CN113538932A - Non-signalized intersection resource scheduling method under cooperative vehicle and road environment - Google Patents

Non-signalized intersection resource scheduling method under cooperative vehicle and road environment Download PDF

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CN113538932A
CN113538932A CN202110784499.XA CN202110784499A CN113538932A CN 113538932 A CN113538932 A CN 113538932A CN 202110784499 A CN202110784499 A CN 202110784499A CN 113538932 A CN113538932 A CN 113538932A
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intersection
vehicle
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fsa
road
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侯运锋
潘志纯
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University of Shanghai for Science and Technology
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    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/07—Controlling traffic signals
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/07—Controlling traffic signals
    • G08G1/08—Controlling traffic signals according to detected number or speed of vehicles
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
    • H04W4/30—Services specially adapted for particular environments, situations or purposes
    • H04W4/40—Services specially adapted for particular environments, situations or purposes for vehicles, e.g. vehicle-to-pedestrians [V2P]
    • H04W4/44—Services specially adapted for particular environments, situations or purposes for vehicles, e.g. vehicle-to-pedestrians [V2P] for communication between vehicles and infrastructures, e.g. vehicle-to-cloud [V2C] or vehicle-to-home [V2H]

Abstract

The invention provides a method for scheduling non-signalized intersection resources in a vehicle-road cooperative environment, which is characterized by comprising the following steps of: step S1: the autonomous vehicle reaches an intersection induction area with an intelligent road side unit; step S2: the autonomous vehicle sends the state information to the intelligent road side unit in real time through the collaborative information interaction system; step S3: the intelligent road side unit calculates the traffic flow and the queuing length of each entrance lane according to the received state information and the established non-signalized intersection resource scheduling model, and calculates an optimal vehicle passing sequence; step S4: the intelligent road side unit sends the optimal vehicle passing sequence to the autonomous vehicle in real time; step S5: the autonomous vehicles sequentially pass through the intersection according to the instruction of the intelligent road side unit and the current optimal parallel sequence; step S6: and (4) judging whether a new autonomous vehicle arrives, executing the step S5 if no new autonomous vehicle arrives, and returning to the step S2 if a new autonomous vehicle arrives. Compared with the traditional signalized intersection control method, the signalized intersection resource scheduling method under the cooperative vehicle road environment provided by the invention effectively reduces the queuing length of the vehicles and improves the vehicle throughput capacity of the intersection.

Description

Non-signalized intersection resource scheduling method under cooperative vehicle and road environment
Technical Field
The invention relates to the technical field of intelligent traffic, in particular to a non-signalized intersection resource scheduling method under a vehicle-road cooperative environment.
Background
With the development of technologies such as wireless communication and new generation internet, an Intelligent Vehicle Infrastructure Communication Systems (IVICS), referred to as a Vehicle-road coordination System for short, has become the latest development direction of an Intelligent Transportation System (ITS). The intelligent traffic system is based on advanced technologies such as sensors, information communication and the like, dynamic real-time information interaction between vehicles and roads is carried out in all directions, effective cooperation between the vehicles and the roads of people is fully realized, traffic safety is guaranteed, traffic efficiency is improved, and therefore the safe, efficient and environment-friendly road traffic system is formed.
Under the cooperative environment of the vehicle and the road, particularly, a vehicle passing control method of an intersection is a research focus of all countries, and the intersection is used as a basic component of a modern traffic network and is also a main occurrence place of accidents and traffic jam. The traditional method is to optimize the intersection frequently through signal lamp alternation or directly increase roads, the former is difficult to achieve the expected effect, and the latter needs a lot of time, space and resources, so the two methods have certain problems.
Disclosure of Invention
The invention aims to provide a method for scheduling resources of a signalless intersection under a vehicle-road cooperative environment, which can effectively improve the vehicle throughput capacity of the intersection.
In order to achieve the above object, the present invention provides a method for scheduling non-signalized intersection resources in a vehicle-road collaborative environment, which is characterized by comprising the following steps:
step S1: the autonomous vehicle reaches an intersection induction area with an intelligent road side unit;
the autonomous vehicle is a vehicle with an intelligent vehicle-mounted unit, and the intelligent vehicle-mounted unit acquires vehicle driving state information through a sensor and vehicle-mounted electronic equipment.
