Driving trajectory push method, device, electronic device and navigation device

Abstract

The present disclosure provides a driving trajectory push method, device, electronic device and navigation device, which relate to the data processing field, especially to the intelligent transportation and big data fields. The specific implementation scheme is: obtaining a push request of a target truck, wherein the push request carries the target truck attributes, target starting point and target end point of the target truck; querying whether there is target waypoint information matching the push request, wherein the target waypoint information includes the positioning information of at least one target waypoint passed by the target representative route, the target representative route is obtained by similarity aggregation of historical driving trajectories, and the representative truck attributes corresponding to the target representative route are obtained by aggregating the truck attributes corresponding to the historical driving trajectories; in response to querying the target waypoint information, the trajectory is restored based on the push request and the target waypoint information to obtain the target driving trajectory; outputting the target driving trajectory.

Description

Technical Field

[0001] The present disclosure relates to the field of data processing technology, in particular to the field of intelligent transportation and big data, and specifically provides a driving trajectory push method, device, electronic device and navigation device. Background Art

[0002] Compared with passenger car driving navigation, trucks have various traffic restrictions, such as time-based restrictions and localized restrictions, which makes truck navigation face the challenge of path planning under traffic restrictions for loaded trucks. At the same time, due to the physical properties of trucks themselves, different driving routes may mean different travel costs and prices. Summary of the invention

[0003] The present invention provides a driving track push method, device, electronic device and navigation device.

[0004] According to a first aspect of the present disclosure, a method for pushing a driving trajectory is provided, comprising: obtaining a push request for a target truck, wherein the push request carries target truck attributes, a target starting point, and a target end point of the target truck; querying whether there is target waypoint information matching the push request, wherein the target waypoint information includes positioning information of at least one target waypoint passed by a target representative route, the target representative route is obtained by performing similarity aggregation on historical driving trajectories, and representative truck attributes corresponding to the target representative route are obtained by aggregating truck attributes corresponding to historical driving trajectories; in response to querying the target waypoint information, performing trajectory restoration based on the push request and the target waypoint information to obtain the target driving trajectory; and outputting the target driving trajectory.

[0005] According to a second aspect of the present disclosure, a driving trajectory push device is provided, including: an acquisition module, used to obtain a push request of a target truck, wherein the push request carries a target truck attribute, a target starting point, and a target end point of the target truck; a query module, used to query whether there is target waypoint information matching the push request, wherein the target waypoint information includes positioning information of at least one target waypoint passed by a target representative route, the target representative route is obtained by similarity aggregation of historical driving trajectories, and the representative truck attribute corresponding to the target representative route is obtained by aggregating the truck attributes corresponding to the historical driving trajectories; a restoration module, used to restore the trajectory based on the push request and the target waypoint information in response to querying the target waypoint information, so as to obtain the target driving trajectory; and an output module, used to output the target driving trajectory.

[0006] According to a third aspect of the present disclosure, an electronic device is provided, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the above-mentioned method.

[0007] According to a fourth aspect of the present disclosure, a non-transitory computer-readable storage medium storing computer instructions is provided, wherein the computer instructions are used to enable a computer to execute the above method.

[0008] According to a fifth aspect of the present disclosure, a computer program product is provided, including a computer program, and the computer program implements the above method when executed by a processor.

[0009] According to a sixth aspect of the present disclosure, a navigation device is provided, including: the above-mentioned driving track pushing device.

[0010] It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present disclosure, nor is it intended to limit the scope of the present disclosure. Other features of the present disclosure will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] The accompanying drawings are used to better understand the present solution and do not constitute a limitation of the present disclosure.

[0012] Figure 1 is a flow chart of a method for pushing a driving trajectory according to an embodiment of the present disclosure;

[0013] Figure 2 is a schematic diagram of a truck attribute aggregation method according to an embodiment of the present disclosure;

[0014] Figure 3 is a schematic diagram of a pushing device for a driving track according to an embodiment of the present disclosure;

[0015] Figure 4 It is a block diagram of an electronic device used to implement the driving trajectory push method of the embodiment of the present disclosure. DETAILED DESCRIPTION

[0016] The following is a description of exemplary embodiments of the present disclosure in conjunction with the accompanying drawings, including various details of the embodiments of the present disclosure to facilitate understanding, which should be considered as merely exemplary. Therefore, it should be recognized by those of ordinary skill in the art that various changes and modifications may be made to the embodiments described herein without departing from the scope and spirit of the present disclosure. Similarly, for the sake of clarity and conciseness, descriptions of well-known functions and structures are omitted in the following description.

