Method and system for wireless communication between unmanned vehicles

Abstract

The invention relates to the technical field of wireless communication, in particular to a wireless communication method and system for unmanned vehicles, and the method comprises the steps: determining a communication index between a center vehicle and each target vehicle at each moment; based on the total communication switching frequency of the central vehicle in the preset current period and the difference between the communication indexes between the central vehicle and all the target vehicles at each moment and the maximum communication index, determining the adjustment coefficient of the next period, and combining the difference between the evaluation threshold of the current period and the maximum value in all the communication indexes in the period to determine the communication switching frequency of the central vehicle. Determining an evaluation threshold value of the next period; and based on the evaluation threshold of the next period, performing communication switching between the center vehicle and the target vehicle in the next period. According to the method and the device, the communication quality between the unmanned vehicles is improved by reducing the influence of multipath interference and high communication switching frequency on the communication quality.

Description

Technical Field

[0001] This application relates to the field of wireless communication technologies, and particularly to a wireless communication method and system for unmanned vehicles. Background Art

[0002] As a large-scale systematic network, the vehicle-to-everything (V2X) network follows specific communication protocols and data interaction standards, and realizes intelligent traffic management, intelligent dynamic information services, and vehicle intelligent control through vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication methods. Unmanned vehicles use the V2X network for wireless communication to ensure the timeliness and reliability of information transmission. Therefore, the communication quality of unmanned vehicles within the V2X network directly affects their driving safety performance.

[0003] V2V communication in the V2X network can share and interact information such as vehicle conditions and road conditions among unmanned vehicles without the assistance of infrastructure, and can effectively avoid communication connection interruptions caused by the fixed and scattered distribution of infrastructure in V2I communication. Since V2V communication is constantly changing due to the driving environment of unmanned vehicles, unmanned vehicles need to perform communication switching with different vehicles to ensure their communication quality. Currently, the switching strategy for V2V communication in the V2X network has insufficient accuracy in estimating the influence degree of multi-source data on communication quality in the driving environment, resulting in a decrease in the effectiveness of its switching decision, and further causing a decline in the communication quality of wireless communication among unmanned vehicles. Summary of the Invention

[0004] In order to solve the above technical problems, the purpose of this application is to provide a wireless communication method and system for unmanned vehicles, and the specific technical solutions adopted are as follows: In a first aspect, an embodiment of this application provides a wireless communication method for unmanned vehicles, and the method includes the following steps: S1: Construct a neighborhood centered on the current unmanned vehicle, denote the current unmanned vehicle as the central vehicle, denote the unmanned vehicle that has established V2V communication with the central vehicle as the target vehicle, and obtain in real time the volume and position of all obstacles in the neighborhood, the number of V2V communication switches of the central vehicle, and all types of communication parameters of each target vehicle; S2: Based on the communication situation between the central vehicle and its target vehicles, iteratively adjust the evaluation threshold for communication switching between the central vehicle and its target vehicles, and the adjustment process is as follows: Based on the distances from the central vehicle to each target vehicle and all obstacles at each moment, as well as the volumes of each obstacle, determine the adjustment ratio of the shape parameter to determine the shape parameter, and combine the channel model to obtain the signal-to-interference-plus-noise ratio (SINR) between the central vehicle and each of its target vehicles at each moment; At each moment and within the preset time duration before it, respectively analyze the dispersion degree of the SINR between the central vehicle and each of its target vehicles, as well as the dispersion degree of each type of communication parameter of each target vehicle, determine the concentration degree, and combine the chaos degree of the SINR between the central vehicle and all its target vehicles at each moment, and the chaos degree of all target vehicles with respect to each type of communication parameter, to determine the communication index between the central vehicle and each of its target vehicles at each moment; Based on the total communication switching frequency of the central vehicle within the preset current period, and the difference between the communication index between the central vehicle and all its target vehicles at each moment and the maximum communication index, determine the adjustment coefficient of the evaluation threshold for the next period, and combine the difference between the evaluation threshold of the current period and the maximum value of all communication indices within the current period to determine the evaluation threshold for the next period; S3: Based on the evaluation threshold for the next period, perform communication switching between the central vehicle and its target vehicles within the next period.

[0005] Preferably, the expression for the adjustment ratio of the shape parameter is: ; where, represents the adjustment ratio of the shape parameter between the central vehicle and target vehicle k at moment t; represents the distance from the central vehicle to target vehicle k at moment t; , respectively represent the distance from obstacle n in the neighborhood of the central vehicle to the central vehicle, and the distance from obstacle n to target vehicle k at moment t; represents the volume of the nth obstacle in the neighborhood of the central vehicle at moment t; represents the number of all obstacles in the neighborhood of the central vehicle at moment t; norm( ) represents the normalization function.

