Path planning methods, devices, electronic equipment and storage media

Abstract

This application provides a route planning method, apparatus, electronic device, and storage medium. The method includes: acquiring a start point and an end point; determining initial routes planned by various navigation applications, including third-party navigation applications and a currently used target navigation application; predicting a first number of vehicles on each first initial route in a future time period; predicting a second number of vehicles on the target initial route in a future time period; predicting the congestion level of each first initial route in a future time period based on the first and second vehicle numbers; and planning a travel route based on the congestion level of each first initial route in a future time period. This method considers the future number of users planning routes through various navigation applications, making the routes planned for vehicles closer to the actual optimal routes, thereby saving users' travel time.

Description

Technical Field

[0001] This application relates to the field of vehicle navigation, and more specifically, to a path planning method, apparatus, electronic device, and storage medium in the field of vehicle navigation. Background Technology

[0002] Navigation technology uses a positioning system to obtain the user's accurate location and plans a travel route based on the user's set start and end points, thus providing traffic navigation for the user.

[0003] Current navigation applications primarily plan routes based on current traffic conditions and do not consider future changes in traffic flow. For example, based on current traffic conditions, a certain route may have low traffic volume, and the navigation application might use this route as the intended travel path. However, in reality, a large number of vehicles may pass through this route in the next half hour, causing congestion. If vehicles follow the route planned by the navigation application, it will contribute to traffic congestion, delaying travel plans and reducing user travel efficiency. Summary of the Invention

[0004] This application provides a route planning method, apparatus, electronic device, and storage medium. The method takes into account the impact of future traffic conditions on vehicle travel, making the planned route for the vehicle closer to the actual optimal route, thereby saving users' travel time.

[0005] Firstly, a path planning method is provided, which includes:

[0006] Get the start and end points;

[0007] Determine the initial path between the start and end points of each navigation application's plan, including third-party navigation applications and the currently used target navigation application;

[0008] Predict the number of vehicles on each initial path in the future time period. The initial path is the initial path planned by the target navigation application.

[0009] Predict the number of second vehicles on the target initial path in a future time period. The target initial path includes the path that is the same as any first initial path among the second initial paths. The second initial path is the initial path planned by a third-party navigation application.

[0010] Based on the number of the first and second vehicles, predict the congestion level of each initial path in the future time period;

[0011] Based on the congestion level of each first initial path in the future time period, a travel route is determined among each first initial path.

[0012] In the above technical solution, since it is impossible for all users to use the same navigation application, and different navigation applications may plan different routes, this application embodiment, after predicting the future number of vehicles on the first initial path planned by the currently used target navigation application, also predicts the future number of vehicles on the target initial path planned by a third-party navigation application. Since the target initial path is the same as the first initial path in the second initial path planned by the third-party navigation application, the total number of vehicles planning to travel on the first initial path through each navigation path can be obtained based on these two future vehicle numbers, thereby obtaining the future congestion level of the first initial path. That is, this application embodiment considers the number of vehicles planning routes through various navigation applications in the future, which can obtain a traffic situation closer to reality, thereby making the route planned for vehicles closer to the actual optimal route and further saving users' travel time.

[0013] In conjunction with the first aspect, in some possible implementations, the prediction of the number of first vehicles on each first initial path in a future time period includes:

[0014] Obtain vehicle navigation route information that has been planned by the target navigation application;

[0015] Based on the vehicle navigation route information, determine the number of the first vehicles on each of the first initial routes planned by the target navigation application in the future time period;

[0016] Predict the number of second vehicles on the target's initial path in future time periods, including:

[0017] Acquire market share of various navigation applications;

[0018] Based on the market share of each navigation application and the number of first vehicles, predict the number of second vehicles on the target initial path in the future time period.

[0019] Combining the first aspect and the above implementation methods, in some possible implementation methods, based on the market share of each navigation application and the number of first vehicles, the number of second vehicles on the target initial path in a future time period is predicted, including:

[0020] Calculate the sum of the number of first vehicles on each first initial path in the future time period to obtain the total number of first vehicles;

[0021] The second total number of vehicles is determined based on the market share of the target navigation application, the first total number of vehicles, and the market share of third-party navigation applications. The second total number of vehicles is the sum of the number of vehicles on each second initial path in the future time period.

[0022] Based on the total number of second vehicles and the number of each second initial path, determine the number of second vehicles for the target initial path in the future time period.

[0023] Combining the first aspect and the above implementation methods, in some possible implementation methods, the number of second vehicles for the target initial path in a future time period is determined based on the total number of second vehicles and the number of each second initial path, including:

[0024] Based on the number of vehicles on each second initial path, the total number of second vehicles is allocated to each second initial path to obtain the number of second vehicles on each second initial path in the future time period.

[0025] Determine the number of second vehicles on the target initial path in the future time period from the number of second vehicles on each second initial path in the future time period.

[0026] In conjunction with the first aspect and the above implementation methods, in some possible implementations, after determining the initial path between the start and end points of each navigation application plan, the method further includes:

[0027] In each initial path, identify the congested path and determine the congested sections and congestion types on the congested path;

[0028] Determine the congestion relief time for congested road segments within the congestion path based on the congestion type;

[0029] Based on the congestion relief time, the probability of congestion relief when vehicles arrive at the congested section from the starting point is predicted, and congestion paths with the probability of congestion relief are obtained.

[0030] Based on the congestion level of each initial path in the future time period, determine the travel route among each initial path, including:

[0031] Based on the congestion level of each initial path in the future time period and the congested paths with the possibility of congestion relief, the travel path is determined among each initial path.

[0032] Combining the first aspect and the above implementation methods, in some possible implementations, the starting point and ending point are carried in the route planning request. Based on the congestion level of each first initial path in the future time period, the travel route is determined from each first initial path, including:

[0033] If multiple route planning requests are obtained at the same time and the multiple route planning requests meet the preset conditions, different travel routes are planned for different route planning requests based on the congestion level of each first initial path in the future time period. The preset conditions are that the distance between any two starting points in the multiple route planning requests is less than a first preset distance, and the distance between any two ending points in the multiple route planning requests is less than a second preset distance.

[0034] Combining the first aspect and the above implementation methods, in some possible implementation methods, the travel route is determined among the first initial paths based on the congestion level of each first initial path in the future time period, including:

[0035] Among the various initial paths, the first initial path whose congestion level is lower than the preset congestion level in the future time period is determined as the travel path.