[0017] At present, the path planning of trucks is completely based on algorithms and graph search path planning, which will lead to the following problems:

[0018] Truck traffic restrictions are complex and inaccurate traffic restriction data can lead to unreasonable route planning results.

[0019] When the truck traffic restriction data is correct, if in reality some truck drivers with traffic restrictions have a high violation rate, increasing the weight of the traffic restriction section in route planning will result in a legal route but an unreasonable detour.

[0020] Graph search algorithms rely on the weights of the entire graph. It is inevitable that the weights will be unreasonable, which will cause unreasonable route planning.

[0021] Truck navigation faces the problem of dealing with complex traffic restrictions. If the recall route calculation algorithm is strict and the truck traffic restriction data is incorrect, a large number of large-scale detours and local detours with low rationality will be generated, which will cause drivers to usually judge that the cost of travel is too high and will not adopt them. Therefore, it is particularly important to introduce routes that drivers often take as route planning guidelines.

[0022] The main problem faced in the truck trajectory application is that each driving trajectory corresponds to a set of truck attribute information. There are many combinations of different trajectory parameters, which leads to personalized trajectories and cannot generate target driving trajectories well.

[0023] In order to solve the above problems, according to an embodiment of the present disclosure, the present disclosure provides a method for pushing a driving trajectory. Figure 1 is a flow chart of a method for pushing a driving trajectory according to an embodiment of the present disclosure, such as Figure 1 As shown, the method may include the following steps:

[0024] Step S102, obtaining a push request of the target truck, wherein the push request carries the target truck attributes, the target starting point and the target end point of the target truck.

[0025] The target truck in the above steps may be a truck that needs to be routed, and the truck may be an autonomous truck or a manually driven truck, which is not specifically limited in the present disclosure. The target truck attributes may be physical attributes of the target truck, including but not limited to: length, width, height, gross weight, load, axle weight, number of axles, and truck type, where the truck type may be a heavy truck, a medium truck, a light truck, or a mini truck.

[0026] In an optional embodiment, a truck driver can plan a route through navigation software installed on an electronic device. The driver can input the physical properties of driving the truck in the navigation interface and select the target starting point and target end point. At this time, the client can package the information input by the driver, generate a push request, and send it to the navigation server for route planning.

[0027] In another optional embodiment, a truck driver can plan a route through a navigation device installed on the truck. The driver can input the physical properties of driving the truck on the display screen of the navigation device and select the target starting point and target terminal, thereby generating a push request and transmitting it to the processor of the navigation device, which performs route planning.

[0028] Step S104, query whether there is target waypoint information that matches the push request, wherein the target waypoint information includes the positioning information of at least one target waypoint passed by the target representative route, the target representative route is obtained by aggregating the similarity of the historical driving trajectories, and the representative truck attributes corresponding to the target representative route are obtained by aggregating the truck attributes corresponding to the historical driving trajectories.

[0029] The historical driving track in the above steps may include but is not limited to: the historical driving track of the target truck and the historical driving track of the third-party truck. It should be noted that the historical driving track in this embodiment is not the historical driving data of a specific truck, and cannot reflect the personal information of a specific driver. Moreover, the historical driving track comes from public data.

[0030] The target waypoints in the above steps can be the coordinate points corresponding to the two ends of each section in the target representative route. The positioning information can be obtained by positioning different target waypoints through positioning modules such as Beidou navigation.

[0031] In an optional embodiment, historical driving trajectories can be introduced to expand the path planning source, and the legitimacy of the routes within the clustering attribute range can be guaranteed by similarity aggregation of historical driving trajectories and aggregation of truck attributes. Among them, similarity aggregation can be based on the similarity of driving trajectories, and driving trajectories with similarity higher than a certain threshold (for example, 90%, but not limited to this) are grouped into a cluster, and representative routes are selected from each cluster; truck attribute aggregation can be sorted according to attribute values ​​from small to large, and attribute values ​​of a certain quantile (for example, 0.75 quantile, but not limited to this) are selected as representative truck attributes after aggregation. For several representative routes and corresponding representative truck attributes after clustering, all the waypoints in the representative routes can be extracted through waypoint extraction to obtain several waypoint information.

