CN110570049A - expressway mixed traffic flow convergence collaborative optimization bottom layer control method - Google Patents

expressway mixed traffic flow convergence collaborative optimization bottom layer control method Download PDF

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CN110570049A
CN110570049A CN201910886980.2A CN201910886980A CN110570049A CN 110570049 A CN110570049 A CN 110570049A CN 201910886980 A CN201910886980 A CN 201910886980A CN 110570049 A CN110570049 A CN 110570049A
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孙湛博
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Abstract

the invention relates to a conflux cooperative optimization bottom layer control method for mixed traffic flow of a highway, 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 confluence model; simulating a cooperative control strategy set; judging whether the vehicles can smoothly complete confluence; if the judgment result is that the vehicle can smoothly complete the confluence, the vehicle continues to run according to the speed of the microscopic follow-up model; if the judgment result is that the vehicles can not smoothly complete the confluence, the specific situation of the vehicles in the confluence process needs to be further judged, and a corresponding cooperative control strategy is made according to the specific situation; and optimizing the running track of the target vehicle according to the made cooperative control strategy to obtain the cooperative optimization control strategy related to the target vehicle, and acting the cooperative optimization control strategy on the target vehicle to control the running of the target vehicle. The method can ensure that the vehicles run stably at a higher speed, thereby improving the vehicle traffic capacity of the expressway ramps in the confluence area.

Description

Expressway mixed traffic flow convergence collaborative optimization bottom layer control method
Technical Field
The invention relates to a conflux cooperative optimization bottom layer control method for mixed traffic flow of a highway, belonging to the field of traffic engineering.
Background
the expressway entrance ramp is used as a traffic demand input link of the whole expressway system, is also a link in which congestion is easy to generate, and has very important significance for smooth and stable operation of the whole expressway system. With the emergence and development of the intelligent networked automobile, a future expressway faces a traffic condition that the intelligent networked automobile and the traditional human driving vehicles are mixed. Decision control under the mixed traffic flow environment of intelligent networked automobiles and traditional human-driven vehicles is a long-standing practical problem in future trips. Therefore, the research on the highway ramp confluence optimization control under the mixed traffic flow environment has important significance.
For the problem of cooperative convergence optimization control of expressway ramps, some model methods have been proposed, but most of decision control methods studied at the present stage are decision methods designed under a traffic environment with an assumed automatic vehicle permeability of 100%, and are only discussed under a specific scene, so that a combined sequencing scene of an automatic vehicle and a human vehicle cannot be completely described, and a corresponding track optimization scheme is provided.
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 highway mixed traffic flow convergence cooperative optimization bottom layer control method, which aims to solve the bottom layer problem existing when highway ramp traffic flows converge into a main road in a mixed traffic state of human-driven vehicles (namely traditional driven vehicles) and automatic driven vehicles (namely intelligent networked vehicles). Human-driven vehicles are non-optimally controllable vehicles and autonomous vehicles are optimally controllable vehicles.
the technical scheme adopted by the invention for realizing the aim is as follows:
A highway mixed traffic flow convergence collaborative optimization bottom layer control method comprises the following steps:
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;
S2, acquiring the time and the speed of an upstream monitoring point of the vehicle in the mixed traffic flow before the vehicle passes through the confluence area, and predicting the initial trajectory of the vehicle between the upstream monitoring point and the confluence terminal point by using the micro-following model; a road section with a certain distance between the upstream monitoring point and the confluence starting point; the confluence starting point is positioned between the upstream monitoring point and the confluence terminal point; a section between the confluence start point and the confluence end point constitutes the confluence area;
S3, adding acceleration constraint, distance constraint and safety constraint based on the microcosmic car-following model, and establishing a convergence model;
s4, aiming at various situations which can not smoothly complete the convergence in the convergence process under the mixed traffic flow scene, a cooperative control strategy set is prepared;
S5, judging whether the vehicle can smoothly complete the confluence through the confluence model based on the initial track of the vehicle; if the judgment result is that the vehicle can smoothly complete the confluence, the vehicle continues to run according to the speed of the microscopic follow-up model; if the judgment result is that the vehicles cannot smoothly complete the confluence, further judging the specific situation of the vehicles in the confluence process, and making a cooperative control strategy corresponding to the cooperative strategy set simulated in the step S4 for the vehicles according to the specific situation of the vehicles in the confluence process, so as to execute a step S6;
And S6, determining the vehicles which participate in the confluence process and can be controlled in an optimized mode as target vehicles, optimizing the running tracks of the target vehicles through the cooperative control strategy made in the step S5, solving the optimization problem into an optimal control problem of discrete time state constraint, solving the cooperative optimization control strategy related to the target vehicles through a dynamic planning idea, applying the cooperative optimization control strategy related to the target vehicles, and controlling the running of the target vehicles.
