A vehicle travel path method, apparatus, and storage medium

Abstract

The application provides a vehicle driving path method. The method comprises the following steps: determining path constraint conditions based on the minimum fuel consumption value and the minimum driving time of the vehicles to be controlled in a region, and determining the driving path of each vehicle according to the starting node and the ending node of each vehicle; determining the time constraint condition of the driving path based on the fact that one intersection in the same time region has one arriving vehicle, and the time constraint condition of the driving path determined based on the road speed limit value of each vehicle between two intersections; adjusting the driving path based on the time constraint condition, and performing path control on the vehicles in the region based on the adjustment result. The method of the application realizes the optimization of the whole traffic network, reduces the total fuel consumption, and improves the traffic operation efficiency.

Description

Technical Field

[0001] This application relates to the field of automotive technology, and more particularly to a method, apparatus and storage medium for a vehicle driving path. Background Technology

[0002] Urban traffic flow is one of the most important factors determining the value of a city. As the population and income grow, so do the number of vehicles, especially in large cities where traffic relies heavily on vehicles. Therefore, controlling and alleviating traffic congestion and improving traffic flow quality has become a challenging problem. Improving the quality of traffic control can bring huge benefits to the entire city. The problem of improving traffic flow will be addressed from the perspective of operations research, and this problem is known as the vehicle routing problem.

[0003] Many researchers have proposed different algorithms to solve vehicle routing problems for various needs. Some have proposed a two-part optimization method based on connecting vehicles. The first part involves a consultation acceleration system that maximizes the intersection throughput of moving vehicles; the second part minimizes the delay of stopping vehicles by suggesting optimal stopping locations. Others have suggested completely eliminating traffic lights and replacing them with cooperative vehicle intersection control (CVIC) based on model predictive control theory.

[0004] However, their optimization occurs locally at each intersection and cannot optimize the entire traffic network. Summary of the Invention

[0005] This application provides a vehicle travel path method, apparatus, and storage medium for solving traffic optimization problems.

[0006] In a first aspect, this application provides a method for determining a vehicle's driving path, including:

[0007] The path constraints are determined based on minimizing the fuel consumption and travel time of the vehicles to be controlled within the region, and the travel path of each vehicle is determined according to the start and end nodes of each vehicle.

[0008] The time constraint for determining the travel path is based on an intersection with one arriving vehicle within the same time area, and the time constraint for determining the travel path for each vehicle between two intersections is based on the road speed limit value.

[0009] The driving path is adjusted based on the time constraints, and the path control of vehicles in the area is performed based on the adjustment results.

[0010] Secondly, this application provides a vehicle travel path control device, comprising:

[0011] The determination module determines path constraints based on minimizing the fuel consumption and travel time of the vehicles to be controlled within the region, and determines the travel path of each vehicle based on the start and end nodes of each vehicle.

[0012] The constraint module determines the travel path based on the time constraint condition that there is one arriving vehicle at an intersection within the same time area, and the time constraint condition that each vehicle determines the travel path based on the road speed limit value between two intersections.

[0013] The control module adjusts the driving path based on the time constraints and performs path control on vehicles within the area based on the adjustment results.

[0014] Thirdly, this application provides a vehicle driving path control device, including: a processor, a memory, and a communication interface, wherein the processor, memory, and communication interface are connected via a bus;

[0015] The memory stores computer-executed instructions;

[0016] The processor executes computer execution instructions stored in the memory to implement a vehicle driving path method as described in any of the preceding claims.

[0017] Fourthly, this application provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, are used to implement a vehicle driving path method as described in any of the preceding claims.

[0018] This application provides a method, device, and storage medium for optimizing vehicle travel routes in traffic. It determines path constraints based on minimizing fuel consumption and travel time for vehicles to be controlled within a region, and determines the travel route for each vehicle according to its start and end points. It also determines the travel route based on the condition that only one vehicle arrives at an intersection within the same time region, and the road speed limit for each vehicle between two intersections. The travel routes are adjusted based on these time constraints, and the adjusted routes are used for path control within the region. Since the path constraints are determined based on minimizing fuel consumption and travel time for vehicles to be controlled within the region, and the travel route for each vehicle is determined according to its start and end points, this method optimizes overall traffic, reduces total fuel consumption, and improves traffic efficiency. Attached Figure Description

[0019] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0020] Figure 1 A flowchart illustrating an embodiment of the traffic optimization vehicle routing method provided in this application;

[0021] Figure 2a A flowchart illustrating an embodiment of the traffic optimization vehicle routing method provided in this application for path selection;

[0022] Figure 2b This is an example diagram illustrating an extreme case where fuel consumption costs far exceed travel time costs.

