Road condition prediction method and device, vehicle and storage medium

Abstract

The invention relates to the field of vehicles, in particular to a road condition prediction method and device, a vehicle and a storage medium, and the method comprises the steps: obtaining broadcast information transmitted by a Bluetooth mesh network; extracting a network identifier and a target credential in the broadcast information, adding a Bluetooth mesh network based on the network identifier and the target credential, and determining a plurality of target other vehicle nodes in the Bluetooth mesh network according to the navigation route of the target vehicle; the active request is sent to a plurality of target other vehicle nodes, each target other vehicle node generates an assembled data packet according to the active request, and the assembled data packet is transmitted to the target vehicle through the Bluetooth mesh network; and receiving the assembled data packet sent by each target other vehicle node, and predicting the congestion condition in the navigation route according to the assembled data packet. Therefore, the problem that the driving experience of a user is poor due to the fact that the road condition cannot be accurately predicted in real time under the condition of a complex road or a congested road in the prior art is solved.

Description

Technical Field

[0001] The present application relates to the field of vehicles, and in particular to a road condition prediction method, device, vehicle and storage medium. Background Art

[0002] With the rapid development of social economy, the demand for in-vehicle navigation systems in self-driving travel and daily commuting is increasing, especially in remote areas or when network coverage is poor, offline navigation function is particularly important. To meet this demand, offline maps and Bluetooth mesh technology are introduced to build a navigation and information exchange platform without a network environment. With its self-organizing network capability and multi-hop communication characteristics, Bluetooth mesh network enables in-vehicle devices to transmit information to each other in the absence of Internet connection, achieving a certain degree of road condition sharing and prediction.

[0003] Although Bluetooth mesh networking can achieve road condition prediction without a network, there are still many problems in the current technical solutions. For example, during peak hours or in traffic congestion scenarios, the network load will increase dramatically, which not only increases data transmission delays, but may also cause data packet loss, seriously affecting the real-time and accuracy of road condition information. Secondly, related technologies mainly focus on road condition prediction for straight roads, and have limited ability to predict future road condition information for complex intersections, turning sections or nonlinear roads. This limitation significantly reduces the practicality and user satisfaction of the navigation system in actual driving environments, affecting the driver's route decision-making and driving safety. Summary of the invention

[0004] The present application provides a road condition prediction method, device, vehicle and storage medium to solve the problem that the road condition cannot be accurately and real-time predicted in the case of complex roads or congested roads in the related technology, resulting in poor driving experience for users.

[0005] The first aspect of the present application provides a road condition prediction method, comprising the following steps: obtaining broadcast information sent by a Bluetooth mesh network; extracting the network identifier and target credentials in the broadcast information, joining the Bluetooth mesh network based on the network identifier and target credentials, and determining multiple target other vehicle nodes in the Bluetooth mesh network according to the navigation route of the target vehicle; sending an active request to multiple target other vehicle nodes, wherein each target other vehicle node generates an assembly data packet according to the active request and transmits it to the target vehicle via the Bluetooth mesh network; receiving the assembly data packet sent by each target other vehicle node, and predicting the congestion in the navigation route according to the assembly data packet.

[0006] Optionally, multiple target other vehicle nodes in the Bluetooth mesh network are determined based on the navigation route of the vehicle, including: acquiring all other vehicle nodes in the Bluetooth mesh network; taking vehicle nodes on the navigation route among all other vehicle nodes as valid vehicle nodes; and determining multiple target other vehicle nodes based on the current position of the target vehicle and a preset distance therebetween.

[0007] Optionally, the preset interval distance and the number of target other vehicle nodes are determined according to the current road condition of the target vehicle. The worse the current road condition is, the smaller the interval distance is and the greater the number of target other vehicle nodes is.

[0008] Optionally, after joining the Bluetooth mesh network based on the network identification and target credentials, it also includes: configuring the device node address, device role and network parameters of the target vehicle; establishing a connection with all other vehicle nodes in the Bluetooth mesh network based on the device node address, device role and device network parameters.