Step S2: the autonomous vehicle sends state information to the intelligent road side unit in real time through the collaborative information interaction system;
the vehicle state information comprises vehicle ID, speed, position, vehicle type and intention (left turn, right turn or straight going), the driving state information is sent to an intelligent road side unit through a wireless communication network, the intelligent road side unit is used as a brain of a crossing and carries out information interaction with the autonomous vehicles in the area of the crossing, and real-time state information of the vehicles is obtained.
Step S3: the intelligent road side unit calculates the traffic flow and the queuing length of each entrance lane according to the received state information and the established non-signalized intersection resource scheduling model, and calculates an optimal vehicle passing sequence;
step S4: the intelligent road side unit sends the optimal vehicle passing sequence to the autonomous vehicle in real time;
the following equations illustrate that the calculated optimal transit sequence is based on calculating all arriving vehicles at the intersection at the current time and vehicles passing through the intersection, and does not consider the vehicle which arrives at the intersection most recently next, i.e., the calculated event sequence is a controllable event sequence
Figure BDA0003158629680000021
Step S5: the autonomous vehicles sequentially pass through the intersection according to the indication of the intelligent road side unit and the current optimal same-row sequence;
the following equations illustrate that the system can return to the initial state from the current state through the calculated pass sequence, i.e., all vehicles arriving at the intersection and passing through the intersection at the current time can travel away from the intersection through the calculated pass sequence.
Figure BDA0003158629680000031
Step S6: and judging whether a new autonomous vehicle arrives, if not, executing the step S5, and if so, returning to the step S2.
It should be noted that although we have calculatedNext nkAn optimal vehicle passing sequence within a step, which is however calculated based on the vehicle conditions at the intersection at the present moment, may be performed while a new vehicle arrives at the intersection (occurrence of an uncontrollable event). Therefore, in practice, only the first step in the calculated optimal traffic sequence is executed, and after the execution of the first step is finished, the optimization process is executed again based on the latest state and parameters of the system, and the traffic control strategy of the next step is calculated.
Further, in step S3, the signalless intersection resource scheduling model is a GFSA model constructed by finite automata (FSA).
The establishment of the signalless intersection resource scheduling model comprises the following steps:
to better describe the process of constructing the model, the symbols used in the model are explained as follows, as shown in fig. 1: the numbers 1-8 represent road sections 1-8; the letters a, b, c, d, e represent the road weights a, b, c, d, e, respectively; the letters s, r, l denote straight, right-turn, left-turn vehicles, respectively. Event sigmaxzkWhere x, z is 1, …,8, a, …, e, k is l, s, r, indicating that a k-type vehicle is driven into z from x, e.g., event σ1asIndicating that a straight-going vehicle enters the right-of-way a from the road section 1; event sigmaaelIndicating that a left-turning vehicle drives from the right-of-way a to the right-of-way e; event alphaxkWhere x is 1, …,4, k is l, s, r, indicating that a k-type vehicle arrives at the intersection from the road segment x, e.g., event α1sIt means that a straight-traveling vehicle arrives at the intersection through the section 1. The arrival of a vehicle, event alpha, is due to the system's inability to prevent the vehicle from reaching the intersectionxkIs an uncontrollable event. However, the system can control whether the vehicle enters the intersection and the driving condition in the intersection, so that the corresponding event of the vehicle driving in the intersection is controllable, namely the event sigmaxzkIs a controllable event.