[0032] In order to improve the push efficiency of online driving trajectories, the aggregation process of historical driving trajectories and the extraction process of waypoints can be executed by offline modules, and the final waypoint information and data representing truck attributes, starting point information, etc. are stored correspondingly, where the starting point information is used as an index. The storage format of the above information is shown in Table 1 below, where the start and end number pairs represent the numbers of the areas where the start and end points are located, and the numbers are determined based on the regional divisions across the country; the link value of the route can be the positioning information of the section passed by the route (i.e., the waypoint information), and the data field format of the attribute trajectory distance result (i.e., representing the truck attributes) is "length|width|height|gross weight|load|axle weight|number of axles|truck type".

[0033] Table 1

[0034] Start and end number pair Characterize the route pathway link sequence Trajectory attribute clustering results 10701|10856 15690237330_16326560490 7.0|2.51|3.8|40.0|40.0|9.0|4|4

[0035] In another optional embodiment, after receiving the push request, the online module can use the target starting point and the target end point as indexes to search in the offline service for corresponding target waypoint information. If there are one or more, it indicates that there are one or more available trajectory routes. Then, the target truck attributes are matched and filtered with the truck attributes of the above-mentioned trajectory routes to obtain the final target waypoint information.

[0036] Step S106, in response to querying the target waypoint information, restore the track based on the push request and the target waypoint information to obtain the target driving track.

[0037] The target driving trajectory in the above steps can be an online real-time route, which needs to be pushed according to the actual route planning needs of the truck driver.

[0038] In an optional embodiment, the target waypoint information only records the waypoints passed in the target representative route, but does not record the specific sections between different waypoints. Therefore, the path planning model in the online module can be used to combine the target starting and ending points, target truck attributes and target waypoint information to restore the trajectory and generate the target driving trajectory produced in the offline module. At this time, the driving trajectory can be a trajectory composed of multiple line segments.

[0039] Step S108, outputting the target driving trajectory.

[0040] In an optional embodiment, after generating the target driving trajectory, the navigation server can send the target driving trajectory to the client, which will display it on a high-precision map, so that the truck driver can view the route planning and select the driving trajectory used for navigation.

[0041] In another optional embodiment, after the processor of the navigation device generates the target driving trajectory, the target driving trajectory can be transmitted to a display screen for display. The display screen displays the target driving trajectory on a high-precision map, so that the truck driver can view the route planning and select the driving trajectory used for navigation.

[0042] Through the solution provided by the above-mentioned embodiments of the present disclosure, by performing similarity aggregation on historical behavior trajectories and truck attribute aggregation, the legitimacy of the routes within the range of clustering attributes is guaranteed, the rationality of the routes is also increased, the richness of truck route recalls is supplemented, and a diversified route recall source guided by the experienced routes taken by truck drivers is provided, so that the navigation service is more in line with the preferences and habits of truck drivers, and meets the basic needs of truck drivers for low-cost travel costs, solving the problems of poor rationality and legitimacy of driving trajectory push and high travel costs.

[0043] In the above embodiment of the present disclosure, querying whether there is target waypoint information that matches the push request includes: querying whether there is initial waypoint information that matches the target start point and the target end point; in response to querying the initial waypoint information, obtaining a first truck attribute of the initial waypoint information; matching the first truck attribute with the target truck attribute to obtain the target waypoint information.

[0044] In an optional embodiment, the target starting point and the target end point can be used as indexes to query whether there is corresponding initial waypoint information in the offline server. If there is one or more, it indicates that there is an available trajectory route; then the target truck attributes and the truck attributes of the trajectory route are matched to obtain the final target waypoint information.

[0045] Through the above steps, through the dual matching of the starting point information and the truck attributes, the purpose of personalized trajectory push can be achieved, and the rationality and legality of the actual driving trajectory can be taken into account.

[0046] In the above-mentioned embodiment of the present disclosure, querying whether there is initial waypoint information includes: determining a target number pair based on a target starting point and a target end point, wherein the target number pair includes the number of a starting area where the target starting point is located and the number of an end area where the target end point is located; and querying whether there is initial waypoint information corresponding to the target number pair.

[0047] In an optional embodiment, the starting and ending areas of each driving track can be numbered according to the regional divisions across the country, and the driving track can be identified by a number pair. For example, if the starting point of the driving track is in area 1 and the end point is in area 2, the number pair can be represented as "1|2". Further, the number pair can be directly used as an index to query whether there is corresponding initial waypoint information.

[0048] Through the above steps, the target starting point and the target end point are identified by numbering, which can not only reduce the storage space of the start and end point information, but also improve the query efficiency of the initial waypoint information.

