Ad-hoc network method and device of Internet of Vehicles, storage medium and electronic equipment

Abstract

The embodiment of the invention provides an ad hoc network method and device for the Internet of Vehicles, a storage medium and electronic equipment, and the method comprises the steps: obtaining vehicle behavior data transmitted by a target vehicle in a broadcast range of a main vehicle; the vehicle behavior data is used for representing the driving state of the target vehicle; the target vehicle refers to a specified vehicle in a plurality of vehicles in a broadcast range of the main vehicle; calculating a networking correlation score corresponding to the target vehicle according to navigation path data of the main vehicle in a preset driving interval in the driving direction and the vehicle behavior data; the networking correlation score represents the correlation between the target vehicle and the main vehicle in the networking process of the Internet of Vehicles; and under the condition that the networking correlation score is greater than a preset score threshold value, adding the target vehicle into the ad hoc network of the main vehicle. According to the invention, the technical problem of low information transmission efficiency in the Internet of Vehicles is solved.

Description

Technical Field

[0001] The embodiments of the present application relate to the field of intelligent connected vehicles, and more specifically, to a method, device, storage medium and electronic device for self-organizing networking of vehicle networks. Background Art

[0002] C-V2X collaborative driving technology refers to the technology that uses communication and data sharing between vehicles to achieve collaborative driving and intelligent traffic management between vehicles. However, the existing C-V2X technology uses PC5 direct connection to broadcast messages for vehicle-road interconnection used for collaborative driving, which will receive invalid messages that are not relevant to the vehicle itself, and cannot achieve personalized and targeted information transmission, resulting in low information transmission efficiency. Summary of the invention

[0003] The embodiments of the present application provide a method, device, storage medium and electronic device for self-organizing a vehicle network, so as to at least solve the technical problem of low efficiency of information transmission in the vehicle network in the related art.

[0004] According to one aspect of an embodiment of the present application, a method for self-organizing networking of an Internet of Vehicles is provided, comprising: obtaining vehicle behavior data sent by a target vehicle within a broadcast range of a main vehicle; the vehicle behavior data is used to characterize the driving state of the target vehicle; the target vehicle refers to a specified one of multiple vehicles within the broadcast range of the main vehicle; based on navigation path data of a preset driving interval in a driving direction of the main vehicle and the vehicle behavior data, a networking correlation score corresponding to the target vehicle is calculated; the networking correlation score characterizes the correlation between the target vehicle and the main vehicle during the Internet of Vehicles networking process; when the networking correlation score is greater than a preset score threshold, the target vehicle is added to the self-organizing network of the main vehicle.

[0005] According to another aspect of an embodiment of the present application, a vehicle network self-organizing network device is also provided, including: a data acquisition module, used to acquire vehicle behavior data sent by a target vehicle within the broadcast range of a main vehicle; the vehicle behavior data is used to characterize the driving state of the target vehicle; the target vehicle refers to a specified one of multiple vehicles within the broadcast range of the main vehicle; a correlation evaluation module, used to calculate the networking correlation score corresponding to the target vehicle based on the navigation path data of a preset driving interval in the driving direction of the main vehicle and the vehicle behavior data; the networking correlation score characterizes the correlation between the target vehicle and the main vehicle during the vehicle network networking process; a networking module, used to add the target vehicle to the self-organizing network of the main vehicle when the networking correlation score is greater than a preset score threshold.

[0006] According to another aspect of the embodiments of the present application, a computer-readable storage medium is provided, in which a computer program is stored, wherein the computer program is configured to execute the steps of any of the above method embodiments when run.

[0007] According to another aspect of the embodiments of the present application, a computer program product or a computer program is provided, the computer program product or the computer program includes computer instructions, the computer instructions are stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device performs the steps in any of the above method embodiments.

[0008] According to another aspect of the embodiments of the present application, there is further provided an electronic device, including a memory and a processor, wherein the memory stores a computer program, and the processor is configured to execute the steps in any one of the above method embodiments through the computer program.

[0009] Through the present application, the host vehicle can calculate the networking relevance score of the target vehicle based on the navigation path data of the preset driving interval of the host vehicle in the driving direction and the vehicle behavior data of the target vehicle, wherein the navigation path data provides the route information of the host vehicle's future driving, and the vehicle behavior data reflects the real-time driving status and behavior pattern of the target vehicle. The navigation path data and the vehicle behavior data are integrated and analyzed, so that the host vehicle can predict whether the target vehicle will interact within its preset driving interval, thereby judging whether the target vehicle should be added to the self-organizing network, optimizing the scope and direction of information transmission, and improving the effectiveness of vehicle network communication; further, according to the networking relevance score, the target vehicles within the broadcast range are actively screened, and the vehicles that are highly relevant to the host vehicle's own driving direction and the preset driving interval are identified, so as to realize the directional transmission of information, rather than just indiscriminately receiving all vehicle information, avoiding the reception and processing of invalid information, solving the problem of low efficiency of information transmission in the vehicle network, and significantly improving the pertinence and efficiency of information transmission. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] Figure 1 It is a schematic diagram of an application scenario of a self-organizing method for an Internet of Vehicles according to an embodiment of the present application;

[0011] Figure 2 It is a flowchart of an optional vehicle networking self-organizing method according to an embodiment of the present application;

[0012] Figure 3 is a schematic structural diagram of an optional vehicle-mounted unit in an embodiment of the present application;

[0013] Figure 4is a flow chart of another optional vehicle networking self-organizing network method according to an embodiment of the present application;

[0014] Figure 5 is a structural block diagram of an optional vehicle networking ad hoc network device according to an embodiment of the present application;

[0015] Figure 6 It is a block diagram of a computer system structure of an optional electronic device according to an embodiment of the present application. DETAILED DESCRIPTION

[0016] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present application.

