Path planning method and device, electronic device, and storage medium

Abstract

One or more embodiments of the present specification provide a path planning method and apparatus, an electronic device, and a storage medium; the method may include: selecting multiple driving routes whose departure and destination are matched from the driving routes of at least one vehicle in historical travel; calculating the trajectory similarity between the selected multiple driving routes to obtain at least one similar route set, the trajectory similarity between the driving routes in the same similar route set meets the similarity condition, and the driving routes in the same similar route set all correspond to the same type of vehicle; when there is a path to be planned for a target vehicle, determining a target similar route set that meets the departure and destination of the path to be planned and matches the type of the target vehicle, and selecting a candidate path for the path to be planned from the target similar route set.

Description

Technical Field

[0001] One or more embodiments of the present specification relate to the field of navigation technology, and in particular, to a path planning method and device, an electronic device, and a storage medium. Background Art

[0002] In the field of cargo distribution, different from the driving route planning of ordinary transport vehicles, there are some special means of transportation whose driving routes are not fixed but change dynamically with various factors, or the allowed driving routes are difficult to determine and the cost is high.

[0003] For example, due to the safety risks posed by hazardous chemicals vehicles, hazardous chemicals transportation must be carried out on roads approved by relevant departments. Due to different policies in different regions, hazardous chemical routes are mostly unstructured information, and there is no electronic route for hazardous chemical transportation formulated by authoritative departments. Therefore, it is difficult for hazardous chemical companies to plan routes when transporting goods to unfamiliar destinations.

[0004] For example, in the maritime field, routes can be designed with reference to nautical charts. Route design needs to consider factors such as route depth, ship draft, seasonal changes, etc., resulting in high route production costs and long update cycles. In related technologies, ships usually use empirical routes for navigation, but there are no global digital routes for empirical routes. Summary of the invention

[0005] In view of this, one or more embodiments of the present specification provide a path planning method and device, an electronic device, and a storage medium.

[0006] To achieve the above objectives, one or more embodiments of this specification provide the following technical solutions:

[0007] According to a first aspect of one or more embodiments of this specification, a path planning method is proposed, including:

[0008] Selecting a plurality of travel routes with matching departure places and destinations from the travel routes of at least one means of transportation in historical trips;

[0009] Calculating trajectory similarities between the selected multiple driving routes to obtain at least one similar route set, wherein the trajectory similarities between the driving routes in the same similar route set meet the similarity condition, and the driving routes in the same similar route set all correspond to the same type of transportation;

[0010] When there is a path to be planned for the target vehicle, a set of target similar routes that meet the departure and destination of the path to be planned and match the type of the target vehicle is determined, and a candidate path for the path to be planned is selected from the set of target similar routes.

[0011] According to a second aspect of one or more embodiments of this specification, a path planning method is proposed, including:

[0012] Acquire at least one similar route set, wherein the driving routes in the same similar route set all correspond to the same type of transportation, the at least one similar route set is obtained by calculating the trajectory similarity between a plurality of driving routes and making the trajectory similarity between the driving routes in the same similar route set meet a similarity condition, and the plurality of driving routes are obtained by selecting driving routes with matching departure points and destinations from driving routes of at least one transportation vehicle in historical trips;

[0013] Obtaining a departure point and a destination of a path to be planned for a target vehicle, and determining a target similar route set that meets the departure point and the destination of the path to be planned and matches the type of the target vehicle;

[0014] A candidate path is selected from the target similar route set as the path to be planned.

[0015] According to a third aspect of one or more embodiments of this specification, a path planning device is provided, including:

[0016] A selection unit selects a plurality of travel routes with matching departure places and destinations from the travel routes of at least one means of transportation in historical travels;

[0017] a calculation unit, calculating the trajectory similarity between the selected multiple driving routes to obtain at least one similar route set, wherein the trajectory similarity between the driving routes in the same similar route set meets the similarity condition, and the driving routes in the same similar route set all correspond to the same type of transportation;

[0018] The planning unit, when there is a path to be planned for a target vehicle, determines a set of target similar routes that meet the departure and destination of the path to be planned and match the type of the target vehicle, and selects a candidate path for the path to be planned from the set of target similar routes.

[0019] According to a fourth aspect of one or more embodiments of this specification, a path planning device is provided, including:

[0020] an acquisition unit, acquiring at least one similar route set, wherein the driving routes in the same similar route set all correspond to the same type of transportation tools, the at least one similar route set is obtained by calculating the trajectory similarity between a plurality of driving routes and making the trajectory similarity between the driving routes in the same similar route set meet a similarity condition, and the plurality of driving routes are obtained by selecting driving routes with matching departure points and destinations from driving routes of at least one transportation tool in historical trips;

[0021] A determination unit, which obtains a departure point and a destination of a path to be planned for a target vehicle, and determines a target similar route set that meets the departure point and the destination of the path to be planned and matches the type of the target vehicle;

[0022] A selection unit selects a candidate path from the target similar route set as the path to be planned.