[0049] In the above embodiment of the present disclosure, the first truck attribute and the target truck attribute are matched to obtain the target waypoint information, including: determining whether the first attribute value of the first truck attribute is greater than the target attribute value of the target truck attribute; in response to the first attribute value being greater than the target attribute value, determining the initial waypoint information as the target waypoint information.

[0050] In an optional embodiment, the matching rule for truck attributes may be that a hit is available if the attribute values ​​of all attributes in the truck attributes are greater than or equal to the target truck attributes. That is, when all attribute values ​​of the first truck attributes are greater than all attribute values ​​of the target attributes, the initial waypoint information corresponding to the first truck attributes is determined to be the target waypoint information finally screened out.

[0051] Through the above steps, the target waypoint information is filtered by matching the truck attributes, so as to achieve the purpose of personalized push of the driving trajectory.

[0052] In the above embodiment of the present disclosure, trajectory restoration is performed based on the push request and the target waypoint information to obtain the target driving trajectory, including: determining multiple target sections based on the target truck attributes, wherein the multiple target sections include: a section between the target starting point and the first target waypoint, a section between two adjacent target waypoints, and a section between the last target waypoint and the target end point; based on the multiple target sections, generating the target driving trajectory.

[0053] In an optional embodiment, the specific road section between any two coordinate positions can be determined based on the target starting point, target end point, and target truck attributes by adding the target waypoints in the target waypoint information to calculate the route, and then different road sections can be combined to generate a target driving trajectory that is pushed online in real time.

[0054] Through the above steps, by restoring the trajectory according to the target starting point, target end point, and target truck attributes, the target push trajectory is accurately generated to improve the push accuracy.

[0055] In the above embodiment of the present disclosure, the method also includes: determining a number pair of a first driving trajectory in a historical driving trajectory, wherein the number pair includes a number of an area where a starting point of the first driving trajectory is located and a number of an area where an end point of the first driving trajectory is located; performing similarity aggregation on first driving trajectories of the same number pair to obtain at least one aggregated cluster; obtaining a second driving trajectory corresponding to a minimum weight in at least one aggregated cluster to obtain at least one representative route.

[0056] The number pairs in the above steps may be determined by numbering the national regions using the above method. The preset weight may be a preset minimum weight indicating that the weight is small and meets the similarity aggregation requirements.

[0057] In an optional embodiment, a specific implementation scheme for similarity aggregation of historical driving data is as follows: the starting and ending points of each driving track in the historical driving track are numbered to obtain a number pair. The number pair is used as an aggregation index to calculate the similarity of the driving tracks within the number pair, and the driving tracks with high similarity are aggregated into an aggregation cluster. The weight of each driving track in the cluster is calculated, and the driving track with a small weight in the cluster is selected as the representative route.

[0058] Through the above steps, by aggregating the similarity of historical driving trajectories, the purpose of estimating the travel range of the actual driving route is achieved, while taking into account the rationality of the route.

[0059] In the above embodiment of the present disclosure, performing similarity aggregation on the first driving trajectories of the same numbered pair to obtain at least one aggregated cluster includes: determining the first similarity of the first driving trajectories of the same numbered pair based on the road sections included in the first driving trajectories of the same numbered pair; and aggregating the first driving trajectories of the same numbered pair based on the first similarity to obtain at least one aggregated cluster.

[0060] In an optional embodiment, the similarity calculation can be implemented by the following method: determine whether the proportion of the same road sections in the two driving trajectories exceeds 90%. If so, it is determined that the similarity of the two driving trajectories is high, and the driving trajectories with high similarity can be clustered into an aggregate cluster; if not, that is, the similarity between a driving trajectory and each cluster is not high, then the driving trajectory can be used as a new cluster.

[0061] Through the above steps, by aggregating the first driving trajectories of the same number pairs, the legitimacy and rationality of the path planning can be improved, and the richness of the truck route recall can be supplemented.

[0062] In the above embodiment of the present disclosure, the method further includes: acquiring the driving time of the second driving trajectory; and determining the weight of the second driving trajectory based on the driving time.

[0063] In an optional embodiment, the time taken for the entire driving trajectory (ie, the driving time) may be estimated as a weight, and a driving trajectory with a smaller weight may be selected as a representative route.

[0064] Through the above steps, the weight of the driving trajectory is determined by the driving time, ensuring that the representative route can save the driver's driving time and improve the driver's experience and favorability.

