Route recommendation method, device, cloud device, storage medium and program product

Abstract

The embodiments of the present application provide a route recommendation method, device, cloud device, storage medium and program product. It relates to the field of automotive electronic technology. The method includes: receiving vehicle data sent by a vehicle terminal; wherein the vehicle data is the perception data and positioning information of the current lane where the vehicle terminal is located. The vehicle data and the cloud information within the first preset range are fused to obtain fusion result data; the cloud information includes map data information, road congestion information and traffic light information including intersections. Based on the fusion result data, a lane-level recommended route for the vehicle terminal is generated; the lane-level recommended route is sent to the vehicle terminal; wherein the lane-level recommended route is used to indicate the driving route of the vehicle terminal. The method is used to achieve the effect of improving the accuracy of the navigation route.

Description

Technical Field

[0001] The present application relates to the field of automotive electronic technology, and in particular to a route recommendation method, device, cloud device, storage medium and program product. Background Art

[0002] Currently, users need to obtain real-time traffic information while driving, so they need to navigate the road conditions ahead.

[0003] In the prior art, the vehicle flow map ahead is mainly predicted during the navigation process, and then driving suggestions are provided to the user.

[0004] However, in the prior art, since only the vehicle flow map ahead is predicted, the vehicle flow map cannot reflect the actual road conditions of multiple roads ahead in detail, resulting in the user being unable to determine the optimal route, thereby wasting a lot of driving time. Summary of the invention

[0005] The embodiments of the present application provide a route recommendation method, apparatus, cloud device, storage medium and program product to achieve the effect of improving the accuracy of navigation routes.

[0006] In a first aspect, an embodiment of the present application provides a route recommendation method, comprising:

[0007] Receiving vehicle data sent by a vehicle terminal; wherein the vehicle data is perception data and positioning information of a current lane in which the vehicle terminal is located;

[0008] The vehicle data and the cloud information within the first preset range are fused to obtain fusion result data; the cloud information includes map data information, road congestion information, and traffic light information including intersections;

[0009] Generate a lane-level recommended route for the vehicle terminal based on the fusion result data; send the lane-level recommended route to the vehicle terminal; wherein the lane-level recommended route is used to indicate a driving route for the vehicle terminal.

[0010] In a possible implementation manner, the perception information represents data of a perception object within a second preset range around the vehicle terminal;

[0011] The vehicle data and the cloud information within the first preset range are fused to obtain fusion result data, including:

[0012] Restoring the map data information within the first preset range in preset cloud navigation data to obtain restored map data information within the first preset range;

[0013] Matching the road congestion information and the traffic light information at the intersection with the restored map data information to obtain matching result information;

[0014] The perception information is restored in the cloud navigation data, and the trajectory of each perception object in the perception information is predicted to generate the running trajectory of each perception object at the next moment;

[0015] According to the matching result information and the running trajectory, fusion result data is obtained.

[0016] In a possible implementation manner, the positioning information includes the current location of the vehicle terminal;

[0017] Generating a lane-level recommended route for the vehicle terminal according to the fusion result data includes:

[0018] Determine lane information of a first turning point at a preset distance ahead of the current position according to the fusion result data;

[0019] Determine the lane where the first turning point is located according to the lane information, and determine the end point of the lane;

[0020] A lane-level recommended route for the vehicle terminal is generated based on the current position and the destination.

[0021] In a possible implementation, the method further includes:

[0022] According to a preset monitoring distance, each road section on the lane-level recommended route is monitored and updated in turn to generate an updated lane-level recommended route.

[0023] In a possible implementation, monitoring and updating each road section on the lane-level recommended route in sequence according to a preset monitoring distance to generate an updated lane-level recommended route includes:

[0024] According to a preset monitoring distance, each road section on the lane-level recommended route is monitored in turn to generate lane information of each road section;

[0025] According to the lane information of each road section, the lane-level recommended route in each road section is determined in turn;

[0026] Determining, according to the distance between the vehicle terminal and the end of the lane-level recommended route, that the end of the lane-level recommended route is the next turning point;

[0027] The lane-level recommended route is updated according to the lane where the next turning point is located and the end point of the lane to generate an updated lane-level recommended route.

[0028] In a possible implementation manner, the method for acquiring the cloud information within the first preset range includes:

[0029] Determining map data information of a first preset range in preset cloud navigation data according to the positioning information in the vehicle data;

[0030] The road congestion information within the first preset range and the traffic light information including the intersection are obtained through the preset service equipment.

[0031] In a possible implementation, the method further includes:

[0032] Determining a weight value of a lane passed by the vehicle terminal according to the lane information of the vehicle terminal;

[0033] If it is determined that the weight value is greater than a preset weight threshold, the lane passed by the vehicle terminal is determined to be a deviated lane, and the lane-level recommended route is re-determined.

[0034] In a second aspect, an embodiment of the present application provides a route recommendation device, including:

[0035] A receiving module, used to receive vehicle data sent by a vehicle terminal; wherein the vehicle data is the sensing data and positioning information of the current lane where the vehicle terminal is located;

[0036] A fusion module, used to fuse the vehicle data and the cloud information within a first preset range to obtain fusion result data; the cloud information includes map data information, road congestion information, and traffic light information including intersections;

[0037] A generation module, used for generating a lane-level recommended route for the vehicle terminal according to the fusion result data;

[0038] A sending module is used to send the lane-level recommended route to the vehicle terminal; wherein the lane-level recommended route is used to indicate the driving route of the vehicle terminal.