Further, the step S4 specifically includes:
assuming that the ramp and the main road are both one-way lanes, the vehicle k on the ramp is converged into the interval between two vehicles of the continuous traffic flow of the main road, and the two vehicles of the continuous traffic flow of the main road are respectively used as vehiclesand a vehicleShow, wherein the vehicleIndicating the front vehicle, vehicleRepresenting a rear vehicle;
Aiming at various conditions possibly occurring in the confluence process in the mixed traffic flow scene, vehicles are driven based on the microcosmic following modelvehicle k and vehiclethe relationship between them is divided into that the confluence can be smoothly completed and that the confluence cannot be smoothly completed; the failure to smoothly complete the confluence is divided into four cases, the first case is denoted as R1, which indicates that the vehicle k and the vehicleThe distance between the two parts is too close, and the constraint condition that the confluence can be smoothly completed is not satisfied; the second case is denoted as R2 and represents vehicle k and vehicletoo close to each other, and can not meet the requirement of smooth completionA constraint condition of convergence; the third case is denoted as R3 and represents vehicle k and vehicleAnd with vehiclesmeets the basic spacing requirement but the confluence process is not comfortable; the fourth case is denoted as R4 and represents vehicle k and vehicleAnd with vehiclesThe distances between the two parts are too close to meet the constraint condition of smooth confluence;
aiming at four conditions that confluence cannot be smoothly completed, vehicles which can be optimally controlled need to be cooperatively controlled; h represents a human driving vehicle, which is a vehicle which cannot be optimally controlled; an automatic driving vehicle is represented by A and is an optimally controllable vehicle; n represents that no front vehicle participates in the confluence or no rear vehicle participates in the confluence; and prescribes the order of combination of the vehicles as vehicles in turnvehicle k and vehicle(for example: representing the vehicle by HAN)For human driving, for vehicle k for automatic driving, for vehicleThe rear vehicle is not involved in the confluence. )
Based on different vehicle combinations, different vehicle type combinations and the four conditions that confluence cannot be smoothly completed, a collaborative control strategy set is prepared, and is shown in the following table:
In the above table, the non-optimization means that the vehicle has no corresponding control strategy under the condition that the convergence cannot be smoothly completed correspondingly, at this time, the vehicle k on the ramp takes the end of the ramp as a stopped virtual front vehicle, and continuously decelerates or even stops to wait by following the microscopic following model until the vehicle interval meeting the convergence appears on the main road, and the vehicle k is converged into the main road;
The acceleration of the vehicle k is controlled by a decision variable u of the vehicle k at the time tk(t) satisfies:
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:
And v isk(t)+uk(t)τ≥0；
the unknown control state of the vehicle k is that the control mode of the vehicle k can be to control the vehicle k to decelerate, also can control the vehicle k to accelerate or not control the vehicle k, and at the moment t, the decision variable u of the vehicle k is determinedk(t) satisfies:
vk(t)+uk(t)τ≤ve，
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:
i.e. uk(t)＝0；
The control vehicleacceleration of the vehicle at time tDecision variables ofSatisfies the following conditions:
And is
The uncontrolled vehicleMake the vehicle at the time tDecision variables ofsatisfies the following conditions:
namely, it is
the control vehicleDecelerating the vehicle at time tDecision variables ofSatisfies the following conditions:
And is
The uncontrolled vehicleMake the vehicle at the time tDecision variables ofsatisfies the following conditions:
namely, it is
Wherein u isk(t) as a decision variable for vehicle k at time t, representing the acceleration of vehicle k at time t; v. ofk(t) is the speed of vehicle k at time t;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) indicates that the vehicle k is at tthe speed of the moment of the hand,Representing the speed of the car k at time t before the car k follows);As the vehicle at time trepresents the vehicle at time tacceleration of (2);Is a vehicleVelocity at time t;Is a vehicle predicted according to the micro-following modelSafe following speed at time t + tau (whereindicating vehicle at time tThe relative distance between the car and the car before the car is driven,indicating vehiclesat the speed at the time of the t-instant,Indicating vehiclesThe speed of the car before the following at the time t);As the vehicle at time tRepresents the vehicle at time tacceleration of (2);Is a vehiclevelocity at time t;Is a vehicle predicted according to the micro-following modelSafe following speed at time t + tau (whereIndicating vehicle at time tThe relative distance between the car and the car before the car is driven,Indicating vehiclesAt the speed at the time of the t-instant,Indicating vehiclesthe 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(ii) a The control area is a road section between a control starting point and the convergence end point; the control starting point is located between the upstream monitoring point and the confluence starting point;
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 confluence can be smoothly completed 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.