[0036] Secondly, a path planning device is provided, the device comprising:

[0037] The acquisition module is used to obtain the start and end points;

[0038] The first determining module is used to determine the initial path between the starting point and the ending point planned by each navigation application, including third-party navigation applications and the target navigation application currently in use.

[0039] The first prediction module is used to predict the number of vehicles on each first initial path in a future time period. The first initial path is the initial path planned by the target navigation application.

[0040] The second prediction module is used to predict the number of second vehicles on the target initial path in a future time period. The target initial path includes the path that is the same as any first initial path among the second initial paths. The second initial path is the initial path planned by a third-party navigation application.

[0041] The third prediction module is used to predict the congestion level of each first initial path in the future time period based on the first number of vehicles and the second number of vehicles.

[0042] The second determining module is used to determine the travel route among the first initial paths based on the congestion level of each first initial path in the future time period.

[0043] In conjunction with the second aspect, in some possible implementations, the first prediction module includes:

[0044] The first acquisition unit is used to acquire vehicle navigation path information that has been planned by the target navigation application;

[0045] The first determining unit is used to determine the number of first vehicles in a future time period for each first initial path planned by the target navigation application based on the vehicle navigation path information.

[0046] The second prediction module includes:

[0047] The second acquisition unit is used to acquire the market share of each navigation application.

[0048] The prediction unit is used to predict the number of second vehicles on the target initial path in a future time period based on the market share of each navigation application and the number of first vehicles.

[0049] Combining the second aspect and the above implementation methods, in some possible implementations, the prediction unit includes:

[0050] The calculation subunit is used to calculate the sum of the number of first vehicles on each first initial path in a future time period to obtain the total number of first vehicles;

[0051] The first determining subunit is used to determine the second total number of vehicles based on the market share of the target navigation application, the first total number of vehicles, and the market share of the third-party navigation application. The second total number of vehicles is the sum of the number of vehicles in each second initial path in the future time period.

[0052] The second determining subunit is used to determine the number of second vehicles for the target initial path in a future time period based on the total number of second vehicles and the number of each second initial path.

[0053] In conjunction with the second aspect and the above implementation methods, in some possible implementation methods, the second determining subunit is further used for:

[0054] Based on the number of vehicles on each second initial path, the total number of second vehicles is allocated to each second initial path to obtain the number of second vehicles on each second initial path in the future time period.

[0055] Determine the number of second vehicles on the target initial path in the future time period from the number of second vehicles on each second initial path in the future time period.

[0056] In conjunction with the second aspect and the above implementation methods, in some possible implementation methods, the first determining module is also used for:

[0057] In each initial path, identify the congested path and determine the congested sections and congestion types on the congested path;

[0058] Determine the congestion relief time for congested road segments within the congestion path based on the congestion type;

[0059] Based on the congestion relief time, the probability of congestion relief when vehicles arrive at the congested section from the starting point is predicted, and congestion paths with the probability of congestion relief are obtained.

[0060] The second determining module is also used for:

[0061] Based on the congestion level of each initial path in the future time period and the congested paths with the possibility of congestion relief, the travel path is determined among each initial path.

[0062] In conjunction with the second aspect and the above implementation methods, in some possible implementations, the start point and end point are carried in the path planning request, and the second determining module is also used for:

[0063] If multiple route planning requests are obtained at the same time and the multiple route planning requests meet the preset conditions, different travel routes are planned for different route planning requests based on the congestion level of each first initial path in the future time period. The preset conditions are that the distance between any two starting points in the multiple route planning requests is less than a first preset distance, and the distance between any two ending points in the multiple route planning requests is less than a second preset distance.

[0064] In conjunction with the second aspect and the above implementation methods, in some possible implementation methods, the second determining module is further used for:

[0065] In each initial path, the initial path with a congestion level lower than the preset congestion level in the future time period is determined as the travel path.

[0066] Thirdly, an electronic device is provided, including a memory and a processor. The memory is used to store executable program code, and the processor is used to call and run the executable program code from the memory, causing the electronic device to perform the methods of the first aspect or any possible implementation thereof.

[0067] Fourthly, a computer program product is provided, comprising: computer program code, which, when run on a computer, causes the computer to perform the methods described in the first aspect or any possible implementation thereof.

[0068] Fifthly, a computer-readable storage medium is provided that stores computer program code, which, when executed on a computer, causes the computer to perform the methods described in the first aspect or any possible implementation thereof. Attached Figure Description

[0069] Figure 1 This is a schematic diagram of the structure of a traffic navigation system provided in an embodiment of this application;

[0070] Figure 2 This is a schematic flowchart of a path planning method provided in an embodiment of this application;

[0071] Figure 3 This is a schematic diagram of the structure of a path planning device provided in an embodiment of this application;

[0072] Figure 4This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0073] The technical solutions in this application will be clearly and thoroughly described below with reference to the accompanying drawings. In the description of the embodiments of this application, unless otherwise stated, " / " means "or," for example, A / B can mean A or B. "And / or" in the text is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone. Furthermore, in the description of the embodiments of this application, "multiple" refers to two or more than two.

[0074] Hereinafter, the terms "first" and "second" are used for descriptive purposes only and should not be construed as implying or suggesting relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature.

[0075] Before providing a detailed explanation of the path planning method provided in the embodiments of this application, let's first introduce the application scenarios provided in the embodiments of this application.

[0076] Currently, traffic navigation systems perform dynamic route planning based on static road networks and real-time traffic information to determine the optimal driving route that meets certain conditions. Most traffic navigation systems have wireless connectivity, allowing them to obtain real-time traffic information and select navigation routes accordingly. However, existing traffic navigation systems do not consider the impact of future traffic conditions on route planning. This means that if a user travels along a route planned by the navigation system, they may encounter severe congestion in the future, causing delays and reducing user trust in the system, thus rendering it somewhat meaningless.

[0077] Based on this, embodiments of this application provide a route planning method that takes into account the impact of future traffic conditions on vehicle travel, making the planned route for the vehicle closer to the actual optimal route, thereby saving users' travel time.