[0039] In a possible implementation manner, the perception information represents data of a perception object within a second preset range around the vehicle terminal;

[0040] The fusion module is specifically used for:

[0041] Restoring the map data information within the first preset range in preset cloud navigation data to obtain restored map data information within the first preset range;

[0042] Matching the road congestion information and the traffic light information at the intersection with the restored map data information to obtain matching result information;

[0043] The perception information is restored in the cloud navigation data, and the trajectory of each perception object in the perception information is predicted to generate the running trajectory of each perception object at the next moment;

[0044] According to the matching result information and the running trajectory, fusion result data is obtained.

[0045] In a possible implementation manner, the positioning information includes the current location of the vehicle terminal;

[0046] The generating module is specifically used for:

[0047] Determine lane information of a first turning point at a preset distance ahead of the current position according to the fusion result data;

[0048] Determine the lane where the first turning point is located according to the lane information, and determine the end point of the lane;

[0049] A lane-level recommended route for the vehicle terminal is generated based on the current position and the destination.

[0050] In a possible implementation, the device further includes:

[0051] The updating module is used to monitor and update each road section on the lane-level recommended route in turn according to a preset monitoring distance, and generate an updated lane-level recommended route.

[0052] In a possible implementation manner, the update module is specifically configured to:

[0053] According to a preset monitoring distance, each road section on the lane-level recommended route is monitored in turn to generate lane information of each road section;

[0054] According to the lane information of each road section, the lane-level recommended route in each road section is determined in turn;

[0055] Determining, according to the distance between the vehicle terminal and the end of the lane-level recommended route, that the end of the lane-level recommended route is the next turning point;

[0056] The lane-level recommended route is updated according to the lane where the next turning point is located and the end point of the lane to generate an updated lane-level recommended route.

[0057] In a possible implementation, the device further includes an acquisition module, the acquisition module is used to acquire cloud information within the first preset range, and the acquisition module is specifically used to:

[0058] Determining map data information of a first preset range in the cloud navigation data according to the positioning information in the vehicle data;

[0059] The road congestion information within the first preset range and the traffic light information including the intersection are obtained through the preset service equipment.

[0060] In a possible implementation manner, the device is further specifically used for:

[0061] Determining a weight value of a lane passed by the vehicle terminal according to the lane information of the vehicle terminal;

[0062] If it is determined that the weight value is greater than a preset weight threshold, the lane passed by the vehicle terminal is determined to be a deviated lane, and the lane-level recommended route is re-determined.

[0063] In a third aspect, an embodiment of the present application provides a cloud device, including: a memory, a processor;

[0064] The memory stores computer-executable instructions;

[0065] The processor executes the computer-executable instructions stored in the memory, so that the processor executes the above first aspect and / or various possible implementations of the first aspect.

[0066] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, in which computer-executable instructions are stored. When the computer-executable instructions are executed by a processor, they are used to implement the first aspect above and / or various possible implementations of the first aspect.

[0067] In a fifth aspect, an embodiment of the present application provides a computer program product, including a computer program, which, when executed by a processor, implements the above first aspect and / or various possible implementation methods of the first aspect.

[0068] The route recommendation method, device, cloud device, storage medium and program product provided in the embodiments of the present application receive vehicle data sent by a vehicle terminal; wherein the vehicle data is the perception data and positioning information of the current lane where the vehicle terminal is located. The vehicle data and the cloud information within the first preset range are fused to obtain fusion result data; the cloud information includes map data information, road congestion information and traffic light information including intersections. Based on the fusion result data, a lane-level recommended route for the vehicle terminal is generated; the lane-level recommended route is sent to the vehicle terminal; wherein the lane-level recommended route is used to indicate the driving route of the vehicle terminal. In this solution, the real-time vehicle data of the vehicle terminal is uploaded to the cloud device; the cloud device obtains the latest cloud information within the first preset range based on the vehicle data, and generates a lane-level recommended route for the vehicle terminal in combination with the vehicle data and the cloud information, and sends the lane-level recommended route to the vehicle terminal; when the vehicle terminal receives the lane-level recommended route, it drives according to the lane-level recommended route. Therefore, placing the calculation of the recommended route on the cloud device can refer to more road conditions and real-time information, while reducing the computing power consumption of the vehicle terminal, making the lane-level recommended route calculation more accurate, so that the vehicle terminal can increase its trust in the lane-level recommended route, so as to achieve perfect vehicle control and achieve the effect of improving the accuracy of the navigation route. BRIEF DESCRIPTION OF THE DRAWINGS

[0069] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0070] Figure 1 A schematic diagram of a route recommendation method provided in an embodiment of the present application Figure 1 ;

[0071] Figure 2 A schematic diagram of a route recommendation method provided in an embodiment of the present application Figure 2 ;

[0072] Figure 3 A schematic diagram of a route recommendation method provided in an embodiment of the present application Figure 3 ;

[0073] Figure 4 A schematic diagram of the structure of a route recommendation device provided in an embodiment of the present application;

[0074] Figure 5 A schematic diagram of the structure of another route recommendation device provided in an embodiment of the present application;

[0075] Figure 6 A schematic diagram of the structure of a cloud device provided in an embodiment of the present application.

[0076] The above drawings have shown clear embodiments of the present application, which will be described in more detail later. These drawings and text descriptions are not intended to limit the scope of the present application in any way, but to illustrate the concept of the present application to those skilled in the art by referring to specific embodiments. DETAILED DESCRIPTION

[0077] Exemplary embodiments will be described in detail herein, examples of which are shown in the accompanying drawings. When the following description refers to the drawings, the same numbers in different drawings represent the same or similar elements unless otherwise indicated. The implementations described in the following exemplary embodiments do not represent all implementations consistent with the present application. Instead, they are merely examples of devices and methods consistent with some aspects of the present application as detailed in the appended claims.

