CN110473404B - Cooperative optimization bottom layer control method for mixed traffic flow at urban crossroad - Google Patents

Cooperative optimization bottom layer control method for mixed traffic flow at urban crossroad Download PDF

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CN110473404B
CN110473404B CN201910886987.4A CN201910886987A CN110473404B CN 110473404 B CN110473404 B CN 110473404B CN 201910886987 A CN201910886987 A CN 201910886987A CN 110473404 B CN110473404 B CN 110473404B
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孙湛博
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Southwest Jiaotong University
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Abstract

The invention provides a cooperative optimization bottom control method for mixed traffic flow at an urban crossroad, belonging to the field of traffic engineering. The method comprises the following steps: determining a microscopic following model; predicting an initial track of the vehicle; establishing a conflict coordination model aiming at the conflict type of the crossroad; simulating a cooperative control strategy set; judging whether the vehicle can smoothly pass through the conflict area; if the vehicle can pass through smoothly, the vehicle continues to run according to the speed of the microscopic follow-up model; if the vehicle cannot pass through the conflict area smoothly, the specific situation of the vehicle in the process of passing through the conflict area needs to be judged, and a corresponding cooperative control strategy is made; and optimizing the running track of the target vehicle according to the made cooperative control strategy, resolving the optimization problem into an optimal control problem of discrete time state constraint, solving by using a dynamic programming idea to obtain the cooperative optimization control strategy about the target vehicle, acting the cooperative optimization control strategy on the target vehicle, and controlling the running of the target vehicle.

Description

Cooperative optimization bottom layer control method for mixed traffic flow at urban crossroad
Technical Field
The invention relates to a cooperative optimization bottom control method for mixed traffic flow at an urban crossroad, belonging to the field of traffic engineering.
Background
With the continuous advance of the research and development of the intelligent networking technology, the intelligent networking vehicle can be driven on the road, and the characteristics of intelligence and safety are added, so that the intelligent networking vehicle is certainly popularized, but the traditional manually-driven vehicle cannot be completely replaced for a long time, and therefore, the future road traffic condition is certainly a mixed traffic condition of the intelligent networking vehicle and the traditional manually-driven vehicle. Meanwhile, with the rapid development of the intelligent networking system, research on intelligent networking vehicles is receiving more and more attention.
The plane intersection is the position of a conflict node and a bottleneck of a road traffic network, and the traffic flow control of the intersection is always the core of traffic organization and management. Because of the saturated traffic flow in the urban road and the lack of a very practical and effective intersection traffic control mode, some plane intersections become congestion places and accident multiple points of urban traffic.
Most of the existing research on intelligent networked vehicles only considers the assumed CAV permeability of 100% (i.e. all vehicles are intelligent networked vehicles), but this assumption is not realistic at least in the near future. Meanwhile, the conventional collaborative decision-making research mostly considers the situation of a highway or an urban express way, and the research on the traffic situation of an urban non-signal intersection is less. Therefore, the research on the urban intersection conflict collaborative optimization control in the mixed traffic flow environment is of great significance.
Disclosure of Invention
And defining the vehicle track optimization control problem as a bottom layer problem. The solution of the underlying problem needs to consider specific constraints including road geometric constraints, safety constraints, and vehicle type constraints according to the scene and the optimization objective. The invention provides a method for controlling a mixed traffic flow collaborative optimization bottom layer of an urban crossroad, which is suitable for the urban crossroad without traffic signal lamp indication, and aims to solve the bottom layer problem of the urban crossroad in the mixed traffic state of traditional driving vehicles (namely human driving vehicles) and intelligent internet vehicles (namely automatic driving vehicles). The traditional driving vehicle is a vehicle which cannot be controlled optimally, and the intelligent networked vehicle is a vehicle which can be controlled optimally.
The technical scheme adopted by the invention for realizing the aim is as follows:
a cooperative optimization bottom layer control method for mixed traffic flow at an urban crossroad comprises the following steps:
s1, determining a micro-following model, and describing a following state of the vehicle by using the micro-following model, wherein the following state of the vehicle comprises the speed, the acceleration and the position of the vehicle;
s2, acquiring the time and speed of the vehicle passing through the upstream monitoring point of the conflict area in the mixed traffic flow, and predicting the initial trajectory of the vehicle between the upstream monitoring point and the conflict end point by using the micro-following model; a road section with a certain distance between the upstream monitoring point and the conflict starting point; the collision starting point is positioned between the upstream monitoring point and the collision terminal point; the road sections between the collision starting point and the collision terminal point form a collision area;
s3, adding acceleration constraint, distance constraint and safety constraint to establish a conflict coordination model aiming at the conflict type of the crossroad based on the microcosmic car-following model; the conflict types of the crossroad comprise cross conflict and confluence conflict;
s4, aiming at various situations which can not pass through the conflict area smoothly and possibly occur in the process that the vehicle passes through the conflict area under the mixed traffic flow scene, a cooperative control strategy set is prepared;
s5, judging whether the vehicle can smoothly pass through a conflict area or not by the conflict coordination model based on the vehicle initial track; if the judgment result is that the vehicle can smoothly pass through the conflict area, the vehicle continuously drives according to the speed of the microcosmic following model; if the judgment result is that the vehicle cannot smoothly pass through the conflict area, further judging the specific situation of the vehicle in the process of passing through the conflict area, and making a cooperative control strategy corresponding to the cooperative strategy set simulated in the step S4 for the vehicle according to the specific situation of the vehicle in the process of passing through the conflict area, so as to execute a step S6;
and S6, determining the optimally controlled vehicles participating in the process that the vehicles pass through the conflict area as target vehicles, optimizing the running tracks of the target vehicles according to the cooperative control strategy made in the step S5, resolving the optimization problem into an optimal control problem of discrete time state constraint, solving the cooperative optimization control strategy related to the target vehicles by using a dynamic planning idea, acting the cooperative optimization control strategy related to the target vehicles on the target vehicles, and controlling the running of the target vehicles.
Preferably, the conflict type of the crossroad is cross conflict; the cross conflict means that vehicles in different driving directions run in a cross mode at a large angle; the step S3 specifically includes:
suppose that both the X and Y lanes are one-way lanes and the intersection of the X and Y lanes is (X)C，YC)；
Defining a conflict area: determining a collision starting point and a collision end point by combining the geometric length of the road and the microcosmic car-following model, wherein the collision area on the X road is from the collision starting point XcsTo the conflicting terminal XceStopping; collision area self-conflict starting point Y on Y roadcsTo the conflict end point YceIf not, the conflict area is (X)cs，Ycs)～(Xce，Yce)；
Defining a control area: control area on X road from vehicle distance conflict starting point XcsControl starting point X of a certain distanceDTo the cross point (X)C，YC) Stopping; control area on Y road from vehicle distance collision starting point YcsControl starting point Y of a certain distanceDTo the cross point (X)C，YC) If not, the control region is (X)D，YD)～(XC，YC) (ii) a The control starting point is positioned between the upstream monitoring point and the collision starting point;
suppose that vehicle k on the X road is in the control zone (X)D，YD)～(XC，YC) The interval between two vehicles that vehicle k will pass through a continuous flow on the Y road; k 'for each of two vehicles of the continuous stream on the Y-road'1And k'2Is represented by, wherein vehicle k'1Denotes a front vehicle, vehicle k'2Representing a rear vehicle;
at time t, the relative distance between vehicle k on the X road and vehicle k' on the Y road is represented by lk,k′(t) denotes, then:
Figure BDA0002207613840000021
wherein x isk(t) represents the position of the vehicle k on the X road at time t, yk′(t) represents the position of the vehicle k' on the Y road at time t;
u for conflict cooperative utilityk(t) indicating whether the vehicle k can smoothly pass through the collision area on the X road; the conflict cooperative utility Uk(t) is represented as follows:
Figure BDA0002207613840000031
wherein, | uk(t) | represents the absolute value of the acceleration of the vehicle k on the X road at the time t;
Figure BDA0002207613840000032
denotes vehicle k 'on Y road'2The absolute value of the acceleration at time t;
Figure BDA0002207613840000033
at time t, represents vehicle k ' on the X road and vehicle k ' on the Y road '1The relative distance therebetween;
Figure BDA0002207613840000034
at time t, represents vehicle k ' on the X road and vehicle k ' on the Y road '2The relative distance therebetween;
Figure BDA0002207613840000035
representing a safety body of the intelligent networked vehicle;
Figure BDA0002207613840000036
representing a safety body of a conventionally driven vehicle; bsafeRepresents a maximum allowable deceleration; phiAThe method comprises the following steps of (1) providing an intelligent networked vehicle set; phiHη for conventional driving vehicle1Indicating a safety factor of η2Representing a polite coefficient; the intelligent networked vehicle is an optimally controllable vehicle and is represented by a letter A; the traditional driving vehicle is a vehicle which cannot be controlled optimally and is indicated by a letter H;
polite coefficient η2The expression of (a) is as follows:
Figure BDA0002207613840000037
wherein v isthIs a given threshold speed, β1And β2Is a constant.
For collaborative decision Ik(t + τ) is represented as follows:
Figure BDA0002207613840000038
wherein, IkA value of (t + τ) of 1 indicates that the vehicle k can smoothly pass through the collision region at time t + τ; i iskA value of 0 for (t + τ) indicates that vehicle k cannot smoothly pass through the collision zone at time t + τ.
