Vehicle driving path detection method and device, vehicle, storage medium and product

Abstract

The invention discloses a vehicle driving path detection method and device, a vehicle, a storage medium and a product, and relates to the technical field of path detection.The vehicle driving path detection method specifically comprises the steps that an initial planned path and an initial actual path are obtained, determining initial path grids corresponding to the initial planning path and the initial actual path in the target tile map; updating the initial planning path according to each initial path grid to obtain a target planning path, and updating the initial actual path according to each initial path grid to obtain a target actual path; and determining a path similarity between the target planned path and the target actual path, and determining a path anomaly detection result corresponding to the target actual path based on the path similarity.

Description

Technical Field

[0001] The present application relates to the field of vehicle technology, and in particular to a method for evaluating vehicle rental value, a terminal device, a storage medium, and a computer program product. Background Art

[0002] With the continuous development of the automobile industry, online car-hailing has become the first choice for more and more users in their daily travel. Protecting the travel rights of users has become an important factor in the smooth development of online car-hailing operators.

[0003] In related technologies, in order to effectively protect the travel rights and interests of users, online car-hailing operators usually collect the actual driving routes of online car-hailing vehicles after each travel task is completed, and use the iBOAT (Isolation-Based Online Anomalous Trajectory Detection) algorithm to compare the actual driving routes obtained with multiple standard driving routes, so as to judge whether the online car-hailing vehicle has taken a detour based on the comparison results.

[0004] However, when the actual driving trajectory of the vehicle matches a standard driving path, the iBOAT algorithm will assume that the online ride-hailing vehicle does not take a detour. Therefore, when the online ride-hailing vehicle takes a detour, if the detour route it chooses matches a standard driving path, the iBOAT algorithm may easily obtain an incorrect path detection result. Summary of the invention

[0005] The main purpose of the present application is to provide a vehicle driving path detection method, path detection equipment, vehicle, storage medium and computer program product, aiming to solve the technical problem of low accuracy of path detection results obtained by path detection equipment in related technologies.

[0006] To achieve the above object, the present application proposes a method for detecting a vehicle driving path, the method comprising:

[0007] Acquire an initial planned path and an initial actual path, and determine initial path grids corresponding to the initial planned path and the initial actual path in the target tile map;

[0008] The initial planned path is updated according to each of the initial path grids to obtain a target planned path, and the initial actual path is updated according to each of the initial path grids to obtain a target actual path;

[0009] Determine a path similarity between the target planned path and the target actual path, and determine a path anomaly detection result corresponding to the target actual path based on the path similarity.

[0010] In one embodiment, the initial path grid includes a first initial path grid and a second initial path grid, and the step of determining the initial path grids corresponding to the initial planned path and the initial actual path in the target tile map includes:

[0011] Determine a preset tile map level, and cut the preset initial tile map according to the tile map level to obtain a target tile map;

[0012] Projecting each initial planned navigation point included in the initial planned path to the target tile map to determine a first initial path grid corresponding to each initial planned navigation point in the target tile map;

[0013] Each initial actual navigation point included in the initial actual path is projected onto the target tile map to determine that each initial actual navigation point corresponds to a second initial path grid in the target tile map.

[0014] In one embodiment, the step of updating the initial planned path according to each of the initial path grids to obtain a target planned path includes:

[0015] Determine first grid coordinates corresponding to each of the first initial path grids;

[0016] determining each first enhanced path grid coordinate based on each of the first grid coordinates, and determining each first enhanced path grid according to each of the first enhanced path grid coordinates;

[0017] Each of the first enhanced path grids is added to the initial planned path to update the target planned path.

[0018] In one embodiment, the step of updating the initial actual path according to each of the initial path grids to obtain the target actual path includes:

[0019] Determine the second grid coordinates corresponding to each of the second initial path grids;

[0020] determining each second enhanced path grid coordinate based on each of the second grid coordinates, and determining each second enhanced path grid according to each of the second enhanced path grid coordinates;

[0021] Each of the second enhanced path grids is added to the initial actual path to update the target actual path.

[0022] In one embodiment, after the step of determining that each of the initial actual navigation points corresponds to a second initial path grid in the target tile map, the method further includes:

[0023] Determine the distances between adjacent grids corresponding to the second initial path grids;

[0024] Screening each of the second initial path grids according to each of the adjacent grid distances to determine an abnormal second initial path grid included in each of the second initial path grids;

[0025] Each of the abnormal second initial path grids is deleted, and the step of updating the initial planned path according to each of the initial path grids to obtain a target planned path is performed.