[0078] Currently, users need to obtain real-time traffic information while driving, so they need to navigate the road conditions ahead.

[0079] In one example, the vehicle flow map ahead is predicted during navigation, and driving suggestions are provided to the user. However, in the prior art, since only the vehicle flow map ahead is predicted, the vehicle flow map cannot reflect the actual road conditions of multiple roads ahead in detail, resulting in the user being unable to determine the optimal route, thereby wasting a lot of driving time.

[0080] In combination with the above scenarios, it can be seen that in the prior art, there is a technical problem of low accuracy of navigation routes.

[0081] The route recommendation method provided in this application solves the technical problem of low accuracy of navigation routes.

[0082] The technical solution of the present application and how the technical solution of the present application solves the above-mentioned technical problems are described in detail below with specific embodiments. The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be repeated in some embodiments. The embodiments of the present application will be described below in conjunction with the accompanying drawings.

[0083] Figure 1 A schematic diagram of a route recommendation method provided for this application Figure 1 ,like Figure 1 As shown, the method includes:

[0084] S101. Receive vehicle data sent by a vehicle terminal; wherein the vehicle data is perception data and positioning information of a current lane in which the vehicle terminal is located.

[0085] For example, the execution subject of this embodiment may be a cloud device, or a terminal device, or a route recommendation device or device, or other devices or equipment that can execute this embodiment, and there is no limitation to this. In this embodiment, the execution subject is introduced as a cloud device.

[0086] First, after the vehicle terminal is powered on, the vehicle system of the vehicle terminal establishes a connection with the cloud device and completes the authentication. The connection can be established through the built-in SIM card in the vehicle, and the specific connection method is not limited. Authentication refers to the vehicle function authentication. The https protocol is used to upload the unique identifier of the vehicle terminal and request to download the functional permissions of the current vehicle. That is, different vehicle functions can be assigned according to different vehicles. This interaction process will use Token authentication. After the authentication is successfully completed, the vehicle terminal obtains the vehicle data and reports the vehicle data to the cloud device at a preset frequency. Among them, the vehicle data is the perception data and positioning information of the current lane where the vehicle terminal is located; the positioning information includes the current location of the vehicle terminal; the perception information represents the data of the perception object within the second preset range around the vehicle terminal. For example, the perception data includes detailed information of the perception object within the preset range around the vehicle (for example, 100 meters, or 200 meters, etc., which is not limited), such as timestamp, perception object ID, relative position, relative vehicle speed, perception object type (such as trucks, vans, cars, SUVs, vans, bicycles, electric vehicles, pedestrians, etc.), turn signal information and brake light information. Location information, including timestamp, current location information, lane, vehicle speed and vehicle direction. Finally, the cloud device receives the vehicle data sent by the vehicle terminal.

[0087] S102, fusing the vehicle data and the cloud information within the first preset range to obtain fusion result data; the cloud information includes map data information, road congestion information, and traffic light information including intersections.

[0088] For example, based on the vehicle data, cloud information within a first preset range is determined in the preset cloud navigation data, and the vehicle data and the cloud information within the first preset range are fused to obtain fusion result data. The cloud information includes map data information, road congestion information, and traffic light information including intersections; the preset range of the first preset range can be 2km, etc., which is not limited.

[0089] S103. Generate a lane-level recommended route for the vehicle terminal based on the fusion result data; send the lane-level recommended route to the vehicle terminal; wherein the lane-level recommended route is used to indicate the driving route of the vehicle terminal.

[0090] Exemplarily, the fusion result data represents the actual road condition under the combination of multi-source data within the first preset range. According to the fusion result data, a lane-level recommended route for the vehicle terminal is generated and sent to the vehicle terminal. The lane-level recommended route is the recommended optimal driving route for the vehicle terminal. After receiving the lane-level recommended route, the vehicle terminal drives according to the lane-level recommended route.

[0091] The route recommendation method provided in the embodiment of the present application receives vehicle data sent by a vehicle terminal; wherein the vehicle data is the perception data and positioning information of the current lane where the vehicle terminal is located. The vehicle data and the cloud information within the first preset range are fused to obtain fusion result data; the cloud information includes map data information, road congestion information, and traffic light information including intersections. Based on the fusion result data, a lane-level recommended route for the vehicle terminal is generated; the lane-level recommended route is sent to the vehicle terminal; wherein the lane-level recommended route is used to indicate the driving route of the vehicle terminal. In this solution, the real-time vehicle data of the vehicle terminal is uploaded to the cloud device; the cloud device obtains the latest cloud information within the first preset range based on the vehicle data, and generates a lane-level recommended route for the vehicle terminal in combination with the vehicle data and the cloud information, and sends the lane-level recommended route to the vehicle terminal; when the vehicle terminal receives the lane-level recommended route, it drives according to the lane-level recommended route. Therefore, placing the calculation of the recommended route on the cloud device can refer to more road conditions and real-time information, while reducing the computing power consumption of the vehicle terminal, making the lane-level recommended route calculation more accurate, so that the vehicle terminal can increase its trust in the lane-level recommended route, so as to achieve perfect vehicle control and achieve the effect of improving the accuracy of the navigation route.

[0092] Figure 2 A schematic diagram of a route recommendation method provided for this application Figure 2 ,like Figure 2 As shown, in this embodiment Figure 1 Based on the embodiment, a route recommendation method is described in detail, and the method includes:

[0093] S201. Receive vehicle data sent by a vehicle terminal; wherein the vehicle data is perception data and positioning information of a current lane in which the vehicle terminal is located.

