CN114745699A - Vehicle-to-vehicle communication mode selection method and system based on neural network and storage medium - Google Patents
Vehicle-to-vehicle communication mode selection method and system based on neural network and storage medium Download PDFInfo
- Publication number
- CN114745699A CN114745699A CN202210661399.2A CN202210661399A CN114745699A CN 114745699 A CN114745699 A CN 114745699A CN 202210661399 A CN202210661399 A CN 202210661399A CN 114745699 A CN114745699 A CN 114745699A
- Authority
- CN
- China
- Prior art keywords
- communication
- service data
- vehicles
- vehicle
- duration
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
Images
Classifications
-
- 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]
- H04W4/46—Services specially adapted for particular environments, situations or purposes for vehicles, e.g. vehicle-to-pedestrians [V2P] for vehicle-to-vehicle communication [V2V]
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
- G06N3/045—Combinations of networks
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
- H04W4/02—Services making use of location information
- H04W4/023—Services making use of location information using mutual or relative location information between multiple location based services [LBS] targets or of distance thresholds
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
- H04W4/02—Services making use of location information
- H04W4/025—Services making use of location information using location based information parameters
- H04W4/026—Services making use of location information using location based information parameters using orientation information, e.g. compass
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
- H04W4/02—Services making use of location information
- H04W4/025—Services making use of location information using location based information parameters
- H04W4/027—Services making use of location information using location based information parameters using movement velocity, acceleration information
-
- 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]
- H04W4/42—Services specially adapted for particular environments, situations or purposes for vehicles, e.g. vehicle-to-pedestrians [V2P] for mass transport vehicles, e.g. buses, trains or aircraft
-
- 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]
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
- Y02D30/00—Reducing energy consumption in communication networks
- Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks
Landscapes
- Engineering & Computer Science (AREA)
- Signal Processing (AREA)
- Computer Networks & Wireless Communication (AREA)
- Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- General Health & Medical Sciences (AREA)
- General Physics & Mathematics (AREA)
- Computational Linguistics (AREA)
- Data Mining & Analysis (AREA)
- Evolutionary Computation (AREA)
- Biomedical Technology (AREA)
- Molecular Biology (AREA)
- Computing Systems (AREA)
- General Engineering & Computer Science (AREA)
- Biophysics (AREA)
- Mathematical Physics (AREA)
- Software Systems (AREA)
- Artificial Intelligence (AREA)
- Life Sciences & Earth Sciences (AREA)
- Health & Medical Sciences (AREA)
- Aviation & Aerospace Engineering (AREA)
- Mobile Radio Communication Systems (AREA)
Abstract
The invention discloses a vehicle-to-vehicle communication mode selection method, a system and a storage medium based on a neural network, wherein the method comprises the following steps: acquiring the positions and speeds of the two vehicles, and calculating the actual distance between the two vehicles; determining the waiting communication time length and the communication maintaining time length of the two vehicles based on the speed of the two vehicles, the actual distance and the communication distance threshold; acquiring the transmission completion time length of a transmission service data packet; comparing the waiting communication time length, the communication maintaining time length and the transmission completing time length, and selecting a communication mode of two vehicles among direct communication, relay communication and direct communication of the two vehicles after the actual distance of the two vehicles reaches a communication distance threshold according to a comparison result; taking the positions and the speeds of two vehicles in historical data, the service data types of service data packets, the service data quantity, the service data size and the service data transmission rate as input, taking a corresponding two-vehicle communication mode as output, and carrying out neural network training to obtain a vehicle-vehicle communication mode selection model; a vehicle-to-vehicle communication mode is subsequently determined based on the selection model.
Description
Technical Field
The invention relates to the field of vehicle-to-vehicle communication, in particular to a vehicle-to-vehicle communication mode selection method, a system and a storage medium based on a neural network.
Background
A Communication Based Train operation Control (CBTC) System is widely used in the world, and has a main function of transmitting information between a Train and the ground through wireless Communication, and after Train data is collected and processed by the ground, the data is distributed to each Train. However, the train-ground-train communication involves too many subsystems and interfaces, so that the equipment structure is complex and the information transmission has large delay, which affects the efficiency and safety of train operation. In order to improve Train operation efficiency and safety, Train-to-Train communication (T2T) technology has been developed and applied to Train operation control systems, which perform direct Train-to-Train communication to reduce the circulation of data in trackside equipment. Despite the many advantages of direct vehicle-to-vehicle communication, trackside equipment in the system is still necessary. Under the condition that vehicle-to-vehicle direct communication and vehicle-ground-vehicle communication coexist, how to select a proper communication mode according to different train running conditions is very important for reducing the time delay of data transmission and ensuring the integrity of the data transmission.