[0026] In one embodiment, the step of determining the path similarity between the target planned path and the target actual path includes:

[0027] Determine the number of first matching grids generated between the target planned path and the target actual path;

[0028] The path similarity between the target planned path and the target actual path is determined according to the first matching grid number.

[0029] In one embodiment, the step of determining the number of first matching grids generated between the target planned path and the target actual path includes:

[0030] Determine each first character subscript corresponding to the target planned path, and determine each second character subscript corresponding to the target actual path;

[0031] Determine first reverse character subscripts corresponding to the first character subscripts, and replace the second character subscripts with the first reverse character subscripts to obtain a target subscript sequence;

[0032] A longest ascending subsequence is determined according to the target index sequence, and a first matching grid quantity generated between the target planned path and the target actual path is determined based on the longest ascending subsequence.

[0033] In one embodiment, the step of determining the path similarity between the target planned path and the target actual path according to the first matching grid number includes:

[0034] Determine the first number of grids corresponding to the target planned path, and determine the second number of grids corresponding to the target actual path;

[0035] Comparing the first number of grids with the second number of grids to determine a target number of grids;

[0036] Based on the target grid number and the first matching grid number, a path similarity between the target planned path and the target actual path is determined.

[0037] In one embodiment, after the step of determining the number of first matching grids generated between the target planned path and the target actual path, the method further includes:

[0038] Traversing the target actual path to obtain a grid matching result, and determining a matching abnormal grid included in the target actual path according to the grid matching result;

[0039] Detecting each adjacent grid of the matching abnormal grid to obtain each detection result, and updating the first matching grid quantity according to each detection result to obtain a second matching grid quantity;

[0040] The path similarity between the target planned path and the target actual path is determined according to the second matching grid number.

[0041] In one embodiment, the step of determining the path anomaly detection result corresponding to the actual path of the target based on the path similarity includes:

[0042] Determining a preset similarity threshold;

[0043] When it is detected that the path similarity reaches the similarity threshold, determining that the path anomaly detection result corresponding to the target actual path is that the driving route is normal;

[0044] When it is detected that the path similarity does not reach the similarity threshold, it is determined that the path anomaly detection result corresponding to the target actual path is a driving route anomaly.

[0045] In addition, to achieve the above-mentioned purpose, the present application also proposes a path detection device, which includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program is configured to implement the steps of the vehicle driving path detection method as described above.

[0046] In addition, to achieve the above-mentioned purpose, the present application also proposes a vehicle, which includes the path detection device as described above.

[0047] In addition, to achieve the above-mentioned purpose, the present application also proposes a storage medium, which is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by a processor, the steps of the vehicle driving path detection method described above are implemented.

[0048] In addition, to achieve the above-mentioned purpose, the present application also provides a computer program product, which includes a computer program, and when the computer program is executed by a processor, the steps of the vehicle driving path detection method as described above are implemented.

[0049] The vehicle driving path detection method provided in the embodiment of the present application obtains an initial planned path and an initial actual path, and determines the initial path grids corresponding to the initial planned path and the initial actual path in the target tile map; updates the initial planned path according to each of the initial path grids to obtain a target planned path, and updates the initial actual path according to each of the initial path grids to obtain a target actual path; determines the path similarity between the target planned path and the target actual path, and determines the path anomaly detection result corresponding to the target actual path based on the path similarity.

[0050] In this embodiment, during operation, the electronic device first obtains an initial planned path generated by the vehicle before completing the driving task, and obtains an initial actual path generated by the vehicle after completing the driving task. The path detection device then grids the initial planned path and the initial driving path to determine the initial path grids of the initial planned path and the initial driving path in the target tile map. Afterwards, the path detection device updates the initial planned path according to each initial path grid to connect each initial path grid of the initial planned path in the target tile map to construct the target planned path. At the same time, the path detection device updates the initial actual path according to each initial path grid to connect each initial path grid of the initial actual path in the target tile map to construct the target actual path. Finally, the path detection device calculates the path similarity between the target planned path and the target actual path, and determines the path anomaly detection result corresponding to the target actual path based on the path similarity.