[0094] For example, Figure 3 A schematic diagram of a route recommendation method provided for this application Figure 3 ,like Figure 3As shown, it includes a vehicle terminal (Vehicle) and a cloud device (cloud); the vehicle terminal includes: local-connect module (i.e. local connection module), Collect module (i.e. collection module), local-position module (i.e. local position module), Perception module (i.e. perception module), local-Lane module (i.e. local lane module), Map module (i.e. map module); the cloud device includes: big data platform, Datafusion module (i.e. data fusion module), DBProvide module (i.e. DBP providing module), Traffic module (i.e. traffic module), Decision module (i.e. decision module), Guide module (i.e. guide module), Route module (i.e. route module), icloud-Lane module (i.e. cloud service-lane module), icloud-connect module (i.e. cloud service-connection module).

[0095] according to Figure 3 It can be seen that after the vehicle terminal is powered on, the vehicle system of the vehicle terminal establishes a connection with the cloud device. After successfully completing the authentication, the Collect module begins to receive the perception data from the Perception module. These data include detailed information about the perceived objects within the second preset range around the vehicle, such as timestamp, perceived object ID, relative position, relative speed, perceived object type (such as trucks, vans, cars, SUVs, vans, bicycles, electric vehicles, pedestrians, etc.), turn signal information and brake light information. At the same time, the Collect module obtains positioning information from the local-position module, including timestamp, current position information, lane, vehicle speed and vehicle head direction. The perception data and positioning information are reported to the local-connect module at a frequency of 20Hz, and are further sent to the cloud device through the local-connect module. The local-Lane module receives the lane-level recommended route issued by the cloud device, and notifies the intelligent driving module and the Map module for rendering. At the same time, the Map module receives the positioning information in real time and synchronously updates the perception data around the vehicle terminal itself to ensure that the vehicle system always provides the latest driving support. The cloud device receives vehicle data sent by the local-connect module of the vehicle terminal, and the vehicle data is data of the current lane where the vehicle terminal is located.

[0096] S202, fusing the vehicle data with the cloud information within the first preset range to obtain fusion result data; the cloud information includes map data information, road congestion information, and traffic light information including intersections.

[0097] In one example, the perception information represents data of perceived objects within a second preset range around the vehicle terminal; step S202 includes: restoring the map data information within the first preset range in the preset cloud navigation data to obtain the restored map data information within the first preset range; matching the road congestion information and the traffic light information including the intersection with the restored map data information to obtain matching result information; restoring the perception information in the cloud navigation data, and predicting the trajectory of each perceived object in the perception information to generate the running trajectory of each perceived object at the next moment; and obtaining fusion result data based on the matching result information and the running trajectory.

[0098] In one example, a method for acquiring cloud information within a first preset range includes: determining map data information within the first preset range in cloud navigation data based on positioning information in vehicle data; and acquiring road congestion information within the first preset range and traffic light information including intersections through a preset service device.

[0099] Exemplarily, the vehicle data includes the perception data and positioning information of the current lane where the vehicle terminal is located, the positioning information includes the current location of the vehicle terminal, and the perception information represents the data of the perception object within the second preset range around the vehicle terminal. Cloud information includes map data information, road congestion information, and traffic light information including intersections. The preset cloud navigation data is the navigation map data of the entire area deployed in the cloud, and the cloud navigation data is used to determine the map data information in the cloud information within the first preset range, that is, the map data information is the data of the local area in the cloud navigation data. For example, the cloud can determine the city where the positioning information is located based on the positioning information, and call the navigation map data corresponding to the overall area of ​​the city, and then determine the map data information within the first preset range in the navigation map data corresponding to the overall area of ​​the city. The first preset range and the second preset range may be the same or different, and there is no limitation on this.

[0100] In this step, the cloud device can fuse the vehicle data and the cloud information within the first preset range to obtain the fusion result data. Specifically, the map data information within the first preset range is restored in the cloud navigation data to obtain the restored map data information within the first preset range. The road congestion information (traffic) and traffic light information (traafic-light) obtained from the third-party service are matched with the restored map data information of the first preset range to obtain the matching result information. And when processing the state information of the traffic light in the traffic light information, it is necessary to compare the timestamp information of multiple packages of positioning data, and the time of the cloud device is used as the basis. Specifically, the difference is made according to the timestamps of adjacent positioning data packets, and the data packet with the time closest to the time of the cloud device is used as the benchmark to avoid network delays. Misjudgment of the traffic post's passability. Then, the perception information is restored in the cloud navigation data, and the trajectory of each moving perception object in the perception information is predicted. According to the running trajectory and light prompts of the backtracking perception object, the running trajectory at the next moment is predicted, and marked for use in subsequent processes. Among them, the prediction method can use a particle filter algorithm for prediction, which is not limited.

[0101] It should be noted that for positioning information and perception data, in order to ensure the alignment of the timestamps of the data sources, time synchronization is performed, which is completed by backtracking the data and then interpolating the data to ensure the time consistency of different data sources.

[0102] Further, according to Figure 3 It can be seen that the cloud information within the first preset range is obtained in the following ways: after the cloud device receives the positioning information and perception data of the vehicle terminal through the Datafusion module and caches it, it obtains the map data information within the first preset range (for example, the default first preset range is 2km, which is not limited to this) from the DBProvide module according to the positioning information in the vehicle data, and obtains the road congestion information and traffic light information including the intersection within the first preset range (for example, the default is 2km, which is not limited to this) provided by the preset service device through the Traffic module. It should be noted that the positioning information and perception data cached by the cloud device can be used for big data analysis and data backtracking; the preset service device is used to provide third-party services, such as data services.

[0103] S203: Generate a lane-level recommended route for the vehicle terminal based on the fusion result data.