[0023] Figure 2c This is an example diagram illustrating an extreme case where the cost of travel time far exceeds the cost of fuel consumption.

[0024] Figure 3 A flowchart of an embodiment of the traffic optimization vehicle travel path method with path time constraints provided in this application;

[0025] Figure 4 A specific example diagram illustrating the traffic optimization vehicle routing method provided in this application;

[0026] Figure 5 This is a schematic diagram of a vehicle travel path control device provided in an embodiment of this application;

[0027] Figure 6 This is a schematic diagram of a vehicle travel path control device provided in an embodiment of this application.

[0028] The accompanying drawings illustrate specific embodiments of this application, which will be described in more detail below. These drawings and descriptions are not intended to limit the scope of the concept in any way, but rather to illustrate the concept of this application to those skilled in the art through reference to particular embodiments. Detailed Implementation

[0029] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0030] First, let me explain the terms used in this application:

[0031] Boolean variable: This is a very useful logical variable that can be used to control the flow of a program and make decisions. It contains two values: true or false. If a Boolean variable is used in an expression, it is assigned an integer value of 1 or 0 depending on whether the variable value is true or false.

[0032] In existing technologies, many researchers have proposed different algorithms to solve vehicle routing problems for various needs. Some have proposed a two-part optimization method based on connecting vehicles. The first part involves a consultation acceleration system that maximizes the intersection throughput of moving vehicles; the second part minimizes the delay of stopping vehicles by suggesting optimal stopping locations. Others have suggested completely eliminating traffic lights and replacing them with cooperative vehicle intersection control (CVIC) based on model predictive control theory. However, the optimization of existing technologies occurs locally at each intersection and cannot optimize the entire traffic network.

[0033] To address the aforementioned problems, this application provides a traffic optimization method for vehicle travel routes, which optimizes the entire traffic network. The specific technical concept is as follows:

[0034] The overall traffic network is optimized by simultaneously controlling the speed and route of vehicles. Both of these quantities can be precisely controlled in autonomous vehicles. The objective function is set to minimize the collective fuel consumption and travel time of all vehicles in the network, thereby optimizing the entire traffic network.

[0035] The method provided in this application is intended to solve the above-mentioned technical problems of the prior art.

[0036] The technical solution of this application and how the technical solution of this application solves the above-mentioned technical problems are described in detail below with specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments. The embodiments of this application will now be described with reference to the accompanying drawings.

[0037] Figure 1 A flowchart of an embodiment of the traffic optimization vehicle routing method provided in this application is shown below. Figure 1 As shown, the entity executing this method can be a control device or management platform that controls an autonomous vehicle. This device / platform can execute the following steps through hardware, software, or a combination of both. The method includes:

[0038] S101. Determine the path constraints based on the minimum fuel consumption and minimum travel time of the vehicles to be controlled within the region, and determine the travel path of each vehicle according to the start node and end node of each vehicle.

[0039] With the start and end points of each vehicle determined, the system server can control the path. The system server can use various hybrid algorithms to find the path that minimizes overall vehicle fuel consumption and travel time, and then control vehicle operation based on the calculated minimum path. For example, a hybrid integer linear rule algorithm can be used to constrain vehicle paths, ensuring that vehicles follow paths that meet the conditions, starting and ending at each vehicle's respective start and end points.

[0040] Among them, the mixed integer linear rule formula is defined as the minimum fuel consumption value and the minimum driving time.

[0041] min C1∑ i,j,k X ijk W ij +C2∑ j,k T jk

[0042] Where i represents the starting node, j represents the ending node, and k represents the k-th vehicle. X ijk It is a Boolean variable, representing that the k-th vehicle passes through the j-th intersection from the i-th intersection; W ij T represents the weight of the road segment from the i-th intersection to the j-th intersection; jk C1 is a continuous decision variable, representing the total time it takes for car k to reach intersection j; C2 represents the fuel consumption cost (unit: yuan / liter) and C3 represents the travel time cost (unit: yuan / minute).

[0043] In this system, all vehicles' paths begin and end at their respective start and end nodes. For the start node, each vehicle must leave the start node and cannot re-enter it. For the end node, each vehicle cannot leave after entering the end node. For other nodes, if a vehicle enters another node, it must leave that node and cannot return to it. For all non-adjacent nodes, the weight is set to infinity to exclude all non-adjacent nodes.