[0078] Please refer to Figure 1 , Figure 1 This is a schematic diagram of the structure of a traffic navigation system provided in an embodiment of this application. Figure 1 As shown, the traffic navigation system includes a traffic analysis and planning module 101, a third-party navigation application 102, and a navigation device 103. The traffic analysis and planning module 101 is connected to the third-party navigation application 102 via an interface. The third-party navigation application may include at least one navigation application other than the target navigation application. Figure 1 The following is an example of a third-party navigation application.

[0079] The navigation device 103 is used to obtain the starting point and the ending point, and send the starting point and the ending point to the traffic analysis and planning module 101.

[0080] The traffic analysis and planning module 101 is used to send the starting point and the ending point to a third-party navigation application 102.

[0081] The third-party navigation application 102 is used to plan an initial route based on the starting point and the ending point, and then sends the initial route to the traffic analysis and planning module 101.

[0082] The traffic analysis and planning module 101 is used to determine the number of first vehicles on each first initial path planned by the target navigation application in a future time period based on the start point and end point, and to predict the number of second vehicles on each second initial path sent by the third-party navigation application 102 in a future time period. Furthermore, based on the number of first and second vehicles, it predicts the congestion level of each first initial path in a future time period, and then plans the travel route between the start point and end point. The process by which the traffic analysis and planning module 101 predicts the future congestion level of each initial path will be described in detail later and will not be repeated here.

[0083] In addition, after the traffic analysis and planning module 101 plans the travel route, it can also send the planned travel route to the navigation device 103 so that the navigation device 103 can display the travel route to the user.

[0084] Optionally, the traffic navigation system may further include a traffic data acquisition module 104. This traffic data acquisition module 104 collects real-time traffic flow data and sends it to the traffic analysis and planning module 101, enabling the traffic analysis and planning module 101 to determine the current traffic conditions based on the real-time traffic flow data, and thus determine the initial route. The process by which the traffic analysis and planning module 101 determines the initial route will be described in detail later and will not be repeated here.

[0085] The traffic analysis and planning module 101 can be integrated into the navigation device 103 or deployed in the cloud; this embodiment does not limit this. The navigation device 103 can be a mobile phone or a vehicle-mounted system.

[0086] The path planning method provided in the embodiments of this application will be explained in detail below.

[0087] Figure 2 This is a schematic flowchart illustrating a path planning method provided in an embodiment of this application. The method is exemplarily applied to... Figure 1 The traffic analysis and planning module shown is as follows: Figure 2 As shown, the method includes the following steps.

[0088] Step 201: Obtain the starting point and the ending point.

[0089] The starting point is the user's origin, and the ending point is the user's desired destination. After the user inputs the starting and ending points on the navigation device, the device generates a route planning request based on the user's input and sends it to the traffic analysis and planning module, which then plans a travel route for the user. This route planning request includes the starting and ending points. Optionally, the route planning request may also include the time the user interacted with the navigation device.

[0090] Step 202: Determine the initial path between the start and end points of each navigation application plan.

[0091] The navigation applications include third-party navigation applications and the target navigation application currently in use. The third-party navigation applications are navigation applications other than the target navigation application. The initial paths include the first initial paths planned by the target navigation application and the second initial paths planned by the third-party navigation application.

[0092] In some embodiments, step 202 can be implemented by: acquiring real-time traffic flow data of the road segment between the starting point and the ending point; determining the current traffic conditions based on the real-time traffic flow data; and determining the initial path between the starting point and the ending point based on the current traffic conditions.

[0093] Real-time traffic flow data can be acquired through a traffic data acquisition module, which uses roadside infrastructure to collect real-time data on the entire road network, including road location information and surrounding vehicle information. This roadside infrastructure can include wireless devices such as base stations and positioning anchors. After acquiring the real-time traffic flow data for the entire road network, the traffic data acquisition module sends this data to the traffic analysis and planning module. This allows the traffic analysis and planning module to determine the current traffic conditions based on the real-time traffic flow data of the road segments between the starting and ending points, and thus determine the initial path.

[0094] Optionally, after collecting real-time traffic flow data for the entire road network, the traffic data acquisition module can also directly determine the current traffic conditions of the entire road network based on the real-time traffic flow data, and send the current traffic conditions of the entire road network to the traffic analysis and planning module, so that the traffic analysis and planning module can determine the initial path between the starting point and the ending point based on the current traffic conditions of the road segments between the starting point and the ending point.

[0095] Since the traffic data acquisition module and the traffic analysis and planning module determine the current traffic conditions in the same way based on real-time traffic flow data, the following explanation will take the traffic analysis and planning module's method of determining the current traffic conditions as an example.

[0096] Once the traffic analysis and planning module obtains the user's travel plan, i.e., the start and end points, it can determine the road segments between these points. After receiving real-time traffic flow data for the entire road network from the traffic data collection module, it determines the real-time traffic flow data for each road segment between the start and end points from this data. This means determining the number of online vehicles on each road segment within the area between the start and end points and comparing it with the vehicle capacity of the corresponding road segment to determine the traffic condition of each road segment at the current time. This current traffic condition can be represented by the congestion level. A higher congestion level indicates that the road segment is more congested at the current time, and vice versa.

[0097] After obtaining the current traffic conditions, the traffic analysis and planning module can use multiple road segments with low congestion levels from the starting point to the ending point as the first initial path for the target navigation application planning.

[0098] For the same starting and ending points, different navigation applications may plan different paths. Therefore, after obtaining the first initial paths between the starting and ending points of the target navigation plan, an interface can be called to obtain the second initial paths between the starting and ending points of other third-party navigation applications. Subsequently, by predicting the number of vehicles on the same paths as the first initial paths in the second initial paths, a more realistic number of vehicles on the first initial paths can be obtained. This makes the paths planned for vehicles closer to the actual optimal paths, further saving users' travel time.

[0099] Therefore, the traffic analysis and planning module can also call the interfaces of other third-party navigation applications. By inputting the start and end points in the third-party navigation application, it can obtain the second initial path planned for the user by the third-party navigation application. For example, the start and end points can be input in the third-party navigation application, which will plan three paths based on the start and end points and send these three paths as second initial paths to the traffic analysis and planning module. The traffic analysis and planning module then obtains the second initial paths planned by the third-party navigation application.

[0100] Furthermore, the embodiments of this application can also consider the current traffic congestion situation, such as a traffic accident causing congestion on a certain road segment at the current time, i.e., a congested road segment, and predict the possibility of congestion being relieved when the vehicle arrives at the congested road segment by combining the time required to handle such congestion situations under normal circumstances.