[0104] In one example, the positioning information includes the current position of the vehicle terminal; step S203 includes: determining the lane information of the first turning point at a preset distance in front of the current position based on the fusion result data; determining the lane where the first turning point is located based on the lane information, and determining the end point of the lane; generating a lane-level recommended route for the vehicle terminal based on the current position and the end point.

[0105] Exemplarily, the cloud device can determine the lane information of the first turning point at a preset distance in front of the current position from the Guide module based on the fusion result data; for example, the preset distance is 200 meters, 300 meters, etc.; the lane information includes: the distance from the vehicle, the turning lane number, the turning direction (left turn, right turn, etc.), etc.; taking 200 meters as an example, the lane information of the first turning point 200 meters in front of the vehicle terminal itself is obtained. According to the lane information, the lane where the first turning point is located is determined, and the end point of the lane is determined as the destination. According to the current position and the end point, the lane-level recommended route of the vehicle terminal is generated. The current position of the vehicle terminal itself is regarded as the starting node, and the end point is regarded as the destination. The basic lane-level recommended route between the starting position and the destination is calculated using the A* algorithm.

[0106] Specifically, the A* algorithm includes the following:

[0107] 1. Initialization

[0108] Node definition: Each lane section is considered as a node (a lane (which can be pre-defined according to the map data) is divided into multiple lane sections according to the virtual and real lines). The node contains the following attributes:

[0109] a. Basic lane attributes (shape point, curvature, length, speed limit)

[0110] b. Traffic status (severe congestion, congestion, smooth traffic)

[0111] c. Edge properties (solid line, dashed line)

[0112] d. Perceived object situation (existence, type, speed, predicted behavior)

[0113] e. Distance to traffic lights and remaining time

[0114] f. Open List: Initialize the list containing the starting point node.

[0115] g. Result list (Closed List): initialized to empty.

[0116] h. Path dictionary: used to record the parent node of each node to facilitate path tracing.

[0117] 2. Weight calculation mechanism (weights can be adjusted according to actual conditions)

[0118] For each node, the total weight (g(n)+h(n)) is calculated using the following formula:

[0119] g(n) (actual cost) calculation:

[0120] Basic weight: Each lane is assigned a basic weight.

[0121] a. If there is a perceptual object:

[0122] Dynamic perception:

[0123] 1). Always driving at a constant speed, adjust the basic weight. Specifically, if it is greater than or equal to the vehicle speed, the basic weight minus the first preset value; if it is less than the vehicle speed, the basic weight plus the second preset value. Among them, the first preset value and the second preset value can be user-defined values, and there is no limit to this. For example, the first preset value is 10, or 20, or 25, etc., and there is no limit to this; the second preset value is 2, or 3, or 4, etc., and there is no limit to this.

[0124] 2) Predict lane changes and adjust weights. Specifically, if the vehicle changes to the lane, the basic weight + the third preset value; if the vehicle leaves the lane, the basic weight - the fourth preset value. The third preset value and the fourth preset value can be user-defined values, which are not limited. For example, the third preset value is 40, 45, or 60, etc., which are not limited; the fourth preset value is 10, 11, or 12, etc., which are not limited.

[0125] 3) Predict sudden braking and adjust weights. Specifically, if the self-lane brakes suddenly, the basic weight + the fifth preset value; if the other lanes brake suddenly, the basic weight - the sixth preset value. Among them, the fifth preset value and the sixth preset value can be user-defined values, and there is no limit to this. For example, the fifth preset value is 30, or 40, or 60, etc., and there is no limit to this; the sixth preset value is 8, or 11, or 12, etc., and there is no limit to this.

[0126] Static perception:

[0127] 1) For objects that will not move for a long time, such as no-entry signs, ice cream buckets, etc., the basic weight + the seventh preset value. The seventh preset value is 80 or 90, etc., and there is no limit on this.

[0128] 2) For objects that can be removed in a short time, such as dense vehicles and traffic accidents, the basic weight + the eighth preset value. The eighth preset value is 60 or 65, etc., and there is no limit on this.

[0129] b. Traffic information: severe congestion, basic weight + ninth preset value; congestion, basic weight + tenth preset value; smooth, basic weight + eleventh preset value. Among them, the ninth preset value is greater than the tenth preset value, and the tenth preset value is greater than the eleventh preset value; the ninth, tenth, and eleventh preset values ​​are all user-defined values ​​and are not limited to them. For example, the ninth preset value is 60, the tenth preset value is 40, and the eleventh preset value is 10, etc.

[0130] c. Lane edge attributes: solid line, basic weight + 12th preset value; dotted line, basic weight + 13th preset value. The 12th and 13th preset values ​​are user-defined values ​​and are not limited. For example, the 12th preset value is 30, the 13th preset value is 5, etc.

[0131] d. Number of lane changes: If the number of lane changes exceeds the threshold (e.g. 3 times, adjustable), additional weight is added according to the driving style. For aggressive driving, the basic weight + the 14th preset value; for steady driving, the basic weight + the 15th preset value. Among them, the 14th and 15th preset values ​​are user-defined values ​​and are not limited to them. For example, the 14th preset value is 20, the 15th preset value is 50, etc.

[0132] e. Traffic light information: The remaining time of the green light is less than 5 seconds, the basic weight + the 16th preset value. The 16th preset value is a value set by the user and is not limited to this. For example, the 16th preset value is 30, etc.

[0133] h(n) (heuristic cost estimate) calculation:

[0134] According to the expected distance to the target lane, the Euclidean distance method is used. Combined with the traffic conditions, if the distance to the target is close and the traffic is smooth, the expected cost can be reduced, the basic weight-the seventeenth preset value. Among them, the seventeenth preset value is a value set by the user, and there is no limitation on this. For example, the seventeenth preset value is 5, etc. It should be noted that the above multiple preset values ​​can have the same value or different values, and there is no limitation on this.