[0006] Preferably, the method for determining the shape parameter includes: The shape parameter between the central vehicle and target vehicle k at moment t has the following expression: ; where, , S respectively represent a preset first value and a preset second value, where the preset first value is greater than the preset second value.

[0007] Preferably, the method for obtaining the SINR between the central vehicle and each of its target vehicles at each moment is: At each moment, the communication transmission power among all communication parameter types of each target vehicle is used as the scale parameter of the channel model. Among them, the shape parameter between the central vehicle and each of its target vehicles at each moment is used as the shape parameter of the channel model, and the received power - probability density function of the transmitted signal received by the central vehicle from each target vehicle within the channel model is obtained. The mean value of the product of all received powers and their corresponding probabilities in the received power - probability density function is used as the received signal power of the central vehicle from each target vehicle; The received signal power of the central vehicle receiving the target signal k is divided by the sum of the received signal powers of the central vehicle and all other target signals except the target signal k, and the obtained ratio is used as the signal - to - interference - plus - noise ratio (SINR) between the central vehicle and its target signal k. By traversing all target signals, the SINR between the central vehicle and each of its target signals is obtained.

[0008] Preferably, the determination process of the concentration degree is as follows: The SINR between the central vehicle and each of its target vehicles, as well as all communication parameter types of each target vehicle, are collectively referred to as the parameters of each target vehicle. The reciprocal of the standard deviation of various types of parameters of the target vehicle within the preset time duration up to and including the current moment is calculated as the concentration degree of various types of parameters.

[0009] Preferably, the expression of the communication index between the central vehicle and each of its target vehicles at each moment is: ; where represents the communication index between the central vehicle and its target vehicle k at time t; represents the total number of parameter types of all types of parameters of the target vehicle k; represents the entropy weight of the s - th type of parameter of all target vehicles at time t; the normalized value of the s - th type of parameter of the target vehicle k at time t; represents the concentration degree of the s - th type of parameter of the target vehicle k at time t; represents a constant greater than 0 preset.

[0010] Preferably, the expression of the adjustment coefficient for the next cycle is: ; where represents the adjustment coefficient for the next cycle; Q represents the total number of communication handovers of the central vehicle within the current cycle; represents the communication index between the central vehicle and its target vehicle v at time u within the current cycle; represents the maximum value of the communication index between the central vehicle and all its target vehicles at time u within the current cycle; represents the total number of target vehicles of the central vehicle at time u within the current cycle; represents the total duration of the current cycle; denotes the exponential function with the natural constant as the base; norm[ ] denotes the normalization function.

[0011] Preferably, the expression of the evaluation threshold for the next period is: ; in the formula, and respectively represent the evaluation thresholds for the next period and the current period, where the initial evaluation threshold is a preset third value; represents the maximum value among the communication indices between the central vehicle and all its target vehicles at all times within the current period.

[0012] Preferably, the communication switching between the central vehicle and all its target vehicles in the next period includes: Calculate the sum of the communication indices between the central vehicle and all its target vehicles at each moment in the next period, denoted as the communication sum value; regard the target vehicles whose communication indices between the central vehicle and all its target vehicles are greater than the evaluation threshold for the next period as characteristic vehicles, and calculate the sum of the communication indices between the central vehicle and all its characteristic vehicles at each moment in the next period; In the next period, if the time when the sum of the communication indices is continuously greater than the communication sum value is greater than the preset time threshold, then disconnect the V2V communication connection between the central vehicle and the remaining target vehicles except the characteristic vehicles. In the second aspect, the embodiment of the present application also provides a wireless communication system for unmanned vehicles, including a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, it implements the steps of the wireless communication method for unmanned vehicles described in any one of the above.