Disclosure of Invention
The invention aims to solve the problems in the prior art and provides a method, a system and a storage medium for selecting a vehicle-to-vehicle communication mode based on a neural network.
In a first aspect, the present invention provides a vehicle-to-vehicle communication mode selection method based on a neural network, the method comprising the steps of:
s1, acquiring the position and speed of two vehicles running in the same direction
And
calculating the actual distance between two vehicles
；
S2, determining the communication waiting time when the actual distance of the two vehicles reaches the communication distance threshold based on the speed, the actual distance and the communication distance threshold L of the two vehicles
And communication maintenance duration
；
S3, obtaining the transmission completion time length needed by the service data packet to be transmitted
；
S4, comparing the communication maintaining time of the two vehicles with the transmission completing time required by the service data packet, if yes
Then the two vehicles relay communication, if
Go to S5;
s5, comparing the waiting communication time of the two vehicles with the transmission completion time required by the service data packet, if yes
Then two vehicles wait for the actual distance of the two vehicles to reach the threshold of the communication distanceDirect communication; if it is not
If yes, the two vehicles carry out relay communication;
s6, taking the positions and speeds of two vehicles in the vehicle-vehicle communication historical data, the service data types, the service data quantity, the service data size and the service data transmission rate of the service data packets as input, and taking the corresponding vehicle-vehicle communication mode determined based on the steps S1 to S5 as output to carry out neural network training to obtain a vehicle-vehicle communication mode selection model;
and S7, determining a vehicle-to-vehicle communication mode based on the selection model in the subsequent train operation.
Preferably, the method for determining the waiting communication duration and the communication maintaining duration in step S2 includes:
comparing the speeds of the two vehicles, and comparing the actual distance between the two vehicles with the communication distance threshold;
when the rear vehicle speed is higher than the front vehicle speed, if
If the two vehicles wait for the communication time
Duration of communication maintenance
(ii) a If it is not
If so, the waiting time of the two vehicles
Duration of communication maintenance
；
When the rear vehicle speed is equal to the front vehicle speed, if
If so, the waiting time of the two vehicles
Duration of communication maintenance
(ii) a If it is used
If so, the waiting time of the two vehicles
Duration of communication maintenance
；
When the rear vehicle speed is lower than the front vehicle speed, if
If so, the waiting time of the two vehicles
Duration of communication maintenance
(ii) a If it is not
If the two vehicles wait for the communication time
Duration of communication maintenance
。
Preferably, the transmission completion duration in step S3 includes a data transmission duration
And establishing a communication delay
。
Preferably, the data transmission duration is determined based on a service data type, a service data quantity, a service data size, and a service data transmission rate of the service data packet, where the service data transmission rate is related to the service data type.
Preferably, the data transmission duration
Wherein
Represents the service data type of the service data packet, j belongs to (1, 2...., M), M represents the quantity of the service data type,
indicating the corresponding service data type
The traffic data transmission rate, N denotes the traffic data amount of the traffic data packet,
indicating the size of the ith service data in the service data packet.
In a second aspect, the present invention provides a vehicle-to-vehicle communication mode selection system, the system including:
the historical parameter module is used for acquiring the positions and the speeds of two vehicles running in the same direction in the vehicle-vehicle communication historical data, the service data type of a service data packet, the quantity of service data, the size of the service data and the transmission rate of the service data; the method comprises the steps of obtaining a communication distance threshold and establishing communication time delay;
the calculation module is used for calculating the communication waiting time and the communication maintaining time when the actual distance between the two vehicles reaches the communication distance threshold based on the positions and the speeds of the two vehicles in the historical data and the communication distance threshold, and calculating the transmission completion time required by the service data packet to be transmitted based on the service data type, the service data quantity, the service data size, the service data transmission rate and the communication time delay; a communication mode for determining the two vehicles based on the waiting communication time length, the communication maintaining time length and the transmission completion time length;
the neural network module is used for inputting the positions and the speeds of the two vehicles in the vehicle-vehicle communication historical data, the service data types of the service data packets, the service data quantity, the service data size and the service data transmission rate, taking the corresponding two-vehicle communication mode calculated by the calculation module as output, and performing neural network training to obtain a selection model of the vehicle-vehicle communication mode;
the real-time parameter module is used for acquiring the positions and the speeds of two vehicles running in the same direction, the service data type of a service data packet, the service data quantity, the service data size and the service data transmission rate, and is used for acquiring a communication distance threshold and establishing communication time delay;
and the selection module is used for determining whether the communication behaviors of the two vehicles are direct communication, relay communication or waiting direct communication through a selection model established by the neural network module based on the positions and the speeds of the two vehicles running in the same direction, the service data type of a service data packet, the service data quantity, the service data size and the service data transmission rate.