[0101] In some embodiments, a congested path can be determined in each initial path, and congested segments and congestion types on the congested path can be determined; based on the congestion type, the congestion relief time of the congested segments in the congested path can be determined; based on the congestion relief time, the probability of congestion relief when a vehicle arrives at the congested segment from the starting point can be predicted, thereby obtaining a congested path with the probability of congestion relief.

[0102] The traffic analysis and planning module obtains real-time traffic flow data for the entire road network, i.e., the current traffic conditions of each initial path. Based on the current traffic conditions, it identifies congested paths among the initial paths. For example, the path with the highest congestion level among the initial paths can be identified as the congested path. Furthermore, it can identify the specific road segments experiencing congestion within the congested path, thus identifying the congested road segments and the reasons for the congestion, i.e., the congestion type. This allows us to obtain the congested road segments and congestion types on the congested path; for example, road segment A might be congested due to a rear-end collision. Typically, different congestion types require different processing times. Therefore, based on the congestion type, the congestion relief time for the congested road segments within the congested path can be determined.

[0103] After obtaining the congestion relief time, the time required for a vehicle to reach the congested section can be predicted based on the current traffic conditions. If the congestion relief time is longer than the time required for the vehicle to reach the congested section, it means that the congested section will still be congested when the vehicle arrives, and in this case, the initial path including that congested section can be disregarded. If the congestion relief time is shorter than the time required for the vehicle to reach the congested section, it means that the congestion in that section has been relieved when the vehicle arrives. Subsequently, based on the congested paths including that congested section and the future congestion levels of each initial path, a travel route can be planned for the user.

[0104] Step 203: Predict the number of vehicles on each initial path in the future time period.

[0105] The preset time period can be set in advance, and for example, it can be set to half an hour. However, this application embodiment does not limit this.

[0106] When a user operates a navigation device and the target navigation application plans a travel route for that user, it is simultaneously planning travel routes for other users. Some segments of these users' routes overlap with segments of the initial route. Therefore, these users will also traverse the initial route in the future. This simultaneous planning of travel routes for other users includes situations where the target navigation application has already completed planning and these users are already in the navigation process, and / or situations where the target navigation application has completed planning, but the users have not yet traveled at the current time, but will travel at some time after the current time, and will also traverse the initial route in the future.

[0107] Therefore, the implementation process of step 203 can be as follows: obtain the vehicle navigation path information that the target navigation application has planned; and determine the number of vehicles in the future time period for each first initial path planned by the target navigation application based on the vehicle navigation path information.

[0108] Specifically, when a user enters the start and end points in the target navigation application, there may be vehicles already navigating within the application. These vehicles travel along the routes planned by the application, and some road segments may overlap with parts of the initial route. Therefore, based on the vehicle navigation route information already planned in the target navigation application, the number of vehicles on certain road segments of the initial route in future time periods can be obtained. These vehicle numbers represent the actual number of vehicles traveling on certain road segments of the initial route in future times.

[0109] This allows us to determine which vehicles will pass through certain sections of each initial path in the future time period based on the vehicle navigation path information that has already been planned, and thus obtain the number of vehicles on each initial path in the future time period.

[0110] For example, a target navigation application might plan four initial paths from the starting point to the ending point: A, B, C, and D. Based on the vehicle navigation path information already planned, it's determined that 1000 vehicles might pass through initial path A within the next half hour, 500 vehicles might pass through a segment of initial path B, 200 vehicles might pass through initial path C, and 800 vehicles might pass through initial path D. Therefore, the number of vehicles on initial path A within the next half hour is 1000, the number on initial path B is 500, the number on initial path C is 200, and the number on initial path D is 800.

[0111] Step 204: Predict the number of second vehicles on the target initial path in the future time period. The target initial path includes the path that is the same as any of the first initial paths in each of the second initial paths.

[0112] In some embodiments, the implementation process of step 204 can be divided into the following two steps: Step 2041: Obtain the market share of each navigation application; Step 2042: Based on the market share of each navigation application and the number of first vehicles, predict the number of second vehicles on the target initial path in the future time period.

[0113] For example, the market share of a navigation app can be the percentage of users of that app relative to the total population of the region. For ease of description, market share will be referred to as "market share rate" hereafter. For instance, the target navigation app's market share could be 50%, and third-party navigation apps could include a first navigation app and a second navigation app, with the first app having a market share of 40% and the second app having a market share of 10%.

[0114] After obtaining the market share of each navigation application, step 2042 can be performed, which involves predicting the second number of vehicles on the target initial path in the future time period based on the first vehicle count on each first initial path and the market share of each navigation application. Since the target initial path is the same as the first initial path among the second initial paths planned by the third-party navigation application, the total number of vehicles traveling on the first initial path via each navigation path can be obtained based on these two future vehicle counts. The target initial path being the same as the first initial path can mean that all segments of the target initial path are exactly the same as all segments of the first initial path, or that most segments of the target initial path are the same as segments of the first initial path. For example, if 3 / 4 of the segments of the target initial path are the same as segments of the first initial path, then the target initial path can be considered the same as the first initial path.

[0115] As an example, for starting point 1 and ending point 2, the target navigation application plans four initial paths A, B, C and D. The number of vehicles on the first initial path A in the next half hour is 1000, the number of vehicles on the first initial path B in the next half hour is 500, the number of vehicles on the first initial path C in the next half hour is 200, and the number of vehicles on the first initial path D in the next half hour is 800.

[0116] If the target navigation application has a market share of 50%, the total number of vehicles on the first initial path in the future time period is calculated by dividing the number of vehicles on the first initial path by the market share of the target navigation application. That is, the total number of vehicles on the first initial path A in the next half hour is 1000 / 50% = 2000; the total number of vehicles on the first initial path B in the next half hour is 500 / 50% = 1000; the total number of vehicles on the first initial path C in the next half hour is 200 / 50% = 400; and the total number of vehicles on the first initial path D in the next half hour is 800 / 50% = 1600. This method essentially assumes that all paths planned by each navigation application are the same between the start and end points, and that the proportion of vehicles on each path to the total number of vehicles on the planned paths is also the same.