Step D1: constructing an FSA model of each road weight;
the process of constructing the FSA model of each road weight is as follows:
FSA models of road weights a to e are shown in FIGS. 2(a) to 2(e), respectivelyRespectively denoted as G1～G5。G1The specific definitions of all states in (1) are as follows: state 0 indicates that the road right a is empty; state 1 indicates that the right-of-way a is occupied by a left-turn vehicle driven in from the road section 1; state 2 represents the right-of-way a being left-turn by a right-of-way e; state 3 indicates that the right-of-way a is simultaneously occupied by a left-turn vehicle entering from the road segment 1 and a left-turn vehicle entering from the right-of-way e; state 4 represents that the right of way a is occupied by a straight-going vehicle driven in by the road segment 1; state 5 indicates that the right-of-way a is occupied by a right-turn vehicle entering from the road segment 1; state 6 indicates that right a is occupied by a straight-ahead vehicle driven in by right b. FSA model and G of road weights b, c, d1With strong analogity, G is generated2～G4。G5The specific definitions of all states in (1) are as follows: state 0 indicates that the road right e is empty; state 1 indicates that the right-of-way e is occupied by a left-turn vehicle driven in by the right-of-way a; state 2 represents that the right-of-way e is occupied by a left-turn vehicle driven in by the right-of-way c; state 3 indicates that the right-of-way e is simultaneously occupied by a left-turn vehicle driven by the right-of-way a and a left-turn vehicle driven by the right-of-way c; state 4 indicates that the right-of-way e is occupied by a left-turn vehicle driven in by the right-of-way b; state 5 indicates that the right-of-way e is simultaneously occupied by a left-turn vehicle driven by the right-of-way b and a left-turn vehicle driven by the right-of-way d; state 6 indicates that right e is occupied by a left turn vehicle driven by right d.
Step D2: constructing an FSA model of arrival of the autonomous vehicle in each direction of the intersection;
the process of constructing the FSA model of the arrival of the vehicle in each direction of the intersection is as follows:
3(a) -3 (d) simulate the process of a vehicle on a road segment 1-4 arriving at an intersection. The models shown in FIGS. 3(a) to 3(d) are denoted by G6～G9。G6The specific definitions of all states in (1) are as follows: state 0 indicates that no vehicle on road segment 1 has reached the intersection; state 1 indicates that a right-turning vehicle on road segment 1 arrives at the intersection; state 2 represents a straight-ahead vehicle on road segment 1 arriving at the intersection; state 3 represents a left-turning vehicle on road segment 1 arriving at the intersection; state 4 represents a left-turning vehicle and a right-turning vehicle on road segment 1 arriving at the intersection; form ofState 5 represents a left-turning vehicle and a straight-going vehicle on road segment 1 arriving at the intersection. Due to FSA model and G of 2, 3 and 4 road sections6With strong analogity, G is generated7～G9。
Step D3: generating an intersection FSA model from each of the road-right FSA models and an intersection FSA model for the vehicle to arrive in each direction in parallel;
the intersection FSA model is generated as follows:
the FSAs are both independent and interact with each other, and interact through common events with each other. The model of the crossing consists of G1～G9In parallel, generating:
G＝G1||G2||…||G9＝(X,E,f,Γ,x0)
step D4: defining an event occurrence condition function of the intersection FSA model, and constructing the FSA model under the universal meaning of the intersection;
step D5: and introducing an event occurrence condition function into the FSA model to form a GFSA model.
The process of defining the event occurrence condition function of the intersection FSA model and constructing the GFSA model of the intersection is as follows:
in the control process of the model, the occurrence time of the event sequence is usually considered, so that the condition c is introduced, and the condition c defines the clock condition which needs to be met when all events occur. And defining pi (sigma) as the time length of the event sigma from the current moment in the latest triggering. t (σ) is an event required for occurrence of event σ, and if σ has not occurred, t (σ) — infinity is defined as controllable event σ ∈ E without loss of generalitycThe occurrence condition of (d) is denoted as pi (σ ') > t (σ '), and the event set is denoted as E ═ σ { σ ≧ t (σ '), where1,σ2,…,σmAnd satisfy E ═ E }c∪Euc，EucIs an uncontrollable event. Setting the state of the system at the current moment, the time length of the event sigma occurring at the latest time from the current moment, the time required by the event sigma occurring at the latest time and the event triggered by selection as xk、πk(σ)、tk(σ)、akDefine psik＝ψ(xk,πk(σ1),…,πk(σm),tk(σ1),…,tk(σm),ak) To trigger akA waiting time is required.