[0044] S102. Time constraints for determining the travel path based on an intersection with one arriving vehicle within the same time area, and time constraints for determining the travel path of each vehicle between two intersections based on the road speed limit value.

[0045] The system server imposes time constraints on each vehicle to prevent collisions at intersections. Two vehicles are not allowed to arrive at the same intersection simultaneously; the next vehicle can only enter the intersection a certain time after the previous vehicle has left. If a vehicle crosses two intersections, the minimum travel time from one intersection to the next must be specified. ijThis is used to control the speed of vehicles traveling between two intersections.

[0046] The next vehicle must arrive at the intersection no earlier than the shortest clearance time α.

[0047] Among them, the shortest travel time is ∫ ij Set according to the road's speed limit.

[0048] S103. Adjust the driving route based on time constraints, and perform route control on vehicles within the area based on the adjustment results.

[0049] The driving routes are adjusted according to the above time constraints, and the system server performs route control on vehicles within the area based on the adjusted driving routes.

[0050] In this application, the vehicle is an autonomous vehicle, and the traffic optimization vehicle path method proposed in this application can be successfully integrated into the autonomous vehicle framework. The autonomous vehicle will ensure and enforce the accuracy of the method and the robustness of its application.

[0051] This application provides a traffic optimization method for vehicle travel routes. It determines path constraints based on minimizing the fuel consumption and travel time of vehicles to be controlled within a region, and determines the travel route for each vehicle according to its start and end points. It also determines the travel route based on the condition that only one vehicle arrives at an intersection within the same time region, and the road speed limit between two intersections for each vehicle. The travel routes are adjusted based on these time constraints, and the adjusted routes are used for path control within the region. Since the path constraints are determined based on minimizing the fuel consumption and travel time of vehicles to be controlled within the region, and the travel route for each vehicle is determined according to its start and end points, this method optimizes overall traffic, reduces total fuel consumption, and improves traffic efficiency.

[0052] like Figure 2a The diagram shown is a flowchart of an embodiment of the traffic optimization vehicle travel path selection method provided in this application, as follows: Figure 2a As shown, the method includes:

[0053] S201. Determining path constraints based on mixed-integer linear rule formulas:

[0054] min C1∑ i,j,k X ijk W ij +C2∑ j,k T jk

[0055] Step S201 is a specific method for determining path constraints based on minimizing the fuel consumption and travel time of vehicles to be controlled within the region, which is provided on the basis of step S101.

[0056] Where i represents the starting node, j represents the ending node, and k represents the k-th vehicle; X ijk It is a Boolean variable, X, which is true if and only if the k-th car passes through the j-th intersection from the i-th intersection. ijk True; W ij T represents the weight of the road segment from the i-th intersection to the j-th intersection; jk C1 is a continuous decision variable, representing the total time it takes for car k to reach intersection j; C2 represents the fuel consumption cost (unit: yuan / liter) and C3 represents the travel time cost (unit: yuan / minute).

[0057] The values ​​of C1 and C2 can be set according to requirements, and one can take precedence over the other.

[0058] Fuel consumption cost and driving time cost can be set according to actual conditions. Choose an appropriate value based on your needs, and one of these two values ​​can take precedence over the other.

[0059] The choice of values ​​for C1 and C2 will affect the specific solution. For example... Figure 2b This is an example diagram illustrating an extreme case where fuel consumption costs far exceed travel time costs. Figure 2c This is an example diagram illustrating an extreme case where the cost of travel time far exceeds the cost of fuel consumption. Figure 2b It demonstrates the path selection when fuel consumption costs outweigh travel time costs, leading the method to choose a more time-consuming but shorter path to reduce fuel consumption. Figure 2c This illustrates a scenario where the travel time cost is high. In this case, a longer but faster route would be chosen to reduce the travel time cost.

[0060] Among them, Figure 2b and Figure 2c In the diagram, higher density dots represent longer travel times, and the numbers marked on each street represent the time it takes for a car to travel through the street at maximum speed.

[0061] The following steps S202, S203, S204, and S205, based on step S101, provide a specific method for determining the travel path of each vehicle based on its start and end nodes:

[0062] S202. Based on the fact that each vehicle will not re-enter a defined driving path after leaving the starting node:

[0063]

[0064]

[0065] For the starting node, each vehicle must leave its starting node and can never enter it again.