[0009] Optionally, predicting congestion in the navigation route based on the assembled data packet includes: parsing first target information, second target information and positioning information of each target other vehicle node in the assembled data packet; determining the congestion in the navigation route based on the first target information, the second target information and the positioning information, wherein the first target information is positively correlated with the congestion situation, and the second target information is negatively correlated with the congestion situation.

[0010] Optionally, before determining the congestion situation in the navigation route based on the first target information, the second target information and the positioning information, it also includes: detecting whether there is positioning information that is not in the valid area; if so, discarding the assembled data packets of the target other vehicle nodes corresponding to the positioning information that is not in the valid area.

[0011] Optionally, the assembled data packet includes: one or more of positioning information, vehicle control domain information, congestion level, inertial navigation information, and navigation route information.

[0012] The second aspect of the present application provides a road condition prediction device, including: an acquisition module, used to acquire broadcast information sent by a Bluetooth mesh network; a determination module, used to extract the network identifier and target credentials in the broadcast information, join the Bluetooth mesh network based on the network identifier and target credentials, and determine multiple target other vehicle nodes in the Bluetooth mesh network according to the navigation route of the target vehicle; a sending module, used to send active requests to multiple target other vehicle nodes, wherein each target other vehicle node generates an assembly data packet according to the active request and transmits it to the target vehicle via the Bluetooth mesh network; a prediction module, used to receive the assembly data packet sent by each target other vehicle node, and predict the congestion in the navigation route according to the assembly data packet.

[0013] Optionally, the determination module is further used to obtain all other vehicle nodes in the Bluetooth mesh network; take the vehicle nodes on the navigation route among all other vehicle nodes as valid vehicle nodes; and determine multiple target other vehicle nodes based on the current position of the vehicle and a preset distance therebetween.

[0014] Optionally, the preset interval distance and the number of target other vehicle nodes are determined according to the current road condition of the target vehicle. The worse the current road condition is, the smaller the interval distance is and the greater the number of target other vehicle nodes is.

[0015] Optionally, the road condition prediction device also includes: a configuration module, which is used to configure the device node address, device role and network parameters of the target vehicle after joining the Bluetooth mesh network based on the network identification and target credentials; and establish a connection with all other vehicle nodes in the Bluetooth mesh network based on the device node address, device role and device network parameters.

[0016] Optionally, the prediction module is further used to parse the first target information, the second target information and the positioning information of each target other vehicle node in the assembled data packet; determine the congestion situation in the navigation route based on the first target information, the second target information and the positioning information, wherein the first target information is positively correlated with the congestion situation, and the second target information is negatively correlated with the congestion situation.

[0017] Optionally, the road condition prediction device also includes: a discarding module, which is used to detect whether there is positioning information that is not in the valid area before determining the congestion situation in the navigation route based on the first target information, the second target information and the positioning information; if so, the assembled data packet of the target other vehicle nodes corresponding to the positioning information that is not in the valid area is discarded.

[0018] Optionally, the assembled data packet includes: one or more of positioning information, vehicle control domain information, congestion level, inertial navigation information, and navigation route information.

[0019] The third aspect of the present application provides a vehicle, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement a road condition prediction method as described in the above embodiment.

[0020] The fourth aspect of the present application provides a computer-readable storage medium on which a computer program is stored. The program is executed by a processor to implement a road condition prediction method as described in the above embodiment.

[0021] Therefore, this application has at least the following beneficial effects:

[0022] The embodiment of the present application can join the Bluetooth mesh network based on the network identifier and target credentials in the broadcast information, and determine multiple target other vehicle nodes in the Bluetooth mesh network according to the navigation route. By receiving the driving data of the target other vehicle nodes in a targeted manner, the risk of network flooding is effectively reduced, and the network load is reduced, so that the navigation road condition information can be accurately and long-distance predicted. Thus, the problem that the road conditions cannot be accurately and real-time predicted in the case of complex roads or congested roads in the related technology, resulting in the user's driving experience and other problems, is solved.