[0065] In the above embodiment of the present disclosure, the method also includes: obtaining second truck attributes corresponding to the second driving trajectory; sorting the second truck attributes according to the attribute values ​​of the second truck attributes to obtain sorted truck attributes; and determining, from the sorted truck attributes, the truck attributes corresponding to the preset sorting position as the representative truck attributes.

[0066] The preset sorting position in the above steps may be set by the user in advance according to the actual application scenario and push requirements. For example, the preset sorting position may be the 0.75 quantile, but is not limited thereto.

[0067] In an optional embodiment, if Figure 2 As shown, all attribute values ​​in the target truck attributes can be sorted separately, and the attribute value with the 0.75 quantile of the sorted value is taken as the representative truck attribute, that is, as the estimated passable value of the attribute.

[0068] Through the above steps, by sorting the truck attributes and filtering the representative truck attributes, the purpose of personalized aggregation of truck attributes is achieved.

[0069] In the above embodiment of the present disclosure, the method also includes: determining the frequency of occurrence of the representative route in the historical driving trajectory; based on the frequency of occurrence, determining the target restoration route in the representative route, wherein the frequency of occurrence of the target restoration route is greater than a preset frequency; extracting the waypoints passed by the target restoration route to obtain at least one waypoint information.

[0070] The above-mentioned occurrence frequency may refer to the frequency at which different truck drivers drive along the driving trajectory in the historical driving trajectory. The higher the occurrence frequency, the more likely the truck driver is to choose the driving trajectory.

[0071] In an optional embodiment, the number of occurrences of the representative route can be determined based on all historical driving trajectories, that is, the occurrence frequency of the representative route can be obtained, and then a target restoration route that has been verified by a high-frequency driver can be selected based on the occurrence frequency.

[0072] Through the above steps, high-frequency representative routes are screened by frequency of occurrence as target restoration routes, so as to achieve the purpose of extracting and aggregating high-frequency historical routes to fill the deficiency of micro-algorithm path recall.

[0073] In the above-mentioned embodiment of the present disclosure, extracting the waypoints passed by the target restoration route and obtaining at least one waypoint information includes: determining multiple third paths between the starting point and the end point of the target restoration route based on the truck attributes corresponding to the target restoration route; generating a new driving trajectory based on the multiple third paths; determining a second similarity between the new driving trajectory and the target restoration route; in response to the second similarity being greater than a preset similarity, obtaining at least one waypoint information based on the waypoints corresponding to the multiple third paths.

[0074] The preset similarity in the above steps may be a minimum similarity used to indicate that the new driving trajectory has a high similarity to the target restored route, and the new driving trajectory may be considered to be the same as the target restored route.

[0075] In an optional embodiment, during the extraction of waypoints, the route can be calculated based on the starting and ending links of the target restored route and in combination with the truck attributes to generate a new driving trajectory, and the new driving trajectory and the target restored route are similarly calculated. If the similarity is low, a path link is removed from the difference section as a waypoint, and the calculation is continued until the new driving trajectory obtained by adding waypoints has a high similarity with the target restored route. At this time, all waypoints can be saved to obtain the waypoint information.

[0076] Through the above steps, by extracting the waypoints of the target restored route, the purpose of automatically generating the waypoint information, saving storage memory, and improving the push efficiency of the driving route is achieved.

[0077] A preferred embodiment of the present disclosure is described in detail below. The method specifically includes: for a large number of truck trajectories, firstly, the start and end points are numbered according to the start and end points of the trajectories to generate the start and end point number pairs of the trajectories; the truck trajectories under the same number pair are extracted through the start and end point number pairs, and the truck attributes saved when the historical driving trajectories are collected are also extracted. By calculating the similarity of the driving trajectories, the trajectories with a similarity higher than 90% are classified into a cluster, and the route weights of the routes in the cluster are calculated respectively (the route weights are obtained by calculating the estimated time of the routes), and the driving trajectories with smaller weights and less time consumption in the cluster are obtained as representative routes; at the same time, the attribute information corresponding to the routes in the cluster is aggregated, and the aggregation method is to sort the sets of attributes such as length, width, height, and weight from small to large actual values, and take the value of 0.75 quantile as the aggregated value, and use this set of values ​​as the attributes of the representative routes. Finally, several representative routes after clustering and the corresponding attribute information sets can be obtained.