[0017] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0018] According to one aspect of an embodiment of the present application, a vehicle network ad hoc networking method is provided. Optionally, in this embodiment, the vehicle network ad hoc networking method can be applied to, but is not limited to, Figure 1 The hardware environment shown includes a host vehicle 102 and a target vehicle 104. The target vehicle 104 can be connected to the host vehicle 102 via a network.

[0019] The above network may include but is not limited to at least one of the following: wired network, wireless network. The above wired network may include but is not limited to at least one of the following: wide area network, metropolitan area network, local area network, and the above wireless network may include but is not limited to at least one of the following: WIFI (Wireless Fidelity), Bluetooth.

[0020] The vehicle networking self-organizing method of the embodiment of the present application can be executed by the host vehicle 102, or by the target vehicle 104, or by both the host vehicle 102 and the target vehicle 104. The host vehicle 102 can also execute the vehicle networking self-organizing method of the embodiment of the present application by a client installed thereon.

[0021] Taking the target vehicle 104 as an example to execute the vehicle network self-organizing networking method in this embodiment, the main vehicle 102 transmits the navigation path data of the preset driving interval in its driving direction to the target vehicle 104 by broadcasting, and the target vehicle 104 receives the navigation path data and obtains its own vehicle behavior data, and then calculates the networking relevance score corresponding to the target vehicle based on the navigation path data and vehicle behavior data of the preset driving interval in the driving direction of the main vehicle. When the networking relevance score is greater than the preset score threshold, the target vehicle is added to the self-organizing network of the main vehicle.

[0022] Taking the vehicle 102 as an example to execute the vehicle networking self-organizing method in this embodiment, Figure 2 is a flow chart of an optional vehicle networking self-organizing method according to an embodiment of the present application, such as Figure 2 As shown, the process of the method may include the following steps:

[0023] Step S202, obtaining vehicle behavior data sent by a target vehicle within the broadcast range of the host vehicle; the vehicle behavior data is used to characterize the driving state of the target vehicle; the target vehicle refers to a specified vehicle among multiple vehicles within the broadcast range of the host vehicle.

[0024] The vehicle network self-organizing network method in this embodiment can be applied to the field of intelligent connected vehicles to improve the safety and efficiency of collaborative driving between vehicles, especially in complex road environments and traffic conditions, by improving the communication range and information transmission efficiency, thereby improving vehicle decision-making capabilities.

[0025] Among them, in the vehicle networking self-organizing method, the main vehicle refers to the vehicle that initiates the networking process, which broadcasts self-vehicle messages through the on-board unit (OBU), including its current position, heading angle, and part of the navigation path information. The broadcast range refers to the maximum effective distance that a vehicle (main vehicle or remote vehicle) can cover when broadcasting information using the vehicle communication system. The target vehicle is one of all potential vehicles within the communication range of the main vehicle. Their information is collected by the main vehicle. After subsequent screening by the algorithm, the vehicles with scores higher than the threshold will be identified as vehicles with networking potential and become the target of directional information transmission. In the embodiment of the present application, the main vehicle is denoted as m and the target vehicle is denoted as n.

[0026] Vehicle behavior data refers to information that can characterize the driving status of the target vehicle, including but not limited to the current position, speed, acceleration, heading angle, and environmental perception data of the target vehicle. Vehicle behavior data is used to evaluate the cooperative driving potential of the target vehicle and the host vehicle, and to determine the priority and direction of information transmission, thereby improving the efficiency of information transmission and the safety and efficiency of traffic under the conditions of vehicle networking.

[0027] Optionally, the host vehicle m communicates with a target vehicle n within the broadcast range to obtain vehicle behavior data sent by the target vehicle n.

[0028] Step S204, calculating the networking relevance score corresponding to the target vehicle based on the navigation path data and vehicle behavior data of the preset driving interval in the driving direction of the host vehicle; the networking relevance score represents the relevance between the target vehicle and the host vehicle in the Internet of Vehicles networking process.

[0029] The communication between the host vehicle and the target vehicle is realized through an on-board unit (OBU). It can be understood that both the host vehicle and the target vehicle include an on-board unit OBU. Figure 3 is a schematic diagram of the structure of an optional vehicle-mounted unit in an embodiment of the present application, such as Figure 3 As shown, the on-board unit OBU includes an inter-vehicle communication module, an information processing module and a positioning module. The inter-vehicle communication module is connected to the information processing unit and is used to receive and send vehicle information. The information processing module calculates the information transmission weight of the target vehicle based on the navigation path data of the preset driving interval in the driving direction of the main vehicle and the vehicle behavior data of the target vehicle, determines the networking relevance between the target vehicle and the main vehicle, and controls the sending direction and content of the information based on the calculation results, and determines which information needs to be transmitted to the target vehicle in a directional manner to improve the efficiency and accuracy of information transmission. The positioning module is used to obtain the real-time location information of the vehicle and measure the heading angle of the vehicle. Combined with the vehicle positioning information, it is used to evaluate the relative position and direction between vehicles, thereby determining the priority of information transmission.

[0030] The on-board unit OBU of the main vehicle and the on-board unit OBU of the target vehicle send self-vehicle messages in the form of broadcasts, and receive vehicle-side messages within the communication range. Within the broadcast range of the main vehicle, in addition to basic V2X messages, the main vehicle sends navigation path data containing the preset driving interval of the main vehicle in the driving direction to the target vehicle. The preset driving interval of the main vehicle in the driving direction refers to the estimated driving range of the main vehicle on the navigation path, which is usually based on the current position and driving direction of the main vehicle, combined with the driving speed and expected range of the vehicle, to calculate the driving range of the main vehicle in the future. The navigation path data of the preset driving interval refers to the preset driving route information of the vehicle, which includes a series of path point coordinates arranged in time or distance order. For example, the navigation path data of the preset driving interval of the main vehicle in the driving direction can be 3 kilometers of navigation path data in the driving direction of the main vehicle.