[0135] 3. Main steps

[0136] 1). Select the node with the smallest f(n)=g(n)+h(n) from the candidate list as the current node.

[0137] 2). Check whether the current node is the target node. If so, end the algorithm and return the path.

[0138] 3). Otherwise, move the current node into the result list.

[0139] 4). Traverse the adjacent nodes of the current node and calculate g(n) and h(n) of each adjacent node.

[0140] 5). For each adjacent node:

[0141] If the node is in the result list, skip it.

[0142] If the node is not in the candidate list, add and record the parent node.

[0143] If the node is in the candidate list, check whether its g(n) needs to be updated (i.e. a shorter path is found). If necessary, update and record the parent node.

[0144] 6). Repeat steps 1 to 5 until the target node is found.

[0145] Through the above detailed description and weight calculation mechanism, the A* algorithm can effectively select lanes by considering multiple factors to achieve the purpose of improving driving safety and efficiency. In actual applications, the weight value may need to be adjusted according to the actual scenario to ensure the effectiveness of the algorithm.

[0146] Data delivery:

[0147] like Figure 3 As shown, the Decision module (i.e., the decision module) notifies the icloud-lane module (cloud service-lane module) of the selected lane-level recommended route, and sends the lane-level recommended route to the vehicle computer through the icloud-lane module.

[0148] Therefore, through the above process, the Decision module can effectively calculate the optimal lane and improve driving safety and efficiency.

[0149] S204: monitor and update each road section on the lane-level recommended route in turn according to a preset monitoring distance, and generate an updated lane-level recommended route.

[0150] In one example, step S204 includes: monitoring each road section on the lane-level recommended route in turn according to a preset monitoring distance to generate lane information for each road section; determining the lane-level recommended route in each road section in turn according to the lane information for each road section; determining the end of the lane-level recommended route as the next turning point according to the distance between the vehicle terminal and the end of the lane-level recommended route; updating the lane-level recommended route according to the lane where the next turning point is located and the end of the lane to generate an updated lane-level recommended route.

[0151] Exemplarily, the cloud device can update the real-time recommended lanes in sections. Specifically, based on the lane-level recommended route, the road conditions within the preset monitoring distance in front of the vehicle terminal itself are continuously monitored, and the lane information of each section of the road is generated in sequence; wherein, the preset monitoring distance can be 200 meters, 100 meters, etc., and there is no limitation on this. According to the lane information of each section of the road, the lane-level recommended route within the monitoring distance in front is updated in real time in sequence. If it is updated to the preset distance before the end of the lane-level recommended route, for example, it is updated to 100 meters before the end of the lane-level recommended route, that is, the distance between the vehicle terminal and the end of the lane-level recommended route is 100 meters, which can be regarded as 100 meters before the first turning point, and the end of the lane-level recommended route is determined to be the next turning point. At this time, when the vehicle passes the next turning point, the basic route needs to be recalculated from the current position. Specifically, the lane where the next turning point is located and the end of the lane are determined, and according to the lane where the next turning point is located and the end of the lane, the A* algorithm is used again to update the lane-level recommended route to generate an updated lane-level recommended route.

[0152] Therefore, through the interconnection between cloud devices and vehicle terminals, the real-time positioning information and perception information of the vehicle terminal, the map data information of the cloud device, road congestion information, traffic light information including intersections, the user's personal driving habits and the recommended driving information of the current section are comprehensively processed, and the optimal lane-level recommended route can be calculated and recommended in real time.

[0153] S205. Send the lane-level recommended route to the vehicle terminal; wherein the lane-level recommended route is used to indicate the driving route of the vehicle terminal.

[0154] For example, the cloud device may send a lane-level recommended route to the vehicle terminal, where the lane-level recommended route indicates the driving route of the vehicle terminal. After receiving the lane-level recommended route, the vehicle terminal drives according to the lane-level recommended route.

[0155] S206. Determine a weight value of the lane passed by the vehicle terminal according to the lane information of the vehicle terminal.

[0156] Exemplarily, in order to prevent route deviation, the cloud device can determine the weight value of the lane passed by the vehicle terminal based on the lane information of the vehicle terminal. For example, a lane-level recommended route is currently generated. Since the lane information is generated based on a preset monitoring distance, the vehicle terminal can determine all the distances passed by the lane-level recommended route based on multiple lane information, and then determine the actual lane-level recommended route passed by the vehicle terminal, and the weight value of the lane-level recommended route.

[0157] S207: If it is determined that the weight value is greater than a preset weight threshold, the lane passed by the vehicle terminal is determined to be a deviated lane, and the lane-level recommended route is re-determined.

[0158] Exemplarily, the cloud device can compare the weight value with a preset weight threshold. If it is determined that the weight value is greater than the weight threshold, it is judged as a serious deviation, and the lane passed by the vehicle terminal is determined to be a deviated lane, triggering a mechanism for re-planning the basic route, and re-determining the lane-level recommended route according to the above steps.