[0078] This method can be used to obtain the estimated attribute value of the high-frequency route that is actually passable based on the user attributes, which can not only achieve the purpose of personalized trajectory aggregation, but also estimate the actual passable range of the high-frequency route, taking into account the rationality of the route.

[0079] In the technical solution disclosed in the present invention, the acquisition, storage and application of historical driving trajectories and truck attributes involved are in compliance with the provisions of relevant laws and regulations and do not violate public order and good morals.

[0080] According to an embodiment of the present disclosure, the present disclosure also provides a pushing device for a driving track.

[0081] Figure 3is a schematic diagram of a pushing device for a driving track according to an embodiment of the present disclosure, such as Figure 3 As shown, the device may include the following steps:

[0082] The acquisition module 32 is used to acquire a push request of a target truck, wherein the push request carries the target truck attributes, the target starting point and the target end point of the target truck;

[0083] A query module 34 is used to query whether there is target waypoint information matching the push request, wherein the target waypoint information includes location information of at least one target waypoint passed by the target representative route, the target representative route is obtained by aggregating similarity of historical driving trajectories, and the representative truck attribute corresponding to the target representative route is obtained by aggregating truck attributes corresponding to the historical driving trajectories;

[0084] A restoration module 36, configured to restore the target driving trajectory based on the push request and the target waypoint information in response to the query of the target waypoint information;

[0085] The output module 38 is used to output the target driving trajectory.

[0086] In the above-mentioned embodiment of the present disclosure, the query module includes: a query unit, which is used to query whether there is initial waypoint information that matches the target starting point and the target end point; an acquisition unit, which is used to acquire the first truck attribute of the initial waypoint information in response to querying the initial waypoint information; and a matching unit, which is used to match the first truck attribute with the target truck attribute to obtain the target waypoint information.

[0087] In the above embodiment of the present disclosure, the query unit is also used to determine a target number pair based on the target starting point and the target end point, and to query whether there is initial waypoint information corresponding to the target number pair, wherein the target number pair includes the number of the starting area where the target starting point is located and the number of the end area where the target end point is located.

[0088] In the above embodiment of the present disclosure, the matching unit is also used to determine whether the first attribute value of the first truck attribute is greater than the target attribute value of the target truck attribute, and in response to the first attribute value being greater than the target attribute value, determine the initial waypoint information as the target waypoint information.

[0089] In the above-mentioned embodiment of the present disclosure, the restoration module includes: a determination unit, which is used to determine multiple target sections based on the attributes of the target truck, wherein the multiple target sections include: a section between the target starting point and the first target waypoint, a section between two adjacent target waypoints, and a section between the last target waypoint and the target end point; and a generation unit, which is used to generate a target driving trajectory based on the multiple target sections.

[0090] In the above-mentioned embodiment of the present disclosure, the device also includes: a determination module, used to determine a number pair of a first driving trajectory in a historical driving trajectory, wherein the number pair includes a number of an area where a starting point of the first driving trajectory is located and a number of an area where an end point of the first driving trajectory is located; a first aggregation module, used to perform similarity aggregation on first driving trajectories of the same number pair to obtain at least one aggregation cluster; and a processing module, used to obtain a second driving trajectory having a weight less than a preset weight in at least one aggregation cluster to obtain at least one representative route.

[0091] In the above embodiment of the present disclosure, the first aggregation module includes: a similarity determination unit, which is used to determine the first similarity of the first driving trajectories of the same numbered pair based on the road sections included in the first driving trajectories of the same numbered pair; and a first aggregation unit, which is used to aggregate the first driving trajectories of the same numbered pair based on the first similarity to obtain at least one aggregation cluster.

[0092] In the above embodiment of the present disclosure, the device further includes: an acquisition module, which is also used to acquire the driving time of the second driving trajectory; and the determination module is also used to determine the weight of the second driving trajectory based on the driving time.

[0093] In the above-mentioned embodiment of the present disclosure, the device also includes: an acquisition module, which is also used to obtain the second truck attributes corresponding to the second driving trajectory; a sorting module, which is used to sort the second truck attributes according to the attribute values ​​of the second truck attributes to obtain the sorted truck attributes; and a determination module is also used to determine, from the sorted truck attributes, the truck attributes corresponding to the preset sorting position as the representative truck attributes.

[0094] In the above-mentioned embodiment of the present disclosure, the device also includes: a frequency determination unit, used to determine the frequency of occurrence of a representative route in a historical driving trajectory; a route determination unit, used to determine a target restoration route in the representative route based on the frequency of occurrence, wherein the frequency of occurrence of the target restoration route is greater than a preset frequency; and an extraction unit, used to extract the waypoints passed by the target restoration route to obtain at least one waypoint information.