[0023] Additional aspects and advantages of the present application will be given in part in the description below, and in part will become apparent from the description below, or will be learned through the practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] The above and / or additional aspects and advantages of the present application will become apparent and easily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

[0025] Figure 1 A structural diagram of a road condition prediction system provided according to an embodiment of the present application;

[0026] Figure 2 A flowchart of a road condition prediction method provided according to an embodiment of the present application;

[0027] Figure 3 A schematic diagram of a congestion scenario provided according to an embodiment of the present application;

[0028] Figure 4 An example diagram of data assembly provided according to an embodiment of the present application;

[0029] Figure 5 A schematic diagram of a road condition prediction method provided according to an embodiment of the present application;

[0030] Figure 6 A block diagram of a road condition prediction device provided according to an embodiment of the present application;

[0031] Figure 7 It is a schematic diagram of the structure of a vehicle provided according to an embodiment of the present application. DETAILED DESCRIPTION

[0032] Embodiments of the present application are described in detail below, and examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present application, and should not be construed as limiting the present application.

[0033] The road condition prediction method, device, vehicle and storage medium of the embodiment of the present application are described below with reference to the accompanying drawings. In response to the problems mentioned in the above background technology, the present application provides a road condition prediction method, in which a Bluetooth mesh network is joined based on the network identification and target credentials in the broadcast information, and multiple target other vehicle nodes in the Bluetooth mesh network are determined according to the navigation route. By receiving the driving data of the target other vehicle nodes in a targeted manner, the risk of network flooding is effectively reduced, and the load level of the network is reduced, so that the navigation road condition information can be accurately and long-distance predicted. Thereby, the problem in the related technology that in the case of complex roads or congested roads, the road conditions cannot be accurately predicted in real time, resulting in problems such as the user's driving experience.

[0034] Before introducing the present application, the system involved in the present application is first described. Figure 1 As shown, it mainly includes: navigation application module, data processing module, mesh Bluetooth module, vehicle control domain module, GPS module and inertial navigation module.

[0035] Among them, the navigation application module is used for HMI display, showing navigation routes and road conditions. Users can select the destination and choose the planned route for navigation. The data processing module is used to receive data in the Bluetooth mesh network, perform unpacking and deserialization operations, and analyze the data in the data packet to calculate the congestion of a certain section of road. The mesh Bluetooth module supports Bluetooth networking function and is used to transmit data. The vehicle control domain module is mainly used to collect information such as vehicle braking and steering wheel usage, historical vehicle speed, etc. The GPS module is used to collect the vehicle's latitude and longitude, altitude, speed and heading. Inertial navigation module: intervenes when the GPS signal cannot meet the navigation requirements to assist in completing navigation.

[0036] Specifically, Figure 2 A flowchart of a road condition prediction method provided in an embodiment of the present application.

[0037] like Figure 2 As shown, the road condition prediction method includes the following steps:

[0038] In step S101 , broadcast information sent by the Bluetooth mesh network is obtained.

[0039] Among them, the Bluetooth mesh network is the Bluetooth mesh network mentioned above, which can be formed into the largest possible Bluetooth mesh network in the form of a zone. The range of the network may be an irregular shape. The Bluetooth mesh network supports data transfer, so even very far nodes can realize remote data transmission through relay nodes, so you can know the road condition information at any point within the network range, and in the Bluetooth mesh network, each vehicle needs to be a data provider and user, that is, it needs to send its own data and receive data from other vehicles. In the actual execution process, the embodiment of the present application can first receive information from the Bluetooth broadcast and identify whether it is sent by the Bluetooth mesh network of the navigation network.

[0040] In step S102, the network identifier and target credentials in the broadcast information are extracted, the Bluetooth mesh network is joined based on the network identifier and the target credentials, and multiple target other vehicle nodes in the Bluetooth mesh network are determined according to the navigation route of the target vehicle.

[0041] It can be understood that after obtaining the broadcast information sent by the Bluetooth mesh network, the embodiment of the present application can decrypt the network identification and credentials attached to the broadcast information, scan the surrounding Bluetooth, find the Bluetooth mesh network, and use the network identification and credentials to connect to the Bluetooth mesh network.

[0042] Furthermore, after joining the Bluetooth mesh network based on the network identification and target credentials, the embodiment of the present application also requires some configuration of the device, including: configuring the device node address, device role and network parameters of the target vehicle so that the device can participate in network communication and operation. Establishing a connection with all other vehicle nodes in the Bluetooth mesh network based on the device node address, device role and device network parameters.