[0023] According to a fifth aspect of one or more embodiments of this specification, an electronic device is provided, including:

[0024] processor;

[0025] a memory for storing processor-executable instructions;

[0026] The processor implements the method described in any of the above embodiments by running the executable instructions.

[0027] According to a sixth aspect of one or more embodiments of the present specification, a computer-readable storage medium is provided, on which computer instructions are stored, and when the instructions are executed by a processor, the steps of the method described in any of the above embodiments are implemented.

[0028] In the path planning scheme of this specification, in the case where the route between the departure point and the destination of the target vehicle is difficult to determine, the historical driving routes of other vehicles between the two places can be used as a reference, and the routes that more vehicles have traveled can be selected as candidate routes to be recommended to the user. Therefore, multiple driving routes that match the departure point and destination can be selected from the driving routes of at least one vehicle in the historical itinerary, and then the trajectory similarity between the selected multiple driving routes is calculated to determine the driving routes with similar trajectories (driving routes with similar trajectories are regarded as the same routes). In addition to considering the dimension of selecting routes traveled by more vehicles, since there are large differences in the selection of driving routes for different types of vehicles, the dimension of traffic type can be further considered. Therefore, when dividing the set of similar routes, in addition to the similarity of trajectories, it is also necessary to set the type of vehicle to be the same.

[0029] Based on the above division of similar route sets, when there is a path to be planned for the target vehicle, a target similar route set that meets the departure and destination of the path to be planned and matches the type of the target vehicle can be determined, so that a candidate path can be selected from the target similar route set as the path to be planned. Through the above path planning method based on historical itineraries and matching the same type of vehicles, the cost of path planning can be reduced while ensuring that the planned path is actually feasible and accurate. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] In order to more clearly illustrate the technical solutions of the embodiments of this specification, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this specification. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0031] Figure 1 It is a schematic diagram of the architecture of a path planning system provided by an exemplary embodiment.

[0032] Figure 2 It is a flow chart of a path planning method provided by an exemplary embodiment.

[0033] Figure 3 It is a schematic diagram of positioning data denoising provided by an exemplary embodiment.

[0034] Figure 4 It is a schematic diagram of compressed trajectory data provided by an exemplary embodiment.

[0035] Figure 5 A flowchart of compressing trajectory data provided by an exemplary embodiment.

[0036] Figure 6 It is a schematic diagram of calculating trajectory similarity provided by an exemplary embodiment.

[0037] Figure 7 is a flowchart of another path planning method provided by an exemplary embodiment.

[0038] Figure 8 It is a structural schematic diagram of a device provided by an exemplary embodiment.

[0039] Fig. 9 It is a block diagram of a path planning device provided by an exemplary embodiment.

[0040] Fig.10 It is a block diagram of another path planning device provided by an exemplary embodiment. DETAILED DESCRIPTION

[0041] Exemplary embodiments will be described in detail herein, examples of which are shown in the accompanying drawings. When the following description refers to the drawings, the same numbers in different drawings represent the same or similar elements unless otherwise indicated. The implementations described in the following exemplary embodiments do not represent all implementations consistent with one or more embodiments of this specification. Instead, they are merely examples of devices and methods consistent with some aspects of one or more embodiments of this specification as detailed in the appended claims.

[0042] It should be noted that: in other embodiments, the steps of the corresponding method are not necessarily performed in the order shown and described in this specification. In some other embodiments, the steps included in the method may be more or less than those described in this specification. In addition, a single step described in this specification may be decomposed into multiple steps for description in other embodiments; and multiple steps described in this specification may be combined into a single step for description in other embodiments.

[0043] In the field of cargo distribution, different from the driving route planning of ordinary transport vehicles, there are some special means of transportation whose driving routes are not fixed but change dynamically with various factors, or the allowed driving routes are difficult to determine and the cost is high.

[0044] For example, due to the safety risks posed by hazardous chemicals vehicles, hazardous chemicals transportation must be carried out on roads approved by relevant departments. Due to different policies in different regions, hazardous chemical routes are mostly unstructured information, and there is no electronic route for hazardous chemical transportation formulated by authoritative departments. Therefore, it is difficult for hazardous chemical companies to plan routes when transporting goods to unfamiliar destinations.

[0045] For example, in the maritime field, routes can be designed with reference to nautical charts. Route design needs to consider factors such as route depth, ship draft, seasonal changes, etc., resulting in high route production costs and long update cycles. In related technologies, ships usually use empirical routes for navigation, but there are no global digital routes for empirical routes.

[0046] In view of the problem in related technologies that it is difficult to plan driving routes for some special vehicles, this specification aims to provide a path planning solution, which uses the historical driving routes of each vehicle as a reference, calculates the similarity of the historical driving routes, and combines the types of vehicles to select routes that have been traveled by more vehicles as candidate routes and recommend them to users.

[0047] See also Figure 1 , Figure 1 FIG. 1 is a schematic diagram of the architecture of a path planning system provided by an exemplary embodiment. Figure 1As shown, the system may include a server 11 , several electronic devices, such as a mobile phone 12 , a mobile phone 13 , a computer 14 , a computer 15 , and a network 16 .