[0095] In the above-mentioned embodiment of the present disclosure, the extraction unit is also used to determine multiple third paths between the starting point and the end point of the target restoration route based on the truck attributes corresponding to the target restoration route; generate a new driving trajectory based on the multiple third paths; determine a second similarity between the new driving trajectory and the target restoration route; in response to the second similarity being greater than a preset similarity, obtain at least one waypoint information based on the waypoints corresponding to the multiple third paths.

[0096] According to an embodiment of the present disclosure, the present disclosure also provides a navigation device, including the above-mentioned driving trajectory pushing device.

[0097] According to an embodiment of the present disclosure, the present disclosure also provides an electronic device, a readable storage medium and a computer program product.

[0098] Figure 4 A schematic block diagram of an example electronic device 400 that can be used to implement an embodiment of the present disclosure is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present disclosure described and / or required herein.

[0099] like Figure 4 As shown, the device 400 includes a computing unit 401, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 402 or a computer program loaded from a storage unit 408 into a random access memory (RAM) 403. In the RAM 403, various programs and data required for the operation of the device 400 can also be stored. The computing unit 401, the ROM 402, and the RAM 403 are connected to each other via a bus 404. An input / output (I / O) interface 405 is also connected to the bus 404.

[0100] A number of components in the device 400 are connected to the I / O interface 405, including: an input unit 406, such as a keyboard, a mouse, etc.; an output unit 407, such as various types of displays, speakers, etc.; a storage unit 408, such as a disk, an optical disk, etc.; and a communication unit 409, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 409 allows the device 400 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0101] The computing unit 401 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the computing unit 401 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, digital signal processors (DSPs), and any appropriate processors, controllers, microcontrollers, etc. The computing unit 401 performs the various methods and processes described above, such as the push method of the driving trajectory. For example, in some embodiments, the push method of the driving trajectory may be implemented as a computer software program, which is tangibly contained in a machine-readable medium, such as a storage unit 408. In some embodiments, part or all of the computer program may be loaded and / or installed on the device 400 via ROM 402 and / or communication unit 409. When the computer program is loaded into RAM 403 and executed by the computing unit 401, one or more steps of the push method of the driving trajectory described above may be performed. Alternatively, in other embodiments, the computing unit 401 may be configured to execute the push method of the driving trajectory by any other appropriate means (e.g., by means of firmware).

[0102] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0103] The program code for implementing the method of the present disclosure may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that the program code, when executed by the processor or controller, enables the functions / operations specified in the flow chart and / or block diagram to be implemented. The program code may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0104] In the context of the present disclosure, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or equipment. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or equipment, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0105] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the computer. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0106] The systems and techniques described herein may be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), and the Internet.

[0107] A computer system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The relationship of client and server is generated by computer programs running on respective computers and having a client-server relationship with each other. The server may be a cloud server, a server of a distributed system, or a server combined with a blockchain.

[0108] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps recorded in this disclosure can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solutions disclosed in this disclosure can be achieved, and this document does not limit this.

[0109] The above specific implementations do not constitute a limitation on the protection scope of the present disclosure. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present disclosure shall be included in the protection scope of the present disclosure.

Claims

1. A method for pushing a driving trajectory, comprising: Obtaining a push request of a target truck, wherein the push request carries a target truck attribute, a target starting point, and a target destination of the target truck; Query whether there is target waypoint information matching the push request, wherein the target waypoint information includes positioning information of at least one target waypoint passed by the target representative route, the target waypoint is a coordinate point corresponding to both ends of each road section in the target representative route, the target representative route is obtained by similarity aggregation of historical driving trajectories, the representative truck attribute corresponding to the target representative route is obtained by aggregating the truck attributes corresponding to the historical driving trajectories, and the representative truck attribute is an attribute value of a truck attribute at a preset quantile; In response to querying the target waypoint information, performing trajectory restoration based on the push request and the target waypoint information to obtain the target driving trajectory; The target driving trajectory is output.

2. The method according to claim 1, wherein: Querying whether there is the target waypoint information matching the push request includes: Query whether there is initial waypoint information matching the target starting point and the target end point; In response to querying the initial waypoint information, acquiring a first truck attribute of the initial waypoint information; The first truck attribute is matched with the target truck attribute to obtain the target waypoint information.