[0043] In one embodiment of the present application, multiple target other vehicle nodes in a Bluetooth mesh network are determined based on a navigation route of a vehicle, including: acquiring all other vehicle nodes in the Bluetooth mesh network; taking vehicle nodes on the navigation route among all other vehicle nodes as valid vehicle nodes; and determining multiple target other vehicle nodes based on a current position of the target vehicle and a preset distance therebetween.

[0044] The preset separation distance and the number of target other vehicle nodes are determined according to the current road condition of the target vehicle. The worse the current road condition, the smaller the separation distance and the greater the number of target other vehicle nodes. The preset separation distance can be set according to actual conditions, such as a setting range of 30 to 200m, without specific limitation.

[0045] It can be understood that after joining the Bluetooth mesh network, the embodiment of the present application can obtain all other vehicle nodes in the Bluetooth mesh network. According to the navigation route of the vehicle, the embodiment of the present application can determine whether a certain node is in the navigation route according to the GPS positioning information. If it is not on the navigation route and there is no overlapping section in the subsequent driving route, then the node is determined to be an invalid node, such as Figure 3 As shown, the target vehicle drives from point A to point C, so the vehicles near point B will not be counted as valid nodes. Then filter again among the remaining nodes, and the screening criteria are whether the data provided by them (positioning information, navigation information, vehicle control information, congestion information, etc.) are detailed and accurate. If they do not meet the requirements, filter out these nodes again, and then do not receive data from this node in the future. If it meets the requirements, then according to the positioning information, take 2 to 3 vehicles at a certain distance as the target other vehicle nodes, and then subscribe to the driving data of these target other vehicle nodes. Therefore, the embodiment of the present application is to select a part of the high-quality nodes on the navigation route, and publish messages by subscription / publishing, avoiding the Bluetooth broadcast method, thereby reducing the load of the network, and effectively reducing the risk of network flooding, significantly improving the communication efficiency between nodes, and reducing the demand for hardware performance.

[0046] In the actual implementation process, if there is a serious traffic jam, the interval distance will be reduced and the subscribed vehicles will be increased to improve the accuracy of the prediction. If the traffic is relatively smooth, the interval distance can be extended to reduce the Bluetooth load and reduce power consumption.

[0047] It should be noted that nodes that are no longer on the navigation route, such as vehicles coming from the opposite lane and vehicles on nearby parallel roads, are added to a list that will not be processed to reduce the amount of calculation.

[0048] In step S103 , an active request is sent to a plurality of target other vehicle nodes, wherein each target other vehicle node generates an assembled data packet according to the active request and transmits it to the target vehicle via the Bluetooth mesh network.

[0049] The embodiment of the present application can send an active request message to other target vehicle nodes in the Bluetooth mesh network. After receiving the message, all networks in the mesh network, such as Figure 4 As shown, the embodiment of the present application requests a target other vehicle node within the communication range through the mesh Bluetooth network (without relying on relay node forwarding to reduce network load), and the requested Bluetooth node returns its own node data. After receiving the data returned by the node, the nodes on the vehicle route are filtered. Furthermore, the target other vehicle node encapsulates the positioning information, vehicle control domain information, congestion level, inertial navigation information, navigation route and other information into an assembled data packet, and transmits the assembled data packet through the Bluetooth mesh network after serialization.

[0050] The following is a detailed description of the application scenarios in actual navigation. Figure 3 As shown in the figure, congestion occurs near point C and point B. Point B is not on the navigation route and does not subscribe to the vehicle node therein. Point C is on the navigation route. The vehicle at point C collects the required data, assembles data packets and sends data packets, but due to the long distance, it cannot be directly transmitted to the vehicle at point A. At this time, the mesh routing will automatically select a reachable path. After reachability analysis, the vehicle at point D is within the Bluetooth range of the vehicles at points A and C at the same time. With the vehicle at point D as a springboard, after the transfer, the target vehicle at point A receives the data and marks the road ahead as congested, thereby realizing the beyond-visual-range congestion judgment.