Further, 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 highway mixed traffic flow convergence collaborative optimization bottom layer control method which is used for modeling a highway ramp convergence process in a mixed traffic flow state and respectively providing corresponding collaborative convergence track optimization strategies aiming at various mixed permutation and combination scenes of automatic driving vehicles and human driving vehicles. The method describes a microscopic car following state by using a microscopic following model, considers traffic characteristics, geometric constraints and safety constraints of the highway, resolves a collaborative confluence track optimization problem into a discrete time state constraint optimal control problem, and provides a solution method based on dynamic programming to effectively solve the problem.
Proved by a large number of simulation experiments, when a cooperative confluence track optimization control strategy is introduced, the vehicle conflict times under the confluence behavior can be effectively reduced, and the confluence efficiency can be effectively improved: the traffic capacity of a single ramp is improved by 8-10%.
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 convergence diagram of a highway ramp in a mixed traffic flow scene in the embodiment of the invention.
Fig. 2 is a schematic diagram of a first case (R1) in which the vehicle cannot smoothly complete the confluence in the embodiment of the present invention.
fig. 3 is a schematic diagram of a second case (R2) in which the vehicle cannot smoothly complete the confluence in the embodiment of the present invention.
Fig. 4 is a schematic diagram of a third case (R3) where the vehicle cannot smoothly complete the confluence in the embodiment of the present invention.
fig. 5 is a schematic diagram of a fourth case (R4) where the vehicle cannot smoothly complete the confluence in the embodiment of the present invention.
Fig. 6 is a diagram of a confluence trajectory in the second case (R2) where confluence cannot be smoothly performed and in the case of no cooperative optimization control, for three vehicles in which a combination of vehicles and a combination of vehicle types participating in the confluence process are HHA in the embodiment of the present invention.
Fig. 7 is a diagram of a confluence trajectory in the second case (R2) where confluence cannot be smoothly performed and in the case where cooperative optimization control is performed, for three vehicles in which a combination of vehicles and a combination of vehicle types participating in the confluence process are HHA in the embodiment of the present invention.
fig. 8 is a confluence trajectory diagram in the first case (R1) where the confluence cannot be smoothly completed and in the case of no cooperative optimization control, for three vehicles in which the vehicle combination and the vehicle type combination participating in the confluence process are HAH in the embodiment of the present invention.