[0117] In the above example, the total number of vehicles on the first initial path determined by the target navigation application is 1000 + 500 + 200 + 800 = 2500. A. The proportion of the number of vehicles on the first initial path to the total number of vehicles is 1000 / 2500 = 40%. B. The proportion of the number of vehicles on the first initial path to the total number of vehicles is 500 / 2500 = 20%. C. The proportion of the number of vehicles on the first initial path to the total number of vehicles is 200 / 2500 = 8%. D. The proportion of the number of vehicles on the first initial path to the total number of vehicles is 800 / 2500 = 32%.

[0118] Therefore, the above method is equivalent to each navigation application planning four initial paths (A, B, C, and D) for the starting point 1 and the ending point 2. The proportion of vehicles on initial path A is 40%, initial path B is 20%, initial path C is 8%, and initial path D is 32%. However, in reality, the paths planned by each navigation application may differ. For example, in a third-party navigation application, the first application might only plan three initial paths (A, B, and D), while the second application might plan four initial paths (A, B, C, and E). Furthermore, the proportion of vehicles on each initial path relative to the total number of vehicles on the planned paths may also differ. Therefore, the method for determining the total number of vehicles on each initial path is only applicable when all navigation applications plan the same paths for the same starting and ending points, and assume that the proportion of vehicles on each initial path is also the same.

[0119] Therefore, considering that different navigation applications may plan different paths for the same starting point and the same ending point, after predicting the future number of vehicles on the first initial path planned by the currently used target navigation application, this embodiment of the application also predicts the future number of vehicles on the same path as the first initial path in the second initial path planned by the third-party navigation application. Based on these two future vehicle numbers, the total number of vehicles on the first initial path can be predicted more accurately, thereby obtaining the future congestion level of the first initial path. Therefore, this method has strong applicability, not only applicable to the case where the paths between the starting point and the ending point planned by different navigation applications are different, but also applicable to the case where the paths between the starting point and the ending point planned by different navigation applications are the same.

[0120] Based on this, for example, the implementation process of step 2042 can be as follows: calculate the sum of the number of first vehicles for each first initial path in the future time period to obtain the total number of first vehicles; determine the total number of second vehicles based on the market share of the target navigation application, the total number of first vehicles, and the market share of third-party navigation applications, wherein the total number of second vehicles is the sum of the number of vehicles for each second initial path in the future time period; and determine the number of second vehicles for the target initial path in the future time period based on the total number of second vehicles and the number of each second initial path.

[0121] Since the market share of each navigation application will not change significantly within a certain time range, we can first determine the total number of vehicles on each initial path planned by the target navigation application in the future time period, calculate the ratio between the total number of vehicles and the market share of the target navigation application, and then multiply this ratio by the market share of other third-party navigation applications as the second total number of vehicles on the second initial path planned by the third-party navigation application in the future time period.

[0122] For example, if the target navigation application plans that the first initial route A will have 1000 vehicles in the next half hour, the first initial route B will have 500 vehicles, the first initial route C will have 200 vehicles, and the first initial route D will have 800 vehicles, then the total number of vehicles on the first initial routes determined by the target navigation application is 1000 + 500 + 200 + 800 = 2500. If the target navigation application has a 50% market share, and third-party navigation applications (including a first navigation application and a second navigation application) have a 40% market share and a 10% market share, then the total number of vehicles on the second initial routes planned by the first navigation application in the future time period is calculated to be 2500 / 50% × 40% = 2000, and the total number of vehicles on the second initial routes planned by the second navigation application in the future time period is 2500 / 50% × 10% = 500.

[0123] After obtaining the total number of second vehicles, the number of second vehicles for the target initial path in the future time period can be determined based on the total number of second vehicles and the number of second initial paths planned by the third-party navigation application.

[0124] For example, the above operation can be implemented as follows: based on the number of each second initial path, the total number of second vehicles is allocated to each second initial path to obtain the number of second vehicles on each second initial path in the future time period; among the number of second vehicles on each second initial path in the future time period, the number of second vehicles on the target initial path in the future time period is determined.

[0125] As an example, the total number of second vehicles can be evenly distributed across each second initial path, assuming that the number of vehicles on each second initial path is the same. The total number of second vehicles can be divided by the number of vehicles on each second initial path to obtain the number of second vehicles on each second initial path. Then, the number of second vehicles on the target initial path that is the same as the first initial path can be determined.

[0126] It should be noted that if the quotient of the total number of second vehicles and the number of second initial paths is not an integer, that is, the total number of second vehicles cannot be divided evenly, then the number of second vehicles on a certain second initial path can be adjusted to be 2 more than the number of second vehicles on other second initial paths, or the number of second vehicles on two certain second initial paths can be adjusted to be 1 more than the number of second vehicles on other second initial paths, so that the sum of the number of second vehicles on each second initial path is the total number of second vehicles.

[0127] Based on the above example, for starting point 1 and ending point 2, if the target navigation application plans four initial paths (A, B, C, and D), the first navigation application plans three second initial paths (A, B, and D), and the second navigation application plans four second initial paths (A, B, C, and E). The total number of second vehicles planned by the first navigation application for the second initial paths in the future time period is 2000, and the total number of second vehicles planned by the second navigation application for the second initial paths in the future time period is 500. Therefore, the number of second vehicles on each of the second initial paths planned by the first navigation application in the future time period is 2000 / 3, and the number of second vehicles on each of the second initial paths planned by the second navigation application in the future time period is 500 / 4 = 125. Since 2000 / 3 is not an integer, the number of second vehicles on each of the second initial paths planned by the first navigation application can be adjusted adaptively. For example, the number of second vehicles on the three second initial paths can be set to 666, 667, and 667, or the number of second vehicles on the three second initial paths can be set to 666, 666, and 668. The three second initial paths planned by the first navigation application are the same as the first initial path. Therefore, the number of second vehicles on the three target initial paths A, B, and D planned by the first navigation application can be obtained. Three of the four second initial paths planned by the second navigation application are the same as the first initial path. Therefore, the number of second vehicles on the three target initial paths A, B, and C is 125 each.

[0128] As another example, the traffic analysis and planning module can also obtain the historical traffic flow on each second initial path, determine the ratio of each second initial path based on the historical traffic flow on each second initial path, and then determine the number of second vehicles on each second initial path based on the ratio of the total number of second vehicles to each second initial path, thereby obtaining the number of second vehicles on the target initial path.