[0048] The server 11 may be a physical server including an independent host, or the server 11 may be a virtual server carried by a host cluster. During operation, the server 11 may run a program on the server side of a certain application to implement the relevant functions of the application. For example, when the server 11 runs a program of a path planning platform, it may be implemented as the server end of the path planning platform. In the technical solutions of one or more embodiments of the present specification, the server 11 may cooperate with a client running on electronic devices such as mobile phone 12, mobile phone 13, computer 14 and computer 15 to implement the path planning solution of the present specification.

[0049] Mobile phone 12, mobile phone 13, computer 14 and computer 15 are just one type of electronic devices that can be used by users. In fact, users can obviously also use electronic devices such as tablet devices, laptops, PDAs (Personal Digital Assistants), wearable devices (such as smart glasses, smart watches, etc.), etc., and one or more embodiments of this specification do not limit this. During operation, the electronic device can run a program on the client side of an application to implement the relevant functions of the application. For example, when the electronic device runs a program of a path planning platform, it can be implemented as a client of the path planning platform.

[0050] It should be pointed out that the client application of the path planning platform can be pre-installed on the electronic device so that the client can be started and run on the electronic device; of course, when an online "client" such as HTML5 technology is adopted, there is no need to install the corresponding application on the electronic device to obtain and run the client.

[0051] The network 16 for interaction between the electronic devices 12-15 and the server 11 may include various types of wired or wireless networks. In one embodiment, the network 16 may include a public switched telephone network (PSTN) and the Internet. A long connection may be established between the server 11 and the mobile phones 12-13 through the network 16, so that the server 11 sends push messages to the mobile phones 12-13 through the long connection.

[0052] See also Figure 2 , Figure 2 FIG. 1 is a flow chart of a path planning method provided by an exemplary embodiment. Figure 2 As shown, the method is applied to the server and may include the following steps:

[0053] Step 202: Select multiple travel routes with matching departure points and destinations from the travel routes of at least one vehicle in historical travels.

[0054] The path planning scheme of this specification aims to analyze the similarity of mobile user locations and extract similar paths of mobile users to predict the driving route. The mobile users include multiple vehicles of various types, such as hazardous chemical vehicles, aviation vehicles (such as shipping vehicles), etc.

[0055] As an exemplary embodiment, a vehicle can upload positioning data in real time during driving, so that the driving trajectory data corresponding to the itinerary of the vehicle can be obtained based on the positioning data. Specifically, the positioning data uploaded by at least one vehicle in a historical itinerary can be obtained, and the trajectory data corresponding to the historical itinerary can be generated based on the positioning data, so that the trajectory data can be segmented to obtain multiple driving routes of the historical itinerary.

[0056] For example, to calculate the route of a vehicle or ship, the itinerary trajectory can be defined first. A vehicle or ship will pass through multiple positioning points in a complete itinerary, including time and space positioning data. To this end, the itinerary content can be defined including the trajectory point sequence, trajectory start point, trajectory end point, trajectory length, and trajectory point number. As shown in Table 1:

[0057]

[0058]

[0059] Table 1

[0060] The trajectory point sequence is a sequence of trajectory points of a complete travel path, sorted according to the travel trajectory time. Among them, the trajectory point includes time, longitude, latitude, altitude and other information, which can be obtained from the satellite positioning data received regularly by vehicles, ships and other means of transportation. The trajectory start and end points are the trajectory points where the travel trajectory starts and ends, the trajectory length is the total mileage of the complete journey of the vehicle or ship, and the number of trajectory points is the number of trajectory points in the trajectory point sequence. As shown in Table 2:

[0061] Field Name type describe gmtCreate String Track time Ln double longitude Lat double latitude altitude double altitude

[0062] Table 2

[0063] Since the vehicle may have one or more starts and stops during the entire journey, such as rest, maintenance, etc., the trajectory data of the entire journey can be divided into multiple driving routes. For example, as mentioned above, when the positioning data includes the location information of the vehicle and the positioning time information corresponding to the location information, the trajectory data can be generated according to the location information; then, the travel departure time information and travel end time information of the vehicle are determined according to the positioning time information and the residence time threshold, and the trajectory data is segmented according to the travel departure time information and the travel end time information to obtain multiple driving routes.

[0064] For example, a residence time threshold can be set as a basis for determining whether a vehicle has stopped. For example, a residence time threshold is set at 18 minutes for a vehicle. When the vehicle's residence time exceeds 18 minutes, it is determined that the vehicle has ended its journey. Alternatively, a driving speed threshold or a driving range threshold can be set to determine whether the vehicle has started and ended its journey. For example, when the vehicle's operating radius within 5 minutes exceeds 600 meters, it is determined that the vehicle's journey has started. Alternatively, the vehicle can actively report the start and end time of the journey. Of course, different values ​​of residence time thresholds and driving range thresholds can be set for different types of vehicles, and their specific values ​​can be flexibly set according to actual conditions, and this manual does not limit this.