Fig. 9 is a confluence trajectory diagram in the first case (R1) where confluence cannot be smoothly performed and in the case where cooperative optimization control is provided, for three vehicles in which a combination of vehicles and a combination of vehicle types participating in the confluence process are HAH in 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 highway mixed traffic flow convergence collaborative optimization bottom layer control method comprises the following steps:
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;
s2, acquiring the time and the speed of an upstream monitoring point of the vehicle in the mixed traffic flow before the vehicle passes through the confluence area, and predicting the initial trajectory of the vehicle between the upstream monitoring point and the confluence terminal point by using the micro-following model; a road section with a certain distance between the upstream monitoring point and the confluence starting point; the confluence starting point is positioned between the upstream monitoring point and the confluence terminal point; a section between the confluence start point and the confluence end point constitutes the confluence area;
S3, adding acceleration constraint, distance constraint and safety constraint based on the microcosmic car-following model, and establishing a convergence model;
s4, aiming at various situations which can not smoothly complete the convergence in the convergence process under the mixed traffic flow scene, a cooperative control strategy set is prepared;
S5, judging whether the vehicle can smoothly complete the confluence through the confluence model based on the initial track of the vehicle; if the judgment result is that the vehicle can smoothly complete the confluence, the vehicle continues to run according to the speed of the microscopic follow-up model; if the judgment result is that the vehicles cannot smoothly complete the confluence, further judging the specific situation of the vehicles in the confluence process, and making a cooperative control strategy corresponding to the cooperative strategy set simulated in the step S4 for the vehicles according to the specific situation of the vehicles in the confluence process, so as to execute a step S6;
And S6, determining the vehicles which participate in the confluence process and can be controlled in an optimized mode as target vehicles, optimizing the running tracks of the target vehicles through the cooperative control strategy made in the step S5, solving the optimization problem into an optimal control problem of discrete time state constraint, solving the cooperative optimization control strategy related to the target vehicles through a dynamic planning idea, applying the cooperative optimization control strategy related to the target vehicles, and controlling the running of the target vehicles.
Examples
As shown in fig. 1, the schematic diagram of converging a highway ramp in a mixed traffic flow scene is shown, where both the main road and the ramp are one-way lanes, a plurality of traffic flows randomly arranged by the autonomous vehicles a and the human-driven vehicles H are arranged on the main road, and a plurality of traffic flows randomly arranged by the autonomous vehicles a and the human-driven vehicles H are required to converge between the traffic flows on the main road through a converging region on the ramp. Assume that the autonomous vehicle a and the human-driven vehicle H on the main road and the ramp both follow the microscopic follow-up model.
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.
wherein, the microcosmic following model is as follows:
v(t+τ)＝vmic(L(t),v(t),vlead(t))， (1)
u(t)＝(v(t+τ)-v(t))/τ， (2)
x(t+τ)＝x(t)-v(t)τ-0.5u(t)τ2， (3)
Equation (1) is a generalized vehicle-following model describing the speed of a vehicle at a time t + τ, where L (t) is the inter-vehicle distance between the vehicle and the vehicle before the vehicle is followed at the time t; v (t) is the speed of the vehicle at time t, vlead(t) is the speed of the preceding vehicle followed by the vehicle at time t. u (t) represents the acceleration of the vehicle at time t. The position of the vehicle at time t is represented by x (t), and the position at time t + τ is represented by x (t + τ).
S2, acquiring the time and the speed of an upstream monitoring point of the vehicle in the mixed traffic flow before the vehicle passes through the confluence area, and predicting the initial trajectory of the vehicle between the upstream monitoring point and the confluence terminal point by using a micro-following model; a road section with a certain distance between the upstream monitoring point and the confluence starting point; the confluence starting point is positioned between the upstream monitoring point and the confluence terminal point; the section between the confluence start point and the confluence end point constitutes a confluence area.
and S3, adding acceleration constraint, distance constraint and safety constraint based on the microcosmic car-following model, and establishing a confluence model.
wherein, the model of converging is as follows:
Assuming that vehicle k on the ramp is in the merge area, it will merge two vehicles into the continuous flow of the main roadAndWherein the vehicleIs a front vehicle or a vehicleThe rear vehicle.
Equation (4) is used to express the effect of convergence, and reflects the comfort level during convergence, namely, the inter-vehicle distance during convergence, and the converged vehicle k on the ramp and the rear vehicle of the main road during convergenceis calibrated. Wherein the content of the first and second substances,What is shown is the utility of the bus action when unconstrained. lathe length of the vehicle body is long,In order to achieve the minimum safe headway distance between the autonomous vehicle and the front vehicle,The minimum safe head distance between the vehicle and the front vehicle is driven by human. During confluence, the confluent vehicle k actually follows the front vehicle of the main roadrunning and the rear vehicle of the main roadThe actual following bus vehicles k run, and their accelerations can be calculated from the vehicle-following model.an absolute value representing the acceleration of the vehicle k;Rear vehicle for indicating main roadAbsolute value of acceleration of (a); bsafeIndicating the maximum allowed deceleration. PhiAfor autonomous vehicle set, phiHA collection of vehicles for human driving. Eta1And η2Respectively representing a safety factor and a polite coefficient, and a safety factor eta1Is a constant, polite coefficient eta2In piecewise continuous form, V is shown in equation (5)thIs a given speed threshold, veto a desired speed, β1and beta2is a constant. In equation (6) < i >k(t + τ) represents a convergence decision, a value of 0 indicates that the vehicle k cannot smoothly complete convergence at time t + τ, and a value of 1 indicates that the vehicle k can smoothly complete convergence at time t + τ.