[0066] Where i represents node (from), j represents node (to), and k represents the k-th vehicle; X ijk X is a Boolean decision variable, which states that X passes through the j-th intersection from the i-th intersection if and only if the k-th vehicle passes through the j-th intersection from the i-th intersection. ijk If true; S is the set of starting nodes, and E is the set of ending nodes.

[0067] S203. Determine the driving path based on the fact that each vehicle does not leave after entering the end node:

[0068]

[0069]

[0070] For each terminal node, each vehicle must enter its terminal node and may not leave afterward.

[0071] Where i represents the starting node, j represents the ending node, and k represents the k-th vehicle; X ijk X is a Boolean decision variable, which states that X passes through the j-th intersection from the i-th intersection if and only if the k-th vehicle passes through the j-th intersection from the i-th intersection. ijk If true; S is the set of starting nodes, and E is the set of ending nodes.

[0072] S204. Based on the condition that a vehicle must leave after entering a node p in the driving path and not return, the driving path is determined as follows:

[0073]

[0074] For all other nodes, if a vehicle enters node p, it must leave it and can never return to it.

[0075] Where i represents the starting node, j represents the ending node, and k represents the k-th vehicle; X pjk X is a Boolean decision variable, which states that X is true if and only if the k-th car passes through the j-th intersection from the p-th intersection. pjk True; X ipk X is a Boolean decision variable, which states that X passes through the p-th intersection from the i-th intersection if and only if the k-th car passes through the p-th intersection from the i-th intersection. ipk If true; S is the set of starting nodes, and E is the set of ending nodes.

[0076] S205. The weight of non-adjacent nodes in the travel path of any vehicle is an infinite number M:

[0077] W ij =M(∞)

[0078] For all non-adjacent nodes, there is no weight; we will assign weight W. ij Set it to an infinite number M to prevent it from being included in the method.

[0079] Where i represents the starting node, j represents the ending node, and W ij This represents the weight of the road segment from the i-th intersection to the j-th intersection.

[0080] The system server controls vehicle operation and applies path constraints to vehicles using mixed-integer linear rule formulas to ensure that vehicles follow paths that meet the conditions. These paths start and end at the respective start and end points of each vehicle.

[0081] like Figure 3 The diagram shown is a flowchart of an embodiment of the traffic optimization vehicle travel path method with path time constraints provided in this application. Figure 3 As shown, the method includes:

[0082] S301, any vehicle needs at least a minimum travel time to move from one intersection to the next. ij ,∫ ij Speed ​​limits are set according to the speed limits of each street:

[0083] T jk -T ik ≥∫ ij -M*(1-X ijk )

[0084] Step S301, based on step S102, provides a specific method for determining the time constraint conditions for each vehicle to travel between two intersections using the road speed limit values.

[0085] If a car crosses two intersections, the minimum travel time required to get from one intersection to the next is ∫ ij The driving time must not be shorter than the set minimum driving time to avoid speeding.

[0086] Among them, ∫ ij Speed ​​limits are set according to the speed limits of each street.

[0087] S302. If the first vehicle exits an intersection after time α, the second vehicle will arrive no earlier than the shortest clearance time α.

[0088] M*y jk1k2 ≥T jk1 -T jk2 -α

[0089] T jk1 -T jk2 ≤-α+M*y jk1k2 +M*A jk1 +M*A jk2

[0090] T jk1 -T jk2 ≥α-M*(1-y jk1k2 )-M*A jk1 -M*A jk2

[0091] Step S302 is a specific method for determining the travel path based on the time constraint condition that there is one arriving vehicle at an intersection within the same time area, which is provided on the basis of step S102.

[0092] To avoid a collision, two vehicles are not allowed to arrive at the same intersection simultaneously. The second vehicle must arrive no earlier than the shortest clearance time α. Assume the first vehicle will exit the intersection after time α.

[0093] Where y jk1k2 It is a Boolean variable, only when T jk1 -T jk2 It is true when A ≥ α, where A jk1 and A jk2 This statement is true only if vehicles k1 and k2 both reach node j:

[0094]

[0095]

[0096] S303. If there is any intersection j that no vehicle has reached, then T jk Zero:

[0097]

[0098] For an unvisited intersection, if vehicle k has not reached intersection j, then T... jk It is zero.

[0099] Where i represents the starting node, j represents the ending node, and k represents the k-th vehicle; X ijk X is a Boolean decision variable, which states that X passes through the j-th intersection from the i-th intersection if and only if the k-th vehicle passes through the j-th intersection from the i-th intersection. ijk True; T jk It is a continuous decision variable that represents the total time it takes for car k to reach intersection j.