[0065] In this embodiment, the positioning data often contains noise. When the positioning data includes the location information of the vehicle and the positioning time information corresponding to the location information, the positioning data can be maintained in the following manner to improve the accuracy of subsequent path planning: when the positioning time information corresponding to any location information does not match the reference time, delete the location information and the corresponding positioning time information; when any location information does not meet the preset location range, delete the location information and the corresponding positioning time information; when the linear speed of the vehicle at any location information does not meet the preset linear speed range, delete the location information and the corresponding positioning time information.

[0066] For example, there is usually noise in positioning data, which will affect the accuracy of similar distances of routes. Therefore, positioning data denoising adopts a rule-based approach. The basic data range rules include denoising based on the difference between positioning time and system time (as reference time), whether the longitude and latitude range is reasonable, whether denoising is performed within the preset range, etc.

[0067] In addition, see Figure 3 , Figure 3 FIG. 1 is a schematic diagram of positioning data denoising provided by an exemplary embodiment. Figure 3As shown, the linear speed range can also be set as the basis for judging whether it is a noise point, and then the linear speed of the positioning data is calculated for denoising. For example, the linear speed is calculated by the distance and time difference between two points adjacent in time, and the drift rule for the vehicle can be defined as: the speed between two consecutive trajectory points is greater than 160km / h, which is recorded as one drift. Continuous drift is defined as: the distance between multiple consecutive points is greater than 2km. For example, Figure 3 The middle track points AH and JL are continuous drift. Of course, the distance threshold for determining whether there is continuous drift can be flexibly set according to actual conditions, for example, in addition to 2km, it can also be set to 10km, and this specification does not limit this.

[0068] Step 204 , calculating the trajectory similarities between the selected multiple driving routes to obtain at least one similar route set, the trajectory similarities between the driving routes in the same similar route set meet the similarity condition, and the driving routes in the same similar route set all correspond to the same type of transportation.

[0069] Before calculating the similarity, the driving route can be compressed to reduce the amount of data, thereby reducing the amount of calculation for similarity calculation to improve calculation efficiency. For example, when the driving route is a curve, the curve can be approximated as a series of points, thereby reducing the number of points. As an exemplary embodiment, when any driving route is a curve, the driving route is compressed to obtain a driving feature route for characterizing the geometric shape of the driving route, so as to calculate the similarity between each driving feature route as the trajectory similarity. As for the method of compressing the driving route, the distance from all points on the route to the connecting line between the beginning and the end can be combined.

[0070] For example, Figure 4 FIG. 1 is a schematic diagram of compressed trajectory data provided by an exemplary embodiment. Figure 4 As shown, the starting point of the driving route L is A, the end point is B, and the line between the two points is K. For example, the distance between the track point C on the driving route L and the line K is d1, the distance between the track point D on the driving route L and the line K is d2, and the distance between the track point D on the driving route L and the line K is d3.

[0071] As for the specific method of compressing the driving route, the starting point and the end point of any driving route may be connected to form a line segment, and the distances from all points on the driving route to the line segment are calculated to determine the maximum distance value. If the maximum distance value is within the tolerance range, the point between the starting point and the end point of any driving route is deleted; otherwise, the driving route is divided into two driving routes by the point corresponding to the maximum distance value on the driving route to continue to calculate the maximum distance values ​​separately until all points between the starting point and the end point of the driving route are deleted.

[0072] For ease of understanding, the following is an example in conjunction with Figure 5 for illustration.

[0073] After segmenting the journey, the positioning data of the driving route can be obtained. Since a single complete journey of a vehicle or a ship may cover a long distance and the trajectory points included in the driving trajectory are numerous, directly calculating the similarity of the driving route will result in a large amount of calculation and low efficiency. To improve the calculation efficiency, the travel trajectory data can be compressed first to generate trajectory compression data, and then the similarity calculation can be performed. The definition of the travel trajectory data is shown in Table 3:

[0074] Field Name type describe Vhc_id String Vehicle ID O_area_code String Departure area code D_area_code String Arrival area code trajectoryPointList List <trajectorypoint> < / trajectorypoint> Compressed trajectory point sequence

[0075] Table 3

[0076] The compression idea is as follows: Virtually connect a straight line between the start and end points of each curve, calculate the distances from all points on the virtual line to the straight line, and find the maximum distance value dmax. Compare dmax with the tolerance D: If dmax < D, then all intermediate points on this curve are discarded; If dmax ≥ D, then retain the trajectory point corresponding to dmax, and take this point as the boundary to divide the curve into two partial curves, and repeat the above method for compression on these two partial curves.

[0077] Please refer to Figure 5 , Figure 5 which is a flowchart of compressing trajectory data provided by an exemplary embodiment. As Figure 5 shown, the process of compressing trajectory data may include the following steps:

[0078] Step 502, generate the vehicle trajectory originPoints according to the obtained vehicle positioning point sequence.

[0079] Step 504, obtain the start and end positioning points of the vehicle trajectory originPoints.