[0013] The present application has at least the following beneficial effects: Based on the signals collected in real time by the sensing system of the unmanned vehicle and the signals received from the vehicle network, the present application obtains multi-source data affecting the V2V communication quality, and further estimates the influence degree of the current driving environment on the V2V communication of the unmanned vehicle, avoiding the V2V communication between vehicles from switching to a communication link with larger signal interference and fading, so as to improve the communication quality after switching; further, by analyzing the time-varying characteristics of the V2V communication of the unmanned vehicle, an evaluation threshold is constructed to adjust the switching strategy of the wireless communication between vehicles, avoiding frequent switching caused by the rapid change of the vehicle communication state, ensuring that the unmanned vehicle is less affected by signal interference and fading after switching, while improving the effectiveness of the switching decision and enhancing the communication quality between the unmanned vehicles. Description of the Drawings

[0014] To more clearly illustrate the technical solutions and advantages in the embodiments of the present application or the prior art, the following will briefly introduce the accompanying drawings required for the description of the embodiments or the prior art. Obviously, the accompanying drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0015] Figure 1 It is a flowchart of the steps of a wireless communication method for unmanned vehicles provided by an embodiment of the present application; Figure 2 It is a schematic diagram of the evaluation threshold acquisition process provided by an embodiment of the present application. Detailed implementation manners

[0016] In order to further elaborate on the technical means and effects adopted by the present application to achieve the predetermined invention purpose, the following, in combination with the accompanying drawings and preferred embodiments, details the specific implementation manners, structures, features and effects of the wireless communication method and system for unmanned vehicles proposed according to the present application. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. In addition, the specific features, structures or characteristics in one or more embodiments can be combined in any suitable form.

[0017] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those of ordinary skill in the technical field to which the present application belongs.

[0018] The following specifically describes the specific solutions of the wireless communication method and system for unmanned vehicles provided by the present application in conjunction with the accompanying drawings.

[0019] Please refer to Figure 1 , which shows a flowchart of the steps of a wireless communication method for unmanned vehicles provided by an embodiment of the present application. The method includes the following steps: S1: Construct a neighborhood centered on the current unmanned vehicle. Denote the current unmanned vehicle as the central vehicle, and denote the unmanned vehicles that have established V2V communication with the central vehicle as target vehicles. Real-time obtain the volume and position of all obstacles in the neighborhood, the number of V2V communication switches of the central vehicle, and all communication parameters of each target vehicle.

[0020] The high-speed movement of unmanned vehicles and the complex urban road conditions, on the one hand, result in differences in the throughput and the number of communication connections of V2V communication between different vehicles. On the other hand, the attenuation degree of the signal by the channel between unmanned vehicles changes relatively fast, resulting in strong time-variability of the communication quality of V2V communication of unmanned vehicles.

[0021] The driverless vehicle obtains multi-source data affecting the wireless communication quality between driverless vehicles based on the signals collected by its on-vehicle sensors and received from the vehicle networking. There are many types of sensors in the driverless vehicle sensing system, and there are various types and methods of collecting signals. In this embodiment, for the driverless vehicle equipped with a lidar sensor, multi-source data is obtained, specifically as follows: Taking the current driverless vehicle as the center, a neighborhood is constructed with a radius of R. The current driverless vehicle is denoted as the center vehicle. The lidar sensor is used to obtain the point cloud data within the neighborhood of the center vehicle, and a target detection algorithm is used to obtain the positions and quantities of obstacles in the point cloud data.

[0022] It should be noted that there are many commonly used target detection algorithms. In this embodiment, the Complex-YOLO algorithm is adopted. In the actual application process, as other implementation manners, implementers can also adopt other target detection algorithms according to specific situations.

[0023] Among them, the Complex-YOLO algorithm is a well-known technology, and its specific principle will not be elaborated here.

[0024] It is further explained that the value of the neighborhood radius R is set artificially. In this embodiment, the value of the neighborhood radius R is 150m. In the actual application process, implementers can also set it by themselves according to specific situations, and this embodiment does not make special restrictions.

[0025] Furthermore, according to the point cloud data of the identified obstacles, the triangulation method is used to obtain the distance between the vehicle and the obstacles, and the point cloud processing algorithm is used to measure the size of the obstacles. Specifically, in this embodiment, the point cloud of the obstacles is fitted with a minimum bounding box, and the volume of the obstacles is the volume of its minimum bounding box.

[0026] Among them, the triangulation method and the minimum bounding box are both well-known technologies, and their specific principles will not be elaborated here.

[0027] Furthermore, the driverless vehicle that conducts V2V communication with the center vehicle is denoted as the target vehicle, and all types of communication parameters of the target vehicle and the V2V communication switching times of the center vehicle are collected in real time. In this embodiment, all types of communication parameters include: the signal transmission power, switching success rate, throughput, and connection number of the target vehicle.