[0159] The route recommendation method provided in the embodiment of the present application receives vehicle data sent by a vehicle terminal; wherein the vehicle data is the perception data and positioning information of the current lane where the vehicle terminal is located. The vehicle data and the cloud information within the first preset range are fused to obtain fusion result data; the cloud information includes map data information, road congestion information, and traffic light information including intersections. Based on the fusion result data, a lane-level recommended route for the vehicle terminal is generated. According to the preset monitoring distance, each section of the road on the lane-level recommended route is monitored and updated in turn to generate an updated lane-level recommended route. The lane-level recommended route is sent to the vehicle terminal; wherein the lane-level recommended route is used to indicate the driving route of the vehicle terminal. According to the lane information of the vehicle terminal, the weight value of the lane passed by the vehicle terminal is determined. If it is determined that the weight value is greater than the preset weight threshold, the lane passed by the vehicle terminal is determined to be a deviated lane, and the lane-level recommended route is re-determined. Therefore, placing the calculation of the recommended route on the cloud device can refer to more road conditions and real-time information, while reducing the computing power consumption of the vehicle terminal, making the lane-level recommended route calculation more accurate, so that the vehicle terminal can increase its trust in the lane-level recommended route, so as to achieve perfect vehicle control and achieve the effect of improving the accuracy of the navigation route.

[0160] Figure 4 A schematic diagram of the structure of a route recommendation device provided in this application, such as Figure 4 As shown, the route recommendation device 30 provided in this embodiment includes:

[0161] The receiving module 31 is used to receive vehicle data sent by the vehicle terminal; wherein the vehicle data is the sensing data and positioning information of the current lane where the vehicle terminal is located;

[0162] A fusion module 32 is used to fuse the vehicle data and the cloud information within the first preset range to obtain fusion result data; the cloud information includes map data information, road congestion information, and traffic light information including intersections;

[0163] A generation module 33, used to generate a lane-level recommended route for the vehicle terminal according to the fusion result data;

[0164] The sending module 34 is used to send the lane-level recommended route to the vehicle terminal; wherein the lane-level recommended route is used to indicate the driving route of the vehicle terminal.

[0165] Figure 5 A schematic diagram of the structure of another route recommendation device provided in an embodiment of the present application, Figure 4 Based on the embodiment shown, Figure 5 As shown, the perception information represents data of a perceived object within a second preset range around the vehicle terminal;

[0166] The fusion module 32 is specifically used for:

[0167] Restoring the map data information within the first preset range in the preset cloud navigation data to obtain restored map data information within the first preset range;

[0168] Matching the road congestion information and the traffic light information including the intersection with the restored map data information to obtain matching result information;

[0169] The perception information is restored in the cloud navigation data, and the trajectory of each perception object in the perception information is predicted to generate the running trajectory of each perception object at the next moment;

[0170] According to the matching result information and the running trajectory, the fusion result data is obtained.

[0171] In a possible implementation, the positioning information includes the current location of the vehicle terminal;

[0172] The generating module 33 is specifically used for:

[0173] According to the fusion result data, the lane information of the first turning point at a preset distance ahead of the current position is determined;

[0174] According to the lane information, determine the lane where the first turning point is located and determine the end point of the lane;

[0175] Generate a lane-level recommended route for the vehicle terminal based on the current location and destination.

[0176] In a possible implementation, the device further includes:

[0177] The updating module 41 is used to monitor and update each road section on the lane-level recommended route in turn according to a preset monitoring distance, and generate an updated lane-level recommended route.

[0178] In a possible implementation, the updating module 41 is specifically configured to:

[0179] According to the preset monitoring distance, each section of the road on the lane-level recommended route is monitored in turn to generate lane information for each section of the road;

[0180] According to the lane information of each road section, the lane-level recommended route in each road section is determined in turn;

[0181] According to the distance between the vehicle terminal and the end of the lane-level recommended route, determining the end of the lane-level recommended route as the next turning point;

[0182] The lane-level recommended route is updated according to the lane where the next turning point is located and the end point of the lane, and an updated lane-level recommended route is generated.

[0183] In a possible implementation, the device further includes an acquisition module 42, the acquisition module 42 is used to acquire cloud information within a first preset range, and the acquisition module 42 is specifically used to:

[0184] Determining map data information of a first preset range in the cloud navigation data according to the positioning information in the vehicle data;

[0185] Through the preset service equipment, the road congestion information within the first preset range and the traffic light information including the intersection are obtained.

[0186] In a possible implementation, the device is further specifically used for:

[0187] Determine the weight value of the lane passed by the vehicle terminal according to the lane information of the vehicle terminal;

[0188] If it is determined that the weight value is greater than a preset weight threshold, the lane passed by the vehicle terminal is determined to be a deviated lane, and the lane-level recommended route is re-determined.

[0189] The device provided in this embodiment can execute the method provided in the above method embodiment. Its implementation principle and technical effect are similar, and this embodiment will not be described in detail here.

[0190] Figure 6 This is a schematic diagram of the structure of the cloud device provided by this application. Figure 6 As shown, the cloud device 50 provided in this embodiment includes: at least one processor 501 and a memory 502. Optionally, the device 50 also includes a communication component 503. The processor 501, the memory 502 and the communication component 503 are connected via a bus 504.

[0191] In a specific implementation process, at least one processor 501 executes the computer-executable instructions stored in the memory 502, so that at least one processor 501 executes the above method.

[0192] The specific implementation process of the processor 501 can be found in the above method embodiment, and its implementation principle and technical effect are similar, so this embodiment will not be repeated here.

[0193] In the above embodiments, it should be understood that the processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), etc. A general-purpose processor may be a microprocessor or any conventional processor. The steps of the method disclosed in the invention may be directly implemented as being executed by a hardware processor, or may be executed by a combination of hardware and software modules in the processor.

[0194] The memory may include a high-speed memory (Random Access Memory, RAM), and may also include a non-volatile memory (Non-volatile Memory, NVM), such as at least one disk memory.

[0195] The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, the bus in the drawings of the present application is not limited to only one bus or one type of bus.

[0196] The present application also provides a computer program product, including a computer program, which implements the above method when executed by a processor.