[0051] In step S104, the assembled data packet sent by each target other vehicle node is received, and the congestion situation in the navigation route is predicted according to the assembled data packet.

[0052] In one embodiment of the present application, predicting congestion in a navigation route based on an assembled data packet includes: parsing first target information, second target information and positioning information of each target other vehicle node in the assembled data packet; determining the congestion in the navigation route based on the first target information, the second target information and the positioning information.

[0053] Among them, the first target information is positively correlated with the congestion situation, which can be the congestion level and the number of cars, the length of the car model, the frequency of brake use, the ratio of speed limit to vehicle speed, etc. The second target information is negatively correlated with the congestion situation, which can be the number of lanes. The embodiment of the present application can judge the road congestion situation by analyzing this information, specifically including: first, it is necessary to detect whether there is positioning information that is not in the effective area; if so, the assembly data packet of the target other vehicle nodes corresponding to the positioning information that is not in the effective area is discarded. Then read the control domain information of the vehicle, the vehicle congestion level, the ratio of speed limit to vehicle speed, the frequency of brake use and other information as well as the number of lanes to comprehensively judge the road congestion situation.

[0054] Furthermore, the embodiments of the present application can be combined with GPS and navigation technology. The present invention can retrieve and subscribe to information about vehicles that are about to enter other road sections, thereby achieving accurate prediction of the road conditions of the entire road, no longer limited to the prediction range of straight roads, greatly expanding its application value and functional scope.

[0055] The following is a detailed description of the steps based on actual usage. Figure 5 As shown, the road condition prediction method includes the following steps:

[0056] Step 1: Receive information from Bluetooth broadcasts and identify whether it is sent by the Bluetooth mesh network of the navigation network. If so, decrypt the network identifier and credentials attached therein and join the Bluetooth mesh network.

[0057] Step 2: The node newly added to the Bluetooth mesh network sends an active request message to request the nodes of all vehicles in the network;

[0058] Step 3: Analyze whether the nodes of all vehicles in the network are on the navigation route. If not, do not process them. Otherwise, select a part of high-quality nodes and transmit data through subscription / publishing.

[0059] Step 4: Receive data transmitted by high-quality nodes to determine road congestion.

[0060] According to the road condition prediction method proposed in the embodiment of the present application, the Bluetooth mesh network is joined based on the network identifier and target credentials in the broadcast information, and multiple target other vehicle nodes in the Bluetooth mesh network are determined according to the navigation route. By receiving the driving data of the target other vehicle nodes in a targeted manner, the risk of network flooding is effectively reduced, and the network load is reduced, so that the navigation road condition information can be accurately and long-distance predicted. Therefore, the problem that the road condition cannot be accurately and real-time predicted in the case of complex roads or congested roads in the related technology, resulting in the user's driving experience and other problems, is solved.

[0061] Next, the road condition prediction device proposed according to the embodiment of the present application is described with reference to the accompanying drawings.

[0062] Figure 6 It is a block diagram of a road condition prediction device according to an embodiment of the present application.

[0063] like Figure 6 As shown, the road condition prediction device 10 includes: an acquisition module 100 , a determination module 200 , a sending module 300 and a prediction module 400 .

[0064] Among them, the acquisition module 100 is used to obtain the broadcast information issued by the Bluetooth mesh network; the determination module 200 is used to extract the network identification and target credentials in the broadcast information, join the Bluetooth mesh network based on the network identification and target credentials, and determine multiple target other vehicle nodes in the Bluetooth mesh network according to the navigation route of the target vehicle; the sending module 300 is used to send active requests to multiple target other vehicle nodes, wherein each target other vehicle node generates an assembly data packet according to the active request and transmits it to the target vehicle through the Bluetooth mesh network; the prediction module 400 is used to receive the assembly data packet sent by each target other vehicle node, and predict the congestion in the navigation route according to the assembly data packet.

[0065] In one embodiment of the present application, the determination module 200 is further used to obtain all other vehicle nodes in the Bluetooth mesh network; take the vehicle nodes on the navigation route among all other vehicle nodes as valid vehicle nodes; and determine multiple target other vehicle nodes based on the current position of the target vehicle and the preset distance between them.