In a third aspect, the present invention provides a storage medium, in which computer-executable instructions are stored, and when the computer-executable instructions are loaded and executed by a processor, the method for selecting a vehicle-to-vehicle communication mode based on a neural network is implemented.
In conclusion, the invention has the following beneficial effects: calculating the transmission completion time length required for completing transmission of the service data packet under the comprehensive optimal conditions of the transmission rate, the channel bandwidth occupation and the power consumption of the communication equipment according to the service data type, the service data quantity, the service data size and the service data transmission rate of the service data packet; calculating the waiting communication time length and the communication maintaining time length of the train workshop according to the position and the speed of the train; selecting a proper communication mode between two vehicles running in the same direction by comparing the transmission completion time length determined by the service data packet information, the communication waiting time length determined by the train position and speed information and the communication maintaining time length; and neural network training is carried out on the basis of the service data packet information, the train position and speed information and the corresponding train-to-vehicle communication mode to construct a selection model of the train-to-vehicle communication mode, so that the communication mode can be determined quickly in the following process, and the method is favorable for reasonably reducing the time delay of various data transmission under different driving conditions of the train and ensuring the integrity of the data transmission.
Additional aspects and advantages of the invention will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the invention.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions and advantages of the prior art, the drawings used in the embodiments or the description of the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and other drawings can be obtained by those skilled in the art without creative efforts.
FIG. 1 is a flowchart of steps S1-S5 according to an embodiment of the present invention.
Fig. 2 is a schematic structural diagram of a neural network training model according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages disclosed in the embodiments of the present invention more clearly apparent, the embodiments of the present invention are described in further detail below with reference to the accompanying drawings and the embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the embodiments of the invention and do not delimit the embodiments. All other embodiments obtained by a person of ordinary skill in the art based on the embodiments in the present application without making creative efforts shall fall within the protection scope of the present application. Examples of which are illustrated in the accompanying drawings, wherein like reference numerals refer to the same or similar elements or elements having the same or similar function throughout.
It should be noted that the terms "comprises" and "comprising," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or server that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
A large amount of data of a train is transmitted in sequence by being divided into a plurality of data packets, one data packet can be regarded as the minimum data size to be transmitted by the train, and retransmission is carried out by taking a single data packet with a problem as a unit when the transmission data has a problem. In addition to the outbound and inbound conditions, the remaining time of the train may be regarded as a block of constant speed, and for two trains running in the same direction, the time of direct communication between them is determined by the train distance and the train running condition. If the direct communication time of two trains is not enough to complete the transmission of a single data packet of data to be transmitted, the selection of vehicle-to-vehicle direct communication can cause the transmission of the data packet to fail, and the delay is retransmitted through other modes such as vehicle-to-ground-vehicle communication, which can adversely affect the train operation efficiency and safety. Therefore, under the condition that the direct communication between the train and the ground-train relay communication coexist, the train operation efficiency and the train operation safety can be effectively improved by selecting a proper communication mode according to different train running conditions.