[0100] The system server in this application controls vehicle operation and uses a mixed-integer linear rule formula to impose time constraints on the vehicles, which helps prevent collisions at intersections and ensures timing consistency.

[0101] like Figure 4 The diagram shown is a specific example of the traffic optimization vehicle routing method provided in this application. Figure 4 As shown, the method includes:

[0102] in, Figure 4 The method is given for 16 intersections and 3 vehicles (assuming the intersections are equidistant). The higher the density of points on the link, the lower the speed.

[0103] As can be seen, this method reduces the speed of vehicle 1, allowing vehicle 2 to pass first. This not only prevents vehicles 1 and 2 from colliding at their intersection, but also prevents a collision with vehicle 3 at the upcoming intersection without slowing down any additional vehicles. If the method reduced the speed of vehicle 2 instead of vehicle 1, it would need to reduce the speed of either vehicle 1 or vehicle 3 at the next intersection, resulting in an uneconomical solution. In other words, this method considers the entire path of the vehicle when controlling its speed, not just the upcoming intersection. While doing so, it also selects the shortest path connecting the start and end nodes.

[0104] This method can be applied to more complex traffic scenarios involving more nodes and vehicles.

[0105] One possible embodiment of this application provides a vehicle travel path control device. Figure 5 This is a schematic diagram of a vehicle travel path control device provided in an embodiment of this application, as shown below. Figure 5 As shown, a vehicle driving path control device 50 includes: a determination module 501, a constraint module 502, and a control module 503;

[0106] The determination module 501 determines the path constraints based on the minimum fuel consumption and minimum travel time of the vehicles to be controlled within the region, and determines the travel path of each vehicle according to the start node and end node of each vehicle.

[0107] The constraint module 502 determines the travel path based on the time constraint condition that there is one arriving vehicle at an intersection within the same time area, and the time constraint condition that each vehicle determines the travel path based on the road speed limit value between two intersections.

[0108] The control module 503 adjusts the driving path based on time constraints and performs path control on vehicles within the area based on the adjustment results.

[0109] One possible embodiment of this application provides a vehicle travel path control device. Figure 6 This is a schematic diagram of a vehicle travel path control device provided in an embodiment of this application, such as... Figure 6 As shown, a vehicle travel path control device includes: a processor 601, a memory 602, and a communication interface 603, wherein the processor 601, the memory 602, and the communication interface 603 are connected via a bus 604.

[0110] Memory 602 stores computer-executed instructions;

[0111] The processor 601 executes computer execution instructions stored in the memory 602 to implement any of the vehicle driving path methods described above.

[0112] The specific implementation process of processor 601 can be found in the above method embodiments, and its implementation principle and technical effect are similar. It will not be repeated here.

[0113] In the above Figure 6 In the illustrated embodiments, it should be understood that the processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), etc. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the method disclosed in this invention can be directly implemented by a hardware processor, or implemented by a combination of hardware and software modules within the processor.

[0114] The memory may include random access memory (RAM) and may also include non-volatile memory (NVM), such as at least one disk storage device.

[0115] The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be categorized as address buses, data buses, control buses, etc. For ease of illustration, the buses shown in the accompanying drawings are not limited to a single bus or a single type of bus.

[0116] In one possible embodiment of this application, a computer-readable storage medium is also provided, which stores computer-executable instructions that, when executed by a processor, are used to implement the vehicle driving path method described above.

[0117] The aforementioned computer-readable storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. The computer-readable storage medium can be any available medium accessible to a general-purpose or special-purpose computer.

[0118] An exemplary computer-readable storage medium is coupled to a processor, enabling the processor to read information from and write information to the storage medium. Of course, the computer-readable storage medium can also be a component of the processor. The processor and the computer-readable storage medium can reside in application-specific integrated circuits (ASICs). Alternatively, the processor and the computer-readable storage medium can exist as discrete components in the device.

[0119] The division of units is merely a logical functional division; in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be indirect coupling or communication connection through some interfaces, devices, or units, and may be electrical, mechanical, or other forms.

[0120] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0121] In addition, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0122] If a function is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0123] Those skilled in the art will understand that all or part of the steps of the above-described method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When executed, the program performs the steps of the above-described method embodiments; and the aforementioned storage medium includes various media capable of storing program code, such as ROM, RAM, magnetic disks, or optical disks.