and S4, aiming at various situations which can not smoothly complete the convergence in the convergence process under the mixed traffic flow scene, simulating a cooperative control strategy set. The method comprises the following specific steps:
assuming that the ramp and the main road are both one-way lanes, the vehicle k on the ramp is converged into the interval between two vehicles of the continuous traffic flow of the main road, and the two vehicles of the continuous traffic flow of the main road are respectively used as vehiclesand a vehicleShow, wherein the vehicleindicating the front vehicle, vehicleshowing the rear vehicle.
Aiming at various conditions possibly occurring in the confluence process under the mixed traffic flow scene, the vehicle is driven based on the microcosmic car-following modelVehicle k and vehicleThe relationship between them is divided into that the confluence can be smoothly completed and that the confluence cannot be smoothly completed; the failure to smoothly complete convergence is divided into four cases: the first case is denoted as R1 and shows a vehicle k and a vehicle as shown in fig. 2The distance between the two parts is too close, and the constraint condition that the confluence can be smoothly completed is not satisfied; the second case is denoted as R2 and shows the vehicle k and the vehicle k as shown in fig. 3Too close to each other, failing to meet the requirement of smoothly completing the confluenceThe constraint of (2); the third case is denoted as R3 and shows vehicle k and vehicle k as shown in fig. 4And with vehiclesMeets the basic spacing requirement but the confluence process is not comfortable; the fourth case is denoted as R4 and shows vehicle k and vehicle k as shown in fig. 5And with vehiclesThe distances between the two parts are too close to satisfy the constraint condition of smooth confluence.
Aiming at four conditions that confluence cannot be smoothly completed, vehicles which can be optimally controlled need to be cooperatively controlled; h represents a human driving vehicle, which is a vehicle which cannot be optimally controlled; an automatic driving vehicle is represented by A and is an optimally controllable vehicle; n represents that no front vehicle participates in the confluence or no rear vehicle participates in the confluence; and prescribes the order of combination of the vehicles as vehicles in turnVehicle k and vehicleFor example: representation of a vehicle by HANFor human driving, for vehicle k for automatic driving, for vehiclethe rear vehicle is not involved in the confluence.
Based on different vehicle combinations, different vehicle type combinations and the four conditions that confluence cannot be smoothly completed, a collaborative control strategy set is prepared, and is shown in the following table:
In the above table, no optimization means that the vehicle has no corresponding control strategy under the condition that the convergence cannot be smoothly completed correspondingly, at this time, the vehicle k on the ramp takes the end of the ramp as a stopped virtual front vehicle, and continuously decelerates or even stops to wait by following a microscopic following model until a vehicle interval meeting the convergence appears on the main road, and the vehicle k is converged into the main road;
controlling the acceleration of the vehicle k is a decision variable u for the vehicle k at time tk(t) satisfies:
And v isk(t)+uk(t)τ≤ve； (7-1)
Controlling the deceleration of the vehicle k is a decision variable u for the vehicle k at time tk(t) satisfies:
And v isk(t)+uk(t)τ≥0； (7-2)
When the control state of the vehicle k is unknown, the control mode of the vehicle k can be to control the vehicle k to decelerate, control the vehicle k to accelerate or not control the vehicle k, and at the moment t, the decision variable u of the vehicle k is determinedk(t) satisfies:
vk(t)+uk(t)τ≤ve，
and v isk(t)+uk(t)τ≥0； (7-3)
without controlling the vehicle k, the decision variable u of the vehicle k at time t is setk(t) satisfies:
Controlling a vehicleAcceleration of the vehicle at time tDecision variables ofSatisfies the following conditions:
And is
Not controlling the vehicleMake the vehicle at the time tDecision variables ofSatisfies the following conditions:
Controlling a vehicledecelerating the vehicle at time tDecision variables ofSatisfies the following conditions:
And is
Not controlling the vehicleMake the vehicle at the time tDecision variables ofSatisfies the following conditions:
Wherein u isk(t) as a decision variable for vehicle k at time t, representing the acceleration of vehicle k at time t; v. ofk(t) is the speed of vehicle k at time t;The safe following speed of the vehicle k at the moment of t + tau is predicted according to the microcosmic following model;As the vehicle at time trepresents the vehicle at time tacceleration of (2);is a vehicleVelocity at time t;Is predicted from a microscopic follow-up modelSafe following speed at time t + τ;As the vehicle at time tRepresents the vehicle at time tAcceleration of (2);Is a vehicleVelocity at time t;Is predicted from a microscopic follow-up modelSafe 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 complete the confluence through the confluence model based on the initial track of the vehicle; if the judgment result is that the vehicle can smoothly complete the confluence, the vehicle continues to run according to the speed of the microscopic follow-up model; if the determination result is that the vehicle cannot smoothly complete the confluence, it is necessary to further determine the specific situation of the vehicle during the confluence process, and perform the cooperative control strategy corresponding to the cooperative strategy set simulated in step S4 on the vehicle according to the specific situation of the vehicle during the confluence process, so as to execute step S6.