Therefore, the embodiment of the application provides a neural network-based vehicle-to-vehicle communication mode selection method suitable for the same-direction driving condition, and the method comprises the following steps:
as shown in FIG. 1, in step S1, the positions of two vehicles traveling in the same direction are acquired,
And
calculating the actual distance between two vehicles
。
In some embodiments of the present application, the two train positions are represented by coordinates
And
the front-back relation of the two vehicles and the actual distance between the two vehicles can be defined by the coordinates of the two vehicles
。
Step S2, determining the communication waiting time when the actual distance of the two vehicles reaches the communication distance threshold based on the speed, the actual distance and the communication distance threshold L of the two vehicles
And communication maintenance duration
。
In some embodiments of the present application, the method for determining the waiting communication duration and the communication maintaining duration in step S2 includes:
comparing the speeds of the two vehicles, and comparing the actual distance between the two vehicles with the communication distance threshold;
when the rear vehicle speed is higher than the front vehicle speed, if
If the two vehicles wait for the communication time
Duration of communication maintenance
(ii) a If it is not
Then the two cars wait for communicationDuration of message
Duration of communication maintenance
；
When the rear vehicle speed is equal to the front vehicle speed, if
If so, the waiting time of the two vehicles
Duration of communication maintenance
(ii) a If it is used
If so, the waiting time of the two vehicles
Duration of communication maintenance
；
When the rear vehicle speed is lower than the front vehicle speed, if
If so, the waiting time of the two vehicles
Duration of communication maintenance
(ii) a If it is not
If so, the waiting time of the two vehicles
Duration of communication maintenance
。
Step S3, obtaining the transmission completion time length required by the service data packet to be transmitted
。
In some embodiments of the present application, the transmission completion duration in step S3 includes a data transmission duration
And establishing a communication delay
Is shown as
。
The transmission rate affects the channel bandwidth occupation, power consumption and the like of the device, and for data of types such as text, numerical values and the like, if the highest transmission rate is adopted for transmission, the channel bandwidth occupation and the power consumption of the device are greatly sacrificed compared with the weak reduction in the transmission time, while for data of types such as video, pictures and the like, if the highest transmission rate is adopted for transmission, the transmission time is obviously shortened by sacrificing the channel bandwidth occupation and the power consumption of the device, which is acceptable. When different types of service data are transmitted, different transmission rates are adopted, so that the transmission rate, the occupation of the channel bandwidth of the equipment and the power consumption can be comprehensively optimal. Therefore, in some embodiments of the present application, the data transmission duration is determined based on the service data type of the service data packet, the amount of the service data, the size of the service data, and a service data transmission rate of a preset limit, where the service data transmission rate is related to the service data type.
In some embodiments of the present application, the data transmission duration
In which
Represents the service data type of the service data packet, j belongs to (1, 2.... multidata., M), M represents the number of the service data types,
indicating the corresponding service data type
The traffic data transmission rate, N denotes the traffic data amount of the traffic data packet,
indicating the size of the ith service data in the service data packet.
Step S4, comparing the communication maintaining time of the two vehicles with the transmission completing time required by the service data packet, if yes
And if the direct communication time of the two vehicles is not enough to finish the transmission of the service data packet, the two vehicles adopt relay communication, namely, the two vehicles communicate through ground trackside equipment, so as to avoid larger delay caused by transmission failure and relay retransmission. If it is not
Then, the process goes to step S5.
Step S5, comparing the waiting communication time of the two vehicles with the transmission completion time required by the service data packet, if yes
After the actual distance between the two vehicles reaches the communication distance threshold, the two vehicles directly communicate; if it is not
And the two vehicles relay communication.
When in use
If the train and the train are in direct communication, the time for receiving the service data packet of the train is the sum of the communication waiting time and the transmission finishing time, namely
If the communication is relayed, the time for receiving the service data packet by the receiving train is the time for one train to relay and transmit the service data packet to the ground and then to distribute and transmit the service data packet to another train, which can be approximately seen as twice the time for completing the transmission, that is, the time is the time for completing the transmission
. When the temperature is higher than the set temperature
I.e. by
And the train-to-train direct communication is compared with the ground relay communication, and the receiving train receives the service data packet faster or at the same time, so that the two trains are in direct communication after the actual distance of the two trains reaches the communication distance threshold. When it comes to
I.e. by
Ground relay communication is compared with vehicle-to-vehicle direct communication, and a receiving train receives a service data packet faster, so that two vehicles are selected to communicate through ground relay at the moment.
S6, taking the positions and speeds of two vehicles in the vehicle-vehicle communication historical data, the service data types of the service data packets, the service data quantity, the service data size and the service data transmission rate as inputs, and taking the corresponding two-vehicle communication modes determined based on the steps S1 to S5 as outputs to carry out neural network training to obtain a selection model of the vehicle-vehicle communication mode;
and S7, determining a vehicle-to-vehicle communication mode based on the selection model in the subsequent train operation.