[0129] For example, the traffic analysis and planning module obtains the historical traffic flow of each second initial path at a certain point in time before the current time. The historical traffic flow on second initial path A is 4000, on second initial path B it is 10000, and on second initial path D it is 6000. Then, when determining the number of second vehicles on the three second initial paths A, B, and D planned by the first navigation application, the ratio for second initial path A is 4000 / (4000+10000+6000) = 0.2, the ratio for second initial path B is 10000 / (4000+10000+6000) = 0.5, and the ratio for second initial path D is 6000 / (4000+10000+6000) = 0.3. Given that the total number of vehicles on the second initial path planned by the first navigation application is 2000, we can determine that the number of vehicles on second initial path A is 2000 × 0.2 = 400, the number of vehicles on second initial path B is 2000 × 0.5 = 1000, and the number of vehicles on second initial path D is 2000 × 0.3 = 600. Since the three second initial paths A, B, and D are the same as the first initial path, we can obtain that the number of vehicles on the three target initial paths A, B, and D planned by the first navigation application are 400, 1000, and 600, respectively.

[0130] Furthermore, since users navigate through a target navigation application, the vehicle count obtained from the initial path planned by that application over a future time period is the most accurate. In contrast, the vehicle counts obtained from the initial paths of other third-party navigation applications may not be as accurate. Therefore, the most accurate data can be given a higher weight, while the predicted data can be given a lower weight. That is, the market share of each navigation application is adjusted, adaptively increasing the market share of the target navigation application and correspondingly decreasing the market share of other third-party navigation applications. The adjustment range of the market share can be preset, and this embodiment does not limit this.

[0131] For example, if the target navigation app has a market share of 50%, the first navigation app has a market share of 40%, and the second navigation app has a market share of 10%, the target navigation app's market share can be increased to 80%, the first navigation app's market share can be decreased to 15%, and the second navigation app's market share can be decreased to 5%. Subsequently, when calculating the number of second vehicles on each of the second initial paths planned by the third-party navigation app, the calculated number of second vehicles on each second initial path can be multiplied by the corresponding market share to obtain the final number of second vehicles on each second initial path.

[0132] Step 205: Based on the first number of vehicles and the second number of vehicles, predict the congestion level of each first initial path in the future time period.

[0133] Specifically, the number of first vehicles and the number of second vehicles on each initial path in the future time period can be added together to obtain the total number of vehicles on each initial path in the future time period. Based on this total number of vehicles on each initial path in the future time period, the future congestion level of each initial path can be predicted. The higher the total number of vehicles on a given initial path in the future time period, the higher the future congestion level of that initial path.

[0134] In the example above, the number of vehicles on the four initial paths A, B, C, and D planned by the target navigation application are 1000, 500, 200, and 800 respectively. If the number of vehicles on the three initial paths A, B, and D planned by the first navigation application are 666, 667, and 667 respectively, and the number of vehicles on the three initial paths A, B, and C planned by the second navigation application is 125 each, then the total number of vehicles on the first initial path A is 1000 + 666 + 125 = 1791, the total number of vehicles on the first initial path B is 500 + 667 + 125 = 1292, the total number of vehicles on the first initial path C is 200 + 125 = 325, and the total number of vehicles on the first initial path D is 800 + 667 = 1467.

[0135] For example, multiple congestion levels can be pre-set, each corresponding to a threshold for the total number of vehicles. For instance, Level 1 congestion corresponds to the first threshold, Level 2 to the second threshold, Level 3 to the third threshold, and so on. Level 1 congestion indicates the most severe congestion, with the congestion levels gradually decreasing downwards. Correspondingly, the first threshold for Level 1 congestion is the largest, and the thresholds gradually decrease downwards. When it is determined that the total number of vehicles on a certain initial path in a future time period is higher than the second threshold corresponding to the second congestion level but lower than the first threshold corresponding to the first congestion level, the congestion level of that initial path is determined to be Level 2 congestion.

[0136] Given that the total number of vehicles on the four initial paths A, B, C, and D are 1791, 1292, 325, and 1467 respectively, if we set the first threshold for Level 1 congestion to 1500, the second threshold for Level 2 congestion to 1000, the third threshold for Level 3 congestion to 500, and the fourth threshold for Level 4 congestion to 100, we can determine that the congestion levels of the four initial paths A, B, C, and D are Level 1, Level 2, Level 4, and Level 2 congestion, respectively.

[0137] Step 206: Determine the travel route among the first initial paths based on the congestion level of each first initial path in the future time period.

[0138] In some embodiments, step 206 can be implemented by: identifying the first initial path among the first initial paths whose congestion level is lower than a preset congestion level in the future time period as the travel path. This is equivalent to using the first initial path with lower future congestion level as the user's travel path, thus saving user time and improving user experience when traveling according to this path.

[0139] The preset congestion level can be set in advance, but this application embodiment does not limit this.

[0140] In the example above, if the preset congestion level is level two, then the first initial path C, which is below level two congestion, will be used as the travel path for the user to choose from.

[0141] In addition, after obtaining the initial paths with the possibility of congestion relief in step 202 above, the implementation process of step 206 can be as follows: determine the travel route in each of the first initial paths according to the congestion level of each first initial path in the future time period and the congested paths with the possibility of congestion relief.

[0142] In other words, the embodiments of this application take into account the possibility that the congestion on the road segment will be relieved in the future. If the congestion has been relieved when the vehicle arrives at the congested road segment, the user's travel route can be planned based on the future congestion level of the congested road segment and other first initial paths.

[0143] For example, if the congestion has cleared when a vehicle arrives at the congested section and the future congestion level of that congested route is low, then the congested route can be used as the vehicle's travel route.

[0144] Furthermore, when the number of users and market share of a target navigation application are quite large, there are many users requesting route planning at the same time, and the distance between multiple starting points and multiple ending points in these route planning requests is very close. In addition to predicting and planning routes, it is also possible to plan traffic distribution, allowing different users to take different alternative routes, so as to achieve the effect of unified scheduling and balanced and reasonable use of right-of-way.

[0145] Based on this, in some embodiments, the implementation process of step 206 can also be as follows: when multiple route planning requests are obtained in the same time period and the multiple route planning requests meet the preset conditions, different travel routes are planned for different route planning requests according to the congestion level of each first initial path in the future time period. The preset conditions are that the distance between any two starting points among the multiple starting points carried in the multiple route planning requests is less than a first preset distance, and the distance between any two ending points among the multiple ending points carried in the multiple route planning requests is less than a second preset distance.