[0031] The networking relevance score is a quantitative indicator used to evaluate the degree of relevance between the target vehicle and the host vehicle in the process of networking of the Internet of Vehicles. It is calculated based on the degree of match between the current position and heading angle of the target vehicle and the navigation path data of the preset driving range of the host vehicle. The higher the score, the more relevant the target vehicle is to the host vehicle in information transmission, and therefore it is more likely to become the object of directional information transmission by the host vehicle, thereby improving the efficiency of the Internet of Vehicles self-organizing network and the accuracy of information transmission.

[0032] For example, the main vehicle actively broadcasts the route information planned for the next period of time through its on-board unit OBU, that is, the navigation path data of the preset driving range. After receiving this data, the target vehicle feeds back the current vehicle behavior data of the target vehicle (including position, heading angle, speed, acceleration and driving target, etc.). The main vehicle calculates the matching degree of the target vehicle's position trend with its own according to the current vehicle behavior data of the target vehicle, evaluates the potential intersection probability of the two vehicles, and considers whether the target vehicle has the same driving target or destination as the main vehicle, so as to determine the level of the network relevance score. If the target vehicle's driving route overlaps geographically with the preset driving range of the main vehicle, or the two have a common driving direction, the target vehicle will be given a higher score, which indicates that it has significant value to the information needs of the main vehicle, and naturally becomes the priority object of the main vehicle's directional information transmission, which helps the main vehicle to plan the route in advance and deal with potential traffic conditions.

[0033] For example, in the IoV system, the host vehicle not only broadcasts the navigation path data of its preset driving range, but also collects and analyzes the driving behavior patterns of the target vehicles (such as frequent lane changes, acceleration and deceleration habits) and real-time traffic prediction information based on the road network. The host vehicle conducts deep learning and analysis of the driving behavior patterns of the target vehicles through the on-board unit OBU, and predicts whether the target vehicle exhibits a driving style similar to that of the host vehicle, or whether it may affect the driving safety or efficiency of the host vehicle under specific traffic conditions (such as congestion, traffic accidents). At the same time, combined with traffic prediction information, the host vehicle evaluates the potential interaction value of the target vehicle in its preset driving range, such as whether the target vehicle indicates a traffic congestion trend ahead, or whether it is located in an expected high-traffic area. Based on these analyses, the host vehicle calculates the networking relevance score of the target vehicle. High-scoring target vehicles represent that they have important information value to the host vehicle in the current or future traffic environment, and are therefore preferred by the host vehicle as the recipient of directional information transmission, which helps the host vehicle make timely and accurate driving decisions and improve driving safety and traffic efficiency.

[0034] Optionally, the main vehicle obtains navigation path data of a preset driving interval in its driving direction, and vehicle behavior data sent by the target vehicle, and calculates the networking relevance score corresponding to the target vehicle based on the navigation path data and vehicle behavior data of the preset driving interval in the driving direction of the main vehicle according to a preset networking relevance score calculation method.

[0035] Step S206: When the network correlation score is greater than a preset score threshold, the target vehicle is added to the main vehicle's ad hoc network.

[0036] Optionally, when the networking correlation score is greater than a preset score threshold, the host vehicle establishes a connection with the target vehicle, adds the target vehicle to the host vehicle's ad hoc network, and updates the topology of the ad hoc network.

[0037] Through the embodiments provided by the present application, the host vehicle can calculate the networking relevance score of the target vehicle based on the navigation path data of the preset driving interval of the host vehicle in the driving direction and the vehicle behavior data of the target vehicle, wherein the navigation path data provides the route information of the future driving of the host vehicle, and the vehicle behavior data reflects the real-time driving status and behavior pattern of the target vehicle. The navigation path data and the vehicle behavior data are integrated and analyzed, so that the host vehicle can predict whether the target vehicle will interact within its preset driving interval, thereby judging whether the target vehicle should be added to the self-organizing network, optimizing the scope and direction of information transmission, and improving the effectiveness of vehicle network communication; further, according to the networking relevance score, the target vehicles within the broadcast range are actively screened, and the vehicles that are highly relevant to the own driving direction and the preset driving interval are identified, so as to realize the directional transmission of information, rather than just receiving all vehicle information indiscriminately, avoiding the reception and processing of invalid information, solving the problem of low efficiency of information transmission in the vehicle network, and significantly improving the pertinence and efficiency of information transmission.

[0038] In an exemplary embodiment, the vehicle behavior data includes the current position and heading angle of the target vehicle, wherein the current position and heading angle of the target vehicle can be obtained by a positioning module in an on-board unit (OBU) of the target vehicle.

[0039] In some embodiments, the network relevance score corresponding to the target vehicle is calculated based on the navigation path data and vehicle behavior data of the preset driving interval in the driving direction of the host vehicle, including:

[0040] Based on the current position and navigation path data of the target vehicle, determine the distance score of the target vehicle; the distance score represents the spatial proximity between the current position of the target vehicle and the navigation path data; determine the heading angle similarity score between the heading angle of the target vehicle and the heading angle of the host vehicle; based on the distance score and the heading angle similarity score, determine the networking relevance score corresponding to the target vehicle.

[0041] Among them, the distance score is a quantitative indicator used to evaluate the spatial proximity between the current position of the target vehicle and the navigation path data of the preset driving range of the main vehicle. In the vehicle networking self-organizing network scenario, the distance score directly reflects whether the target vehicle is near the future driving path of the main vehicle and its potential impact on the driving decision of the main vehicle. The higher the distance score, the closer the position of the target vehicle is to the future driving route of the main vehicle, and therefore the more likely it is to have a direct impact on the information needs of the main vehicle, thus becoming an important object of networking.