In some embodiments of the present application, as shown in fig. 2, the neural network training model includes an input layer, a first hidden layer, a second hidden layer, a third hidden layer and an output layer, where the input layer and the first hidden layer both include a driving parameter module and a data parameter module, the driving parameter module of the input layer sets 2 nodes corresponding to the positions and speeds of two vehicles, and the data parameter module of the input layer sets 4 nodes corresponding to the types of service data, the amount of service data, the size of service data and the transmission rate of service data of the input service data packet. The first hidden layer driving parameter module and the data parameter module are respectively provided with a plurality of nodes, two modules in the embodiment are respectively provided with 30 nodes, the second hidden layer is provided with 2 nodes and respectively corresponds to the output of the first hidden layer driving parameter module and the data parameter module, the third hidden layer is used as a fusion layer of the driving parameter module and the data parameter module to be provided with a plurality of nodes, the embodiment is provided with 25 nodes, the output layer is provided with 1 node, and the communication mode of two vehicles is output.
According to the method and the device, the transmission completion time required for completing transmission of the service data packet is calculated under the comprehensive optimal conditions of the transmission rate, the channel bandwidth occupation of the communication equipment and the power consumption through the service data type, the service data quantity, the service data size and the service data transmission rate of the service data packet; calculating the waiting communication time length and the communication maintaining time length of the train workshop according to the position and the speed of the train; selecting a proper communication mode between two vehicles running in the same direction by comparing the transmission completion time length determined by the service data packet information, the communication waiting time length determined by the train position and speed information and the communication maintaining time length; and neural network training is carried out on the basis of the service data packet information, the train position and speed information and the corresponding train-to-vehicle communication mode to construct a selection model of the train-to-vehicle communication mode, so that the communication mode can be determined rapidly in the following process, and the method is favorable for reasonably reducing the time delay of various data transmission under different driving conditions of the train and ensuring the integrity of the data transmission.
The embodiment of the present application further provides a vehicle-to-vehicle communication mode selection system, the system includes:
the historical parameter module is used for acquiring the positions and the speeds of two vehicles running in the same direction in the vehicle-vehicle communication historical data, the service data type of a service data packet, the quantity of service data, the size of the service data and the transmission rate of the service data; the method comprises the steps of obtaining a communication distance threshold and establishing communication time delay;
the calculation module is used for calculating the communication waiting time and the communication maintaining time when the actual distance between the two vehicles reaches the communication distance threshold based on the positions and the speeds of the two vehicles in the historical data and the communication distance threshold, and calculating the transmission completion time required by the service data packet to be transmitted based on the service data type, the service data quantity, the service data size, the service data transmission rate and the communication time delay; a communication mode for determining the two vehicles based on the waiting communication time length, the communication maintaining time length and the transmission completion time length;
the neural network module is used for taking the positions and the speeds of two vehicles in the vehicle-vehicle communication historical data, the service data types of the service data packets, the service data quantity, the service data size and the service data transmission rate as inputs, taking the corresponding two-vehicle communication mode calculated by the calculation module as an output, and performing neural network training to obtain a vehicle-vehicle communication mode selection model;
the real-time parameter module is used for acquiring the positions and speeds of two vehicles running in the same direction, the service data type of a service data packet, the service data quantity, the service data size and the service data transmission rate, and is used for acquiring a communication distance threshold and establishing communication time delay;
and the selection module is used for determining whether the communication behaviors of the two vehicles are direct communication, relay communication or waiting direct communication through a selection model established by the neural network module based on the positions and the speeds of the two vehicles running in the same direction, the service data type of a service data packet, the service data quantity, the service data size and the service data transmission rate.
The specific method for calculating the waiting communication duration, the communication maintaining duration and the transmission completion duration and determining the two-vehicle communication behavior is described in detail in the foregoing method embodiments, and is not described herein again.
The embodiment of the application also provides an electronic device which comprises a memory and a processor, wherein the memory and the processor can be connected through a bus or in other ways. The memory can be used for storing software programs, computer programs and modules, such as the programs/modules corresponding to the vehicle-to-vehicle communication mode selection method based on the neural network; the processor realizes the vehicle-to-vehicle communication mode selection method based on the neural network by executing the computer program and the module in the memory.