and S6, determining the vehicles which participate in the confluence process and can be controlled in an optimized mode as target vehicles, optimizing the running tracks of the target vehicles through 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 through a dynamic programming 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 comprises the following specific 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 t as t0+τ′,t0+2τ′,t0+3τ′,t0+4τ′,…,t0+(N-1)τ′,tf(ii) a The control area is a road section between the control starting point and the convergence end point; the control starting point is positioned between the upstream monitoring point and the confluence starting point;
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; 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 t0Allowable state at time +2 τCentralizing the 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 confluence can be smoothly completed 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 between the control areas according to the cooperative optimization control strategy.
Step S6, solving the collaborative optimization control strategy for the target vehicle by using the idea of dynamic programming, where the model is as follows:
The target vehicle is denoted as target vehicle i. The objective function equation (8) indicates that the target vehicle i travels smoothly enough and the traveling speed approaches the desired speed in the control section, veAt the desired speed. Equation (9) represents the initial state of the target vehicle i, i.e., the target vehicle i is at t0The state of the moment. Equations (10) and (11) represent the transient state of the target vehicle, and equation (12) is the confluent utility constraint on the final state of confluent vehicle k. K is a vehicle combination, represents the vehicle combination directly participating in confluence on the ramp and the main road, and belongs to K1,K2,K3Vehicle combinationThe vehicle directly participating in the confluence includes a vehicle k and a vehiclevehicle with a steering wheelVehicle combinationThe vehicle directly participating in the confluence includes a vehicle k and a vehiclevehicle combinationIndicating that there is a vehicle directly participating in the confluenceA vehicle k; and the target vehicle i is an optimally controllable vehicle of the vehicle combination K. A set of allowable states (i.e., a set of allowable states at each decision time) of the target vehicle i at each stage n is defined, denoted asbelman's recursion formula is shown as equation (13) and equation (14) for solving the sub-problem. Equation (13) represents the target vehicle i from the initial state (t)0State of the moment) to phase 1 (t)0Time + τ) transfer cost; stage 1 (t)0time + τ) is Is the 1 st stage (t)0Time + τ). In the equation (14), the process is carried out,Refers to the target value of each sub-question, i.e. the target vehicle i, from the initial state (t)0State of the moment) to the nth stage (t)0+ n τ time); stage (n-1) (t)0The allowable state at time + (n-1) τ) isthe nth stage (t)0Time + n τ) is Is the nth stage (t)0Time + n τ);is from the (n-1) th stage (t)0+ (n-1) time τ) allowed stateTo the n-th stage (t)0time + n τ) of the memory cellThe transfer cost of (2) is expressed by equation (15).
the method establishes a microscopic traffic flow simulation environment (including car following and converging) through MATLAB programming, and the parameters and values of the simulation experiment environment for realizing the mixed traffic flow of the highway through computer programming are shown in the following table:
fig. 6 is a diagram showing a confluence trajectory in the second case (R2) where confluence cannot be smoothly performed and in the case of no cooperative optimization control, in which three vehicles of which vehicle combinations and vehicle type combinations participating in the confluence process are HHA, wherein (a) is a position-time relationship diagram, (b) is a speed-time relationship diagram, and (c) is an acceleration-time relationship diagram.
fig. 7 is a diagram showing a confluence trajectory in the second case (R2) where confluence cannot be smoothly performed and in the case of cooperative optimization control, in which three vehicles of which vehicle combinations and vehicle type combinations participating in the confluence process are HHA, wherein (a) is a position-time relationship diagram, (b) is a speed-time relationship diagram, and (c) is an acceleration-time relationship diagram.