[0124] Other embodiments of this application will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of this application that follow the general principles of this application and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered exemplary only, and the true scope and spirit of this application are indicated by the following claims.

[0125] It should be understood that this application is not limited to the precise structure described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this application is limited only by the appended claims.

Claims

1. A method for determining a vehicle's travel path, characterized in that, include: The path constraints are determined based on the mixed integer linear rule formula, and the driving path of each vehicle is determined according to the start node and end node of each vehicle. Given the time constraint that a vehicle can arrive at an intersection within the same time zone to determine its travel path, and the requirement that any vehicle needs at least a minimum travel time to move from one intersection to the next. ; The driving path is adjusted based on the time constraints, and the vehicles in the area are controlled based on the adjustment results. The formula for the mixed-integer linear rule is: in, Indicates the starting node. Indicates the termination node. Indicates the first vehicle; if and only if the first The car from the The first intersection passed the first At the intersection, the aforementioned True; the stated Indicates from the first The intersection to the first The segment weights of each intersection; It is a continuous decision variable, representing the car Arriving at the crossroads Total time used; the Indicates fuel consumption cost, the Indicates the cost of travel time; the and One value takes precedence over the other; The Speed ​​limits are set according to the speed limits of each street: in, For an infinitely large number, the stated It is a Boolean decision variable. Indicates automobile Arriving at the crossroads Total time used Indicates automobile Arriving at the crossroads Total time used; The time constraint condition for determining the travel route based on an intersection within the same time region having one arriving vehicle includes: If in time After the first vehicle exits an intersection, the second vehicle must clear customs no earlier than the shortest clearance time. arrive: in, It is a Boolean variable, only when Time is true, among which and Only in vehicles 1 and 2. Arrive at the nodes respectively Time is real: 。 2. The method according to claim 1, characterized in that, The process of determining the travel path of each vehicle based on its start and end points includes: Based on the fact that each vehicle will not re-enter the defined driving path after leaving the starting node: The driving path is determined based on the fact that each vehicle does not leave after entering the termination node: If a vehicle enters a node p in a driving path and must leave without returning, the driving path is determined as follows: In this context, the weights of non-adjacent nodes in the travel path of any vehicle are infinitely large numbers. : in, Indicates the starting node. Indicates the termination node. Indicates the first Vehicle; the vehicle mentioned It is a Boolean decision variable if and only if the first... The car from the The first intersection passed the first At the intersection, the aforementioned True; the stated Indicates from the first The intersection to the first The segment weight of each intersection; The set of starting nodes, It is the set of endpoint nodes.

3. The method according to claim 1, characterized in that, The method also includes: if there are any intersections that vehicles have not reached. ,but Zero: 。 4. A vehicle travel path control device, comprising: The determination module determines the path constraints based on the mixed integer linear rule formula, and determines the driving path of each vehicle according to the start node and end node of each vehicle. The constraint module determines the travel path based on the time constraint that at any given intersection within the same time region, only one vehicle can arrive at the intersection, and that any vehicle requires at least a minimum travel time from one intersection to the next. ; The control module adjusts the driving path based on the time constraints and performs path control on vehicles within the area based on the adjustment results. The formula for the mixed-integer linear rule is: in, Indicates the starting node. Indicates the termination node. Indicates the first vehicle; if and only if the first The car from the The first intersection passed the first At the intersection, the aforementioned True; the stated Indicates from the first The intersection to the first The segment weights of each intersection; It is a continuous decision variable, representing the car Arriving at the crossroads Total time used; the Indicates fuel consumption cost, the Indicates the cost of travel time; the and One value takes precedence over the other; The Speed ​​limits are set according to the speed limits of each street: in, For an infinitely large number, the stated It is a Boolean decision variable. Indicates automobile Arriving at the crossroads Total time used Indicates automobile Arriving at the crossroads Total time used; The time constraint condition for determining the travel route based on an intersection within the same time region having one arriving vehicle includes: If in time After the first vehicle exits an intersection, the second vehicle must clear customs no earlier than the shortest clearance time. arrive: in, It is a Boolean variable, only when Time is true, among which and Only in vehicles 1 and 2. Arrive at the nodes respectively Time is real: 。 5. A vehicle travel path control device, comprising: The processor, memory, and communication interface are connected via a bus. The memory stores computer-executed instructions; The processor executes computer execution instructions stored in the memory to implement a vehicle driving path method as described in any one of claims 1 to 3.

6. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions, which, when executed by a processor, are used to implement a vehicle driving path method as described in any one of claims 1 to 3.

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