[0028] It is further explained that the acquisition of the relevant data of the obstacles within the neighborhood of the center vehicle and all types of communication parameters of the target vehicle of the center vehicle is synchronized in real time, and the data acquisition frequency is f. In this embodiment, the value of the data acquisition frequency f is 10Hz. In the actual application process, as other implementation manners, implementers can also set it by themselves according to specific situations, and this embodiment does not make special restrictions.

[0029] S2: Iteratively adjust the evaluation threshold for communication handover between the central vehicle and its target vehicles based on the communication situation between the central vehicle and its target vehicles.

[0030] S201: Determine the adjustment ratio of the shape parameter based on the distances from the central vehicle to each target vehicle and all obstacles at each moment, as well as the volumes of the obstacles, so as to determine the shape parameter. Then, in combination with the channel model, obtain the signal-to-interference ratio between the central vehicle and each of its target vehicles at each moment.

[0031] Changes in the number, position, and volume of obstacles within the neighborhood of driverless vehicles will directly or indirectly affect the change in signal propagation characteristics, thereby reflecting the time-varying characteristics of the V2V wireless communication channel. Specifically, a large number of vehicles means high communication demand, which increases the congestion of the communication channel, resulting in increased signal interference and reduced communication quality; the closer the distance between vehicles, the smaller the signal attenuation and the higher the communication quality; while the sizes of vehicles and obstacles directly affect the signal transmission path and intensity. Larger vehicles may block the signal propagation path, and in a dense urban environment, other types of obstacles such as buildings and trees will also block the signal, affecting communication quality.

[0032] Considering that in the driving environment of driverless vehicles, the path of the V2V communication signal from the transmitter to the receiver is often not a straight line, but passes through multiple reflections, diffractions, and scatterings. This multipath propagation causes the amplitude of the received signal to change rapidly, forming typical Rayleigh fading or more general Nakagami fading.

[0033] To accurately obtain the degree to which the wireless communication signal between driverless vehicles is affected by its surrounding driving environment, this embodiment uses the Nakagami-m channel model to reflect the signal fading in wireless communication and adjusts the parameters in the channel model in real time according to the changes in the driving environment. Specifically: when there are a large number of obstacles with large volumes between driverless vehicles, the signal mainly reaches the receiver through diffraction and reflection, and the signal fading is more severe, so a smaller shape parameter is set.

[0034] Based on the above analysis, to reduce the error in estimating the signal propagation characteristics by the fixed shape parameter in a time-varying driving environment, this embodiment determines the adjustment ratio of the shape parameter based on the distances from the central vehicle to each target vehicle and all obstacles at each moment, as well as the volumes of the obstacles, so as to determine the shape parameter. Specifically: The adjustment ratio of the shape parameter between the central vehicle and its target vehicle k at time t is expressed as: ; where represents the distance from the central vehicle to target vehicle k at time t; , respectively represent the distance from the obstacle n in the neighborhood of the central vehicle to the central vehicle and the distance from the obstacle n to the target vehicle k at time t; represents the volume of the nth obstacle in the neighborhood of the central vehicle at time t; represents the number of all obstacles in the neighborhood of the central vehicle at time t; norm( ) represents the normalization function.

[0035] It can be understood from the adjustment ratio of the shape parameter between the central vehicle and its target vehicle at the current moment that if the distance between the central vehicle and the target vehicle is closer, and there are more obstacles between the central vehicle and the target vehicle, and the distances from the obstacles to the central vehicle and the target vehicle are closer, the greater the possibility of signal fading. At this time, the shape parameter should be reduced. Therefore, by reducing the adjustment ratio, the shape parameter is reduced.

[0036] Furthermore, based on the adjustment ratio of the shape parameter, the shape parameter is adjusted. The specific process is as follows: the shape parameter between the central vehicle and its target vehicle k at time t has the following expression: ; in the formula, and S respectively represent a preset first value and a preset second value, where the preset first value is greater than the preset second value.

[0037] It should be noted that the preset first value and the preset second value are used to represent the minimum value and the maximum value of the shape parameter in this embodiment. The values of the preset first value and the preset second value are set artificially. In this embodiment, in order to prevent the shape parameter from being too large or too small, the value of the preset first value is 2, and the value of the preset second value is 7. Implementers can also set them according to specific situations by themselves, and this embodiment does not make special restrictions.

[0038] It can be understood from the shape parameters between the central vehicle and its respective target vehicles at each moment that if the possibility of the V2V communication between the central vehicle and the target vehicle being affected by multipath interference is greater, at this time, by reducing the adjustment ratio, the shape parameter is reduced to reduce the channel estimation error.