[0197] The present application also provides a computer-readable storage medium, in which computer-executable instructions are stored. When a processor executes the computer-executable instructions, the above method is implemented.

[0198] The above-mentioned readable storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk. The readable storage medium can be any available medium that can be accessed by a general or special-purpose computer.

[0199] An exemplary readable storage medium is coupled to a processor so that the processor can read information from the readable storage medium and write information to the readable storage medium. Of course, the readable storage medium can also be a component of the processor. The processor and the readable storage medium can be located in an application specific integrated circuit (Application Specific Integrated Circuits, referred to as: ASIC). Of course, the processor and the readable storage medium can also exist in the device as discrete components.

[0200] The division of units is only a logical function division, and there may be other divisions in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interface, device or unit, which can be electrical, mechanical or other forms.

[0201] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0202] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0203] If the function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods of each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc. Various media that can store program codes.

[0204] Those skilled in the art can understand that all or part of the steps of implementing the above-mentioned method embodiments can be completed by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When the program is executed, the steps of the above-mentioned method embodiments are executed; and the aforementioned storage medium includes: ROM, RAM, disk or optical disk and other media that can store program codes.

[0205] Finally, it should be noted that those skilled in the art will readily conceive of other embodiments of the present invention after considering the specification and practicing the invention disclosed herein. The present invention is intended to cover any variations, uses or adaptations of the present invention, which follow the general principles of the present invention and include common knowledge or customary technical means in the art not disclosed by the present invention, are not limited to the precise structure described above and shown in the drawings, and may be modified and changed in various ways without departing from the scope thereof. The scope of the present invention is limited only by the appended claims.

Claims

1. A route recommendation method, characterized in that: include: Receiving vehicle data sent by a vehicle terminal; wherein the vehicle data is perception data and positioning information of a current lane in which the vehicle terminal is located; The vehicle data and the cloud information within the first preset range are fused to obtain fusion result data; the cloud information includes map data information, road congestion information, and traffic light information including intersections; Generate a lane-level recommended route for the vehicle terminal based on the fusion result data; send the lane-level recommended route to the vehicle terminal; wherein the lane-level recommended route is used to indicate a driving route for the vehicle terminal.

2. The method according to claim 1, characterized in that The perception information represents data of a perceived object within a second preset range around the vehicle terminal; The vehicle data and the cloud information within the first preset range are fused to obtain fusion result data, including: Restoring the map data information within the first preset range in preset cloud navigation data to obtain restored map data information within the first preset range; Matching the road congestion information and the traffic light information at the intersection with the restored map data information to obtain matching result information; The perception information is restored in the cloud navigation data, and the trajectory of each perception object in the perception information is predicted to generate the running trajectory of each perception object at the next moment; According to the matching result information and the running trajectory, fusion result data is obtained.

3. The method according to claim 1, characterized in that The positioning information includes the current location of the vehicle terminal; Generating a lane-level recommended route for the vehicle terminal according to the fusion result data includes: Determine lane information of a first turning point at a preset distance ahead of the current position according to the fusion result data; Determine the lane where the first turning point is located according to the lane information, and determine the end point of the lane; A lane-level recommended route for the vehicle terminal is generated based on the current position and the destination.

4. The method according to claim 1, characterized in that: The method further includes: According to a preset monitoring distance, each road section on the lane-level recommended route is monitored and updated in turn to generate an updated lane-level recommended route.

5. The method according to claim 4, characterized in that The monitoring and updating of each road section on the lane-level recommended route is performed in sequence according to a preset monitoring distance to generate an updated lane-level recommended route, including: According to a preset monitoring distance, each road section on the lane-level recommended route is monitored in turn to generate lane information of each road section; According to the lane information of each road section, the lane-level recommended route in each road section is determined in turn; Determining, according to the distance between the vehicle terminal and the end of the lane-level recommended route, that the end of the lane-level recommended route is the next turning point; The lane-level recommended route is updated according to the lane where the next turning point is located and the end point of the lane to generate an updated lane-level recommended route.

6. The method according to any one of claims 1 to 5, characterized in that: The method for obtaining the cloud information within the first preset range includes: Determining map data information of the first preset range in the cloud navigation data according to the positioning information in the vehicle data; The road congestion information within the first preset range and the traffic light information including the intersection are obtained through the preset service equipment.

7. The method according to any one of claims 1 to 5, characterized in that: The method further comprises: Determining a weight value of a lane passed by the vehicle terminal according to the lane information of the vehicle terminal; If it is determined that the weight value is greater than a preset weight threshold, the lane passed by the vehicle terminal is determined to be a deviated lane, and the lane-level recommended route is re-determined.

8. A route recommendation device, characterized in that: include: A receiving module, used to receive vehicle data sent by a vehicle terminal; wherein the vehicle data is the sensing data and positioning information of the current lane where the vehicle terminal is located; An acquisition module, configured to acquire cloud information within a first preset range according to the vehicle data; wherein the cloud information includes map data information, road congestion information, and traffic light information including intersections; A generation module, used to generate a lane-level recommended route for the vehicle terminal based on the vehicle data and the cloud information; A sending module is used to send the lane-level recommended route to the vehicle terminal; wherein the lane-level recommended route is used to indicate the driving route of the vehicle terminal.

9. A cloud device, characterized in that: include: Memory, processor; The memory stores computer-executable instructions; The processor executes the computer-executable instructions stored in the memory, so that the processor performs the method according to any one of claims 1 to 7.

10. A computer-readable storage medium / computer program product, characterized in that: The computer-readable storage medium stores computer-executable instructions, which are used to implement the method according to any one of claims 1 to 7 when executed by a processor; and / or, The computer program product comprises a computer program, and when the computer program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

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