[0042] For example, the distance score of the target vehicle can be determined based on the spatial distance evaluation method of the geographic information system. Specifically, after the host vehicle broadcasts the navigation path data of its preset driving interval, the host vehicle uses the geographic information system (GIS) module in the on-board unit OBU to analyze the geographical distance relationship between the current position of the target vehicle and the navigation path in real time. The GIS module can accurately calculate the straight-line distance between the target vehicle and any point on the navigation path, and then generate a distance distribution map based on these distance data. The host vehicle further analyzes the shortest distance point between the target vehicle and the navigation path in the distance distribution map and uses it as the basis for the distance score. If the shortest distance point of the target vehicle is very close to the navigation path (such as within the preset distance range), or even within the future driving interval of the navigation path, this means that the target vehicle is likely to be on or near the driving path of the host vehicle, so the target vehicle will obtain a higher distance score. This high score indicates that the information of the target vehicle has extremely high potential value to the host vehicle, which helps the host vehicle to plan the path or adjust the driving strategy in advance to cope with possible changes in traffic conditions.

[0043] For example, the distance score of the target vehicle can also be determined by combining the dynamic distance calculation method with the traffic network topology. Specifically, while broadcasting its navigation path data, the main vehicle analyzes the current position of the target vehicle and performs dynamic distance calculation in combination with the current traffic network topology. The traffic network topology includes information such as the connectivity of the road network, intersections, and traffic light locations. The main vehicle estimates the time required for the target vehicle to reach the navigation path of the preset driving range based on the current driving direction and speed through the traffic prediction algorithm in the on-board unit OBU. If the target vehicle is expected to enter the preset driving range of the main vehicle soon, or its driving path has an intersection with the navigation path of the main vehicle at some point in the future, the distance score of the target vehicle will increase.

[0044] The heading angle similarity score is an indicator that evaluates the consistency of the target vehicle's and the host vehicle's heading angles by comparing them. In the VIMO ad hoc network, the heading angle similarity score reflects whether the target vehicle and the host vehicle are traveling in the same or similar direction, and whether they are likely to meet or intersect in the future driving path. The higher the score, the more consistent the target vehicle's driving direction is with the host vehicle, so it has more information transmission value in the ad hoc network, which helps the host vehicle obtain traffic information related to the driving direction in advance and improves the accuracy and safety of decision-making.

[0045] Optionally, the host vehicle determines the distance score of the target vehicle based on the current position and navigation path data of the target vehicle, using a spatial distance evaluation method based on a geographic information system or a dynamic distance calculation method based on the traffic network topology. The distance score is recorded as N d, the heading angle similarity score between the heading angle of the target vehicle and the heading angle of the host vehicle is calculated according to the following formula (1), and the heading angle similarity score is recorded as N θ , weighted fusion of distance score and heading angle similarity score is performed to obtain the network correlation score corresponding to the target vehicle, and the network correlation score is recorded as N n .

[0046]

[0047] Among them, θ m Indicates the heading angle of the main vehicle, θ n Indicates the heading angle of the target vehicle.

[0048] Through this embodiment, the distance score is defined as a quantitative indicator for evaluating the spatial proximity between the current position of the target vehicle and the navigation path data of the host vehicle, so that the host vehicle can accurately evaluate the information value of the target vehicle for its own driving decision based on the consistency between the actual position of the target vehicle and the future driving path. The higher the distance score, the higher the direct relevance and urgency of the information of the target vehicle to the host vehicle, so that it becomes the object of priority communication, avoiding the transmission and processing of invalid information, and significantly improving the efficiency and pertinence of information transmission; by calculating the heading angle similarity score, the host vehicle can determine whether the target vehicle is traveling in a similar direction, so as to decide whether to include it in the self-organizing network, further improving the accuracy and efficiency of information transmission; the distance score and the heading angle similarity score are comprehensively considered to evaluate the relevance between the target vehicle and the host vehicle in the process of the vehicle network self-organizing network in a quantitative manner, not only considering the instantaneous position value of the target vehicle, but also considering the consistency of the vehicle's driving direction, thereby ensuring the comprehensiveness and scientificity of information transmission. Target vehicles with high networking relevance scores will be given priority to join the main vehicle's self-organizing network, realizing targeted and efficient transmission of information, solving the problems of excessive information broadcasting and passive screening of receivers in related technologies, and improving the information transmission efficiency of the intelligent transportation system.

[0049] In an exemplary embodiment, determining a distance score of a target vehicle based on the current position of the target vehicle and navigation path data includes:

[0050] The Euclidean distance between the current position of the target vehicle and the navigation path data is determined; the Euclidean distance represents the positional relationship between the current position of the target vehicle and the navigation path data; the Euclidean distance is normalized to obtain a distance score of the target vehicle.

[0051] Among them, the Euclidean distance is used to measure the positional relationship between the current position of the target vehicle and the navigation path data of the preset driving range of the host vehicle. By calculating the Euclidean distance between the target vehicle and the closest point on the navigation path, it can be judged whether the target vehicle is close to the future driving path of the host vehicle. The smaller the distance, the closer the target vehicle is to the driving path of the host vehicle, and the greater the impact on the driving decision of the host vehicle.

[0052] Direct comparison of Euclidean distances may be affected by the spatial scale, resulting in an inability to fairly evaluate the relative importance of target vehicles at different locations. To solve this problem, in this embodiment, the Euclidean distance is normalized, and the distance score obtained is a standardized indicator that eliminates the spatial scale difference, so that the positional relationship of all target vehicles can be compared under a unified framework. The distance score obtained after normalization more intuitively reflects the relative proximity of the target vehicle to the navigation path of the main vehicle, thereby providing the main vehicle with a more objective and reasonable basis for selecting the communication object, ensuring the efficiency and accuracy of information transmission. Common normalization methods include Gaussian function normalization and linear normalization.

[0053] Optionally, the current position of the target vehicle is denoted as P n =(x n ,y n ), the straight line equation of the navigation path data of the preset driving interval set by the host vehicle in the driving direction can be recorded as Ax+By+C=0, then the Euclidean distance D between the current position of the target vehicle and the navigation path data n It can be calculated using the following formula (2):

[0054]

[0055] Normalize the Euclidean distance to get the distance score N of the target vehicle d .