[0039] Furthermore, according to the shape parameter, using the Nakagami-m channel model, the signal-to-interference ratio between the central vehicle and its respective target vehicles at each moment is determined. Specifically: At each moment, the communication transmission power among all communication parameters of each target vehicle is used as the scale parameter of the Nakagami-m channel model. Among them, the shape parameter between the central vehicle and each of its target vehicles at each moment is used as the shape parameter of the Nakagami-m channel model, and the received power - probability density function of the transmitted signals received by the central vehicle from each target vehicle within the Nakagami-m channel model is obtained. The mean value of the product of all received powers and their corresponding probabilities in the received power - probability density function is used as the received signal power of the transmitted signals received by the central vehicle from each target vehicle; The received signal power of the central vehicle receiving the target signal k is divided by the sum of the received signal powers of the central vehicle and all other target signals except the target signal k. The obtained ratio is used as the signal - to - interference ratio between the central vehicle and its target signal k. By traversing all target signals, the signal - to - interference ratios between the central vehicle and each of its target signals are obtained, which are used to characterize the quality of communication between the central vehicle and its target vehicles during the driving process of the central vehicle. The smaller the signal - to - interference ratio, the better the communication quality between the central vehicle and its target vehicles, and the greater the impact of environmental interference on its V2V communication.

[0040] So far, by analyzing the interference of multipath effect channel estimation, the signal - to - interference ratio between the central vehicle and the target vehicles has been obtained.

[0041] S202: At each moment and within the preset time duration before it, analyze the dispersion degree of the signal - to - interference ratio between the central vehicle and each of its target vehicles, and the dispersion degree of each type of communication parameter of each target vehicle respectively, determine the concentration degree, and combine the chaos degree of the signal - to - interference ratio between the central vehicle and all its target vehicles at each moment, and the chaos degree of all target vehicles with respect to each type of communication parameter to determine the communication index between the central vehicle and each of its target vehicles at each moment.

[0042] The handover success rate, throughput, and connection number of the V2V communication of driverless vehicles reflect the performance of driverless vehicles in the current vehicle - to - vehicle (V2V) communication in the vehicle network. Among them, the handover success rate reflects the communication stability, the throughput reflects the data transmission speed during the communication process, and the connection number shows the utilization of communication resources.

[0043] Therefore, by analyzing the change situation of various communication parameters of the target vehicles received by the central vehicle and combining the signal - to - interference ratio between the central vehicle and the target vehicles, the communication index between the central vehicle and each target vehicle is determined to judge the excellent degree of the communication quality between the central vehicle and the target vehicles. The specific process is as follows: (1) At each moment and within the preset time duration before it, analyze the dispersion degree of the signal - to - interference ratio between the central vehicle and each of its target vehicles, and the dispersion degree of each type of parameter of each target vehicle respectively, and determine the concentration degree. Specifically: The signal-to-interference ratio between the central vehicle and its target vehicles obtained in S202 and all class communication parameters of the target vehicles are collectively referred to as the parameters of the target vehicles. The inverse of the standard deviation of each class parameter of the target vehicle at each moment and within the preset time period before it is calculated as the concentration of each class parameter. The larger the standard deviation of the current class parameter, the more drastic the change of this class parameter before the current moment, and the influence of this class parameter on the switching decision should be reduced in the future to avoid frequent switching of V2V communication between unmanned vehicles.

[0044] (2) Based on the concentration, combined with the degree of confusion of the signal-to-interference ratio between the central vehicle and all its target vehicles at each moment, and the degree of confusion of all target vehicles with respect to each type of communication parameter, the communication index between the central vehicle and each of its target vehicles at each moment is determined, specifically: In the above content, the signal-to-interference ratio between the central vehicle and its target vehicles and all types of communication parameters of the target vehicles are collectively referred to as parameters of the target vehicles. Therefore, in the following content, the word parameter is directly used to replace the signal-to-interference ratio and all types of communication parameters.

[0045] As an implementation method, in this embodiment, the communication index between the center vehicle and its target vehicle k at time t is The expression is: ; In the formula, The total number of categories representing all class parameters of the target vehicle k; represents the entropy weight of the s-th type of parameters of all target vehicles at time t; The normalized value of the s-th parameter of target vehicle k at time t; represents the concentration of the s-th type of parameters of target vehicle k at time t; Indicates a constant greater than 0 to prevent the denominator from being 0. The value of is artificially set. The value of is 0.01. Under the premise of ensuring that the denominator is not 0 and does not excessively affect the calculation result, the implementer can also set it according to the specific application. This embodiment does not impose any special restrictions.