[0066] In one embodiment of the present application, the preset separation distance and the number of target other vehicle nodes are determined according to the current road condition of the target vehicle. The worse the current road condition is, the smaller the separation distance is and the greater the number of target other vehicle nodes is.

[0067] In one embodiment of the present application, the road condition prediction device 10 also includes: a configuration module for configuring the device node address, device role and network parameters of the target vehicle after joining the Bluetooth mesh network based on the network identification and target credentials; and establishing a connection with all other vehicle nodes in the Bluetooth mesh network based on the device node address, device role and device network parameters.

[0068] In one embodiment of the present application, the prediction module is further used to parse the first target information, the second target information and the positioning information of each target other vehicle node in the assembled data packet; determine the congestion situation in the navigation route based on the first target information, the second target information and the positioning information, wherein the first target information is positively correlated with the congestion situation, and the second target information is negatively correlated with the congestion situation.

[0069] In one embodiment of the present application, the road condition prediction device 10 also includes: a discarding module, which is used to detect whether there is positioning information that is not in the valid area before determining the congestion situation in the navigation route based on the first target information, the second target information and the positioning information; if so, the assembled data packet of the target other vehicle nodes corresponding to the positioning information that is not in the valid area is discarded.

[0070] In one embodiment of the present application, the assembled data packet includes: one or more of positioning information, vehicle control domain information, congestion level, inertial navigation information, and navigation route information.

[0071] It should be noted that the above explanations of the road condition prediction method embodiment are also applicable to the road condition prediction device of this embodiment, and will not be repeated here.

[0072] According to the road condition prediction device proposed in the embodiment of the present application, the Bluetooth mesh network is joined based on the network identifier and target credentials in the broadcast information, and multiple target other vehicle nodes in the Bluetooth mesh network are determined according to the navigation route. By receiving the driving data of the target other vehicle nodes in a targeted manner, the risk of network flooding is effectively reduced, and the load of the network is reduced, so that the navigation road condition information can be accurately and long-distance predicted. In this way, the problem that the road condition cannot be accurately and real-time predicted in the case of complex roads or congested roads in the related technology, resulting in poor driving experience for users, etc.

[0073] Figure 7 A schematic diagram of the structure of a vehicle provided in an embodiment of the present application. The vehicle may include:

[0074] A memory 701 , a processor 702 , and a computer program stored in the memory 701 and executable on the processor 702 .

[0075] When the processor 702 executes the program, the road condition prediction method provided in the above embodiment is implemented.

[0076] Furthermore, the vehicle also includes:

[0077] The communication interface 703 is used for communication between the memory 701 and the processor 702 .

[0078] The memory 701 is used to store computer programs that can be executed on the processor 702 .

[0079] The memory 701 may include a high-speed RAM (Random Access Memory) memory, and may also include a non-volatile memory, such as at least one disk memory.

[0080] If the memory 701, the processor 702 and the communication interface 703 are implemented independently, the communication interface 703, the memory 701 and the processor 702 can be connected to each other through a bus and communicate with each other. The bus can be an ISA (Industry Standard Architecture) bus, a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 7 Only one thick line is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0081] Optionally, in a specific implementation, if the memory 701, the processor 702 and the communication interface 703 are integrated on a chip, the memory 701, the processor 702 and the communication interface 703 can communicate with each other through an internal interface.

[0082] The processor 702 may be a CPU (Central Processing Unit), or an ASIC (Application Specific Integrated Circuit), or one or more integrated circuits configured to implement the embodiments of the present application.

[0083] An embodiment of the present application also provides a computer-readable storage medium on which a computer program is stored, and when the program is executed by a processor, the above-mentioned road condition prediction method is implemented.

[0084] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representations of the above terms are not necessarily directed to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or N embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, without contradiction.

[0085] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include at least one of the features. In the description of this application, "N" means at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

[0086] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, fragment or portion of code comprising one or more executable instructions for implementing the steps of a custom logical function or process, and the scope of the preferred embodiments of the present application includes alternative implementations in which functions may not be performed in the order shown or discussed, including performing functions in a substantially simultaneous manner or in reverse order depending on the functions involved, which should be understood by technicians in the technical field to which the embodiments of the present application belong.