[0080] Step 506, input the parameters of the trajectory compression function.

[0081] In this embodiment, the vehicle positioning point sequence can be obtained and defined according to the above travel trajectory to obtain the vehicle trajectory data originPoints. It should be noted that the trajectory compression cannot change the total journey of the vehicle (i.e., the trajectory length cannot be changed) because the start and end positioning points of the trajectory need to be retained.

[0082] This embodiment uses a recursive method to query the point with the maximum distance from the line connecting two points. Define the start and end points of the trajectory: the start position point start and the end position point end. The trajectory point deviation threshold is dMax, the point with the maximum distance from the line segment K formed by start and end is maxPt, the maximum distance is maxDist, and the compressed trajectory is resultPoints.

[0083] Before the recursive query reaches the straight line between two points, initialize and set start as the starting point of the trajectory, start = 0, and end as the point where the trajectory arrives, end = the number of positioning points cnt - 1. Points on the trajectory whose distance to line segment K is less than maxDist can be lost to achieve the purpose of trajectory compression. For example, since the road width can reach 30 meters, in the scenario of calculating the route, it is acceptable for the vehicle to be on the road without caring about which lane it is in, so dMax can be set to 30 meters.

[0084] Step 508, if start < end, go to step 510; otherwise, go to step 520.

[0085] Step 510, calculate the distance curDist[i] from the trajectory point i between start and end to the straight line line between start and end.

[0086] Step 512, calculate the maximum value maxDist and the point maxPt of the values in the curDist[i] array.

[0087] Step 514, if maxDist >= dMax, go to step 516; otherwise, go to step 520.

[0088] The end condition for the recursive query to reach the points on line segment K is that the starting point number is less than the ending point number, that is, start < end. Calculate the distance d from the positioning point curDist[i] between start and end to line segment K between start and end, and then perform a distance comparison to calculate the point maxPt with the maximum distance to line. Compare the distance maxDist from the maximum distance point maxPt to line segment K with dmax. If it is less than the threshold dmax, discard this positioning point; otherwise, retain it in the compressed trajectory sequence resultPoints. Then, divide the entire trajectory into two segments through the maximum distance point maxPt. For segment A, the starting point stat = start and the ending point end = maxPt. For segment B, the starting point start = maxPt and the ending point end = end. Recalculate the query to find the positioning point with the maximum distance to the line segment for the two segmented trajectories until the recursion ends. Finally, sort the compressed trajectory sequence resultPoints according to the time of the positioning points to obtain the compressed trajectory sequence.

[0089] Step 516, save the trajectory point with maxPt to the resultPoints trajectory sequence.

[0090] Step 518A, set start = start, end = maxPt.

[0091] Step 518B, set start=maxPt, end=end.

[0092] Step 520, get resultPoints.

[0093] Step 522, sorting resultPoints according to the positioning point time.

[0094] It should be noted that in order to calculate the similarity of the driving routes and improve the calculation efficiency, the premise of similarity calculation should be set as: select the routes with the same departure and arrival points for similarity calculation. In this regard, for the starting and ending points of the driving route, the map can be grid-coded to obtain multiple grid-coded areas, and the areas can be marked with grids. When two points belong to the same grid-coded area, they can be considered the same.

[0095] In this embodiment, the similarity between two driving routes can be measured by the area of ​​the region enclosed by the two driving routes. Specifically, for any two driving routes, the area of ​​the region enclosed by the two driving routes is calculated, and the similarity between the two driving routes is determined based on the calculated area. Among them, if the trajectories of the two driving routes completely overlap, the area enclosed by the two driving trajectories is a trajectory line with an area of ​​0, and the similarity is the highest. If the area of ​​the area enclosed by the two driving trajectories is large, it means that the two similar deviations are large. Therefore, the similarity between the two driving routes can be set to be negatively correlated with the area of ​​the region enclosed by the two driving routes.

[0096] Furthermore, since different driving routes have different lengths, in order to improve the accuracy of reflecting the trajectory similarity, the calculation can be further combined with the route length. Therefore, the similarity between two driving routes can be set to be positively correlated with the perimeter of the area enclosed by the two driving routes. For example, the formula for the similarity distance between two trajectories can be defined: distance = area / perimeter. Among them, distance represents the trajectory similarity distance, area represents the area of ​​the area enclosed by the two driving routes, and perimeter represents the perimeter of the area enclosed by the two driving routes.

[0097] The two travel trajectories with similarity comparison are enclosed to generate a variable edge area. Since the starting point and destination of the two trajectories are the same and the route direction is the same, the two trajectories can be enclosed to connect the two starting points and the two ending points of the two trajectories, and ensure that the direction of the circle after the merging is consistent.

[0098] For example, see Figure 6 , Figure 6 FIG. 1 is a schematic diagram of calculating trajectory similarity provided by an exemplary embodiment. Figure 6As shown, two trajectories are defined, namely tr1 and tr2, and the end points of tr1 and tr2 are obtained. A line tr1End is generated from the end point of tr1 to the end point of tr2. The tr1End line segment is used as the extension line segment of the tr1 trajectory. Then, tr1End is appended to the end of the tr1 trajectory to generate a new trajectory line tr1New.