Preferably, the conflict type of the crossroad is confluence conflict; the confluent conflict means that vehicles in different driving directions converge and drive in the same direction at a small angle; the step S3 specifically includes:
suppose that both the X and Y lanes are one-way lanes and the intersection of the X and Y lanes is (X)C，YC)；
Defining a conflict area: determining a collision starting point and a collision end point by combining the geometric length of the road and the microcosmic car-following model, wherein the collision area on the X road is from the collision starting point XcsTo the conflicting terminal XceStopping; collision area self-conflict starting point Y on Y roadcsTo the conflict end point YceIf not, the conflict area is (X)cs，Ycs)～(Xce，Yce)；
Defining a control area: control area on X road from vehicle distance conflict starting point XcsControl starting point X of a certain distanceDTo the cross point (X)C，YC) Stopping; control area on Y road from vehicle distance collision starting point YcsControl starting point Y of a certain distanceDTo the cross point (X)C，YC) If not, the control region is (X)D，YD)～(XC，YC) (ii) a The control starting point is positioned between the upstream monitoring point and the collision starting point;
suppose that vehicle k on the X road is in the control zone (X)D，YD)～(XC，YC) The interval between two vehicles that vehicle k will pass through a continuous flow on the Y road; k 'for each of two vehicles of the continuous stream on the Y-road'1And k'2Is represented by, wherein vehicle k'1Denotes a front vehicle, vehicle k'2Representing a rear vehicle;
at time t, the relative distance between vehicle k on the X road and vehicle k' on the Y road is represented by lk,k′(t) denotes, then:
when x isk(t)＜XCAt the time of-r,
Figure BDA0002207613840000041
when in use
Figure BDA0002207613840000042
When the temperature of the water is higher than the set temperature,
Figure BDA0002207613840000043
when in use
Figure BDA0002207613840000044
When the temperature of the water is higher than the set temperature,
Figure BDA0002207613840000045
wherein x isk(t) represents the position of the vehicle k on the X road at time t, yk′(t) represents the position of the vehicle k' on the Y road at time t; r represents the turning radius of the vehicle k at the crossroad;
u for conflict cooperative utilityk(t) indicating whether the vehicle k can smoothly pass through the collision area on the X road; the conflict cooperative utility Uk(t) is represented as follows:
Figure BDA0002207613840000046
wherein, | uk(t) | represents the absolute value of the acceleration of the vehicle k on the X road at the time t;
Figure BDA0002207613840000047
denotes vehicle k 'on Y road'2The absolute value of the acceleration at time t;
Figure BDA0002207613840000048
at time t, represents vehicle k ' on the X road and vehicle k ' on the Y road '1The relative distance therebetween;
Figure BDA0002207613840000049
at time t, represents vehicle k ' on the X road and vehicle k ' on the Y road '2The relative distance therebetween;
Figure BDA00022076138400000410
denotes vehicle k on the X road and vehicle k 'on the Y road'1The vehicle k on the X road is an intelligent networking vehicle;
Figure BDA00022076138400000411
denotes vehicle k on the X road and vehicle k 'on the Y road'1And the vehicle k on the X road is a conventional driving vehicle;
Figure BDA0002207613840000051
denotes vehicle k on the X road and vehicle k 'on the Y road'2The vehicle k on the X road is an intelligent networking vehicle;
Figure BDA0002207613840000052
denotes vehicle k on the X road and vehicle k 'on the Y road'2And the vehicle k on the X road is a conventional driving vehicle; bsafeRepresents a maximum allowable deceleration; phiAThe method comprises the following steps of (1) providing an intelligent networked vehicle set; phiHη for conventional driving vehicle1Indicating a safety factor of η2To representA polite coefficient; the intelligent networked vehicle is an optimally controllable vehicle and is represented by a letter A; the traditional driving vehicle is a vehicle which cannot be controlled optimally and is indicated by a letter H;
polite coefficient η2The expression of (a) is as follows:
Figure BDA0002207613840000053
wherein v isthIs a given threshold speed, β1And β2Is a constant.
For collaborative decision Ik(t + τ) is represented as follows:
Figure BDA0002207613840000054
wherein, IkA value of (t + τ) of 1 indicates that the vehicle k can smoothly pass through the collision region at time t + τ; i iskA value of 0 for (t + τ) indicates that vehicle k cannot smoothly pass through the collision zone at time t + τ.
Further, the step S4 specifically includes:
assuming that both the X and Y lanes are one-way lanes, vehicle k on the X lane will pass through the interval between two vehicles of the continuous flow on the Y lane, each with k'1And k'2Is represented by, wherein vehicle k'1Denotes a front vehicle, vehicle k'2Representing a rear vehicle;
for various situations which can occur in the process that vehicles pass through a conflict area in a mixed traffic flow scene, k 'of the vehicles is determined based on the micro-following model'1K 'of vehicle'2The relationship between the conflict areas is divided into the conflict areas which can be smoothly passed and the conflict areas which can not be smoothly passed; the failure to smoothly pass through the collision region is further divided into four cases, the first case is denoted by R1 and represents the vehicles k and k'1The distance between the two parts is too close to satisfy the constraint condition of smoothly passing through the conflict area; the second case is marked as R2 and represents vehicle k and vehicle k'2Too close to each other, failing to meet the requirement of smooth passing through the punchConstraint conditions of the protruding regions; the third case is marked as R3 and represents vehicle k and vehicle k'1And k 'with vehicle'2The distances between the two parts are too close to meet the constraint condition that the two parts can smoothly pass through a conflict area; the fourth case is marked as R4 and represents vehicle k and vehicle k'1And k 'with vehicle'2The basic spacing requirement is satisfied, but the process of passing through the conflict area is not comfortable;
aiming at four conditions that the vehicle can not smoothly pass through a conflict area, the vehicle which can be optimally controlled needs to be cooperatively controlled; the traditional driving vehicle is represented by H and is a vehicle which cannot be optimally controlled; the method comprises the following steps that A represents an intelligent networked vehicle which is an optimally controllable vehicle; n represents no front vehicle or no rear vehicle; and provides the combination sequence of the vehicles as vehicle k'1K 'of vehicle'2；
Based on different vehicle combinations, different vehicle type combinations and the four conditions that the conflict area can not be smoothly passed, a cooperative control strategy set is planned, and the following table shows that:
Figure BDA0002207613840000061
Figure BDA0002207613840000071
in the above table, the non-optimization means that the vehicle has no corresponding control strategy under the condition that the vehicle cannot smoothly pass through the conflict area; under the condition that optimization cannot be carried out, when the conflict type of the intersection is cross conflict, the vehicle k on the X road takes the intersection of the X road and the Y road as a stopped virtual front vehicle, continuously decelerates and even stops to wait by following the microcosmic following model, and the vehicle k does not pass through the conflict area until the vehicle interval meeting the passing conflict area appears on the Y road; when the conflict type of the crossroad is confluence conflict, the vehicle k on the X road takes the confluence point of the X road and the Y road as a stopped virtual front vehicle, continuously decelerates along the microcosmic car-following model, even stops and waits until the vehicle interval meeting the passing conflict area appears on the Y road, and the vehicle k passes through the conflict area; the confluence point of the X path and the Y path refers to the position where the X path traffic flow is converged into the Y path traffic flow;
the control state is unknown, and in the case R4 where the collision region cannot be smoothly passed, it is necessary to determine the vehicles k and k'1K 'of vehicle'2In the process of passing through the conflict area, which corresponding vehicles form discomfort, and corresponding control strategies are adopted according to the conditions of R1, R2 and R3 that the vehicles cannot pass through the conflict area smoothly;
the acceleration of the vehicle k is controlled by a decision variable u of the vehicle k at the time tk(t) satisfies:
Figure BDA0002207613840000081
and v isk(t)+uk(t)τ≤ve；
The control vehicle k decelerates, and a decision variable u of the vehicle k at the time t is usedk(t) satisfies:
Figure BDA0002207613840000082
and v isk(t)+uk(t)τ≥0；
The uncontrolled vehicle k is a decision variable u for the vehicle k at time tk(t) satisfies:
Figure BDA0002207613840000083
i.e. uk(t)＝0；
The control vehicle k'1The acceleration is to make the vehicle k 'at the time t'1Decision variables of
Figure BDA0002207613840000084
Satisfies the following conditions:
Figure BDA0002207613840000085
and is
Figure BDA0002207613840000086
The vehicle k 'is not controlled'1Is to make the vehicle k 'at time t'1Decision variables of
Figure BDA0002207613840000087
Satisfies the following conditions:
Figure BDA0002207613840000088
namely, it is
Figure BDA0002207613840000089
The control vehicle k'2Deceleration is performed by making the vehicle k 'at time t'2Decision variables of
Figure BDA00022076138400000810
Satisfies the following conditions:
Figure BDA00022076138400000811
and is
Figure BDA00022076138400000812
The vehicle k 'is not controlled'2Is to make the vehicle k 'at time t'2Decision variables of
Figure BDA00022076138400000813
Satisfies the following conditions:
Figure BDA00022076138400000814
namely, it is
Figure BDA00022076138400000815
Wherein u isk(t) as a decision variable for the vehicle k at time t, representing the acceleration of the vehicle k at time t;vk(t) is the speed of vehicle k at time t;
Figure BDA00022076138400000816
is the safe following speed (where L is the safe following speed of the vehicle k at the moment t + tau) predicted according to the microcosmic following modelk(t) represents the relative distance between the vehicle k and its following preceding vehicle at time t, vk(t) represents the speed of the vehicle k at time t,
Figure BDA0002207613840000091
representing the speed of the car k at time t before the car k follows);
Figure BDA0002207613840000092
k 'of vehicle at time t'1Is a decision variable of vehicle k'1Acceleration at time t;
Figure BDA0002207613840000093
is vehicle k'1Velocity at time t;
Figure BDA0002207613840000094
is vehicle k 'predicted from the microscopic follow model'1Safe following speed at time t + tau (where
Figure BDA0002207613840000095
Denotes vehicle k 'at time t'1The relative distance between the car and the car before the car is driven,
Figure BDA0002207613840000096
denotes vehicle k'1At the speed at the time of the t-instant,
Figure BDA0002207613840000097
denotes vehicle k'1The speed of the car before the following at the time t);
Figure BDA0002207613840000098
k 'of vehicle at time t'2Is a decision variable of vehicle k'2At time tAcceleration of the moment;
Figure BDA0002207613840000099
is vehicle k'2Velocity at time t;
Figure BDA00022076138400000910
is vehicle k 'predicted from the microscopic follow model'2Safe following speed at time t + tau (where
Figure BDA00022076138400000911
Denotes vehicle k 'at time t'2The relative distance between the car and the car before the car is driven,
Figure BDA00022076138400000912
denotes vehicle k'2At the speed at the time of the t-instant,
Figure BDA00022076138400000913
denotes vehicle k'2The speed of the car before the following at the time t); τ is the reaction time of the vehicle driving; v. ofeIs the desired speed.