Comparing the confluence trajectory diagrams before and after optimization, it can be seen from the speed-time relationship diagrams (fig. 6 (b) and fig. 7 (b)) and the acceleration-time relationship diagrams (fig. 6 (c) and fig. 7 (c)), that the vehicles participating in confluence have a higher and smoother running speed in the case of cooperative optimization control than in the case of non-cooperative optimization control, and the vehicles in confluence can complete confluence at a smoother speed as soon as possible.
Fig. 8 is a diagram showing a confluence trajectory in the first case (R1) where confluence cannot be smoothly performed and in the case of no cooperative optimization control, in which (a) is a position-time relationship diagram, (b) is a speed-time relationship diagram, and (c) is an acceleration-time relationship diagram, of three vehicles in which a vehicle combination and a vehicle type combination participating in a confluence process are HAH.
Fig. 9 is a diagram showing a confluence trajectory in the first case (R1) where confluence cannot be smoothly performed and in the case where cooperative optimization control is provided, in which (a) is a position-time relationship diagram, (b) is a speed-time relationship diagram, and (c) is an acceleration-time relationship diagram, of three vehicles in which a vehicle combination and a vehicle type combination participating in a confluence process are HAH.
Comparing the confluence trajectory diagrams before and after optimization, it can be seen from the speed-time relationship diagrams (fig. 8 (b) and 9 (b)) and the acceleration-time relationship diagrams (fig. 8 (c) and 9 (c)), that the vehicles participating in confluence have higher and smoother running speed in the case of cooperative optimization control than in the case of non-cooperative optimization control, and the vehicles behind the main road are less affected by the confluence behavior.
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 (4)

1. A highway mixed traffic flow convergence collaborative optimization bottom layer control method is characterized by comprising the following steps:
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;
S2, acquiring the time and the speed of an upstream monitoring point of the vehicle in the mixed traffic flow before the vehicle passes through the confluence area, and predicting the initial trajectory of the vehicle between the upstream monitoring point and the confluence terminal point by using the micro-following model; a road section with a certain distance between the upstream monitoring point and the confluence starting point; the confluence starting point is positioned between the upstream monitoring point and the confluence terminal point; a section between the confluence start point and the confluence end point constitutes the confluence area;
S3, adding acceleration constraint, distance constraint and safety constraint based on the microcosmic car-following model, and establishing a convergence model;
s4, aiming at various situations which can not smoothly complete the convergence in the convergence process under the mixed traffic flow scene, a cooperative control strategy set is prepared;
S5, judging whether the vehicle can smoothly complete the confluence through the confluence model based on the initial track of the vehicle; if the judgment result is that the vehicle can smoothly complete the confluence, the vehicle continues to run according to the speed of the microscopic follow-up model; if the judgment result is that the vehicles cannot smoothly complete the confluence, further judging the specific situation of the vehicles in the confluence process, and making a cooperative control strategy corresponding to the cooperative strategy set simulated in the step S4 for the vehicles according to the specific situation of the vehicles in the confluence process, so as to execute a step S6;
And S6, determining the vehicles which participate in the confluence process and can be controlled in an optimized mode as target vehicles, optimizing the running tracks of the target vehicles through the cooperative control strategy made in the step S5, solving the optimization problem into an optimal control problem of discrete time state constraint, solving the cooperative optimization control strategy related to the target vehicles through a dynamic planning idea, applying the cooperative optimization control strategy related to the target vehicles, and controlling the running of the target vehicles.