[0099] Then get the starting points of tr1 and tr2, connect the starting point of tr1 to the starting point of tr2 to generate the line segment tr2Start, the tr2Start line segment is used as the starting extension of the tr2 trajectory segment, and then append tr2Start to the beginning of the tr2 line segment to generate a new trajectory line tr2New.

[0100] Since tr1 and tr2 have the same starting point and destination, and the same direction, tr1New and tr2New also have the same direction. Therefore, after reversing tr2New, tr2New is reversed and the direction is opposite to tr1New, and the two trajectories are merged to generate a connecting ring. Finally, multiple polygons are generated through the connecting ring, namely multiPolygon.

[0101] After generating multiple polygons, the sum of the areas of all polygons is calculated as area, the sum of the perimeters of all polygons is calculated as perimeter, and then the similarity distance between the two trajectories is calculated. For example, the similarity condition can be set to distance<=6.

[0102] Step 206, when there is a path to be planned for the target vehicle, determine a set of target similar routes that meet the departure and destination of the path to be planned and match the type of the target vehicle, and select a candidate path for the path to be planned from the set of target similar routes.

[0103] Based on the above division of similar route sets, when there is a path to be planned for the target vehicle, a target similar route set that meets the departure and destination of the path to be planned and matches the type of the target vehicle can be determined, so that a candidate path can be selected from the target similar route set as the path to be planned. Through the above-mentioned path planning method based on historical itineraries and matching the same type of vehicles, the cost of path planning can be reduced while ensuring that the planned path is actually feasible and accurate. For example, when the target vehicle is a hazardous chemical vehicle, the departure point can be the same as the departure point of the hazardous chemical vehicle, the destination can be the same as the destination of the hazardous chemical vehicle, and a candidate path can be selected from the similar route set corresponding to the hazardous chemical vehicle for recommendation to the user.

[0104] In this embodiment, when there are multiple similar route sets that meet the departure and destination of the path to be planned and match the type of the target vehicle, the number of vehicles that adopt the same driving route (the trajectory similarity meets the similarity condition) can be further combined for recommendation. For example, the more vehicles that take the same driving route, the higher the probability that the driving route is feasible. Therefore, in this case, the target similar route set can be selected according to the number of driving routes included in each similar route set; wherein, the probability of each similar route set being selected as the target similar route set is positively correlated with the number of driving routes included.

[0105] For example, in order to analyze historical routes and calculate the travel routes of different types of vehicles (such as hazardous chemicals vehicles) or the routes of different types of ships, similar data of vehicle or ship travel trajectories in the past month can be screened for calculation to determine the number of vehicles with a similar route distance between the departure and destination of less than or equal to 6. The vehicle number indicator of the route is used to measure the confidence of the same type of vehicles that can pass or recommend the route.

[0106] See also Figure 7 , Figure 7 FIG. 1 is a flow chart of another path planning method provided by an exemplary embodiment. Figure 7 As shown, the method is applied to the server and may include the following steps:

[0107] Step 702, obtaining at least one similar route set, wherein the driving routes in the same similar route set all correspond to the same type of transportation tools, and the at least one similar route set is obtained by calculating the trajectory similarity between a plurality of driving routes and making the trajectory similarity between the driving routes in the same similar route set meet a similarity condition, and the plurality of driving routes are obtained by selecting driving routes whose departure and destination both match from the driving routes of at least one transportation tool in historical travel.

[0108] Step 704, obtaining the departure point and destination of the path to be planned for the target vehicle, and determining a target similar route set that meets the departure point and destination of the path to be planned and matches the type of the target vehicle.

[0109] Step 706: Select a candidate path from the target similar route set as the path to be planned.

[0110] As mentioned above, the server can obtain the positioning data uploaded by at least one vehicle in a historical trip, generate trajectory data corresponding to the historical trip based on the positioning data, and then segment the trajectory data to obtain multiple driving routes of the historical trip.

[0111] As mentioned above, the positioning data includes the location information of the vehicle and the positioning time information corresponding to the location information. In this case, the trajectory data can be generated according to the location information, and then: the travel departure time information and the travel end time information of the vehicle are determined according to the positioning time information and the residence time threshold, and the trajectory data is segmented according to the travel departure time information and the travel end time information to obtain multiple travel routes.

[0112] As mentioned above, the positioning data includes the location information of the vehicle and the positioning time information corresponding to the location information. In this case, the positioning data can be maintained by at least one of the following methods:

[0113] If the positioning time information corresponding to any position information does not match the reference time, the position information and the corresponding positioning time information are deleted;

[0114] If any location information does not conform to the preset location range, the location information and the corresponding positioning time information are deleted;

[0115] When the linear speed of the vehicle at any position information does not meet the preset linear speed range, the position information and the corresponding positioning time information are deleted.