[0051] In this way, the present application solves the technical problem of low accuracy of path detection results obtained by path detection equipment in related technologies. That is, the present application adopts a method of detecting whether the actual driving path generated by the vehicle matches a single standard driving path, thereby avoiding the situation where the path detection equipment cannot obtain accurate path detection results when the online car-hailing vehicle chooses a detour route that matches a certain standard path due to the use of multiple standard paths. This achieves the technical effect of improving the accuracy of the path detection results obtained by the path detection equipment, thereby protecting the travel rights and interests of users when taking online car-hailing vehicles. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0053] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0054] Figure 1 A schematic diagram of a process flow provided for a first embodiment of a method for detecting a vehicle driving path of the present application;

[0055] Figure 2 A schematic diagram of an initial planned path and an initial actual path involved in an embodiment of a method for detecting a vehicle driving path of the present application;

[0056] Figure 3 A schematic diagram of a tile map involved in an embodiment of a method for detecting a vehicle driving path of the present application;

[0057] Figure 4 A schematic diagram of a first enhanced path grid involved in an embodiment of a method for detecting a vehicle driving path of the present application;

[0058] Figure 5 A schematic diagram of a second enhanced path grid involved in an embodiment of a method for detecting a vehicle driving path of the present application;

[0059] Figure 6 A schematic diagram of an abnormal matching grid involved in an embodiment of a method for detecting a vehicle driving path of the present application;

[0060] Figure 7 A schematic diagram of correction of matching abnormal grids involved in an embodiment of a method for detecting a vehicle driving path of the present application;

[0061] Figure 8 This is a schematic diagram of the module structure of a vehicle driving path detection device according to an embodiment of the present application;

[0062] Fig. 9 Schematic diagram of the device structure of the hardware operating environment involved in the vehicle driving path detection method in the embodiment of the present application.

[0063] The purpose, features and advantages of this application will be further described in conjunction with the embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0064] It should be understood that the specific embodiments described herein are only used to explain the technical solutions of the present application and are not used to limit the present application.

[0065] In order to better understand the technical solution of the present application, a detailed description will be given below in conjunction with the accompanying drawings and specific implementation methods.

[0066] In this embodiment, for ease of description, the following description is made using a path detection device configured on a vehicle, or a mobile terminal, a data storage control terminal, a PC or other terminal connected to an electronic control unit supporting the path detection device as the execution subject.

[0067] Based on the above-mentioned path detection device, the overall concept of the vehicle driving path detection method of the present application is proposed here.

[0068] With the continuous development of the automobile industry, online ride-hailing has become the first choice for more and more users' daily travel. Protecting the travel rights and interests of users has become an important factor in the smooth development of online ride-hailing operators. In related technologies, in order to effectively protect the travel rights and interests of users, online ride-hailing operators usually collect the actual driving path of the online ride-hailing after each travel task is completed, and use the iBOAT algorithm to compare the actual driving path obtained with multiple standard driving paths, so as to judge whether the online ride-hailing has detoured based on the comparison results. However, since the actual driving trajectory of the vehicle matches a standard driving path, the iBOAT algorithm will believe that the online ride-hailing does not have a detour. In this way, when the online ride-hailing takes a detour, if the detour route it chooses matches a standard driving path, it is easy for the iBOAT algorithm to obtain an incorrect path detection result. Technical problems. In addition, some online car-hailing operators will use the EDR (Edit Distance on Real sequence) algorithm and the DTW (Dynamic Time Warping) algorithm to compare the actual driving path with the standard driving path after obtaining the actual driving path of the online car-hailing vehicle to determine whether the online car-hailing vehicle has taken a detour. However, since the EDR algorithm can only indicate the number of corrections between paths, the results obtained by the EDR algorithm usually cannot directly indicate whether the actual driving path is similar to the standard driving path. Similarly, since the DTW algorithm needs to calculate every navigation point, it is more sensitive to the noise points contained in the path, making it difficult to obtain accurate path detection results.

[0069] In response to the above phenomenon, the present application provides a method for detecting a vehicle driving path, which includes: obtaining an initial planned path and an initial actual path, and determining the initial path grids corresponding to the initial planned path and the initial actual path in a target tile map; updating the initial planned path according to each of the initial path grids to obtain a target planned path, and updating the initial actual path according to each of the initial path grids to obtain a target actual path; determining the path similarity between the target planned path and the target actual path, and determining a path anomaly detection result corresponding to the target actual path based on the path similarity.