[0046] It should be noted that the calculation process of entropy weight is a well-known technology. In this embodiment, the entropy weight TOPSIS algorithm is used to calculate the entropy weights of various parameters, and its specific principle will not be repeated here.

[0047] It can be understood from the communication indices between the central vehicle and its target vehicles at each moment that if the entropy weight of the current type of parameter is smaller, it indicates that the change of this type of parameter is more stable, showing that the communication status between the current central vehicle and its target vehicles is good, and the greater the concentration, the more stable the change of this type of parameter before the current moment, indicating that this type of parameter has greater reference significance for evaluating the communication quality between driverless vehicles, the current communication quality is better, and the larger the value of this type of parameter within a certain range at the current moment, it also indicates that the communication quality is better, and the resulting communication index is larger; On the contrary, if the entropy weight of the current type of parameter is larger, it indicates that the change of this type of parameter is more unstable, showing that the communication status between the current central vehicle and its target vehicles is poor, and the smaller the concentration, the more drastic the change of this type of parameter before the current moment, indicating that the current communication quality is poor, and the resulting communication index is smaller, indicating that the communication quality between the central vehicle and its target vehicles at the current moment is worse.

[0048] So far, by analyzing the change stability of different types of communication parameters and the signal-to-interference ratio, the communication index between the central vehicle and the target vehicles has been obtained.

[0049] S203: Based on the total communication switching frequency of the central vehicle within the preset current period, and the difference between the communication indices between the central vehicle and all its target vehicles at each moment compared with the maximum communication index, determine the adjustment coefficient of the evaluation threshold for the next period, and combine the difference between the evaluation threshold of the current period and the maximum value of all communication indices within the current period to determine the evaluation threshold for the next period.

[0050] Since the communication status of different vehicles changes rapidly during road driving, using a fixed communication switching strategy is likely to cause frequent communication switching between vehicles, resulting in a decrease in the effectiveness of switching decisions. Therefore, in this embodiment, the evaluation threshold of the switching strategy is dynamically adjusted to avoid frequent switching of V2V communication between driverless vehicles. The specific process is as follows: (1) Based on the total communication switching frequency of the central vehicle within the preset current period, and the difference between the communication indices between the central vehicle and its target vehicles at each moment compared with the maximum communication index, determine the adjustment coefficient for the next period, specifically: The adjustment coefficient for the next period The expression is: ; where Q represents the total number of communication switches of the central vehicle within the current period; represents the communication index between the central vehicle and its target vehicle v at moment u within the current period; represents the maximum value of the communication indices between the central vehicle and all its target vehicles at moment u within the current period; represents the total number of target vehicles of the central vehicle at moment u within the current period; represents the total duration of the current cycle; represents the exponential function with the natural constant as the base; norm[ ] represents the normalization function.

[0051] It should be noted that the value of the cycle length is a preset value. In this embodiment, the value of the cycle length is 30s. Implementers can also set it by themselves according to specific situations, and this embodiment does not make special restrictions.

[0052] According to the adjustment coefficient, it can be understood that if the number of communication switches is smaller, the difference between the communication index and the maximum value of the communication index is smaller, indicating that the communication status between the central vehicle and the target vehicle is good. A relatively small adjustment coefficient can be appropriately set, so that the evaluation threshold is in a relatively small value range, reducing the frequent communication switches between unmanned vehicles.

[0053] (2) Further, based on the adjustment coefficient of the next cycle and combined with the difference between the maximum communication quality and the evaluation threshold in the current cycle, determine the evaluation threshold of the next cycle, specifically: The expression of the evaluation threshold of the next cycle is: ; where, 、 represent the evaluation thresholds of the next cycle and the current cycle respectively, where the initial evaluation threshold is a preset third value; represents the maximum value among the communication indexes between the central vehicle and all its target vehicles at all times in the current cycle.

[0054] It should be noted that if the central vehicle does not communicate with the unmanned vehicle in the first cycle, the initial evaluation threshold is not considered until the cycle when the central vehicle starts to communicate. Take 0.8 times the maximum communication index between the central vehicle and all its target vehicles at the start communication moment as the initial evaluation threshold. Implementers can also set the initial evaluation threshold by themselves according to specific situations, and this embodiment does not make special restrictions.

[0055] Particularly, if the central vehicle does not communicate in a certain cycle during driving, the evaluation threshold is reset at this time. Take 0.8 times the maximum communication index between the central vehicle and all its target vehicles when the central vehicle starts to communicate again as the initial evaluation threshold of the corresponding cycle.