Further, in step D3, the intersection FSA model may be represented as a quintuple:
G＝(X,E,f,Γ,x0)
in the formula: x is a finite state set; e is a finite set of events; XE → X is the state activation function; Γ is an activity event function, given X ∈ X, { σ ∈ E:f (X, σ)! Where f (x, σ)! The expression f (x, sigma) is defined; x is the number of0Is the initial active state.
Further, in step D3, the GFSA model may be represented as a six-tuple:
G＝(X,E,f,Γ,x0,c)
in the formula (X, E, f, gamma, X)0) Is a common FSA model, and c: E → {0,1} is a conditional function of the occurrence of the event. Given X ∈ X and σ ∈ E, where f (X, σ)! If the current time state x is in the active state and the event σ satisfies the occurrence condition, i.e., c (σ) is 1, then the event σ is allowed to occur, otherwise it does not occur.
Further, in step S3, the driving state information includes vehicle ID, speed, location, vehicle type and intention.
Further, in step S1, the autonomous vehicle is a vehicle having an intelligent on-board unit.
Compared with the prior art, the invention has the advantages that: compared with the traditional control method for the signalized intersection, the non-signalized control method effectively reduces the queuing length of the vehicles and improves the throughput capacity of the vehicles at the intersection.
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FIG. 1 is a schematic view of an application scenario of a non-signalized intersection resource scheduling method in a vehicle-road cooperative environment according to the present invention;
FIG. 2 is an FSA model of the various weights of the present invention;
FIG. 3 is a diagram of the FSA model of vehicle arrival at each direction at an intersection in accordance with the present invention;
FIG. 4 is a schematic flow chart of a non-signalized intersection resource scheduling method in a vehicle-road cooperative environment according to the present invention.
Detailed Description
The method for scheduling resources at non-signalized intersections in a collaborative environment for a vehicle and road according to the present invention will be described in more detail with reference to the drawings, in which preferred embodiments of the present invention are shown, it being understood that those skilled in the art can modify the present invention described herein while still achieving the advantageous effects of the present invention. Accordingly, the following description should be construed as broadly as possible to those skilled in the art and not as limiting the invention.
The invention is described in more detail in the following paragraphs by way of example with reference to the accompanying drawings. The advantages and features of the present invention will become more apparent from the following description. It is to be noted that the drawings are in a very simplified form and are not to precise scale, which is merely for the purpose of facilitating and distinctly claiming the embodiments of the present invention.
As shown in fig. 1, an embodiment of the present invention provides a method for scheduling non-signalized intersection resources in a vehicle-road collaborative environment, where an automaton model established by the method is shown in fig. 2 and fig. 3, a specific processing flow is shown in fig. 4, and the method includes the following processing steps:
step S1: the autonomous vehicle reaches an intersection induction area with an intelligent road side unit;
step S2: the autonomous vehicle sends state information to the intelligent road side unit in real time through the collaborative information interaction system, and the driving state information comprises the speed, the position and the intention of the vehicle;
in this embodiment, an intelligent vehicle-mounted unit needs to be arranged on all vehicles, an intelligent road side unit is arranged at a road intersection, and a single or multiple intelligent road side systems are formed by combination. The cooperative information interaction system is an information transmission carrier of the autonomous vehicle and the intelligent road side system.
The vehicle-mounted unit collects vehicle running state information through the sensor and the vehicle-mounted electronic equipment, the vehicle running state information comprises vehicle ID, speed, position, vehicle type and intention (steering or straight running), and the vehicle running state information is sent to the intelligent road side unit through the wireless communication network.
Step S3: the intelligent road side unit calculates the traffic flow and the queuing length of each entrance lane according to the received state information and the established non-signalized intersection resource scheduling model, and calculates an optimal vehicle passing sequence;
the modeling process of the non-signalized intersection resource scheduling model is as follows:
the symbols used in the model are explained as follows, the numbers 1-8 represent road sections 1-8; the letters a, b, c, d, e represent the road weights a, b, c, d, e, respectively; the letters s, r, l denote straight, right-turn, left-turn vehicles, respectively.