[0146] The first preset distance and the second preset distance can be preset. The first preset distance and the second preset distance can be the same or different. This application embodiment does not limit this.

[0147] For example, when multiple travel plans are identical or have similar starting and ending points, a large number of vehicles can be assigned to routes with lower future congestion, while a smaller number of vehicles can be assigned to routes with higher future congestion. This diverts traffic and prevents all vehicles from choosing the least congested route, which would then become the most congested.

[0148] In this embodiment, since it is impossible for all users to use the same navigation application, and different navigation applications may plan different routes, this embodiment predicts not only the future number of vehicles on the first initial path planned by the currently used target navigation application, but also the future number of vehicles on the target initial path planned by a third-party navigation application. Since the target initial path is the same as the first initial path in the second initial path planned by the third-party navigation application, the total number of vehicles planning to travel along each navigation path on the first initial path can be obtained based on these two future vehicle numbers, thus determining the future congestion level of the first initial path. In other words, this embodiment considers the number of vehicles planning routes through various navigation applications in the future, resulting in a more realistic traffic situation. This makes the planned routes for vehicles closer to the actual optimal routes, further saving users' travel time. Furthermore, this embodiment can perform traffic diversion, that is, when many users request planning at the same time, different travel routes are planned for different users to achieve unified scheduling and balanced and reasonable use of roads.

[0149] All of the above-mentioned optional technical solutions can be combined in any way to form optional embodiments of this application, and the embodiments of this application will not be described in detail one by one.

[0150] Figure 3 This is a schematic diagram of the structure of a path planning device provided in an embodiment of this application. For example, as shown... Figure 3 As shown, the device includes:

[0151] Module 301 is used to obtain the start point and the end point;

[0152] The first determining module 302 is used to determine the initial point between the starting point and the ending point of each navigation application plan, wherein each navigation application includes a third-party navigation application and the currently used target navigation application;

[0153] The first prediction module 303 is used to predict the number of vehicles on each first initial path in a future time period. The first initial path is the initial path planned by the target navigation application.

[0154] The second prediction module 304 is used to predict the number of second vehicles on the target initial path in a future time period. The target initial path includes the path that is the same as any first initial path among the second initial paths. The second initial path is the initial path planned by a third-party navigation application.

[0155] The third prediction module 305 is used to predict the congestion level of each first initial path in the future time period based on the first number of vehicles and the second number of vehicles.

[0156] The second determining module 306 is used to determine the travel route among the first initial paths based on the congestion level of each first initial path in the future time period.

[0157] Optionally, the first prediction module 303 includes:

[0158] The first acquisition unit is used to acquire vehicle navigation path information that has been planned by the target navigation application;

[0159] The first determining unit is used to determine the number of first vehicles in a future time period for each first initial path planned by the target navigation application based on the vehicle navigation path information.

[0160] The second prediction module 304 includes:

[0161] The second acquisition unit is used to acquire the market share of each navigation application.

[0162] The prediction unit is used to predict the number of second vehicles on the target initial path in a future time period based on the market share of each navigation application and the number of first vehicles.

[0163] Optionally, the prediction unit is also used for:

[0164] The calculation subunit is used to calculate the sum of the number of first vehicles on each first initial path in a future time period to obtain the total number of first vehicles;

[0165] The first determining subunit is used to determine the second total number of vehicles based on the market share of the target navigation application, the first total number of vehicles, and the market share of the third-party navigation application. The second total number of vehicles is the sum of the number of vehicles in each second initial path in the future time period.

[0166] The second determining subunit is used to determine the number of second vehicles for the target initial path in a future time period based on the total number of second vehicles and the number of each second initial path.

[0167] Optionally, the second determining subunit is also used for:

[0168] Based on the number of vehicles on each second initial path, the total number of second vehicles is allocated to each second initial path to obtain the number of second vehicles on each second initial path in the future time period.

[0169] Determine the number of second vehicles on the target initial path in the future time period from the number of second vehicles on each second initial path in the future time period.

[0170] Optionally, the first determining module 302 is further configured to:

[0171] In each initial path, identify the congested path and determine the congested sections and congestion types on the congested path;

[0172] Determine the congestion relief time for congested road segments within the congestion path based on the congestion type;

[0173] Based on the congestion relief time, the probability of congestion relief when vehicles arrive at the congested section is predicted, and congestion paths with the probability of congestion relief are obtained.

[0174] The second determining module 306 is also used for:

[0175] Based on the congestion level of each initial path in the future time period and the congested paths with the possibility of congestion relief, the travel path is determined among each initial path.

[0176] Optionally, the start point and end point are carried in the path planning request, and the second determination module 306 is further used for:

[0177] If multiple route planning requests are obtained at the same time and the multiple route planning requests meet the preset conditions, different travel routes are planned for different route planning requests based on the congestion level of each first initial path in the future time period. The preset conditions are that the distance between any two starting points in the multiple route planning requests is less than a first preset distance, and the distance between any two ending points in the multiple route planning requests is less than a second preset distance.

[0178] Optionally, the second determining module 306 is further configured to:

[0179] In each initial path, the initial path with a congestion level lower than the preset congestion level in the future time period is determined as the travel path.

[0180] Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application.

[0181] For example, such as Figure 4As shown, the electronic device 400 includes a memory 401 and a processor 402. The memory 401 stores executable program code 4011, and the processor 402 is used to call and execute the executable program code 4011 to perform a path planning method.

[0182] This embodiment can divide the electronic device into functional modules according to the above method example. For example, each module can correspond to a separate functional module, or two or more functions can be integrated into one processing module. The integrated module can be implemented in hardware. It should be noted that the module division in this embodiment is illustrative and only represents one logical functional division. In actual implementation, there may be other division methods.

[0183] When each functional module is divided according to its corresponding function, the electronic device may include: an acquisition module, a determination module, a first prediction module, a second prediction module, a third prediction module, and a planning module. It should be noted that all relevant content of each step involved in the above method embodiments can be referenced to the functional description of the corresponding functional module, and will not be repeated here.

[0184] The electronic device provided in this embodiment is used to execute the path planning method described above, and thus can achieve the same effect as the implementation method described above.