The embodiment of the application also provides a storage medium, wherein the storage medium stores computer executable instructions, and the computer executable instructions are loaded and executed by the processor to realize the vehicle-to-vehicle communication mode selection method based on the neural network. The storage medium may be one or a combination of more of a magnetic disk, an optical disk, a read-only memory, a random access memory, a flash memory, a hard disk, and the like.
It should be noted that: the precedence order of the above embodiments of the present invention is only for description, and does not represent the merits of the embodiments. While certain embodiments of the present disclosure have been described above, other embodiments are within the scope of the following claims. In some cases, the actions or steps recited in the claims may be performed in a different order than in the embodiments and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing may also be possible or may be advantageous.
The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments.
Those skilled in the art will appreciate that all or part of the steps for implementing the above embodiments may be implemented by hardware, or may be implemented by a program instructing relevant hardware. The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that fall within the spirit and principle of the present invention are intended to be included therein.
Claims (7)
1. A vehicle-to-vehicle communication mode selection method based on a neural network is characterized by comprising the following steps:
s1, acquiring the position and speed of two vehicles running in the same direction
And
calculating the actual distance between two vehicles
；
S2, determining the communication waiting time when the actual distance of the two vehicles reaches the communication distance threshold based on the speed, the actual distance and the communication distance threshold L of the two vehicles
And communication maintenance duration
；
S3, obtaining the transmission completion time length needed by the service data packet to be transmitted
；
S4, comparing the communication maintaining time of the two vehicles with the transmission completing time required by the service data packet, if yes
Then the two vehicles relay communication, if
Go to S5;
s5, comparing the waiting communication time of two vehicles with the transmission completion time needed by the service data packet, if yes
If the actual distance between the two vehicles reaches the communication distance threshold, the two vehicles directly communicate; if it is not
If yes, the two vehicles carry out relay communication;
s6, taking the positions and speeds of two vehicles in the vehicle-vehicle communication historical data, the service data types of the service data packets, the service data quantity, the service data size and the service data transmission rate as inputs, and taking the corresponding two-vehicle communication modes determined based on the steps S1 to S5 as outputs to carry out neural network training to obtain a selection model of the vehicle-vehicle communication mode;
and S7, determining a vehicle-to-vehicle communication mode based on the selection model in the subsequent train running.
2. The neural network-based vehicle-to-vehicle communication mode selection method as claimed in claim 1, wherein the determination method of the waiting communication duration and the communication maintaining duration in step S2 comprises:
comparing the speeds of the two vehicles, and comparing the actual distance between the two vehicles with the communication distance threshold;
when the rear vehicle speed is higher than the front vehicle speed, if
If the two vehicles wait for the communication time
Duration of communication maintenance
(ii) a If it is not
If so, the waiting time of the two vehicles
Duration of communication maintenance
；
When the rear vehicle speed is equal to the front vehicle speed, if
If the two vehicles wait for the communication time
Duration of communication maintenance
(ii) a If it is not
If so, the waiting time of the two vehicles
Duration of communication maintenance
；
When the rear vehicle speed is lower than the front vehicle speed, if
If so, the waiting time of the two vehicles
Duration of communication maintenance
(ii) a If it is used
If so, the waiting time of the two vehicles
Duration of communication maintenance
。
3. The neural-network-based vehicle-to-vehicle communication mode selecting method according to claim 1 or 2, wherein the completion duration of the transmission in step S3 includes a data transmission duration
And establishing a communication delay
。
4. The method of claim 3, wherein the data transmission duration is determined based on the service data type, the service data amount, the service data size and the service data transmission rate of the service data packet, and the service data transmission rate is related to the service data type.
5. The method of claim 4, wherein the data transmission duration is longer than the data transmission duration
Wherein
Indicating to which service data packet belongsJ ∈ (1, 2.... M), M denotes the number of traffic data types,
indicating the corresponding service data type
The traffic data transmission rate of (a), N represents the traffic data amount of the traffic data packet,
indicating the size of the ith service data in the service data packet.