Further, the step S6 specifically includes:
s6-1, predicting the moment t when the target vehicle enters the control area based on the microcosmic car-following model0The time when the user leaves the control area is tf(ii) a And will t0To tfIs divided into N segments on average in discrete time intervals τ', i.e., N ═ tf-t0) T', defining the control decision time as t0+τ′,t0+2τ′,t0+3τ′,t0+4τ′,…,t0+(N-1)τ′,tf；
S6-2, according to the target vehicle at t0The state of the time, the target vehicle is calculated at t0The set of allowable states at time + τ', and the target vehicle from t0State of time t0The allowable states at time + τ' aggregate the transition costs for each allowable state; said target vehicle is at t0The state at time t includes the target vehicle being at t0Speed and position of time of day；
S6-3, according to the target vehicle at t0The allowable state set at the time + tau' is calculated for the target vehicle at t0The allowable state set at time +2 τ', and the target vehicle from t0State at time + τ' to t0The allowable states at time +2 τ' are grouped into transition costs and cumulative costs for each allowable state;
s6-4, sequentially calculating the target vehicle at t according to the method of the step S6-30+3τ′,t0+4τ′,…,t0+(N-1)τ′,tfThe allowable state set of the time is calculated to obtain tfThe cumulative cost of each allowable state in the time-wise allowable state set;
s6-5, judging that the target vehicle is at tfWhether each allowable state in the allowable state set at the moment meets the condition that the allowable state can smoothly pass through the conflict area or not is judged, and the allowable state meeting the condition is brought into the final allowable state set;
s6-6, selecting the allowable state with the minimum accumulated cost in the final allowable state set as tfThe optimal state of the moment;
s6-7, according to tfThe optimal state of the moment is reversely deduced to obtain t0+τ′,t0+2τ′,t0+3τ′,t0+4τ′,…,t0+(N-1)τ′,tfThe optimal state at each moment, and the control decision corresponding to each optimal state is brought into a cooperative optimization control strategy;
and S6-8, controlling the operation of the target vehicle among the control areas according to the cooperative optimization control strategy.
Furthermore, a microscopic traffic flow simulation environment is constructed, and simulation results before and after optimization under different traffic situations are compared.
Compared with the prior art, the method has the beneficial effects that:
the invention provides a collaborative optimization bottom control method for mixed traffic flow at an urban crossroad, which considers the traffic characteristics of the urban crossroad, adds acceleration constraint, distance constraint and safety constraint to establish a conflict collaborative model aiming at different conflict types at the crossroad based on a microscopic following model, and provides a solving method based on dynamic planning to effectively solve the model. The method of the invention is used for carrying out track optimization control on the intelligent internet vehicles in the mixed traffic flow, can reduce vehicle conflict, and effectively improves the passing efficiency of the crossroad and the passing comfort of the vehicles.
Simulation experiments show that: the method can change the vehicle cooperation sequence, so that the vehicle speed is more stable and the acceleration is more stable. For single driving of a traditional driving vehicle, the operation in the mixed traffic flow is easier and the driving is safer. Meanwhile, compared with the common non-optimized situation, the running distance of the individual in the same passing time is increased by 8-12%.
The present invention will be described in further detail with reference to the following detailed description and the accompanying drawings, which are not intended to limit the scope of the invention.
Drawings
Fig. 1 is a schematic structural diagram of a city intersection in the first and second embodiments of the present invention.
Fig. 2 is a microscopic cooperative trajectory diagram of three vehicles in a cross collision with HHA as a vehicle combination in the second situation (R2) where the vehicles cannot smoothly pass through the collision area and without cooperative optimization control according to an embodiment of the present invention.
Fig. 3 is a microscopic cooperative trajectory diagram of three vehicles in a cross collision with HHA as the vehicle combination in the second situation (R2) where the vehicles cannot smoothly pass through the collision zone and under cooperative optimization control according to the embodiment of the present invention.
Fig. 4 is a microscopic cooperative trajectory diagram of three vehicles in a cross collision, in which the vehicle combination condition is HAA, in a third condition (R3) where the vehicles cannot smoothly pass through the collision area and the cooperative optimization control is absent, according to an embodiment of the present invention.
Fig. 5 is a microscopic cooperative trajectory diagram of three vehicles in the case of the third situation (R3) in which the combination of vehicles is HAA and cannot smoothly pass through the collision area in the case of the cooperative optimization control in the cross collision according to the embodiment of the present invention.
Fig. 6 is a microscopic cooperative trajectory diagram of three vehicles whose vehicle combination condition is HHA in the second merge conflict of the embodiment of the present invention, in the second situation (R2) where the vehicles cannot smoothly pass through the conflict area, and in the situation where there is no cooperative optimization control.
Fig. 7 is a microscopic cooperative trajectory diagram in the second merge conflict of the embodiment of the present invention, in the second situation (R2) in which three vehicles whose vehicle combination situation is HHA cannot smoothly pass through the conflict area, and in the cooperative optimization control situation.
Fig. 8 is a microscopic cooperative trajectory diagram in the first case (R1) in which three vehicles having the combination of vehicles HAA fail to smoothly pass through the collision region in the second merge collision according to the embodiment of the present invention and in the case of no cooperative optimization control.
Fig. 9 is a microscopic cooperative locus diagram in the case of cooperative optimization control in the first case (R1) in which three vehicles having a vehicle combination condition of HAA cannot smoothly pass through the collision region in the second merge collision according to the embodiment of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made with reference to the accompanying drawings.
A cooperative optimization bottom layer control method for mixed traffic flow at an urban crossroad comprises the following steps:
s1, determining a micro-following model, and describing a following state of the vehicle by using the micro-following model, wherein the following state of the vehicle comprises the speed, the acceleration and the position of the vehicle;
s2, acquiring the time and speed of the vehicle passing through the upstream monitoring point of the conflict area in the mixed traffic flow, and predicting the initial trajectory of the vehicle between the upstream monitoring point and the conflict end point by using the micro-following model; a road section with a certain distance between the upstream monitoring point and the conflict starting point; the collision starting point is positioned between the upstream monitoring point and the collision terminal point; the road sections between the collision starting point and the collision terminal point form a collision area;
s3, adding acceleration constraint, distance constraint and safety constraint to establish a conflict coordination model aiming at the conflict type of the crossroad based on the microcosmic car-following model; the conflict types of the crossroad comprise cross conflict and confluence conflict;
s4, aiming at various situations which can not pass through the conflict area smoothly and possibly occur in the process that the vehicle passes through the conflict area under the mixed traffic flow scene, a cooperative control strategy set is prepared;
s5, judging whether the vehicle can smoothly pass through a conflict area or not by the conflict coordination model based on the vehicle initial track; if the judgment result is that the vehicle can smoothly pass through the conflict area, the vehicle continuously drives according to the speed of the microcosmic following model; if the judgment result is that the vehicle cannot smoothly pass through the conflict area, further judging the specific situation of the vehicle in the process of passing through the conflict area, and making a cooperative control strategy corresponding to the cooperative strategy set simulated in the step S4 for the vehicle according to the specific situation of the vehicle in the process of passing through the conflict area, so as to execute a step S6;
and S6, determining the optimally controlled vehicles participating in the process that the vehicles pass through the conflict area as target vehicles, optimizing the running tracks of the target vehicles according to the cooperative control strategy made in the step S5, resolving the optimization problem into an optimal control problem of discrete time state constraint, solving the cooperative optimization control strategy related to the target vehicles by using a dynamic planning idea, acting the cooperative optimization control strategy related to the target vehicles on the target vehicles, and controlling the running of the target vehicles.
Example one
A cooperative optimization bottom layer control method for mixed traffic flow at an urban crossroad comprises the following steps:
and S1, determining a micro-following model, and describing the following state of the vehicle by using the micro-following model, wherein the following state of the vehicle comprises the speed, the acceleration and the position of the vehicle.
The present example uses the Gipps following model, which is as follows:
v(t+τ)＝min(ve，v(t)+aτ，vsafe)，
Figure BDA0002207613840000111
Figure BDA0002207613840000121
x(t+τ)＝x(t)+v(t)τ+0.5u(t)τ2，
the velocity of the vehicle at time t + τ is denoted by v (t + τ), where veV (t) + a τ represents the velocity obtained by acceleration at a constant acceleration a, v (t) + a τ being the desired velocitysafeIndicating a minimum safe speed. τ is the reaction time of the vehicle driving; b is a constant deceleration, and the vehicle performs deceleration at the constant deceleration b by default when braking and decelerating; v (t) represents the speed of the vehicle at time t; u (t) represents the acceleration of the vehicle at time t; x (t) represents the position of the vehicle at time t; v. oflead(t) represents the speed of the preceding vehicle followed by the vehicle at time t; x is the number of0Represents the minimum safe distance between the vehicle and the preceding vehicle followed by the vehicle (even if the preceding vehicle suddenly brakes to decelerate to a complete stop (worst case), the distance between the vehicle and the preceding vehicle followed by the vehicle needs to be greater than the minimum safe distance x0)。
S2, acquiring the time and speed of the vehicle passing through the upstream monitoring point of the conflict area in the mixed traffic flow, and predicting the initial trajectory of the vehicle between the upstream monitoring point and the conflict end point by using the micro-following model; a road section with a certain distance between the upstream monitoring point and the conflict starting point; the collision starting point is positioned between the upstream monitoring point and the collision terminal point; the link between the collision start point and the collision end point constitutes a collision area.
S3, adding acceleration constraint, distance constraint and safety constraint to establish a conflict coordination model aiming at the conflict type of the crossroad based on the microcosmic car-following model; the conflict type of the crossroad is cross conflict; the cross collision means that vehicles in different driving directions run across each other at a large angle. The method specifically comprises the following steps:
fig. 1 is a schematic structural diagram of an urban crossroad.
Suppose X way andy roads are all one-way lanes, and the intersection point of the X road and the Y road is (X)C，YC)；
Defining a conflict area: determining a collision starting point and a collision end point by combining the geometric length of the road and the microcosmic car-following model, wherein the collision area on the X road is from the collision starting point XcsTo the conflicting terminal XceStopping; collision area self-conflict starting point Y on Y roadcsTo the conflict end point YceIf not, the conflict area is (X)cs，Ycs)～(Xce，Yce)；
Defining a control area: control area on X road from vehicle distance conflict starting point XcsControl starting point X of a certain distanceDTo the cross point (X)C，YC) Stopping; control area on Y road from vehicle distance collision starting point YcsControl starting point Y of a certain distanceDTo the cross point (X)C，YC) If not, the control region is (X)D，YD)～(XC，YC) (ii) a The control starting point is positioned between the upstream monitoring point and the collision starting point;
suppose that vehicle k on the X road is in the control zone (X)D，YD)～(XC，YC) The interval between two vehicles that vehicle k will pass through a continuous flow on the Y road; k 'for each of two vehicles of the continuous stream on the Y-road'1And k'2Is represented by, wherein vehicle k'1Denotes a front vehicle, vehicle k'2Representing a rear vehicle;
at time t, the relative distance between vehicle k on the X road and vehicle k' on the Y road is represented by lk,k' (t) denotes that:
Figure BDA0002207613840000122
wherein x isk(t) represents the position of the vehicle k on the X road at time t, yk′(t) represents the position of the vehicle k' on the Y road at time t;
u for conflict cooperative utilityk(t) is used to reflect whether the vehicle k can smoothly pass through the X roadA overshoot area; the conflict cooperative utility Uk(t) is represented as follows:
Figure BDA0002207613840000131
wherein, | uk(t) | represents the absolute value of the acceleration of the vehicle k on the X road at the time t;
Figure BDA0002207613840000132
denotes vehicle k 'on Y road'2The absolute value of the acceleration at time t;
Figure BDA0002207613840000133
at time t, represents vehicle k ' on the X road and vehicle k ' on the Y road '1The relative distance therebetween;
Figure BDA0002207613840000134
at time t, represents vehicle k ' on the X road and vehicle k ' on the Y road '2The relative distance therebetween;
Figure BDA0002207613840000135
representing a safety body of the intelligent networked vehicle;
Figure BDA0002207613840000136
representing a safety body of a conventionally driven vehicle; bsafeRepresents a maximum allowable deceleration; phiAThe method comprises the following steps of (1) providing an intelligent networked vehicle set; phiHη for conventional driving vehicle1Indicating a safety factor of η2Representing a polite coefficient; the intelligent networked vehicle is an optimally controllable vehicle and is represented by a letter A; the conventional driving vehicle is a non-optimally controllable vehicle and is denoted by the letter H.
Polite coefficient η2The expression of (a) is as follows:
Figure BDA0002207613840000137
wherein v isthIs a given threshold speed, β1And β2Is a constant.
For collaborative decision Ik(t + τ) is represented as follows:
Figure BDA0002207613840000138
wherein, IkA value of (t + τ) of 1 indicates that the vehicle k can smoothly pass through the collision region at time t + τ; i iskA value of 0 for (t + τ) indicates that vehicle k cannot smoothly pass through the collision zone at time t + τ.
And S4, aiming at various situations which can occur in the process that vehicles pass through the conflict area in the mixed traffic flow scene and can not pass through the conflict area smoothly, simulating a cooperative control strategy set. The method specifically comprises the following steps:
assuming that both the X and Y lanes are one-way lanes, vehicle k on the X lane will pass through the interval between two vehicles of the continuous flow on the Y lane, each with k'1And k'2Is represented by, wherein vehicle k'1Denotes a front vehicle, vehicle k'2Representing a rear vehicle;
for various situations which can occur in the process that vehicles pass through a conflict area in a mixed traffic flow scene, k 'of the vehicles is determined based on the micro-following model'1K 'of vehicle'2The relationship between the conflict areas is divided into the conflict areas which can be smoothly passed and the conflict areas which can not be smoothly passed; the failure to smoothly pass through the collision region is further divided into four cases, the first case is denoted by R1 and represents the vehicles k and k'1The distance between the two parts is too close to satisfy the constraint condition of smoothly passing through the conflict area; the second case is marked as R2 and represents vehicle k and vehicle k'2The distance between the two parts is too close to satisfy the constraint condition of smoothly passing through the conflict area; the third case is marked as R3 and represents vehicle k and vehicle k'1And k 'with vehicle'2The distances between the two parts are too close to meet the constraint condition that the two parts can smoothly pass through a conflict area; the fourth case is marked as R4 and represents vehicle k and vehicle k'1And k 'with vehicle'2The basic spacing requirement is satisfied, but the process of passing through the conflict area is not comfortable;
aiming at four conditions that the vehicle can not smoothly pass through a conflict area, the vehicle which can be optimally controlled needs to be cooperatively controlled; the traditional driving vehicle is represented by H and is a vehicle which cannot be optimally controlled; the method comprises the following steps that A represents an intelligent networked vehicle which is an optimally controllable vehicle; n represents no front vehicle or no rear vehicle; and provides the combination sequence of the vehicles as vehicle k'1K 'of vehicle'2；
Based on different vehicle combinations, different vehicle type combinations and the four conditions that the conflict area can not be smoothly passed, a cooperative control strategy set is planned, and the following table shows that:
Figure BDA0002207613840000141
Figure BDA0002207613840000151
Figure BDA0002207613840000161
in the above table, the non-optimization means that the vehicle has no corresponding control strategy under the condition that the vehicle cannot smoothly pass through the conflict area; under the condition that optimization cannot be carried out, when the conflict type of the intersection is cross conflict, the vehicle k on the X road takes the intersection of the X road and the Y road as a stopped virtual front vehicle, continuously decelerates and even stops to wait by following the microcosmic following model, and the vehicle k does not pass through the conflict area until the vehicle interval meeting the passing conflict area appears on the Y road;
the control state is unknown, and in the case R4 where the collision region cannot be smoothly passed, it is necessary to determine the vehicles k and k'1K 'of vehicle'2In the process of passing through the conflict area, which corresponding vehicles form discomfort, and corresponding control strategies are adopted according to the conditions of R1, R2 and R3 that the vehicles cannot pass through the conflict area smoothly;
the acceleration of the vehicle k is controlled by a decision variable u of the vehicle k at the time tk(t) satisfies:
Figure BDA0002207613840000162
and v isk(t)+uk(t)τ≤ve；
The control vehicle k decelerates, and a decision variable u of the vehicle k at the time t is usedk(t) satisfies:
Figure BDA0002207613840000163
and v isk(t)+uk(t)τ≥0；
The uncontrolled vehicle k is a decision variable u for the vehicle k at time tk(t) satisfies:
Figure BDA0002207613840000164
i.e. uk(t)＝0；
The control vehicle k'1The acceleration is to make the vehicle k 'at the time t'1Decision variables of
Figure BDA0002207613840000165
Satisfies the following conditions:
Figure BDA0002207613840000166
and is
Figure BDA0002207613840000167
The vehicle k 'is not controlled'1Is to make the vehicle k 'at time t'1Decision variables of
Figure BDA0002207613840000168
Satisfies the following conditions:
Figure BDA0002207613840000169
namely, it is
Figure BDA00022076138400001610
The control vehicle k'2Deceleration is performed by making the vehicle k 'at time t'2Decision variables of
Figure BDA00022076138400001611
Satisfies the following conditions:
Figure BDA00022076138400001612
and is
Figure BDA00022076138400001613
The vehicle k 'is not controlled'2Is to make the vehicle k 'at time t'2Decision variables of
Figure BDA00022076138400001614
Satisfies the following conditions:
Figure BDA00022076138400001615
namely, it is
Figure BDA00022076138400001616
Wherein u isk(t) as a decision variable for the vehicle k at time t, representing the acceleration of the vehicle k at time t; v. ofk(t) is the speed of vehicle k at time t;
Figure BDA0002207613840000171
is the safe following speed of the vehicle k at the moment of t + tau predicted according to the microcosmic following model;
Figure BDA0002207613840000172
k 'of vehicle at time t'1Is a decision variable of vehicle k'1Acceleration at time t;
Figure BDA0002207613840000173
is vehicle k'1Velocity at time t;
Figure BDA0002207613840000174
is vehicle k 'predicted from the microscopic follow model'1Safe following speed at time t + τ;
Figure BDA0002207613840000175
k 'of vehicle at time t'2Is a decision variable of vehicle k'2Acceleration at time t;
Figure BDA0002207613840000176
is vehicle k'2Velocity at time t;
Figure BDA0002207613840000177
is vehicle k 'predicted from the microscopic follow model'2Safe following speed at time t + τ; τ is the reaction time of the vehicle driving; v. ofeIs the desired speed.
S5, judging whether the vehicle can smoothly pass through a conflict area or not by the conflict coordination model based on the vehicle initial track; if the judgment result is that the vehicle can smoothly pass through the conflict area, the vehicle continuously drives according to the speed of the microcosmic following model; if the determination result is that the vehicle cannot smoothly pass through the collision area, it is necessary to further determine the specific situation of the vehicle in the process of passing through the collision area, and perform the cooperative control strategy corresponding to the cooperative strategy set formulated in step S4 on the vehicle according to the specific situation of the vehicle in the process of passing through the collision area, so as to execute step S6.
And S6, determining the optimally controlled vehicles participating in the process that the vehicles pass through the conflict area as target vehicles, optimizing the running tracks of the target vehicles according to the cooperative control strategy made in the step S5, resolving the optimization problem into an optimal control problem of discrete time state constraint, solving the cooperative optimization control strategy related to the target vehicles by using a dynamic planning idea, acting the cooperative optimization control strategy related to the target vehicles on the target vehicles, and controlling the running of the target vehicles. The method specifically comprises the following steps:
s6-1, predicting the moment t when the target vehicle enters the control area based on the microcosmic car-following model0The time when the user leaves the control area is tf(ii) a And will t0To tfIs divided into N segments on average in discrete time intervals τ', i.e., N ═ tf-t0) T', defining the control decision time as t0+τ′,t0+2τ′,t0+3τ′,t0+4τ′,…,t0+(N-1)τ′,tf；
S6-2, according to the target vehicle at t0The state of the time, the target vehicle is calculated at t0The set of allowable states at time + τ', and the target vehicle from t0State of time t0The allowable states at time + τ' aggregate the transition costs for each allowable state; said target vehicle is at t0The state at time t includes the target vehicle being at t0Speed and position of the moment;
s6-3, according to the target vehicle at t0The allowable state set at the time + tau' is calculated for the target vehicle at t0The allowable state set at time +2 τ', and the target vehicle from t0State at time + τ' to t0The allowable states at time +2 τ' are grouped into transition costs and cumulative costs for each allowable state;
s6-4, sequentially calculating the target vehicle at t according to the method of the step S6-30+3τ′,t0+4τ′,…,t0+(N-1)τ′,tfThe allowable state set of the time is calculated to obtain tfThe cumulative cost of each allowable state in the time-wise allowable state set;
s6-5, judging that the target vehicle is at tfWhether each allowable state in the allowable state set at the moment meets the condition that the allowable state can smoothly pass through the conflict area or not is judged, and the allowable state meeting the condition is brought into the final allowable state set;
s6-6, selecting the allowable state with the minimum accumulated cost in the final allowable state set as tfOptimal state of the moment；
S6-7, according to tfThe optimal state of the moment is reversely deduced to obtain t0+τ′,t0+2τ′,t0+3τ′,t0+4τ′,…,t0+(N-1)τ′,tfThe optimal state at each moment, and the control decision corresponding to each optimal state is brought into a cooperative optimization control strategy;
and S6-8, controlling the operation of the target vehicle among the control areas according to the cooperative optimization control strategy.