[0056] By analyzing the evaluation threshold, it can be obtained that: if the difference between the evaluation threshold of the current cycle and the maximum value is smaller, it indicates that the communication quality between unmanned vehicles in the current cycle is good, and the adjustment coefficient is also relatively small. Therefore, the communication frequency between unmanned vehicles in the next cycle can be appropriately reduced. Therefore, the evaluation threshold of the next cycle can be appropriately reduced.

[0057] Preferably, the schematic diagram of the evaluation threshold acquisition process provided in this embodiment is as shown in Figure 2 as follows.

[0058] S3: Based on the evaluation threshold of the next cycle, perform communication switching between the central vehicle and its target vehicles within the next cycle.

[0059] According to S2, the evaluation threshold is obtained. Based on the evaluation threshold, the communication switching between the central vehicle and its target vehicles is controlled. The specific process is as follows: Calculate the cumulative sum of the communication indices between the central vehicle and all its target vehicles at each moment within the next cycle, denoted as the communication sum value; consider the target vehicles whose communication indices between the central vehicle and all its target vehicles are greater than the evaluation threshold of the next cycle as characteristic vehicles, and calculate the sum of the communication indices between the central vehicle and all its characteristic vehicles at each moment within the next cycle. Within the next cycle, if the time when the sum of the communication indices is continuously greater than the communication sum value is greater than the preset time threshold, then disconnect the V2V communication connection between the central vehicle and the remaining target vehicles except the characteristic vehicles.

[0060] It should be noted that the value of the time threshold is set artificially. In this embodiment, the value of the time threshold is 1s. In actual application, the implementer can also set it according to the specific situation by himself / herself, and this embodiment does not make special restrictions.

[0061] Based on the same inventive concept as the above method, the embodiment of the present application also provides a wireless communication system for driverless vehicles, including a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, it implements the steps of any one of the above methods for wireless communication between driverless vehicles.

[0062] It should be noted that: the above sequence of the embodiments of the present application is only for description and does not represent the superiority or inferiority of the embodiments. And the above description of specific embodiments of this specification is made. In addition, the processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0063] Each embodiment in this specification is described in a progressive manner. The same or similar parts between each embodiment can be referred to each other, and the key point of each embodiment is to illustrate the differences from other embodiments.

[0064] The above are only the preferred embodiments of the present application and are not used to limit the present application. Any modifications, equivalent replacements, improvements, etc. made within the principle of the present application shall be included in the protection scope of the present application.

Claims

1. A wireless communication method for driverless vehicles, characterized in that, The method includes the following steps: S1: Construct a neighborhood centered on the current driverless vehicle. Denote the current driverless vehicle as the central vehicle, and the driverless vehicles that have established V2V communication with the central vehicle as target vehicles. Real-time obtain the volume and position of all obstacles within the neighborhood, the number of V2V communication switches of the central vehicle, and all communication parameters of each target vehicle. S2: Based on the communication situation between the central vehicle and its target vehicles, iteratively adjust the evaluation threshold for communication switching between the central vehicle and its target vehicles. The adjustment process is as follows: Based on the distances from the central vehicle to each target vehicle and all obstacles at each moment, and the volume of each obstacle, determine the adjustment ratio of the shape parameter to determine the shape parameter, and combine with the channel model to obtain the signal-to-interference-plus-noise ratio (SINR) between the central vehicle and each of its target vehicles at each moment. At each moment and within the preset duration before it, respectively analyze the dispersion degree of the SINR between the central vehicle and each of its target vehicles, and the dispersion degree of each type of communication parameter of each target vehicle, determine the concentration degree, and combine with the chaos degree of the SINR between the central vehicle and all its target vehicles at each moment, and the chaos degree of all target vehicles regarding each type of communication parameter, to determine the communication index between the central vehicle and each of its target vehicles at each moment. Based on the total number of communication switches of the central vehicle within the preset current period, and the difference between the communication index between the central vehicle and all its target vehicles at each moment and the maximum communication index, determine the adjustment coefficient of the evaluation threshold for the next period, and combine with the difference between the evaluation threshold of the current period and the maximum value of all communication indices within the current period to determine the evaluation threshold for the next period. S3: Based on the evaluation threshold for the next period, perform communication switching between the central vehicle and its target vehicles in the next period.