3. The method according to claim 2, wherein: Querying whether the initial waypoint information exists includes: Based on the target starting point and the target end point, determining a target number pair, wherein the target number pair includes a number of a starting area where the target starting point is located and a number of an end area where the target end point is located; Check whether there is initial waypoint information corresponding to the target number pair.

4. The method according to claim 2, wherein: Matching the first truck attribute with the target truck attribute to obtain the target waypoint information includes: determining whether a first attribute value of the first truck attribute is greater than a target attribute value of the target truck attribute; In response to the first attribute value being greater than the target attribute value, determining the initial waypoint information as the target waypoint information.

5. The method according to claim 1, wherein: Performing trajectory restoration based on the push request and the target waypoint information to obtain the target driving trajectory includes: Based on the target truck attributes, a plurality of target road sections are determined, wherein the plurality of target road sections include: a road section between the target starting point and a first target waypoint, a road section between two adjacent target waypoints, and a road section between a last target waypoint and the target end point; Based on the multiple target road sections, the target driving trajectory is generated.

6. The method according to any one of claims 1 to 5, further comprising: Determine a number pair of a first driving track in the historical driving track, wherein the number pair includes a number of an area where a starting point of the first driving track is located and a number of an area where an end point of the first driving track is located; Performing similarity aggregation on the first driving trajectories of the same number pair to obtain at least one aggregation cluster; A second driving trajectory corresponding to the minimum weight in the at least one aggregated cluster is obtained to obtain at least one representative route.

7. The method according to claim 6, wherein: The first driving trajectories of the same numbered pairs are aggregated by similarity to obtain the at least one aggregated cluster, including: Determining a first similarity of the first driving trajectory of the pair with the same number based on the road section included in the first driving trajectory of the pair with the same number; The first driving trajectories of the pairs with the same number are aggregated based on the first similarity to obtain the at least one aggregated cluster.

8. The method according to claim 6, further comprising: Obtaining the driving time of the second driving trajectory; Based on the driving time, a weight of the second driving trajectory is determined.

9. The method according to claim 6, further comprising: Acquire a second truck attribute corresponding to the second driving trajectory; sorting the second truck attributes according to the attribute values ​​of the second truck attributes to obtain sorted truck attributes; From the sorted truck attributes, determine the truck attribute corresponding to the preset sorting position as the representative truck attribute.

10. The method according to claim 6, further comprising: Determining the frequency of occurrence of the representative route in the historical driving trajectory; Based on the occurrence frequency, determining a target restoration route in the representative route, wherein the occurrence frequency of the target restoration route is greater than a preset frequency; The waypoints passed by the target restoration route are extracted to obtain at least one waypoint information.

11. The method according to claim 10, wherein: Extracting the waypoints passed by the target restoration route to obtain the at least one waypoint information includes: Determining a plurality of third paths between a starting point and an end point of the target restoration route based on the truck attributes corresponding to the target restoration route; generating a new driving trajectory based on the plurality of third paths; Determining a second similarity between the new driving trajectory and the target restoration route; In response to the second similarity being greater than a preset similarity, the at least one waypoint information is obtained based on the waypoints corresponding to the plurality of third paths.

12. A driving track pushing device, comprising: An acquisition module, used for acquiring a push request of a target truck, wherein the push request carries a target truck attribute, a target starting point, and a target end point of the target truck; a query module, configured to query whether there is target waypoint information matching the push request, wherein the target waypoint information includes positioning information of at least one target waypoint passed by the target representative route, the target waypoint is a coordinate point corresponding to both ends of each road section in the target representative route, the target representative route is obtained by performing similarity aggregation on historical driving trajectories, the representative truck attribute corresponding to the target representative route is obtained by aggregating truck attributes corresponding to the historical driving trajectories, and the representative truck attribute is an attribute value of a truck attribute at a preset quantile; A restoration module, configured to restore the target driving trajectory based on the push request and the target waypoint information in response to the query of the target waypoint information; An output module is used to output the target driving trajectory.

13. An electronic device comprising: at least one processor; as well as a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method according to any one of claims 1 to 11.

14. A non-transitory computer-readable storage medium storing computer instructions, wherein: The computer instructions are used to cause the computer to execute the method according to any one of claims 1-11.

15. A computer program product comprising a computer program, which, when executed by a processor, implements the method according to any one of claims 1 to 11.

16. A navigation device comprising: The pushing device of the driving track as claimed in claim 12.

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