Taking FIG. 2(a) as an example, model G1Is 0 and the road right a is empty. If a left-turning vehicle enters a from the section 1, the incident sigma1alWhen the system jumps to the state 1, a left-turning vehicle is allowed to drive into the state a from the state e, and in addition, other behaviors of occupying the right of way a cause the vehicle to collide and are strictly prohibited. Suppose that at this time a left-turning vehicle is heading from e to a, accompanied by event σealThe system goes to state 3. At this time, the right-of-way a is occupied by two left-turning vehicles at the same time, and thus other vehicles are not allowed to enter the right-of-way a. Next, if a vehicle leaves a into e, it is accompanied by an event σaelWhen the right-turn vehicle enters the right-turn road, the right-turn vehicle enters the right-turn road through the right-turn road, and the system returns to the state 2. Accompanied by an event σa7lThe vehicle leaves a into the road section 7. At this time, the right a is left empty, and the system returns to the initial state 0. If a vehicle leaves a and enters the section 7 when the system is in state 3, it follows the event σa7lWhen only one left-turn vehicle driven in the road section 1 remains in the step a, the system jumps to the state 1. At this time, if the left-turning vehicle leaves a and goes to e, the right of way a is vacant, and the system returns to the initial state 0.
By analogy, an FSA model of each road weight shown in fig. 2 is constructed from the intersection shown in fig. 1, and G ═ X, E, f, Γ, X0) Is recorded as G1～G5。
According to the scheme as shown in FIG. 1The intersection of (a) constructs an intersection arrival FSA model G ═ X, E, f, Γ, X in each direction as shown in fig. 30) Is recorded as G6～G9。
The FSA models for each right of way are coupled with the vehicle arrival FSA models in each direction at the intersection, introducing the following parallel operations:
given H1＝(Q1,Σ1,δ1,Γ1,q01) And H2＝(Q2,Σ2,δ2,Γ2,q02)，H1And H2Parallel generation of H ═ Ac (Q)1×Q2,E1∪E2,δ,Γ1||2,(q01,q02) Wherein (q) is arbitrarily given1,q2)∈Q1×Q2And σ ∈ Σ, if σ ∈ Γ1(q1)∩Γ2(q2) Then, is δ ((q)1,q2),σ)＝(δ1(q1,σ),δ2(q2σ)); if σ e is Γ1(q1)/Σ2Then, is δ ((q)1,q2),σ)＝(δ1(q1,σ),q2) (ii) a If σ e is Γ2(q2)/Σ1Then, is δ ((q)1,q2),σ)＝(q1,δ2(q2σ)); other cases, δ ((q)1,q2) σ) is not defined. Ac denotes the reachable portion of the finite automaton from the initial state.
Intersection FSA model G1～G9Are generated in parallel. Formally, the intersection FSA model based on the finite physical space is:
G＝G1||G2||…||G9＝(X,E,f,Γ,x0)
constructing GFSAG (X, E, f, gamma, X) on the basis of the intersection FSA model0And c) a model. And defining pi (sigma) as the time length of the event sigma from the current moment in the latest triggering. t (σ) is an event required for occurrence of event σ, and if σ has not occurred, t (σ) — infinity is defined as controllable event σ ∈ E without loss of generalitycThe occurrence condition of (c) is expressed as pi (sigma ') > t (sigma'),and the specific corresponding relation is shown in table 1. The event set is denoted as E ═ σ1,σ2,…,σmAnd satisfy E ═ E }c∪Euc，EucIs an uncontrollable event. Setting the state of the system at the current moment, the time length of the event sigma occurring at the latest time from the current moment, the time required by the event sigma occurring at the latest time and the event triggered by selection as xk、πk(σ)、tk(σ)、akDefine psik＝ψ(xk,πk(σ1),…,πk(σm),tk(σ1),…,tk(σm),ak) To trigger akTime required to wait, nkIndicating the length of the sequence.