[0056] Through this embodiment, the Euclidean distance is introduced to quantify the positional relationship between the current position of the target vehicle and the navigation path data of the host vehicle, so that the host vehicle can accurately identify which target vehicles' current positions are closely related to its own navigation path, thereby giving priority to establishing communication connections with these vehicles, avoiding blind broadcasting of information, and improving the pertinence and efficiency of information transmission.

[0057] In an exemplary embodiment, the Euclidean distance is normalized to obtain a distance score of the target vehicle, including:

[0058] A first difference between a preset maximum Euclidean distance and the Euclidean distance, and a second difference between the maximum Euclidean distance and a preset minimum Euclidean distance are calculated; and a ratio between the first difference and the second difference is determined as a distance score of the target vehicle.

[0059] Optionally, the host vehicle sets the maximum value D of the Euclidean distance of the navigation path data of the target vehicle relative to the preset driving interval in the driving direction of the host vehicle. max and minimum value D min , calculate a first difference between a preset maximum Euclidean distance and the Euclidean distance, and a second difference between the maximum Euclidean distance and a preset minimum Euclidean distance; and determine the ratio between the first difference and the second difference as the distance score of the target vehicle. The distance score N of the target vehicle d The calculation formula can be expressed by the following formula (3):

[0060]

[0061] Through this embodiment, the first difference between the maximum Euclidean distance and the Euclidean distance, and the second difference between the maximum Euclidean distance and the preset minimum Euclidean distance are calculated, and the ratio between the first difference and the second difference is determined as the distance score of the target vehicle. In this way, all distance scores are scaled to the interval [0,1], where 0 represents the farthest distance and 1 represents the closest distance, which is convenient for intuitively evaluating the proximity between the target vehicle and the future driving path of the host vehicle.

[0062] In an exemplary embodiment, determining a networking relevance score corresponding to a target vehicle according to a distance score and a heading angle similarity score includes:

[0063] According to the preset weights, the distance score and the heading angle similarity score are weighted and summed to obtain the networking relevance score corresponding to the target vehicle.

[0064] The purpose of weighted summation is to comprehensively evaluate the networking value between the target vehicle and the host vehicle. It reflects the relative importance of spatial position and driving direction in self-organizing network decision-making by assigning different weights to the distance score and heading angle similarity score. The distance score weight is α, the heading angle similarity score weight is β, and α+β=1 (the values ​​of α and β can be dynamically adjusted according to actual application requirements). The distance score of the target vehicle n is D n , the heading angle similarity score is N θ , then the network relevance score N of target vehicle n n It can be calculated by the following formula: N n =αD n +βN θ For example, α is 0.7, β is 0.3, and the network relevance score N of target vehicle n is n It can be expressed as shown in the following formula (4):

[0065] N n =0.7*N d+0.3*N θ (4)

[0066] Through this embodiment, the distance score and the heading angle similarity score are weighted and summed, which can not only quantify the correlation between the target vehicle and the main vehicle in terms of spatial position and driving direction, but also flexibly adjust the relative weights of the spatial position and driving direction in the networking decision according to application requirements, thereby achieving more accurate and efficient information transmission and self-organizing networking.

[0067] In an exemplary embodiment, the vehicle-road interconnection used for cooperative driving in the existing C-V2X technology broadcasts messages in a PC5 direct connection mode. The effective communication distance of PC5 communication in an unobstructed environment is about 1,200 meters, and the effective communication distance in an actual road environment is only 400 to 500 meters or even shorter. Therefore, in order to solve the problem of limited communication distance, after adding the target vehicle to the main vehicle's self-organizing network, the above-mentioned vehicle network self-organizing network method also includes:

[0068] The target vehicle is taken as the new main vehicle, and based on the new main vehicle, the steps of obtaining the vehicle behavior data sent by the target vehicle within the broadcast range of the main vehicle are continued until the preset condition is met, the networking is stopped, and the target networking is obtained; the preset condition refers to the condition that the distance score is less than or equal to the preset threshold.

[0069] Among them, the preset condition is the rule used in the vehicle network self-organizing algorithm to determine when to stop the networking process. In this embodiment, the preset condition specifically refers to the situation where the distance score of the target vehicle is less than or equal to the preset threshold, wherein the preset threshold can be set according to actual needs. For example, the preset threshold can be set to 0, and the distance score of the target vehicle is less than or equal to 0, indicating that the navigation path data of the preset driving interval in the driving direction of the selected main vehicle has been covered, which means that the self-organizing network has successfully extended to the end of the expected driving interval of the main vehicle, and all target vehicles along the way have been included in the network, thereby ensuring that the main vehicle can obtain key information on the entire driving path, meeting the basic needs of collaborative driving and intelligent traffic management. The preset condition is intended to ensure that the self-organizing network will not expand indefinitely, but selectively include target vehicles that are highly related to the driving path and direction of the main vehicle into the network, so as to control the network scale and avoid excessive consumption of resources while ensuring the efficiency of information transmission.

[0070] When the target vehicle n to be networked is found within the primary broadcast range of the main vehicle, the main vehicle m establishes a connection with the target vehicle n, and the target vehicle n becomes the next hop of the new main vehicle m network. At this time, the target vehicle n becomes the new main vehicle m and continues to search for the next hop of the network within its primary broadcast range, that is, calculate the other remote vehicles i within the broadcast range of the currently networked remote vehicle, and calculate the networking relevance score N of the remote vehicle i i, select the remote vehicle i with a networking relevance score higher than the threshold as the next hop vehicle, and establish a communication connection with the remote vehicle i that meets the conditions. After the connection is successfully established, update the networking information, add the remote vehicle i to the network, and update the network topology and other information. Repeat the above steps within the broadcast range of the remote vehicle after networking, and continue to search for the next hop networking remote vehicle object. Repeat the above steps until the preset condition is met (that is, the distance score N of the remote vehicle within the broadcast range is greater than the threshold). d When both are 0) or there is no next-hop vehicle, the networking is terminated and the networking topology and other information are updated. The remote vehicles within the networking range can transmit road information, traffic information, perception information, etc. to the main vehicle in real time.