The method of the invention constructs a microscopic traffic flow simulation environment through Python, and realizes the parameters and values of the simulation experiment environment of the mixed traffic flow of the urban crossroads (the structure of which is shown in figure 1) by programming as shown in the following table:
Figure BDA0002207613840000181
FIG. 2 is a microscopic cooperative locus diagram of three vehicles in a cross collision, with a vehicle combination HHA, in a second situation (R2) where the vehicles cannot smoothly pass through a collision area and without cooperative optimization control, wherein (a) is a position-time relationship diagram, (b) is a speed-time relationship diagram, and (c) is an acceleration-time relationship diagram, and the time unit in the diagram is s, the position unit is m, the speed unit is m/s, and the acceleration unit is m/s2。
FIG. 3 is a microscopic cooperative locus diagram in the case of cooperative optimization control in the second case (R2) in which three vehicles having a vehicle combination condition HHA cannot smoothly pass through a collision area in a cross collision, wherein (a) is a position-time relationship diagram, (b) is a speed-time relationship diagram, and (c) is an acceleration-time relationship diagram, and the time unit in the diagram is s, the position unit is m, the speed unit is m/s, and the acceleration unit is m/s2。
FIG. 4 is a microscopic cooperative locus diagram of three vehicles in a cross collision, in which the combination of vehicles is HAA, in a third situation (R3) where the vehicles cannot smoothly pass through the collision region, and in the case of no cooperative optimization control, wherein (a) is a position-time relationship diagram, (b) is a speed-time relationship diagram, and (c) is an acceleration-time relationship diagramA time diagram, and the time unit is s, the position unit is m, the speed unit is m/s, and the acceleration unit is m/s2。
FIG. 5 is a microscopic cooperative locus diagram in a cooperative optimization control situation in a third situation (R3) in which three vehicles in a combined vehicle situation of HAA cannot smoothly pass through a collision area in a cross collision, wherein (a) is a position-time relationship diagram, (b) is a speed-time relationship diagram, and (c) is an acceleration-time relationship diagram, and the time unit in the diagram is s, the position unit is m, the speed unit is m/s, and the acceleration unit is m/s2。
In the comparison graph before and after the cross conflict optimization, it is found that: the speed-time relation graph can be obtained by comparing, under the cooperative optimization control, the speed change degrees of the three vehicles become smaller, and the vehicle speed is more stable; the acceleration-time relation graph can be obtained by comparing, and the acceleration is relatively stable without large acceleration and deceleration of the vehicle adopting the cooperative optimization control strategy. Meanwhile, under the condition of no cooperative optimization control, when a corresponding traditional driving vehicle passes through a conflict area, due to the fact that other vehicles participating in the conflict process are excessively decelerated, the vehicle needs to be accelerated and recovered to return to the original vehicle speed to pass through the conflict area, and therefore a driver of the traditional driving vehicle needs to be greatly and frequently accelerated and decelerated to be switched to cooperate with the traditional driving vehicle, driving operation of the traditional driving vehicle is complicated, and great unsafety is brought to vehicle driving; under cooperative optimization control, most of the conventional driving vehicles only adopt a deceleration mode to achieve the purpose of mutual cooperative cooperation when passing through a conflict area, so that the driving is obviously safe, comfortable and reasonable.
In general, by using the collaborative optimization bottom layer control method, the overall traffic flow can be more stable, the running cost is lower, and the driving operation becomes more stable and safer for the single driving of the traditional driving vehicle.
Example two
A cooperative optimization bottom layer control method for mixed traffic flow at an urban crossroad comprises the following steps:
and S1, determining a micro-following model, and describing the following state of the vehicle by using the micro-following model, wherein the following state of the vehicle comprises the speed, the acceleration and the position of the vehicle.
The present example uses the Gipps following model, which is as follows:
v(t+τ)＝min(ve,v(t)+aτ,vsafe)，
Figure BDA0002207613840000191
Figure BDA0002207613840000192
x(t+τ)＝x(t)+v(t)τ+0.5u(t)τ2，
the velocity of the vehicle at time t + τ is denoted by v (t + τ), where veV (t) + a τ represents the velocity obtained by acceleration at a constant acceleration a, v (t) + a τ being the desired velocitysafeIndicating a minimum safe speed. τ is the reaction time of the vehicle driving; b is a constant deceleration, and the vehicle performs deceleration at the constant deceleration b by default when braking and decelerating; v (t) represents the speed of the vehicle at time t; u (t) represents the acceleration of the vehicle at time t; x (t) represents the position of the vehicle at time t; v. oflead(t) represents the speed of the preceding vehicle followed by the vehicle at time t; x is the number of0Represents the minimum safe distance between the vehicle and the preceding vehicle followed by the vehicle, and even if the preceding vehicle suddenly brakes and decelerates to a complete stop (worst case), the distance between the vehicle and the preceding vehicle followed by the vehicle needs to be larger than the minimum safe distance x0。
S2, acquiring the time and speed of the vehicle passing through the upstream monitoring point of the conflict area in the mixed traffic flow, and predicting the initial trajectory of the vehicle between the upstream monitoring point and the conflict end point by using the micro-following model; a road section with a certain distance between the upstream monitoring point and the conflict starting point; the collision starting point is positioned between the upstream monitoring point and the collision terminal point; the link between the collision start point and the collision end point constitutes a collision area.
S3, adding acceleration constraint, distance constraint and safety constraint to establish a conflict coordination model aiming at the conflict type of the crossroad based on the microcosmic car-following model; the conflict type of the crossroad is confluence conflict; the confluent collision means that vehicles in different driving directions travel together in the same direction at a small angle. The method specifically comprises the following steps:
fig. 1 is a schematic structural diagram of an urban crossroad.
Suppose that both the X and Y lanes are one-way lanes and the intersection of the X and Y lanes is (X)C，YC)；
Defining a conflict area: determining a collision starting point and a collision end point by combining the geometric length of the road and a microcosmic car-following model, wherein the collision area on the X road is from a collision starting point XcsTo the conflicting terminal XceStopping; collision area self-conflict starting point Y on Y roadcsTo the conflict end point YceIf not, the conflict area is (X)cs，Ycs)～(Xce，Yce)；
Defining a control area: control area on X road from vehicle distance conflict starting point XcsControl starting point X of a certain distanceDTo the cross point (X)C，YC) Stopping; control area on Y road from vehicle distance collision starting point YcsControl starting point Y of a certain distanceDTo the cross point (X)C，YC) If not, the control region is (X)D，YD)～(XC，YC) (ii) a The control starting point is positioned between the upstream monitoring point and the collision starting point;
suppose that vehicle k on the X road is in the control zone (X)D，YD)～(XC，YC) The interval between two vehicles that vehicle k will pass through a continuous flow on the Y road; k 'for each of two vehicles of the continuous stream on the Y-road'1And k'2Is represented by, wherein vehicle k'1Denotes a front vehicle, vehicle k'2Representing a rear vehicle;
at time t, the relative distance between vehicle k on the X road and vehicle k' on the Y road is represented by lk,k′(t) denotes, then:
when x isk(t)＜XCAt the time of-r,
Figure BDA0002207613840000201
when in use
Figure BDA0002207613840000202
When the temperature of the water is higher than the set temperature,
Figure BDA0002207613840000203
when in use
Figure BDA0002207613840000211
When the temperature of the water is higher than the set temperature,
Figure BDA0002207613840000212
wherein x isk(t) represents the position of the vehicle k on the X road at time t, yk′(t) represents the position of the vehicle k' on the Y road at time t; r represents the turning radius of the vehicle k at the crossroad;
u for conflict cooperative utilityk(t) indicating whether the vehicle k can smoothly pass through the collision area on the X road; the conflict cooperative utility Uk(t) is represented as follows:
Figure BDA0002207613840000213
wherein, | uk(t) | represents the absolute value of the acceleration of the vehicle k on the X road at the time t;
Figure BDA0002207613840000214
denotes vehicle k 'on Y road'2The absolute value of the acceleration at time t;
Figure BDA0002207613840000215
at time t, represents vehicle k ' on the X road and vehicle k ' on the Y road '1The relative distance therebetween;
Figure BDA0002207613840000216
at time t, represents vehicle k ' on the X road and vehicle k ' on the Y road '2The relative distance therebetween;
Figure BDA0002207613840000217
denotes vehicle k on the X road and vehicle k 'on the Y road'1The vehicle k on the X road is an intelligent networking vehicle;
Figure BDA0002207613840000218
denotes vehicle k on the X road and vehicle k 'on the Y road'1And the vehicle k on the X road is a conventional driving vehicle;
Figure BDA0002207613840000219
denotes vehicle k on the X road and vehicle k 'on the Y road'2The vehicle k on the X road is an intelligent networking vehicle;
Figure BDA00022076138400002110
denotes vehicle k on the X road and vehicle k 'on the Y road'2And the vehicle k on the X road is a conventional driving vehicle; bsafeRepresents a maximum allowable deceleration; phiAThe method comprises the following steps of (1) providing an intelligent networked vehicle set; phiHη for conventional driving vehicle1Indicating a safety factor of η2Representing a polite coefficient; the intelligent networked vehicle is an optimally controllable vehicle and is represented by a letter A; the conventional driving vehicle is a non-optimally controllable vehicle and is denoted by the letter H.
Polite coefficient η2The expression of (a) is as follows:
Figure BDA00022076138400002111
wherein v isthIs a given speed threshold, β1And β2Is a constant.