TABLE 1 controllable event occurrence conditions
Table 1Occurrence condition for controllable events
Figure BDA0003158629680000091
The following equation calculates a vehicle passing sequence which takes the least time to completely empty the vehicles currently arriving at the intersection and the passing vehicles in the intersection:
Figure BDA0003158629680000101
the following equations illustrate that the calculated optimal transit sequence is based on calculating all arriving vehicles at the intersection at the present time and the vehicles passing through the intersection, without considering the vehicle that arrives at the intersection most recently next.
Figure BDA0003158629680000102
The following equations illustrate that the system can return to the initial state from the current state through the calculated pass sequence, i.e., all vehicles arriving at the intersection and passing through the intersection at the current time can travel away from the intersection through the calculated pass sequence.
Figure BDA0003158629680000103
The following equation is an iterative process:
in the following equationj-1Is an event aj-1The required waiting time occurs.
Figure BDA0003158629680000104
In the following equation, in the execution of aj-1When, if aj-1When the occurrence condition is satisfied, aj-1The occurrence waiting time is 0; on the contrary, if aj-1If the occurrence condition is not satisfied, aj-1Occurrence wait time of t (a'j-1)-π(a′j-1)；
Figure BDA0003158629680000105
In the following equation, the following equation is used,
Figure BDA0003158629680000106
is an estimate of the time of occurrence of the event sigma. After event σ is triggered, its occurrence time needs to be updated to
Figure BDA0003158629680000107
The occurrence times of other events need not be updated.
Figure BDA0003158629680000108
In the following equation, after the event σ occurs, the time length from the last occurrence time of other events except σ to the current time is the original value plus the occurrence waiting time of the event σ. Since the event has just been triggered, pi (σ) is 0.
Figure BDA0003158629680000111
Step S4: the intelligent road side unit sends the optimal vehicle passing sequence to the autonomous vehicle in real time;
step S5: the autonomous vehicles sequentially pass through the intersection according to the instruction of the intelligent road side unit and the current optimal parallel sequence;
step S6: and (4) judging whether a new autonomous vehicle arrives, executing the step S5 if no new autonomous vehicle arrives, and returning to the step S2 if a new autonomous vehicle arrives.
The established model calculates the next nkAnd (4) recalculating the optimal sequence when a new vehicle arrives at the intersection according to the optimal vehicle passing sequence in the step. In practical application, only the first step in the optimal passing sequence is executed, and after the execution of the first step is finished, the latest state and parameters of the system are based. And when no new vehicle arrives, continuing to execute the step S450, and enabling the vehicles to sequentially pass through the intersection according to the current optimal passing sequence. And when a new vehicle arrives, returning to the step S420, executing the optimization process again, and calculating the next passing control strategy. Thereby resulting in a minimum time to dissipate the vehicle at the intersection.
The technical solution provided by the above embodiments of the present invention can be seen. The invention has the beneficial effects that: the intersection is divided into disjoint physical space road right resources by the model, the mutual cooperative relationship among the road right resources is described, and the traffic control problem of the signalless intersection is converted into the limited resource scheduling problem. On the basis, an objective function for maximizing the traffic efficiency of the intersection is constructed, and the optimal traffic sequence of the vehicle is solved. The experimental results show that: compared with the traditional control method for the signalized intersection, the non-signalized control method effectively reduces the queuing length of the vehicles and improves the throughput capacity of the vehicles at the intersection.
In practical application, the autonomous vehicle can be sent the sequence position and the expected waiting time, and the priority right of passage is possessed when the waiting time exceeds a certain range.
In practical application, the application scenario of the method of the embodiment of the present invention can be realized in a simulation manner, and a road network scenario of an actual intersection can be simulated by using MATLAB simulation software.
In the embodiment of the invention, the established model divides the intersection into mutually disjoint physical space road right resources, describes the mutual cooperative relationship among the road right resources, and further converts the traffic control problem of the signalless intersection into the problem of limited resource scheduling. On the basis, an objective function for maximizing the traffic efficiency of the intersection is constructed, and the optimal traffic sequence of the vehicle is solved. Compared with the traditional control method for the signalized intersection, the non-signalized control method effectively reduces the queuing length of the vehicles and improves the throughput capacity of the vehicles at the intersection.