[0071] The target network is a self-organizing network formed by gradually expanding and stopping according to preset conditions during the process of self-organizing networking of the Internet of Vehicles. It includes the main vehicle and a series of target vehicles selected based on the evaluation of navigation paths and vehicle behavior data.

[0072] Optionally, Figure 4 is a flow chart of another optional vehicle networking self-organizing method according to an embodiment of the present application, such as Figure 4 As shown, the host vehicle m calculates the network relevance score N of the target vehicle n within its broadcast range. n , if N n Greater than the preset score threshold (denoted as N v ), the host vehicle m establishes a communication connection with the target vehicle n and updates the network topology; when the host vehicle m successfully establishes a communication connection with the target vehicle n and incorporates it into the network, the target vehicle n will be converted into the new host vehicle m and continue to execute the steps of obtaining the vehicle behavior data sent by the target vehicles within the broadcast range of the new host vehicle m until the preset conditions (such as N d =0), stop networking, obtain the target networking, and update the networking topology.

[0073] Through this embodiment, when the main vehicle successfully establishes a communication connection with the target vehicle, the target vehicle is converted into the new main vehicle and continues to search for the next hop networking object within its broadcast range until the preset condition (such as the distance score is less than or equal to the preset threshold) is met. The innovation of this chain networking mechanism is that it realizes the dynamic expansion of the self-organizing network through the relay method between vehicles, so that information can be continuously transmitted along the vehicle link on the navigation path, breaking through the physical limitation of the communication distance of a single vehicle, effectively overcoming the defect of the limited communication range of traditional C-V2X technology, and improving the continuity and breadth of information transmission.

[0074] In an exemplary embodiment, the topology of the target network is updated to each vehicle in the target network, so that the multiple vehicles in the target network can transmit real-time environmental information to each other.

[0075] Optionally, after determining the target network, the new main vehicle generates the latest network topology diagram based on the topology of the target network. The network topology diagram records in detail the position of each vehicle in the network and its connection relationship with adjacent vehicles. The updated topology is broadcast to all vehicles in the network through the inter-vehicle communication module of the on-board communication system (such as the on-board unit OBU) to ensure that each vehicle has the latest network status information. After receiving the updated topology, each vehicle updates its locally stored network topology data. Each vehicle in the target network collects surrounding environment information in real time through its environmental perception system (such as camera, radar, laser radar, etc.). The collected information is encoded and converted into a standard communication format for transmission between vehicles. Each vehicle determines the path and target node of its information transmission in the self-organizing network based on the latest topology. Information transmission follows the shortest path principle or chain transmission mechanism, and the encoded real-time environmental information is sent to the next hop node through the communication module of the on-board unit OBU. After receiving the information, the receiving vehicle decodes it and processes it locally or forwards it further as needed.

[0076] Through this embodiment, the topology of the target network is updated to each vehicle in the target network to achieve sharing of real-time environmental information, so that the vehicles can timely understand the traffic conditions and potential risks on the road and make safe driving decisions quickly.

[0077] In an exemplary embodiment, in a vehicle networking collaborative driving scenario, the traditional single broadcast strategy often cannot take into account the quality, efficiency and energy consumption of communication when dealing with network load and communication distance, especially in high-load areas, where broadcasting with a single frequency and power is prone to information overload, signal interference and energy waste. Therefore, to solve the above problems, this embodiment can dynamically adjust the broadcast frequency and power according to the vehicle's position in the network, communication distance and network load to optimize the communication performance of the vehicle networking.

[0078] Before the designated vehicle in the target network sends a message, the positioning module of the designated vehicle's on-board unit OBU is used to identify the target location of the designated vehicle in the target network, and the target broadcast frequency of the designated vehicle is adjusted according to the category of the area where the target location is located. The target broadcast power of the designated vehicle is determined according to the communication distance of the designated vehicle and the network load. The designated vehicle sends the message according to the target broadcast frequency and target broadcast power.

[0079] This category includes network core areas (such as intersections, highway entrances / exits, congested road sections, etc.) and network edge areas. Different categories of areas correspond to different broadcast frequencies. For example, the frequency of the core area is 10 broadcasts per second, while the frequency of the non-core area is reduced to 3 broadcasts per second.

[0080] This embodiment dynamically adjusts the broadcast power of the vehicle-mounted unit according to the network load and communication distance. For example, when the network load is high, not only the broadcast frequency is reduced, but also the power is intelligently adjusted according to the distance of the remote vehicle and the communication quality to ensure the transmission quality of key information while reducing the additional burden on the network.

[0081] Through this embodiment, a mechanism can automatically adjust the broadcast frequency and power according to the vehicle position and network load, ensuring that the vehicle can adopt the optimal communication strategy in different scenarios, thereby improving the communication efficiency and stability of the entire Internet of Vehicles.

[0082] It should be noted that, for the aforementioned method embodiments, for the sake of simplicity, they are all expressed as a series of action combinations, but those skilled in the art should be aware that the present application is not limited by the described order of actions, because according to the present application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required by the present application.

[0083] Through the description of the above implementation methods, those skilled in the art can clearly understand that the method according to the above embodiment can be implemented by means of software plus a necessary general hardware platform, and of course it can also be implemented by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present application, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as ROM (Read-Only Memory) / RAM (Random Access Memory), a disk, or an optical disk), and includes a number of instructions for a terminal device (which can be a mobile phone, a computer, a server, or a network device, etc.) to execute the methods described in each embodiment of the present application.

[0084] According to another aspect of the embodiments of the present application, a vehicle networking self-organizing network device is also provided, which can be used to implement the vehicle networking self-organizing network method provided in the above embodiments, and will not be repeated hereafter. As used below, the term "module" can implement a combination of software and / or hardware for a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, the implementation of hardware, or a combination of software and hardware, is also possible and conceivable.