6. A neural network-based vehicle-to-vehicle communication mode selection system, the system comprising:
the historical parameter module is used for acquiring the positions and the speeds of two vehicles running in the same direction in the vehicle-vehicle communication historical data, the service data type of a service data packet, the quantity of service data, the size of the service data and the transmission rate of the service data; the method comprises the steps of obtaining a communication distance threshold and establishing communication time delay;
the calculation module is used for calculating the communication waiting time and the communication maintaining time when the actual distance between the two vehicles reaches the communication distance threshold based on the positions and the speeds of the two vehicles in the historical data and the communication distance threshold, and calculating the transmission completion time required by the service data packet to be transmitted based on the service data type, the service data quantity, the service data size, the service data transmission rate and the communication time delay; a communication mode for determining the two vehicles based on the waiting communication time length, the communication maintaining time length and the transmission completion time length;
the neural network module is used for taking the positions and the speeds of two vehicles in the vehicle-vehicle communication historical data, the service data types of the service data packets, the service data quantity, the service data size and the service data transmission rate as inputs, taking the corresponding two-vehicle communication mode calculated by the calculation module as an output, and performing neural network training to obtain a vehicle-vehicle communication mode selection model;
the real-time parameter module is used for acquiring the positions and the speeds of two vehicles running in the same direction, the service data type of a service data packet, the service data quantity, the service data size and the service data transmission rate, and is used for acquiring a communication distance threshold and establishing communication time delay;
and the selection module is used for determining whether the communication behaviors of the two vehicles are direct communication, relay communication or waiting direct communication through a selection model established by the neural network module based on the positions and the speeds of the two vehicles running in the same direction, the service data type of a service data packet, the service data quantity, the service data size and the service data transmission rate.
7. A storage medium having stored thereon computer-executable instructions that, when loaded and executed by a processor, implement a neural network-based vehicle-to-vehicle communication mode selection method as defined in any one of claims 1 to 5.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210661399.2A CN114745699B (en) | 2022-06-13 | 2022-06-13 | Vehicle-to-vehicle communication mode selection method and system based on neural network and storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210661399.2A CN114745699B (en) | 2022-06-13 | 2022-06-13 | Vehicle-to-vehicle communication mode selection method and system based on neural network and storage medium |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN114745699A true CN114745699A (en) | 2022-07-12 |
| CN114745699B CN114745699B (en) | 2022-09-02 |
Family
ID=82286827
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210661399.2A Active CN114745699B (en) | 2022-06-13 | 2022-06-13 | Vehicle-to-vehicle communication mode selection method and system based on neural network and storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114745699B (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115442849A (en) * | 2022-11-09 | 2022-12-06 | 成都市以太节点科技有限公司 | Differentiated communication method and device for railway vehicle-mounted millimeter wave terminal and storage medium |
Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP1486931A2 (en) * | 2003-06-12 | 2004-12-15 | Navecom | Communication of alert messages between vehicles on a road and a traffic information network |
| US20110172856A1 (en) * | 2010-01-08 | 2011-07-14 | Wabtec Holding Corp. | Short Headway Communications Based Train Control System |
| CN104580295A (en) * | 2013-10-17 | 2015-04-29 | 宁波视竣信息科技有限公司 | Intelligent broadband communication method and system used for high-speed train in moving state |
| EP3040250A1 (en) * | 2013-08-26 | 2016-07-06 | Jian Liu | Railway train with length exceeding that of platform and configuration system therefor |
| US20170334473A1 (en) * | 2014-02-18 | 2017-11-23 | Nabil N. Ghaly | Method & apparatus for a train control system |
| CN107801157A (en) * | 2017-09-28 | 2018-03-13 | 深圳大学 | Data transmission method, user terminal, vehicle and system based on V2X |
| CN110719570A (en) * | 2019-09-09 | 2020-01-21 | 华为技术有限公司 | Transmission method and communication device for map sensitive information |
| WO2022012166A1 (en) * | 2020-07-14 | 2022-01-20 | 北京交通大学 | Safe train tracking and protection method and apparatus based on relative speed |