2. The wireless communication method for unmanned vehicles according to claim 1, characterized in that The expression for the adjustment ratio of the shape parameter is as follows: ; where represents the adjustment ratio of the shape parameter between the central vehicle and its target vehicle k at time t; represents the distance from the central vehicle to the target vehicle k at time t; , respectively represent the distance from the obstacle n in the neighborhood of the central vehicle to the central vehicle and the distance from the obstacle n to the target vehicle k at time t; represents the volume of the nth obstacle in the neighborhood of the central vehicle at time t; represents the number of all obstacles in the neighborhood of the central vehicle at time t; norm( ) represents the normalization function.

3. The wireless communication method between driverless vehicles according to claim 2, wherein The method for determining the shape parameter includes: Shape parameters between the central vehicle and its target vehicle k at time t The expression is as follows: ; In the formula, and S respectively represent a preset first value and a preset second value, where the preset first value is greater than the preset second value.

4. The wireless communication method for unmanned vehicles according to claim 1, characterized in that, The method for obtaining the SINR between the central vehicle and each of its target vehicles at each moment is: At each moment, use the communication transmit power in all types of communication parameters of each target vehicle as the scale parameter of the channel model. Among them, use the shape parameter between the central vehicle and each of its target vehicles at each moment as the shape parameter of the channel model to obtain the received power - probability density function of the central vehicle receiving the transmitted signals of each target vehicle within the channel model. Take the mean of the product of all received powers and their corresponding probabilities in the received power - probability density function as the received signal power of the central vehicle receiving each target vehicle. Divide the received signal power of the central vehicle receiving the target signal k by the sum of the received signal powers of the central vehicle and all other target signals except the target signal k, and use the obtained ratio as the SINR between the central vehicle and its target signal k. Traverse all target signals to obtain the SINR between the central vehicle and each of its target signals.

5. The wireless communication method for unmanned vehicles according to claim 1, characterized in that, The process for determining the concentration degree is: Collectively refer to the SINR between the central vehicle and each of its target vehicles, and all types of communication parameters of each target vehicle as the parameters of each target vehicle. Calculate the reciprocal of the standard deviation of each type of parameter of the target vehicle at each moment and within the preset duration before it as the concentration degree of each type of parameter.

6. The wireless communication method for unmanned vehicles according to claim 5, wherein The expression of the communication index between the central vehicle and its target vehicles at each moment is as follows: ; In the formula, represents the communication index between the central vehicle and its target vehicle k at moment t; represents the total number of categories of all class parameters of target vehicle k; represents the entropy weight of the s-th class parameter of all target vehicles at moment t; the normalized value of the s-th class parameter of target vehicle k at moment t; represents the concentration degree of the s-th class parameter of target vehicle k at moment t; represents a preset constant greater than 0.

7. The wireless communication method for unmanned vehicles according to claim 1, characterized in that, The expression for the adjustment coefficient of the next cycle is as follows: ; where represents the adjustment coefficient of the next cycle; Q represents the total number of communication switches of the central vehicle within the current cycle; represents the communication index between the central vehicle and its target vehicle v at time u within the current cycle; represents the maximum value of the communication indices between the central vehicle and all its target vehicles at time u within the current cycle; represents the total number of target vehicles of the central vehicle at time u within the current cycle; represents the total duration of the current cycle; represents the exponential function with the natural constant as the base; norm[ ] represents the normalization function.

8. The wireless communication method for unmanned vehicles according to claim 6, characterized in that The expression for the evaluation threshold of the next cycle is as follows: ; where , represent the evaluation thresholds of the next cycle and the current cycle respectively, where the initial evaluation threshold is a preset third value; represents the maximum value among the communication indices between the central vehicle and all its target vehicles at all times within the current cycle.

9. The wireless communication method for unmanned vehicles according to claim 1, characterized in that, The communication switching between the central vehicle and all its target vehicles in the next cycle includes: Calculating the cumulative sum of the communication indices between the central vehicle and all its target vehicles at each moment in the next cycle, denoted as the communication sum value; taking the target vehicles whose communication indices between the central vehicle and all its target vehicles are greater than the evaluation threshold of the next cycle as feature vehicles, and calculating the sum of the communication indices between the central vehicle and all its feature vehicles at each moment in the next cycle; In the next cycle, if the time when the sum of the communication indices is continuously greater than the communication sum value is greater than the preset time threshold, then disconnect the V2V communication connection between the central vehicle and the remaining target vehicles except the feature vehicles.

10. A wireless communication system for autonomous vehicles, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the wireless communication method for driverless vehicles according to any one of claims 1-9.

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