2. the highway mixed traffic flow convergence collaborative optimization floor control method according to claim 1, wherein the step S4 specifically comprises:
Assuming that the ramp and the main road are both one-way lanes, the vehicle k on the ramp is converged into the interval between two vehicles of the continuous traffic flow of the main road, and the two vehicles of the continuous traffic flow of the main road are respectively used as vehiclesAnd a vehicleshow, wherein the vehicleIndicating the front vehicle, vehicleRepresenting a rear vehicle;
Aiming at various conditions possibly occurring in the confluence process in the mixed traffic flow scene, vehicles are driven based on the microcosmic following modelVehicle k and vehicleThe relationship between them is divided into that the confluence can be smoothly completed and that the confluence cannot be smoothly completed; the failure to smoothly complete the confluence is divided into four cases, the first case is denoted as R1, which indicates that the vehicle k and the vehicleThe distance between the two parts is too close, and the constraint condition that the confluence can be smoothly completed is not satisfied; the second case is denoted as R2 and represents vehicle k and vehicleThe distance between the two parts is too close, and the constraint condition that the confluence can be smoothly completed is not satisfied; the third case is denoted as R3 and represents vehicle k and vehicleAnd with vehiclesMeets the basic spacing requirement but the confluence process is not comfortable; the fourth case is denoted as R4 and represents vehicle k and vehicleAnd with vehiclesThe distances between the two parts are too close to meet the constraint condition of smooth confluence;
Aiming at four conditions that confluence cannot be smoothly completed, vehicles which can be optimally controlled need to be cooperatively controlled; h represents a human driving vehicle, which is a vehicle which cannot be optimally controlled; an automatic driving vehicle is represented by A and is an optimally controllable vehicle; n represents that no front vehicle participates in the confluence or no rear vehicle participates in the confluence; and prescribes the order of combination of the vehicles as vehicles in turnvehicle k and vehicle
based on different vehicle combinations, different vehicle type combinations and the four conditions that confluence cannot be smoothly completed, a collaborative control strategy set is prepared, and is shown in the following table:
In the above table, the non-optimization means that the vehicle has no corresponding control strategy under the condition that the convergence cannot be smoothly completed correspondingly, at this time, the vehicle k on the ramp takes the end of the ramp as a stopped virtual front vehicle, and continuously decelerates or even stops to wait by following the microscopic following model until the vehicle interval meeting the convergence appears on the main road, and the vehicle k is converged into the main road;
The acceleration of the vehicle k is controlled by a decision variable u of the vehicle k at the time tk(t) satisfies:
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:
and v isk(t)+uk(t)τ≥0；
The unknown control state of the vehicle k is that the control mode of the vehicle k can be to control the vehicle k to decelerate, also can control the vehicle k to accelerate or not control the vehicle k, and at the moment t, the decision variable u of the vehicle k is determinedk(t) satisfies:
vk(t)+uk(t)τ≤ve，
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:
I.e. uk(t)＝0；
The control vehicleAcceleration of the vehicle at time tdecision variables ofSatisfies the following conditions:
and is
The uncontrolled vehicleMake the vehicle at the time tDecision variables ofSatisfies the following conditions:
namely, it is
The control vehicledecelerating the vehicle at time tDecision variables ofSatisfies the following conditions:
And is
the uncontrolled vehicleMake the vehicle at the time tDecision variables ofsatisfies the following conditions:
Namely, it is
Wherein u isk(t) as a decision variable for vehicle k at time t, representing the acceleration of vehicle k at time t; v. ofk(t) is the speed of vehicle k at time t;Is the safe following speed of the vehicle k at the moment of t + tau predicted according to the microcosmic following model;As the vehicle at time trepresents the vehicle at time tacceleration of (2);Is a vehicleVelocity at time t;Is a vehicle predicted according to the micro-following modelsafe following speed at time t + τ;As the vehicle at time tRepresents the vehicle at time tacceleration of (2);Is a vehicleVelocity at time t;Is a vehicle predicted according to the micro-following modelSafe following speed at time t + τ; τ is the reaction time of the vehicle driving; v. ofeIs the desired speed.
3. the highway mixed traffic flow convergence collaborative optimization floor control method according to claim 1, 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(ii) a The control area is a road section between a control starting point and the convergence end point; the control starting point is located between the upstream monitoring point and the confluence starting point;
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 confluence can be smoothly completed 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′,tfEach moment of timeAnd incorporating the control decision corresponding to each optimal state 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.
4. The highway mixed traffic flow confluence cooperative optimization floor control method according to any one of claims 1-3, wherein 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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