[0087] It should be understood that the various parts of the present application can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiment, the N steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array, a field programmable gate array, etc.

[0088] A person skilled in the art may understand that all or part of the steps in the method for implementing the above-mentioned embodiment may be completed by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, which, when executed, includes one or a combination of the steps of the method embodiment.

[0089] Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limitations on the present application. Ordinary technicians in this field can change, modify, replace and modify the above embodiments within the scope of the present application.

Claims

1. A road condition prediction method, characterized in that: The following steps are involved: Get the broadcast information sent by the Bluetooth mesh network; Extracting a network identifier and a target credential from the broadcast information, joining the Bluetooth mesh network based on the network identifier and the target credential, and determining a plurality of target other vehicle nodes in the Bluetooth mesh network according to a navigation route of the target vehicle; Sending an active request to the plurality of target other vehicle nodes, wherein each target other vehicle node generates an assembled data packet according to the active request and transmits the assembled data packet to the target vehicle via the Bluetooth mesh network; Receive the assembled data packet sent by each target other vehicle node, and predict the congestion situation in the navigation route according to the assembled data packet.

2. The road condition prediction method according to claim 1, characterized in that: The step of determining a plurality of target other vehicle nodes in the Bluetooth mesh network according to the navigation route of the vehicle comprises: Obtain all other vehicle nodes in the Bluetooth mesh network; Taking the vehicle nodes on the navigation route among all the other vehicle nodes as valid vehicle nodes; The multiple target other vehicle nodes are determined according to the current position of the target vehicle and the preset distance therebetween.

3. The road condition prediction method according to claim 2, characterized in that: The preset separation distance and the number of the target other vehicle nodes are determined according to the current road condition of the target vehicle. The worse the current road condition is, the smaller the separation distance is and the greater the number of the target other vehicle nodes is.

4. The road condition prediction method according to claim 2, characterized in that: After joining the Bluetooth mesh network based on the network identifier and the target credentials, further comprising: Configuring the device node address, device role and network parameters of the target vehicle; A connection is established with all other vehicle nodes in the Bluetooth mesh network based on the device node address, the device role and the device network parameters.

5. The road condition prediction method according to claim 1, characterized in that: The predicting the congestion situation in the navigation route according to the assembled data packet includes: Parsing the first target information, the second target information and the positioning information of each target other vehicle node in the assembled data packet; The congestion situation in the navigation route is determined according to the first target information, the second target information and the positioning information, wherein the first target information is positively correlated with the congestion situation, and the second target information is negatively correlated with the congestion situation.

6. The method for predicting road conditions according to claim 5, characterized in that: Before determining the congestion situation in the navigation route according to the first target information, the second target information and the positioning information, the method further includes: Detect whether there is positioning information that is not in the valid area; If so, the assembled data packet of the target other vehicle node corresponding to the positioning information that is not in the valid area is discarded.

7. The method for predicting road conditions according to any one of claims 1 to 6, characterized in that: The assembled data includes: one or more of positioning information, vehicle control domain information, congestion level, inertial navigation information, and navigation route information.

8. A road condition prediction device, characterized in that: include: An acquisition module, used to acquire broadcast information sent by a Bluetooth mesh network; A determination module, configured to extract a network identifier and a target credential from the broadcast information, join the Bluetooth mesh network based on the network identifier and the target credential, and determine a plurality of target other vehicle nodes in the Bluetooth mesh network according to a navigation route of the target vehicle; a sending module, configured to send an active request to the plurality of target other vehicle nodes, wherein each target other vehicle node generates an assembled data packet according to the active request and transmits the assembled data packet to the target vehicle via the Bluetooth mesh network; The prediction module is used to receive the assembled data packet sent by each target other vehicle node, and predict the congestion situation in the navigation route according to the assembled data packet.

9. A vehicle, characterized in that: include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the road condition prediction method according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program or instructions stored thereon for execution, characterized in that: When the computer program or instruction is executed, it is executed by the processor to implement the road condition prediction method as described in any one of claims 1-7.

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