For collaborative decision Ik(t + τ) is represented as follows:
Figure BDA00022076138400002112
wherein, IkA value of (t + τ) of 1 indicates that the vehicle k can smoothly pass through the collision region at time t + τ; i iskA value of 0 for (t + τ) indicates that vehicle k cannot smoothly pass through the collision zone at time t + τ.
And S4, aiming at various situations which can occur in the process that vehicles pass through the conflict area in the mixed traffic flow scene and can not pass through the conflict area smoothly, simulating a cooperative control strategy set. The method specifically comprises the following steps:
assuming that both the X and Y lanes are one-way lanes, vehicle k on the X lane will pass through the interval between two vehicles of the continuous flow on the Y lane, each with k'1And k'2Is represented by, wherein vehicle k'1Denotes a front vehicle, vehicle k'2Representing a rear vehicle;
for various situations which can occur in the process that vehicles pass through a conflict area in a mixed traffic flow scene, k 'of the vehicles is determined based on the micro-following model'1K 'of vehicle'2The relationship between the conflict areas is divided into the conflict areas which can be smoothly passed and the conflict areas which can not be smoothly passed; the failure to smoothly pass through the collision region is further divided into four cases, the first case is denoted by R1 and represents the vehicles k and k'1The distance between the two parts is too close to satisfy the constraint condition of smoothly passing through the conflict area; the second case is marked as R2 and represents vehicle k and vehicle k'2The distance between the two parts is too close to satisfy the constraint condition of smoothly passing through the conflict area; the third case is marked as R3 and represents vehicle k and vehicle k'1And k 'with vehicle'2The distances between the two parts are too close to meet the constraint condition that the two parts can smoothly pass through a conflict area; the fourth case is marked as R4 and represents vehicle k and vehicle k'1And k 'with vehicle'2The basic spacing requirement is satisfied, but the process of passing through the conflict area is not comfortable;
aiming at four conditions that the vehicle can not smoothly pass through a conflict area, the vehicle which can be optimally controlled needs to be cooperatively controlled; by using H-metersThe traditional driving vehicle is a vehicle which cannot be controlled optimally; the method comprises the following steps that A represents an intelligent networked vehicle which is an optimally controllable vehicle; n represents no front vehicle or no rear vehicle; and provides the combination sequence of the vehicles as vehicle k'1K 'of vehicle'2；
Based on different vehicle combinations, different vehicle type combinations and the four conditions that the conflict area can not be smoothly passed, a cooperative control strategy set is planned, and the following table shows that:
Figure BDA0002207613840000221
Figure BDA0002207613840000231
Figure BDA0002207613840000241
in the above table, the non-optimization means that the vehicle has no corresponding control strategy under the condition that the vehicle cannot smoothly pass through the conflict area; under the condition that optimization cannot be carried out, when the conflict type of the intersection is confluence conflict, a vehicle k on the X road takes a confluence point of the X road and the Y road as a stopped virtual front vehicle, and continuously decelerates or even stops to wait by following the microcosmic following model until a vehicle interval meeting a passable conflict area appears on the Y road, and the vehicle k does not pass through the conflict area; the confluence point of the X path and the Y path refers to the position where the X path traffic flow is converged into the Y path traffic flow;
the control state is unknown, and in the case R4 where the collision region cannot be smoothly passed, it is necessary to determine the vehicles k and k'1K 'of vehicle'2In the process of passing through the conflict area, which corresponding vehicles form discomfort, and corresponding control strategies are adopted according to the conditions of R1, R2 and R3 that the vehicles cannot pass through the conflict area smoothly;
the acceleration of the vehicle k is controlled by a decision variable u of the vehicle k at the time tk(t) satisfies：
Figure BDA0002207613840000242
And v isk(t)+uk(t)τ≤ve；
The control vehicle k decelerates, and a decision variable u of the vehicle k at the time t is usedk(t) satisfies:
Figure BDA0002207613840000243
and v isk(t)+uk(t)τ≥0；
The uncontrolled vehicle k is a decision variable u for the vehicle k at time tk(t) satisfies:
Figure BDA0002207613840000244
i.e. uk(t)＝0；
The control vehicle k'1The acceleration is to make the vehicle k 'at the time t'1Decision variables of
Figure BDA0002207613840000245
Satisfies the following conditions:
Figure BDA0002207613840000246
and is
Figure BDA0002207613840000247
The vehicle k 'is not controlled'1Is to make the vehicle k 'at time t'1Decision variables of
Figure BDA0002207613840000248
Satisfies the following conditions:
Figure BDA0002207613840000249
namely, it is
Figure BDA00022076138400002410
The control vehicle k'2Deceleration is performed by making the vehicle k 'at time t'2Decision variables of
Figure BDA00022076138400002411
Satisfies the following conditions:
Figure BDA0002207613840000251
and is
Figure BDA0002207613840000252
The vehicle k 'is not controlled'2Is to make the vehicle k 'at time t'2Decision variables of
Figure BDA0002207613840000253
Satisfies the following conditions:
Figure BDA0002207613840000254
namely, it is
Figure BDA0002207613840000255
Wherein u isk(t) as a decision variable for the vehicle k at time t, representing the acceleration of the vehicle k at time t; v. ofk(t) is the speed of vehicle k at time t;
Figure BDA0002207613840000256
is the safe following speed of the vehicle k at the moment of t + tau predicted according to the microcosmic following model;
Figure BDA0002207613840000257
k 'of vehicle at time t'1Is a decision variable of vehicle k'1Acceleration at time t;
Figure BDA0002207613840000258
is vehicle k'1Velocity at time t;
Figure BDA0002207613840000259
is vehicle k 'predicted from the microscopic follow model'1Safe following speed at time t + τ;
Figure BDA00022076138400002510
k 'of vehicle at time t'2Is a decision variable of vehicle k'2Acceleration at time t;
Figure BDA00022076138400002511
is vehicle k'2Velocity at time t;
Figure BDA00022076138400002512
is vehicle k 'predicted from the microscopic follow model'2Safe following speed at time t + τ; τ is the reaction time of the vehicle driving; v. ofeIs the desired speed.
S5, judging whether the vehicle can smoothly pass through a conflict area or not by the conflict coordination model based on the vehicle initial track; if the judgment result is that the vehicle can smoothly pass through the conflict area, the vehicle continuously drives according to the speed of the microcosmic following model; if the judgment result is that the vehicle cannot smoothly pass through the conflict area, further judging the specific situation of the vehicle in the process of passing through the conflict area, and making a cooperative control strategy corresponding to the cooperative strategy set simulated in the step S4 for the vehicle according to the specific situation of the vehicle in the process of passing through the conflict area, so as to execute a step S6;
and S6, determining the optimally controlled vehicles participating in the process that the vehicles pass through the conflict area as target vehicles, optimizing the running tracks of the target vehicles according to the cooperative control strategy made in the step S5, resolving the optimization problem into an optimal control problem of discrete time state constraint, solving the cooperative optimization control strategy related to the target vehicles by using a dynamic planning idea, acting the cooperative optimization control strategy related to the target vehicles on the target vehicles, and controlling the running of the target vehicles. The method specifically comprises the following steps:
s6-1, predicting the moment t when the target vehicle enters the control area based on the microcosmic car-following model0The time when the user leaves the control area is tf(ii) a And will t0To tfIs divided into N segments on average in discrete time intervals τ', i.e., N ═ tf-t0) T', defining the control decision time as t0+τ′,t0+2τ′,t0+3τ′,t0+4τ′,…,t0+(N-1)τ′,tf；
S6-2, according to the target vehicle at t0The state of the time, the target vehicle is calculated at t0The set of allowable states at time + τ', and the target vehicle from t0State of time t0The allowable states at time + τ' aggregate the transition costs for each allowable state; said target vehicle is at t0The state at time t includes the target vehicle being at t0Speed and position of the moment;
s6-3, according to the target vehicle at t0The allowable state set at the time + tau' is calculated for the target vehicle at t0The allowable state set at time +2 τ', and the target vehicle from t0State at time + τ' to t0The allowable states at time +2 τ' are grouped into transition costs and cumulative costs for each allowable state;
s6-4, sequentially calculating the target vehicle at t according to the method of the step S6-30+3τ′,t0+4τ′,…,t0+(N-1)τ′,tfThe allowable state set of the time is calculated to obtain tfThe cumulative cost of each allowable state in the time-wise allowable state set;
s6-5, judging that the target vehicle is at tfWhether each allowable state in the allowable state set at the moment meets the condition that the allowable state can smoothly pass through the conflict area or not is judged, and the allowable state meeting the condition is brought into the final allowable state set;
s6-6, selecting the allowable state with the minimum accumulated cost in the final allowable state set as tfThe optimal state of the moment;
s6-7, according to tfOptimal state of the momentBackward thrust to obtain t0+τ′,t0+2τ′,t0+3τ′,t0+4τ′,…,t0+(N-1)τ′,tfThe optimal state at each moment, and the control decision corresponding to each optimal state is brought into a cooperative optimization control strategy;
and S6-8, controlling the operation of the target vehicle among the control areas according to the cooperative optimization control strategy.
The method constructs a microscopic traffic flow simulation environment through Python, and realizes the parameters and values of the simulation experiment environment of the mixed traffic flow of the urban crossroads (the structure of which is shown in figure 1) by programming as shown in the following table:
Figure BDA0002207613840000261
Figure BDA0002207613840000271
FIG. 6 is a microscopic cooperative locus diagram in the case of no cooperative optimization control in the second case (R2) in which three vehicles having a vehicle combination HHA cannot smoothly pass through a collision area in a confluent collision, wherein (a) is a position-time relationship diagram, (b) is a speed-time relationship diagram, and (c) is an acceleration-time relationship diagram, and the time unit in the diagram is s, the position unit is m, the speed unit is m/s, and the acceleration unit is m/s2。
FIG. 7 is a microscopic cooperative locus diagram in the cooperative optimization control in the second case (R2) in which three vehicles having a vehicle combination HHA cannot smoothly pass through the collision area in the merge collision, wherein (a) is a position-time relationship diagram, (b) is a speed-time relationship diagram, and (c) is an acceleration-time relationship diagram, and the time unit in the diagram is s, the position unit is m, the speed unit is m/s, and the acceleration unit is m/s2。
FIG. 8 is a microscopic cooperative locus diagram of three vehicles in a merge collision, the combination of vehicles of which is HAA, in the first case (R1) where the vehicles cannot smoothly pass through the collision region, and in the case of no cooperative optimization control, in whichThe graph has time unit of s, position unit of m, speed unit of m/s and acceleration unit of m/s2。
FIG. 9 is a microscopic cooperative locus diagram in the cooperative optimization control in the first case (R1) in which three vehicles in the combination of vehicles HAA cannot smoothly pass through the collision region in the merge collision, wherein (a) is a position-time relationship diagram, (b) is a velocity-time relationship diagram, and (c) is an acceleration-time relationship diagram, and the time unit in the diagram is s, the position unit is m, the velocity unit is m/s, and the acceleration unit is m/s2。
In the comparison graph before and after confluence conflict optimization, the following results are found: the speed-time relation graph can be obtained by comparing, under the cooperative optimization control, the speed change degrees of the three vehicles become smaller, and the vehicle speed is more stable; the acceleration-time relation graph can be obtained by comparing, and the acceleration is relatively stable without large acceleration and deceleration of the vehicle adopting the cooperative optimization control strategy. Meanwhile, under the condition of no cooperative optimization control, when a corresponding traditional driving vehicle passes through a conflict area, due to the fact that other vehicles participating in the conflict process are excessively decelerated, the vehicle needs to be accelerated and recovered to return to the original vehicle speed to pass through the conflict area, and therefore a driver of the traditional driving vehicle needs to be greatly and frequently accelerated and decelerated to be switched to cooperate with the traditional driving vehicle, driving operation of the traditional driving vehicle is complicated, and great unsafety is brought to vehicle driving; under cooperative optimization control, most of the conventional driving vehicles only adopt a deceleration mode to achieve the purpose of mutual cooperative cooperation when passing through a conflict area, so that the driving is obviously safe, comfortable and reasonable.
In general, by using the collaborative optimization bottom layer control method, the overall traffic flow can be more stable, the running cost is lower, and the driving operation becomes more stable and safer for the single driving of the traditional driving vehicle.
While the present invention has been described above by way of example with reference to the accompanying drawings, it is to be understood that the invention is not limited to the specific embodiments shown herein.

Claims (5)

1. A collaborative optimization bottom layer control method for mixed traffic flow at an urban crossroad is characterized by comprising the following steps:
s1, determining a micro-following model, and describing a following state of the vehicle by using the micro-following model, wherein the following state of the vehicle comprises the speed, the acceleration and the position of the vehicle;
s2, acquiring the time and speed of the vehicle passing through the upstream monitoring point of the conflict area in the mixed traffic flow, and predicting the initial trajectory of the vehicle between the upstream monitoring point and the conflict end point by using the micro-following model; a road section with a certain distance between the upstream monitoring point and the conflict starting point; the collision starting point is positioned between the upstream monitoring point and the collision terminal point; the road sections between the collision starting point and the collision terminal point form a collision area;
s3, adding acceleration constraint, distance constraint and safety constraint to establish a conflict coordination model aiming at the conflict type of the crossroad based on the microcosmic car-following model; the conflict types of the crossroad comprise cross conflict and confluence conflict;
s4, aiming at various situations which can not pass through the conflict area smoothly and possibly occur in the process that the vehicle passes through the conflict area under the mixed traffic flow scene, a cooperative control strategy set is prepared;
s5, judging whether the vehicle can smoothly pass through a conflict area or not by the conflict coordination model based on the vehicle initial track; if the judgment result is that the vehicle can smoothly pass through the conflict area, the vehicle continuously drives according to the speed of the microcosmic following model; if the judgment result is that the vehicle cannot smoothly pass through the conflict area, further judging the specific situation of the vehicle in the process of passing through the conflict area, and making a cooperative control strategy corresponding to the cooperative strategy set simulated in the step S4 for the vehicle according to the specific situation of the vehicle in the process of passing through the conflict area, so as to execute a step S6;
s6, determining an optimally controlled vehicle participating in the process that the vehicle passes through a conflict area as a target vehicle, optimizing the running track of the target vehicle according to the cooperative control strategy made in the step S5, resolving the optimization problem into an optimal control problem of discrete time state constraint, solving the cooperative optimization control strategy related to the target vehicle by using a dynamic planning idea, acting the cooperative optimization control strategy related to the target vehicle on the target vehicle, and controlling the running of the target vehicle;
the step S4 specifically includes:
assuming that both the X and Y lanes are one-way lanes, vehicle k on the X lane will pass through the interval between two vehicles of the continuous flow on the Y lane, each with k'1 andk′2is represented by, wherein vehicle k'1Denotes a front vehicle, vehicle k'2Representing a rear vehicle;
for various situations which can occur in the process that vehicles pass through a conflict area in a mixed traffic flow scene, k 'of the vehicles is determined based on the micro-following model'1K 'of vehicle'2The relationship between the conflict areas is divided into the conflict areas which can be smoothly passed and the conflict areas which can not be smoothly passed; the failure to smoothly pass through the collision region is further divided into four cases, the first case is denoted by R1 and represents the vehicles k and k'1The distance between the two parts is too close to satisfy the constraint condition of smoothly passing through the conflict area; the second case is marked as R2 and represents vehicle k and vehicle k'2The distance between the two parts is too close to satisfy the constraint condition of smoothly passing through the conflict area; the third case is marked as R3 and represents vehicle k and vehicle k'1And k 'with vehicle'2The distances between the two parts are too close to meet the constraint condition that the two parts can smoothly pass through a conflict area; the fourth case is marked as R4 and represents vehicle k and vehicle k'1And k 'with vehicle'2The basic spacing requirement is satisfied, but the process of passing through the conflict area is not comfortable;
aiming at four conditions that the vehicle can not smoothly pass through a conflict area, the vehicle which can be optimally controlled needs to be cooperatively controlled; the traditional driving vehicle is represented by H and is a vehicle which cannot be optimally controlled; the method comprises the following steps that A represents an intelligent networked vehicle which is an optimally controllable vehicle;n represents no front vehicle or no rear vehicle; and provides the combination sequence of the vehicles as vehicle k'1K 'of vehicle'2；
Based on different vehicle combinations, different vehicle type combinations and the four conditions that the conflict area can not be smoothly passed, a cooperative control strategy set is planned, and the following table shows that:
Figure FDA0002565015680000021
Figure FDA0002565015680000031
Figure FDA0002565015680000041
in the above table, the non-optimization means that the vehicle has no corresponding control strategy under the condition that the vehicle cannot smoothly pass through the conflict area; under the condition that optimization cannot be carried out, when the conflict type of the intersection is cross conflict, the vehicle k on the X road takes the intersection of the X road and the Y road as a stopped virtual front vehicle, continuously decelerates and even stops to wait by following the microcosmic following model, and the vehicle k does not pass through the conflict area until the vehicle interval meeting the passing conflict area appears on the Y road; when the conflict type of the crossroad is confluence conflict, the vehicle k on the X road takes the confluence point of the X road and the Y road as a stopped virtual front vehicle, continuously decelerates along the microcosmic car-following model, even stops and waits until the vehicle interval meeting the passing conflict area appears on the Y road, and the vehicle k passes through the conflict area; the confluence point of the X path and the Y path refers to the position where the X path traffic flow is converged into the Y path traffic flow;
the control state is unknown, and in the case R4 where the collision region cannot be smoothly passed, it is necessary to determine the vehicles k and k'1K 'of vehicle'2In which corresponding vehicles are uncomfortable in passing through the conflict area, andadopting corresponding control strategies according to the conditions of R1, R2 and R3 that the conflict area can not be smoothly passed;
the acceleration of the vehicle k is controlled by a decision variable u of the vehicle k at the time tk(t) satisfies:
Figure FDA0002565015680000042
and v isk(t)+uk(t)τ≤ve；
The control vehicle k decelerates, and a decision variable u of the vehicle k at the time t is usedk(t) satisfies:
Figure FDA0002565015680000043
and v isk(t)+uk(t)τ≥0；
The uncontrolled vehicle k is a decision variable u for the vehicle k at time tk(t) satisfies:
Figure FDA0002565015680000044
i.e. uk(t)＝0；
The control vehicle k'1The acceleration is to make the vehicle k 'at the time t'1Decision variables of
Figure FDA0002565015680000045
Satisfies the following conditions:
Figure FDA0002565015680000046
and is
Figure FDA0002565015680000047
The vehicle k 'is not controlled'1Is to make the vehicle k 'at time t'1Decision variables of
Figure FDA0002565015680000048
Satisfies the following conditions:
Figure FDA0002565015680000049
namely, it is
Figure FDA00025650156800000410
The control vehicle k'2Deceleration is performed by making the vehicle k 'at time t'2Decision variables of
Figure FDA00025650156800000411
Satisfies the following conditions:
Figure FDA0002565015680000051
and is
Figure FDA0002565015680000052
The vehicle k 'is not controlled'2Is to make the vehicle k 'at time t'2Decision variables of
Figure FDA0002565015680000053
Satisfies the following conditions:
Figure FDA0002565015680000054
namely, it is
Figure FDA0002565015680000055
Wherein u isk(t) as a decision variable for the vehicle k at time t, representing the acceleration of the vehicle k at time t; v. ofk(t) is the speed of vehicle k at time t;
Figure FDA0002565015680000056
is the safe following speed of the vehicle k at the moment t + tau predicted according to the microcosmic following model, wherein Lk(t) represents the relative distance between the vehicle k and its following preceding vehicle at time t,
Figure FDA0002565015680000057
representing the speed of the car k before the car k follows at the time t;
Figure FDA0002565015680000058
k 'of vehicle at time t'1Is a decision variable of vehicle k'1Acceleration at time t;
Figure FDA0002565015680000059
is vehicle k'1Velocity at time t;
Figure FDA00025650156800000510
is vehicle k 'predicted from the microscopic follow model'1Safe following speed at time t + tau, wherein
Figure FDA00025650156800000511
Denotes vehicle k 'at time t'1The relative distance between the car and the car before the car is driven,
Figure FDA00025650156800000512
denotes vehicle k'1The speed of the car before the car is followed at the time t;
Figure FDA00025650156800000513
k 'of vehicle at time t'2Is a decision variable of vehicle k'2Acceleration at time t;
Figure FDA00025650156800000514
is vehicle k'2Velocity at time t;
Figure FDA00025650156800000515
is vehicle k 'predicted from the microscopic follow model'2Safe following speed at time t + tau, wherein
Figure FDA00025650156800000516
Denotes vehicle k 'at time t'2The relative distance between the car and the car before the car is driven,
Figure FDA00025650156800000517
denotes vehicle k'2The speed of the car before the car is followed at the time t; τ is the reaction time of the vehicle driving; v. ofeIs the desired speed.