| CN114670904A (en) * | 2022-04-29 | 2022-06-28 | 西门子交通技术(北京)有限公司 | Train communication system, method, electronic device, and storage medium |
-
2022
- 2022-06-13 CN CN202210661399.2A patent/CN114745699B/en active Active
Patent Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP1486931A2 (en) * | 2003-06-12 | 2004-12-15 | Navecom | Communication of alert messages between vehicles on a road and a traffic information network |
| US20110172856A1 (en) * | 2010-01-08 | 2011-07-14 | Wabtec Holding Corp. | Short Headway Communications Based Train Control System |
| EP3040250A1 (en) * | 2013-08-26 | 2016-07-06 | Jian Liu | Railway train with length exceeding that of platform and configuration system therefor |
| CN104580295A (en) * | 2013-10-17 | 2015-04-29 | 宁波视竣信息科技有限公司 | Intelligent broadband communication method and system used for high-speed train in moving state |
| US20170334473A1 (en) * | 2014-02-18 | 2017-11-23 | Nabil N. Ghaly | Method & apparatus for a train control system |
| CN107801157A (en) * | 2017-09-28 | 2018-03-13 | 深圳大学 | Data transmission method, user terminal, vehicle and system based on V2X |
| CN110719570A (en) * | 2019-09-09 | 2020-01-21 | 华为技术有限公司 | Transmission method and communication device for map sensitive information |
| WO2022012166A1 (en) * | 2020-07-14 | 2022-01-20 | 北京交通大学 | Safe train tracking and protection method and apparatus based on relative speed |
| CN114670904A (en) * | 2022-04-29 | 2022-06-28 | 西门子交通技术(北京)有限公司 | Train communication system, method, electronic device, and storage medium |
Non-Patent Citations (2)
| Title |
|---|
| SHUOMEI MA: "A Virtual Coupling Approach Based on Event-triggering Control for CBTC Systems under Jamming Attacks", 《2020 IEEE 92ND VEHICULAR TECHNOLOGY CONFERENCE (VTC2020-FALL)》 * |
| 李永波: "基于5G 与V2X 的智能有轨电车无线通信***设计及性能测试", 《铁道通信信号第57卷第10期》 * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115442849A (en) * | 2022-11-09 | 2022-12-06 | 成都市以太节点科技有限公司 | Differentiated communication method and device for railway vehicle-mounted millimeter wave terminal and storage medium |
| CN115442849B (en) * | 2022-11-09 | 2023-03-24 | 成都市以太节点科技有限公司 | Differentiated communication method and device for railway vehicle-mounted millimeter wave terminal and storage medium |
Also Published As
| Publication number | Publication date |
|---|---|
| CN114745699B (en) | 2022-09-02 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN103491617A (en) | Apparatus and method for controlling power for v2x communication | |
| CN113099418B (en) | Optimization method of block chain task for data transmission of Internet of vehicles | |
| CN110928658A (en) | Cooperative task migration system and algorithm of vehicle-side cloud cooperative architecture | |
| CN107196771A (en) | Vehicle netbios synchronization dormancy method, system and gateway controller | |
| CN114745699B (en) | Vehicle-to-vehicle communication mode selection method and system based on neural network and storage medium | |
| CN113645273B (en) | Internet of vehicles task unloading method based on service priority | |
| CN113254375A (en) | Data transmission method and device, electronic equipment and storage medium | |
| CN116405904A (en) | TACS network resource allocation method based on deep reinforcement learning | |
| CN111245743A (en) | Information processing method, storage medium, gateway and automobile | |
| Kazmi et al. | A novel deep reinforcement learning-based approach for task-offloading in vehicular networks | |
| CN114979145A (en) | Content distribution method integrating sensing, communication and caching in Internet of vehicles | |
| CN106685608A (en) | Resource scheduling method, device and node of vehicle-road collaborative communication system | |
| CN114995990A (en) | Method and device for unloading computing tasks, electronic equipment and computer storage medium | |
| CN113726573A (en) | Redundant network communication method, device, electronic equipment and storage medium | |
| CN115116245B (en) | Intersection dynamic induction signal control method and system in vehicle mixed traffic environment | |
| CN116437319A (en) | Method for constructing differentiated access ICT fusion information transmission platform | |
| CN113438056B (en) | Fountain code-based Internet of vehicles data transmission method and device and storage medium | |
| Zhao et al. | A research of task-offloading algorithm for distributed vehicles | |
| CN103067518A (en) | In-vehicle network video monitoring system and alarming method thereof | |
| CN112906745B (en) | Integrity intelligent network training method based on edge cooperation | |
| CN112671512B (en) | CAN message sending method, control unit, CAN node and network thereof | |
| CN111866810A (en) | Internet of vehicles frequency spectrum allocation method and equipment | |
| CN113207085A (en) | MEC (Meter coordinated Circuit) assisted speed self-adaptive access method for fleet network | |
| CN111615082A (en) | Heterogeneous automatic driving vehicle formation calculation task collaborative execution method | |
| CN117891613B (en) | Computing unloading and resource allocation method and system based on Internet of vehicles |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |