payment D for driverv
Using after min-max standardization as a result, Sp *S is paid for pedestrian's riskpUsing the knot after min-max standardization
Fruit, Sv *S is paid for driver's riskvUsing after min-max standardization as a result, γ represents the weight of traffic rewarding and punishing, α prolongs
The accidentally weight of payment, β are the weight of risk payment, β=1- α, and α > β, α > γ, and vehicle selection passes through F (vv) positive value is taken, it is no
Then take negative value.
7. the V2P according to claim 6 based on crossing avoids collision method, it is characterised in that: in the step 4, institute
Stating I in IEVRI model is know-nothing, represents the people for being not affected by traffic violation person's influence；E is lurker, is represented by violator's shadow
Ring the people for not yet deciding whether to imitate 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 broken the rules after traffic punishment；I (t), E (t), V (t), R (t) respectively indicate t moment these fourth types
The total quantity of people, meets following formula:
+ R (t)=1 I (t)+E (t)+V (t).
8. the V2P according to claim 7 based on crossing avoids collision method, it is characterised in that: in the IEVRI model
One know-nothing necessarily becomes the possibility of lurker once contacting with lurker, and a lurker has a times of infection ability, that
T moment I will become E with the probability of aE, and E will become R with the probability of δ, become V with the probability of b, and V will become R with the probability of c,
R becomes I with the probability of σ, and minority R may ignore traffic punishment, withProbability become V, c is to a certain extent and at traffic
The dynamics penalized is positively correlated, and punishment is bigger, and c is bigger；
Acquire the equalization point of IEVRI model, it was demonstrated that c plays a crucial role i.e. the stabilization of entire traffic system and differentiation
After introducing traffic punishment, the rate violating the regulations of traffic participant will greatly reduce.
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| 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 |
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