Those of ordinary skill in the art will understand that: the figures are merely schematic representations of one embodiment, and the blocks or flow diagrams in the figures are not necessarily required to practice the present invention.
The invention is not the best known technology.
The above embodiments are merely illustrative of the technical ideas and features of the present invention, and the purpose thereof is to enable those skilled in the art to understand the contents of the present invention and implement the present invention, and not to limit the protection scope of the present invention. All equivalent changes and modifications made according to the spirit of the present invention should be covered within the protection scope of the present invention.
The above description is only a preferred embodiment of the present invention, and does not limit the present invention in any way. It will be understood by those skilled in the art that various changes, substitutions and alterations can be made herein without departing from the spirit and scope of the invention as defined by the appended claims.

Claims (7)

1. A method for scheduling non-signalized intersection resources in a vehicle-road cooperative environment is characterized by comprising the following steps:
step S1: the autonomous vehicle reaches an intersection induction area with an intelligent road side unit;
step S2: the autonomous vehicle sends state information to the intelligent road side unit in real time through the collaborative information interaction system;
step S3: the intelligent road side unit calculates the traffic flow and the queuing length of each entrance lane according to the received state information and the established non-signalized intersection resource scheduling model, and calculates an optimal vehicle passing sequence;
step S4: the intelligent road side unit sends the optimal vehicle passing sequence to the autonomous vehicle in real time;
step S5: the autonomous vehicles sequentially pass through the intersection according to the indication of the intelligent road side unit and the current optimal same-row sequence;
step S6: and judging whether a new autonomous vehicle arrives, if not, executing the step S5, and if so, returning to the step S2.
2. The method for scheduling resources at an intersection without signal under the cooperative environment of the vehicle and the road according to claim 1, wherein in step S3, the model for scheduling resources at an intersection without signal is a GFSA model constructed by finite automata (FSA).
3. The method for scheduling resources at an intersection without signalized intersection under the cooperative environment of the vehicle and road according to claim 2, wherein the establishment of the model for scheduling resources at the intersection without signalized intersection comprises the following steps:
step D1: constructing an FSA model of each road weight;
step D2: constructing an FSA model of arrival of the autonomous vehicle in each direction of the intersection;
step D3: generating an intersection FSA model from each of the road-right FSA models and an intersection FSA model for the vehicle to arrive in each direction in parallel;
step D4: defining an event occurrence condition function of the intersection FSA model, and constructing the FSA model under the universal meaning of the intersection;
step D5: and introducing an event occurrence condition function into the FSA model to form a GFSA model.
4. The method for scheduling resources of an intersection without signal under the cooperative environment of the vehicle and the road according to claim 3, wherein in the step D3, the intersection FSA model can be expressed as a quintuple:
G＝(X,E,f,Γ,x0)
in the formula: x is a finite state set; e is a finite set of events; XE → X is the state activation function; Γ is an activity event function, given X ∈ X, { σ ∈ E:f (X, σ)! Where f (x, σ)! The expression f (x, sigma) is defined; x is the number of0Is the initial active state.
5. The signalized intersection resource scheduling method according to claim 3,
in step D3, the GFSA model may be expressed as a six-tuple:
G＝(X,E,f,Γ,x0,c)
in the formula (X, E, f, gamma, X)0) Is a common FSA model, and c: E → {0,1} is a conditional function of the occurrence of the event. Given X ∈ X and σ ∈ E, where f (X, σ)! If the current time state x is in the active state and the event σ satisfies the occurrence condition, i.e., c (σ) is 1, then the event σ is allowed to occur, otherwise it does not occur.
6. The method for scheduling resources at an intersection without signal under the cooperative environment of the vehicle and the road according to claim 1, wherein in step S3, the driving state information comprises vehicle ID, speed, position, vehicle type and intention.
7. The method for scheduling resources at an intersection without signal under the cooperative vehicle infrastructure environment of claim 1, wherein in step S1, the autonomous vehicle is a vehicle with an intelligent vehicle-mounted unit.
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