[0085] Figure 5 is a structural block diagram of an optional vehicle networking ad hoc network device according to an embodiment of the present application, such as Figure 5 As shown in , the Internet of Vehicles self-organizing network device includes:

[0086] The data acquisition module 502 is used to acquire vehicle behavior data sent by a target vehicle within the broadcast range of the host vehicle; the vehicle behavior data is used to characterize the driving state of the target vehicle; the target vehicle refers to a specified vehicle among multiple vehicles within the broadcast range of the host vehicle;

[0087] The correlation evaluation module 504 is used to calculate the networking correlation score corresponding to the target vehicle based on the navigation path data and vehicle behavior data of the preset driving interval in the driving direction of the host vehicle; the networking correlation score represents the correlation between the target vehicle and the host vehicle in the process of networking of the Internet of Vehicles;

[0088] The networking module 506 is used to add the target vehicle to the self-organizing network of the host vehicle when the networking correlation score is greater than a preset score threshold.

[0089] It should be noted that the data acquisition module 502 in this embodiment can be used to execute the above step S202, the correlation evaluation module 504 in this embodiment can be used to execute the above step S204, and the networking module 506 in this embodiment can be used to execute the above step S206.

[0090] Through the embodiments provided by the present application, the host vehicle can calculate the networking relevance score of the target vehicle based on the navigation path data of the preset driving interval of the host vehicle in the driving direction and the vehicle behavior data of the target vehicle, wherein the navigation path data provides the route information of the future driving of the host vehicle, and the vehicle behavior data reflects the real-time driving status and behavior pattern of the target vehicle. The navigation path data and the vehicle behavior data are integrated and analyzed, so that the host vehicle can predict whether the target vehicle will interact within its preset driving interval, thereby judging whether the target vehicle should be added to the self-organizing network, optimizing the scope and direction of information transmission, and improving the effectiveness of vehicle network communication; further, according to the networking relevance score, the target vehicles within the broadcast range are actively screened, and the vehicles that are highly relevant to the own driving direction and the preset driving interval are identified, so as to realize the directional transmission of information, rather than just receiving all vehicle information indiscriminately, avoiding the reception and processing of invalid information, solving the problem of low efficiency of information transmission in the vehicle network, and significantly improving the pertinence and efficiency of information transmission.

[0091] In an exemplary embodiment, the vehicle behavior data includes the current position and heading angle of the target vehicle, and the correlation evaluation module 504 is further used to determine the distance score of the target vehicle based on the current position and navigation path data of the target vehicle; the distance score represents the spatial proximity between the current position of the target vehicle and the navigation path data; determine the heading angle similarity score between the heading angle of the target vehicle and the heading angle of the host vehicle; and determine the networking correlation score corresponding to the target vehicle based on the distance score and the heading angle similarity score.

[0092] In an exemplary embodiment, the correlation evaluation module 504 is also used to determine the Euclidean distance between the current position of the target vehicle and the navigation path data; the Euclidean distance characterizes the positional relationship between the current position of the target vehicle and the navigation path data; the Euclidean distance is normalized to obtain a distance score of the target vehicle.

[0093] In an exemplary embodiment, the correlation evaluation module 504 is further used to calculate a first difference between a preset maximum Euclidean distance and the Euclidean distance, and a second difference between the maximum Euclidean distance and a preset minimum Euclidean distance; and determine the ratio between the first difference and the second difference as the distance score of the target vehicle.

[0094] In an exemplary embodiment, the correlation evaluation module 504 is further configured to perform weighted summation of the distance score and the heading angle similarity score according to a preset weight to obtain a networking correlation score corresponding to the target vehicle.

[0095] In an exemplary embodiment, after the target vehicle is added to the self-organizing network of the main vehicle, the networking module 506 is also used to use the target vehicle as the new main vehicle, and based on the new main vehicle, continue to execute the step of obtaining the vehicle behavior data sent by the target vehicle within the broadcast range of the main vehicle until the preset condition is met, then stop networking and obtain the target network; the preset condition refers to the condition that the distance score is less than or equal to the preset threshold.

[0096] In an exemplary embodiment, the networking module 506 is further configured to update the topology of the target network to each vehicle in the target network, so that multiple vehicles in the target network can mutually transmit real-time environmental information.

[0097] It should be noted that the above modules can be implemented by software or hardware. For the latter, it can be implemented in the following ways, but not limited to: the above modules are all located in the same processor; or the above modules are located in different processors in any combination.

[0098] According to another aspect of the embodiments of the present application, a computer-readable storage medium is provided, wherein the computer-readable storage medium includes a stored program, wherein the steps of any of the above method embodiments are executed when the program is run.

[0099] In an exemplary embodiment, the computer-readable storage medium may include, but is not limited to, various media capable of storing computer programs, such as a USB flash drive, a ROM, a RAM, a mobile hard disk, a magnetic disk, or an optical disk.

[0100] According to another aspect of the embodiments of the present application, an electronic device is provided, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor is configured to execute the steps in any of the above method embodiments through the computer program. In an exemplary embodiment, the electronic device may further comprise a transmission device and an input / output device, wherein the transmission device is connected to the processor, and the input / output device is connected to the processor.

[0101] For specific examples in this embodiment, reference may be made to the examples described in the above embodiments and exemplary implementation modes, and this embodiment will not be described in detail herein.

[0102] According to another aspect of the embodiment of the present application, a computer program product is also provided, which includes a computer program / instruction, and the computer program / instruction contains a program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from the network through the communication part 609, and / or installed from the removable medium 611. When the computer program is executed by the central processing unit 601, various functions provided by the embodiment of the present application are executed. The above-mentioned serial numbers of the embodiments of the present application are only for description and do not represent the advantages and disadvantages of the embodiments.