[0116] As mentioned above, the server can also compress any driving route to obtain a driving characteristic route for characterizing the geometric shape of any driving route when any driving route is a curve, so as to calculate the similarity between each driving characteristic route as the trajectory similarity.

[0117] As mentioned above, the server may connect the starting point and the end point of any driving route to form a line segment, and calculate the distances from all points on the any driving route to the line segment to determine the maximum distance value, wherein if the maximum distance value is within the tolerance range, the points between the starting point and the end point of any driving route are deleted; otherwise, the any driving route is divided into two driving routes through the points on the any driving route corresponding to the maximum distance value to continue to calculate the maximum distance values ​​separately until the points between the starting points and the end points of all driving routes are deleted.

[0118] As mentioned above, the server can calculate the area of ​​the region enclosed by any two driving routes, and then determine the similarity between the any two driving routes based on the calculated area; wherein the similarity between the two driving routes is negatively correlated with the area of ​​the region enclosed by the two driving routes.

[0119] As mentioned above, the similarity between two driving routes is positively correlated with the perimeter of the area enclosed by the two driving routes.

[0120] As mentioned above, when there are multiple similar route sets that meet the departure and destination points of the path to be planned and match the type of the target transportation vehicle, the target similar route set can be selected based on the number of driving routes included in each similar route set; wherein the probability of each similar route set being selected as the target similar route set is positively correlated with the number of driving routes included.

[0121] As previously mentioned, the types of vehicles include hazardous chemicals vehicles and / or aviation vehicles.

[0122] It should be noted that Figure 7 For the specific implementation process of the embodiment shown, please refer to Figure 2 The relevant parts of the illustrated embodiment will not be described in detail here.

[0123] Corresponding to the above method embodiments, this specification also provides corresponding device embodiments, and the specific implementation process thereof can refer to the above method embodiments and will not be repeated here.

[0124] Figure 8 is a schematic structural diagram of a device provided by an exemplary embodiment. Figure 8 At the hardware level, the device includes a processor 802, an internal bus 804, a network interface 806, a memory 808, and a non-volatile memory 810, and may also include other required hardware. One or more embodiments of this specification may be implemented based on software, such as the processor 802 reading the corresponding computer program from the non-volatile memory 810 into the memory 808 and then running it. Of course, in addition to the software implementation, one or more embodiments of this specification do not exclude other implementations, such as logic devices or a combination of software and hardware, etc., that is, the execution subject of the following processing flow is not limited to each logic unit, but can also be hardware or logic devices.

[0125] Please refer to Fig. 9 , a path planning device can be applied to Figure 8 The device shown in the figure can realize the technical solution of this specification. The device can include:

[0126] A selection unit 91 selects a plurality of travel routes with matching departure points and destinations from the travel routes of at least one vehicle in historical travels;

[0127] A calculation unit 92 calculates trajectory similarities between the selected multiple driving routes to obtain at least one similar route set, wherein the trajectory similarities between the driving routes in the same similar route set meet the similarity condition, and the driving routes in the same similar route set all correspond to the same type of transportation;

[0128] The planning unit 93, when there is a path to be planned for the target vehicle, determines a set of target similar routes that meet the departure and destination of the path to be planned and match the type of the target vehicle, and selects a candidate path for the path to be planned from the set of target similar routes.

[0129] Please refer to Fig.10 Another path planning device can be applied to Figure 8 The device shown in the figure can realize the technical solution of this specification. The device can include:

[0130] An acquisition unit 1001 acquires at least one similar route set, wherein the driving routes in the same similar route set all correspond to the same type of transportation tools, and the at least one similar route set is obtained by calculating the trajectory similarity between a plurality of driving routes and making the trajectory similarity between the driving routes in the same similar route set meet a similarity condition, and the plurality of driving routes are obtained by selecting driving routes whose departure and destination both match from the driving routes of at least one transportation tool in historical travel.

[0131] The determination unit 1002 obtains the departure point and destination of the path to be planned for the target vehicle, and determines a target similar route set that meets the departure point and destination of the path to be planned and matches the type of the target vehicle.

[0132] The selection unit 1003 selects a candidate path from the target similar route set as the path to be planned.

[0133] The systems, devices, modules or units described in the above embodiments may be implemented by computer chips or entities, or by products with certain functions. A typical implementation device is a computer, which may be in the form of a personal computer, a laptop computer, a cellular phone, a camera phone, a smart phone, a personal digital assistant, a media player, a navigation device, an email transceiver, a game console, a tablet computer, a wearable device or a combination of any of these devices.

[0134] In a typical configuration, a computer includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0135] The memory may include non-permanent storage in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0136] Computer-readable media include permanent and non-permanent, removable and non-removable media that can be used to store information by any method or technology. Information can be computer-readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassettes, disk storage, quantum memory, graphene-based storage media or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer-readable media does not include temporary computer-readable media (transitory media), such as modulated data signals and carrier waves.