[0185] When using integrated units, the electronic device may include a processing module and a storage module. The processing module is used to control and manage the operation of the electronic device. The storage module is used to support the execution of program code and data by the electronic device.

[0186] The processing module may be a processor or a controller, which can implement or execute various exemplary logic blocks, modules, and circuits as disclosed in this application. The processor may also be a combination of computing functions, such as a combination of one or more microprocessors, a combination of digital signal processing (DSP) and microprocessors, etc., and the storage module may be a memory.

[0187] This embodiment also provides a computer-readable storage medium storing computer program code. When the computer program code is run on a computer, the computer executes the aforementioned method steps to implement a path planning method in the above embodiment.

[0188] This embodiment also provides a computer program product that, when run on a computer, causes the computer to perform the aforementioned related steps to implement a path planning method as described in the above embodiment.

[0189] In addition, the electronic device provided in the embodiments of this application may include a connected processor and a memory; wherein the memory is used to store instructions, and when the vehicle is running, the processor may call and execute the instructions to make the vehicle perform a path planning method in the above embodiments.

[0190] In this embodiment, the electronic device, computer-readable storage medium, computer program product or chip are all used to execute the corresponding methods provided above. Therefore, the beneficial effects that can be achieved can be referred to the beneficial effects of the corresponding methods provided above, and will not be repeated here.

[0191] Through the above description of the embodiments, those skilled in the art will understand that, for the sake of convenience and brevity, only the division of the above functional modules is used as an example. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above.

[0192] In the embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another device, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0193] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A path planning method, characterized in that, The method includes: Get the start and end points; Determine the initial path between the starting point and the ending point planned by each navigation application, wherein each navigation application includes a third-party navigation application and the currently used target navigation application; Obtain the vehicle navigation route information that the target navigation application has already planned; Based on the vehicle navigation path information, determine the number of vehicles on each of the first initial paths planned by the target navigation application in a future time period, wherein the first initial path is the initial path planned by the target navigation application; Obtain the market share of each of the aforementioned navigation applications; Based on the market share of each navigation application and the number of the first vehicles, the number of the second vehicles in the future time period is predicted for the target initial path. The target initial path includes the path in each second initial path that is the same as any of the first initial paths. The second initial path is the initial path planned by the third-party navigation application. Based on the first number of vehicles and the second number of vehicles, predict the congestion level of each of the first initial paths in the future time period; A travel route is determined among the first initial paths based on the congestion level of each first initial path during the future time period.

2. The method according to claim 1, characterized in that, The step of predicting the number of vehicles on the target initial path in the future time period based on the market share of each navigation application and the number of the first vehicle includes: Calculate the sum of the number of first vehicles on each of the first initial paths during the future time period to obtain the total number of first vehicles; The second total number of vehicles is determined based on the market share of the target navigation application, the first total number of vehicles, and the market share of the third-party navigation application. The second total number of vehicles is the sum of the number of vehicles on each of the second initial paths in the future time period. Based on the total number of the second vehicles and the number of each of the second initial paths, the number of the second vehicles for the target initial path in the future time period is determined.

3. The method according to claim 2, characterized in that, The step of determining the number of second vehicles for the target initial path in the future time period based on the total number of second vehicles and the number of each second initial path includes: Based on the number of each second initial path, the total number of second vehicles is allocated to each second initial path to obtain the number of second vehicles on each second initial path in the future time period; The number of second vehicles on each second initial path during the future time period is determined from the number of second vehicles on each second initial path during the future time period.

4. The method according to any one of claims 1-3, characterized in that, After determining the initial path between the start point and the end point of each navigation application plan, the method further includes: In each of the first initial paths, a congested path is identified, and the congested road segment and congestion type on the congested path are determined; Based on the congestion type, determine the congestion relief time for the congested road segment in the congestion path; Based on the congestion relief time, the probability of congestion relief when a vehicle arrives at the congested section from the starting point is predicted, thus obtaining a congestion path with the possibility of congestion relief. The step of determining a travel route among the first initial paths based on the congestion level of each first initial path in the future time period includes: A travel route is determined among the first initial routes based on the congestion level of each first initial route in the future time period and the congested routes with the possibility of congestion relief.

5. The method according to any one of claims 1-3, characterized in that, The starting point and the ending point are carried in the route planning request. The step of determining the travel route among the first initial paths based on the congestion level of each first initial path in the future time period includes: If multiple route planning requests are obtained at the same time period and the multiple route planning requests meet preset conditions, different travel routes are planned for different route planning requests based on the congestion level of each first initial path in the future time period. The preset conditions are that the distance between any two starting points in the multiple route planning requests is less than a first preset distance, and the distance between any two ending points in the multiple route planning requests is less than a second preset distance.

6. The method according to any one of claims 1-3, characterized in that, The step of determining a travel route among the first initial paths based on the congestion level of each first initial path in the future time period includes: In each of the first initial paths, the first initial path whose congestion level is lower than the preset congestion level in the future time period is determined as the travel path.

7. A path planning device, characterized in that, The device includes: The acquisition module is used to obtain the start and end points; The first determining module is used to determine the initial path between the starting point and the ending point planned by each navigation application, wherein each navigation application includes a third-party navigation application and the currently used target navigation application; The acquisition module is further configured to: acquire vehicle navigation route information for which the target navigation application has performed route planning; The first prediction module is used to determine the number of vehicles in a future time period for each first initial path planned by the target navigation application based on the vehicle navigation path information, wherein the first initial path is the initial path planned by the target navigation application. The acquisition module is also used to: acquire the market share of each of the navigation applications; The second prediction module is used to predict the number of second vehicles in a future time period based on the market share of each navigation application and the number of first vehicles. The target initial path includes the path in each second initial path that is the same as any of the first initial paths. The second initial path is the initial path planned by the third-party navigation application. The third prediction module is used to predict the congestion level of each of the first initial paths in the future time period based on the first number of vehicles and the second number of vehicles. The second determining module is used to determine a travel route among the first initial paths based on the congestion level of each first initial path in the future time period.

8. An electronic device, characterized in that, The electronic device includes: Memory, used to store executable program code; A processor for calling and running the executable program code from the memory, causing the electronic device to perform the method as described in any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed, implements the method as described in any one of claims 1 to 6.

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