[0103] Figure 6 The computer system structure block diagram of the electronic device used to implement the embodiment of the present application is schematically shown. Figure 6 As shown, the computer system 600 includes a CPU (Central Processing Unit) 601, which can perform various appropriate actions and processes according to the program stored in the ROM 602 or the program loaded from the storage part 608 to the RAM 603. Various programs and data required for system operation are also stored in the random access memory 603. The central processing unit 601, the read-only memory 602 and the random access memory 603 are connected to each other through a bus 604. An I / O (Input / Output) interface 605 is also connected to the bus 604.

[0104] The following components are connected to the I / O interface 605: an input section 606 including a keyboard, a mouse, etc.; an output section 607 including a CRT (Cathode Ray Tube), an LCD (Liquid Crystal Display), etc., and a speaker, etc.; a storage section 608 including a hard disk, etc.; and a communication section 609 including a network interface card such as a LAN card, a modem, etc. The communication section 609 performs communication processing via a network such as the Internet. A drive 610 is also connected to the input / output interface 605 as needed. A removable medium 611, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 610 as needed so that a computer program read therefrom is installed into the storage section 608 as needed.

[0105] In particular, according to an embodiment of the present application, the process described in each method flow chart can be implemented as a computer software program. For example, an embodiment of the present application includes a computer program product, which includes a computer program carried on a computer readable medium, and the computer program contains a program code for executing the method shown in the flow chart. In such an embodiment, the computer program can be downloaded and installed from the network through the communication part 609, and / or installed from the removable medium 611. When the computer program is executed by the central processor 601, various functions defined in the system of the present application are executed.

[0106] It should be noted that Figure 6 The computer system 600 of the electronic device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0107] Obviously, those skilled in the art should understand that the above modules or steps of the present application can be implemented by a general computing device, they can be concentrated on a single computing device, or distributed on a network composed of multiple computing devices, they can be implemented by a program code executable by a computing device, so that they can be stored in a storage device and executed by the computing device, and in some cases, the steps shown or described can be executed in a different order from that herein, or they can be made into individual integrated circuit modules, or multiple modules or steps therein can be made into a single integrated circuit module for implementation. Thus, the present application is not limited to any specific combination of hardware and software.

[0108] The above are only preferred embodiments of the present application and are not intended to limit the present application. For those skilled in the art, the present application may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the principles of the present application shall be included in the protection scope of the present application.

Claims

1. A self-organizing method for an Internet of Vehicles, characterized in that: include: Obtain vehicle behavior data sent by target vehicles within the broadcast range of the host vehicle; The vehicle behavior data is used to characterize the driving state of the target vehicle; The target vehicle refers to a designated vehicle among multiple vehicles within the broadcast range of the host vehicle; Calculating a network relevance score corresponding to the target vehicle based on the navigation path data of the preset driving section of the host vehicle in the driving direction and the vehicle behavior data; The networking correlation score represents the correlation between the target vehicle and the host vehicle during the networking process of the Internet of Vehicles; When the networking relevance score is greater than a preset score threshold, the target vehicle is added to the self-organizing network of the host vehicle.

2. The method according to claim 1, characterized in that The vehicle behavior data includes the current position and heading angle of the target vehicle, and the computing of the network relevance score corresponding to the target vehicle based on the navigation path data of the preset driving interval of the host vehicle in the driving direction and the vehicle behavior data includes: Determining a distance score of the target vehicle according to the current position of the target vehicle and the navigation path data; the distance score represents the degree of spatial proximity between the current position of the target vehicle and the navigation path data; Determining a heading angle similarity score between the heading angle of the target vehicle and the heading angle of the host vehicle; A networking relevance score corresponding to the target vehicle is determined according to the distance score and the heading angle similarity score.

3. The method according to claim 2, characterized in that Determining the distance score of the target vehicle according to the current position of the target vehicle and the navigation path data includes: Determining a Euclidean distance between a current position of the target vehicle and the navigation path data; the Euclidean distance characterizes a positional relationship between the current position of the target vehicle and the navigation path data; The Euclidean distance is normalized to obtain a distance score of the target vehicle.

4. The method according to claim 3, characterized in that The normalizing the Euclidean distance to obtain the distance score of the target vehicle includes: Calculating a first difference between a preset maximum Euclidean distance and the Euclidean distance, and a second difference between the maximum Euclidean distance and a preset minimum Euclidean distance; The ratio between the first difference and the second difference is determined as the distance score of the target vehicle.

5. The method according to claim 2, characterized in that: The determining, according to the distance score and the heading angle similarity score, a networking relevance score corresponding to the target vehicle includes: According to preset weights, the distance score and the heading angle similarity score are weighted and summed to obtain a networking relevance score corresponding to the target vehicle.

6. The method according to claim 2, characterized in that After adding the target vehicle to the ad hoc network of the host vehicle, the method further includes: The target vehicle is taken as a new main vehicle, and based on the new main vehicle, the step of obtaining the vehicle behavior data sent by the target vehicle within the broadcast range of the main vehicle is continued until a preset condition is met, and the networking is stopped to obtain the target networking; the preset condition refers to the condition that the distance score is less than or equal to a preset threshold.

7. The method according to claim 6, characterized in that The method further includes: updating the topology structure of the target network to each vehicle in the target network, so that multiple vehicles in the target network can transmit real-time environmental information to each other.

8. A self-organizing network device for Internet of Vehicles, characterized in that: include: A data acquisition module, used to acquire vehicle behavior data sent by target vehicles within the broadcast range of the host vehicle; The vehicle behavior data is used to characterize the driving state of the target vehicle; The target vehicle refers to a designated vehicle among multiple vehicles within the broadcast range of the host vehicle; A correlation evaluation module, used to calculate the network correlation score corresponding to the target vehicle according to the navigation path data of the preset driving interval of the host vehicle in the driving direction and the vehicle behavior data; The networking correlation score represents the correlation between the target vehicle and the host vehicle during the networking process of the Internet of Vehicles; The networking module is used to add the target vehicle to the self-organizing network of the host vehicle when the networking correlation score is greater than a preset score threshold.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, wherein the computer program implements the steps of any one of the methods of claims 1 to 7 when executed by a processor.

10. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

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