[0137] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0138] The above is a description of a specific embodiment of the specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in an order different from that in the embodiments and still achieve the desired results. In addition, the processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0139] The terms used in one or more embodiments of this specification are only for the purpose of describing specific embodiments, and are not intended to limit one or more embodiments of this specification. The singular forms of "a", "said" and "the" used in one or more embodiments of this specification and the appended claims are also intended to include plural forms, unless the context clearly indicates other meanings. It should also be understood that the term "and / or" used herein refers to and includes any or all possible combinations of one or more associated listed items.

[0140] It should be understood that although the terms first, second, third, etc. may be used to describe various information in one or more embodiments of this specification, these information should not be limited to these terms. These terms are only used to distinguish the same type of information from each other. For example, without departing from the scope of one or more embodiments of this specification, the first information may also be referred to as the second information, and similarly, the second information may also be referred to as the first information. Depending on the context, the word "if" as used herein may be interpreted as "at the time of" or "when" or "in response to determining".

[0141] The above description is merely a preferred embodiment of one or more embodiments of the present specification and is not intended to limit one or more embodiments of the present specification. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of one or more embodiments of the present specification shall be included in the scope of protection of one or more embodiments of the present specification.

Claims

1. A path planning method, characterized in that: include: Selecting a plurality of travel routes with matching departure places and destinations from the travel routes of at least one means of transportation in historical trips; Calculating trajectory similarities between the selected multiple driving routes to obtain at least one similar route set, wherein the trajectory similarities between the driving routes in the same similar route set meet the similarity condition, and the driving routes in the same similar route set all correspond to the same type of transportation; When there is a path to be planned for the target vehicle, determine a set of target similar routes that meet the departure and destination of the path to be planned and match the type of the target vehicle, and select a candidate path for the path to be planned from the set of target similar routes; The calculating of the trajectory similarity between the selected multiple driving routes includes: For any two driving routes, calculating the area and perimeter of the area enclosed by the any two driving routes; The similarity between any two driving routes is determined based on the calculated area and perimeter; wherein the similarity between the two driving routes is negatively correlated with the area of ​​the area enclosed by the two driving routes and positively correlated with the perimeter of the area enclosed by the two driving routes.

2. The method according to claim 1, characterized in that Also includes: Acquire positioning data uploaded by at least one means of transport in a historical trip, and generate trajectory data corresponding to the historical trip based on the positioning data; The trajectory data is segmented to obtain multiple driving routes of the historical journey.

3. The method according to claim 2, characterized in that The positioning data includes the location information of the vehicle and the positioning time information corresponding to the location information; Generating trajectory data corresponding to the historical trip according to the positioning data includes: generating the trajectory data according to the location information; The step of segmenting the trajectory data to obtain a plurality of travel routes of the historical trip includes: determining the travel departure time information and the travel end time information of the vehicle according to the positioning time information and the residence time threshold, and segmenting the trajectory data according to the travel departure time information and the travel end time information to obtain a plurality of travel routes.

4. The method according to claim 1, characterized in that: Also includes: In the case that any driving route is a curve, the any driving route is compressed to obtain a driving characteristic route for characterizing the geometric shape of the any driving route, so as to calculate the similarity between the various driving characteristic routes as the trajectory similarity.

5. The method according to claim 4, characterized in that The step of compressing any of the driving routes to obtain a driving characteristic route for characterizing the geometric shape of any of the driving routes comprises: Connecting the starting point and the end point of any driving route to form a line segment, and calculating the distances from all points on any driving route to the line segment to determine a maximum distance value; If the maximum distance value is within the tolerance range, the point between the starting point and the end point of any driving route is deleted; Otherwise, the any driving route is divided into two driving routes by the point on the any driving route corresponding to the maximum distance value to continue to calculate the maximum distance values ​​respectively until the points between the starting point and the end point of all driving routes are deleted.

6. A path planning device, characterized in that: include: A selection unit selects a plurality of travel routes with matching departure places and destinations from the travel routes of at least one means of transportation in historical travels; a calculation unit, calculating the trajectory similarity between the selected multiple driving routes to obtain at least one similar route set, wherein the trajectory similarity between the driving routes in the same similar route set meets the similarity condition, and the driving routes in the same similar route set all correspond to the same type of transportation; a planning unit, when there is a path to be planned for the target vehicle, determining a set of target similar routes that meet the departure and destination of the path to be planned and match the type of the target vehicle, and selecting a candidate path for the path to be planned from the set of target similar routes; The computing unit is specifically used for: For any two driving routes, calculating the area and perimeter of the area enclosed by the any two driving routes; The similarity between any two driving routes is determined based on the calculated area and perimeter; wherein the similarity between the two driving routes is negatively correlated with the area of ​​the area enclosed by the two driving routes and positively correlated with the perimeter of the area enclosed by the two driving routes.

7. An electronic device, characterized in that: include: processor; a memory for storing processor-executable instructions; The processor implements the method according to any one of claims 1 to 5 by running the executable instructions.

8. A computer-readable storage medium, characterized in that: Computer instructions are stored thereon, and when the instructions are executed by a processor, the steps of the method according to any one of claims 1 to 5 are implemented.

Images