2. The urban intersection mixed traffic flow collaborative optimization floor control method according to claim 1, characterized in that the conflict type of the intersection is a cross conflict; the cross conflict means that vehicles in different driving directions run in a cross mode at a large angle; the step S3 specifically includes:
suppose that both the X and Y lanes are one-way lanes and the intersection of the X and Y lanes is (X)C，YC)；
Defining a conflict area: determining a collision starting point and a collision end point by combining the geometric length of the road and the microcosmic car-following model, wherein the collision area on the X road is from the collision starting point XcsTo the conflicting terminal XceStopping; collision area self-conflict starting point Y on Y roadcsTo the conflict end point YceIf not, the conflict area is (X)cs，Ycs)～(Xce，Yce)；
Defining a control area: control area on X road from vehicle distance conflict starting point XcsControl starting point X of a certain distanceDTo the cross point (X)C，YC) Stopping; control area on Y road from vehicle distance collision starting point YcsFrom a control start YD at a distance to a cross-over point (X)C，YC) If not, the control region is (X)D，YD)～(XC，YC) (ii) a The control starting point is positioned between the upstream monitoring point and the collision starting point;
suppose thatVehicle k on the X-way is in the control zone (X)D，YD)～(XC，YC) The interval between two vehicles that vehicle k will pass through a continuous flow on the Y road; k 'for each of two vehicles of the continuous stream on the Y-road'1And k'2Is represented by, wherein vehicle k'1Denotes a front vehicle, vehicle k'2Representing a rear vehicle;
at time t, the relative distance between vehicle k on the X road and vehicle k' on the Y road is represented by lk，k′(t) denotes, then:
Figure FDA0002565015680000061
wherein x isk(t) represents the position of the vehicle k on the X road at time t, yk′(t) represents the position of the vehicle k' on the Y road at time t;
u for conflict cooperative utilityk(t) indicating whether the vehicle k can smoothly pass through the collision area on the X road; the conflict cooperative utility Uk(t) is represented as follows:
Figure FDA0002565015680000062
wherein, | uk(t) | represents the absolute value of the acceleration of the vehicle k on the X road at the time t;
Figure FDA0002565015680000063
denotes vehicle k 'on Y road'2The absolute value of the acceleration at time t;
Figure FDA0002565015680000064
at time t, represents vehicle k ' on the X road and vehicle k ' on the Y road '1The relative distance therebetween;
Figure FDA0002565015680000065
at time t, represents vehicle k ' on the X road and vehicle k ' on the Y road '2The relative distance therebetween;
Figure FDA0002565015680000066
representing a safety body of the intelligent networked vehicle;
Figure FDA0002565015680000067
representing a safety body of a conventionally driven vehicle; bsafeRepresents a maximum allowable deceleration; phiAThe method comprises the following steps of (1) providing an intelligent networked vehicle set; phiHη for conventional driving vehicle1Indicating a safety factor of η2Representing a polite coefficient; the intelligent networked vehicle is an optimally controllable vehicle and is represented by a letter A; the traditional driving vehicle is a vehicle which cannot be controlled optimally and is indicated by a letter H;
for collaborative decision Ik(t + τ) is represented as follows:
Figure FDA0002565015680000068
wherein, IkA value of (t + τ) of 1 indicates that the vehicle k can smoothly pass through the collision region at time t + τ; i iskA value of 0 for (t + τ) indicates that vehicle k cannot smoothly pass through the collision zone at time t + τ.
3. The urban intersection mixed traffic flow collaborative optimization floor control method according to claim 1, characterized in that the conflict type of the intersection is a confluence conflict; the confluent conflict means that vehicles in different driving directions converge and drive in the same direction at a small angle; the step S3 specifically includes:
suppose that both the X and Y lanes are one-way lanes and the intersection of the X and Y lanes is (X)C，YC)；
Defining a conflict area: determining a collision starting point and a collision end point by combining the geometric length of the road and the microcosmic car-following model, wherein the collision area on the X road is from the collision starting point XcsTo the conflicting terminal XceStopping; collision area self-conflict starting point Y on Y roadcsTo the conflict end point YceStop, then conflictThe region is (X)cs，Ycs)～(Xce，Yce)；
Defining a control area: control area on X road from vehicle distance conflict starting point XcsControl starting point X of a certain distanceDTo the cross point (X)C，YC) Stopping; control area on Y road from vehicle distance collision starting point YcsControl starting point Y of a certain distanceDTo the cross point (X)C，YC) If not, the control region is (X)D，YD)～(XC，YC) (ii) a The control starting point is positioned between the upstream monitoring point and the collision starting point;
suppose that vehicle k on the X road is in the control zone (X)D，YD)～(XC，YC) The interval between two vehicles that vehicle k will pass through a continuous flow on the Y road; k 'for each of two vehicles of the continuous stream on the Y-road'1And k'2Is represented by, wherein vehicle k'1Denotes a front vehicle, vehicle k'2Representing a rear vehicle;
at time t, the relative distance between vehicle k on the X road and vehicle k' on the Y road is represented by lk，k′(t) denotes, then:
when x isk(t)＜xCAt the time of-r,
Figure FDA0002565015680000071
when in use
Figure FDA0002565015680000072
When the temperature of the water is higher than the set temperature,
Figure FDA0002565015680000073
when in use
Figure FDA0002565015680000074
In the case of a pair of the above-mentioned,
Figure FDA0002565015680000075
wherein x isk(t) represents the position of the vehicle k on the X road at time t, yk′(t) table shows the position of the vehicle k on the Y road at time t; r is the turning radius of the vehicle k at the crossroad;
u for conflict cooperative utilityk(t) indicating whether the vehicle k can smoothly pass through the collision area on the X road; the conflict cooperative utility Uk(t) is represented as follows:
Figure FDA0002565015680000076
wherein, | uk(t) | represents the absolute value of the acceleration of the vehicle k on the X road at the time t;
Figure FDA0002565015680000077
denotes vehicle k 'on Y road'2The absolute value of the acceleration at time t;
Figure FDA0002565015680000078
at time t, represents vehicle k ' on the X road and vehicle k ' on the Y road '1The relative distance therebetween;
Figure FDA0002565015680000079
at time t, represents vehicle k ' on the X road and vehicle k ' on the Y road '2The relative distance therebetween;
Figure FDA00025650156800000710
denotes vehicle k on the X road and vehicle k 'on the Y road'1The vehicle k on the X road is an intelligent networking vehicle;
Figure FDA00025650156800000711
denotes vehicle k on the X road and vehicle k 'on the Y road'1And the vehicle k on the X road is a conventional driving vehicle;
Figure FDA0002565015680000081
denotes vehicle k on the X road and vehicle k 'on the Y road'2The vehicle k on the X road is an intelligent networking vehicle;
Figure FDA0002565015680000082
denotes vehicle k on the X road and vehicle k 'on the Y road'2And the vehicle k on the X road is a conventional driving vehicle; bsafeRepresents a maximum allowable deceleration; phiAThe method comprises the following steps of (1) providing an intelligent networked vehicle set; phiHη for conventional driving vehicle1Indicating a safety factor of η2Representing a polite coefficient; the intelligent networked vehicle is an optimally controllable vehicle and is represented by a letter A; the traditional driving vehicle is a vehicle which cannot be controlled optimally and is indicated by a letter H;
for collaborative decision Ik(t + τ) is represented as follows:
Figure FDA0002565015680000083
wherein, IkA value of (t + τ) of 1 indicates that the vehicle k can smoothly pass through the collision region at time t + τ; i iskA value of 0 for (t + τ) indicates that vehicle k cannot smoothly pass through the collision zone at time t + τ.
4. The urban intersection mixed traffic flow cooperative optimization floor control method according to any one of claims 1 to 3, wherein the step S6 specifically comprises:
s6-1, predicting the moment t when the target vehicle enters the control area based on the microcosmic car-following model0The time when the user leaves the control area is tf(ii) a And will t0To tfIs divided into N segments on average in discrete time intervals τ', i.e., N ═ tf-t0) T', defining the control decision time as t0+τ′，t0+2τ′，t0+3τ′，t0+4τ′，...，t0+(N-1)τ′，tf；
S6-2, according to the target vehicle at t0The state of the time, the target vehicle is calculated at t0The set of allowable states at time + τ', and the target vehicle from t0State of time t0The allowable states at time + τ' aggregate the transition costs for each allowable state; said target vehicle is at t0The state at time t includes the target vehicle being at t0Speed and position of the moment;
s6-3, according to the target vehicle at t0The allowable state set at the time + tau' is calculated for the target vehicle at t0The allowable state set at time +2 τ', and the target vehicle from t0State at time + τ' to t0The allowable states at time +2 τ' are grouped into transition costs and cumulative costs for each allowable state;
s6-4, sequentially calculating the target vehicle at t according to the method of the step S6-30+3τ′，t0+4τ′，...，t0+(N-1)τ′，tfThe allowable state set of the time is calculated to obtain tfThe cumulative cost of each allowable state in the time-wise allowable state set;
s6-5, judging that the target vehicle is at tfWhether each allowable state in the allowable state set at the moment meets the condition that the allowable state can smoothly pass through the conflict area or not is judged, and the allowable state meeting the condition is brought into the final allowable state set;
s6-6, selecting the allowable state with the minimum accumulated cost in the final allowable state set as tfThe optimal state of the moment;
s6-7, according to tfThe optimal state of the moment is reversely deduced to obtain t0+τ′，t0+2τ′，t0+3τ′，t0+4τ′，...，t0+(N-1)τ′，tfThe optimal state at each moment, and the control decision corresponding to each optimal state is brought into a cooperative optimization control strategy;
and S6-8, controlling the operation of the target vehicle among the control areas according to the cooperative optimization control strategy.
5. The urban crossroad mixed traffic flow collaborative optimization bottom-layer control method according to claim 4, characterized in that a microscopic traffic flow simulation environment is constructed, and simulation results before and after optimization under different traffic situations are compared.
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