[0070] In this way, the present application solves the technical problem of low accuracy of the path detection results obtained by the path detection device in the related technology, that is, the present application adopts a method of detecting whether the actual driving path generated by the vehicle matches a single standard driving path, thereby avoiding the situation that the path detection device cannot obtain accurate path detection results when the online car-hailing vehicle chooses a detour route that matches a certain standard path due to the use of multiple standard paths, thereby achieving the technical effect of improving the accuracy of the path detection results obtained by the path detection device, and protecting the travel rights and interests of users when taking online car-hailing vehicles. At the same time, the present application also projects the initial planned path and the actual planned path onto the target tile map and performs a grid enhancement operation to obtain a target planned path and a target actual path composed of multiple continuous grids, so that the path detection device can project each navigation point contained in each path into the grid in the tile map in the process of obtaining the path anomaly detection result, so as to process each path grid in the tile map, thereby reducing the influence of the noise points contained in the path on the detection result, and further improving the accuracy of the obtained detection result.

[0071] Based on the overall concept of the vehicle driving path detection method of the present application, the embodiment of the present application provides a vehicle driving path detection method, referring to Figure 1 , Figure 1 This is a flow chart of the first embodiment of the vehicle driving path detection method of the present application. In this embodiment, the vehicle driving path detection method includes steps S10 to S30:

[0072] Step S10: obtaining an initial planned path and an initial actual path, and determining initial path grids corresponding to the initial planned path and the initial actual path in the target tile map;

[0073] Please note that, please refer to Figure 2 , Figure 2 This is a schematic diagram of an initial planned path and an initial actual path involved in an embodiment of a method for detecting a vehicle driving path of the present application, such as Figure 2 As shown in Figure 1, the initial planned path is based on the starting position of the online car-hailing vehicle before departure and the driving path constructed based on the destination position that the online car-hailing vehicle needs to go to (i.e. Figure 2 In addition, the initial actual path is the driving path generated by the vehicle moving from the starting position to the end position after the online car-hailing vehicle completes the driving task (i.e. Figure 2 blue route in the figure).

[0074] In this embodiment, when the online-hailing car completes the driving plan, the path detection device first accesses the map navigation device that is communicated with itself to obtain the initial planned path generated by the map navigation device based on the starting location of the online-hailing car and the destination location to which the online-hailing car needs to go. At the same time, the path detection device accesses the mobile terminal device used by the driver to obtain the longitude and latitude parameters recorded by the mobile terminal device during the vehicle's driving, and determines the initial actual path generated in the process of the online-hailing car moving from the starting location to the destination location based on the longitude and latitude parameters. The path detection device then grids the initial planned path and the initial actual path to determine the initial path grids corresponding to the initial planned path and the initial actual path in the target tile map.

[0075] Exemplarily, for example, when the online car-hailing vehicle picks up the target passenger at location A and sends the target passenger to the designated location B, the path detection device first accesses the map navigation device that is connected to the communication with itself, and thereby obtains the initial planned path t0 generated by the map navigation device based on location A and location B through the API (Application Programming Interface) in the map navigation device, which is basically free of noise and meets the expectations of the target passenger. At the same time, the path detection device accesses the mobile terminal device used by the driver of the online car-hailing vehicle to obtain the various longitude and latitude parameters uploaded by the mobile terminal device during driving. The path detection device then projects the various longitude and latitude parameters onto the road network through a preset road tracking service to obtain the initial actual path t1 generated in the process of the vehicle driving from location A to location B. The path detection device then grids the initial planned path t0 and the initial actual path t1 to project the navigation points contained in the initial planned path t0 and the initial actual path t1 onto the target tile map, thereby determining the initial path grids corresponding to the initial planned path t0 and the initial actual path t1 on the target tile map.

[0076] In addition, it should be noted that the specific process of the above-mentioned path detection equipment projecting various latitude and longitude parameters to the road network through the road tracking service is an existing technology, so it will not be repeated here.

[0077] In this way, the path detection device can obtain the initial planned path planned by the online car-hailing vehicle before departure, and the initial actual path generated by the online car-hailing vehicle in the process of driving to the target location. At the same time, the path detection device can project the navigation points contained in the initial planned path and the initial actual path into the target tile map, and determine the initial path grids corresponding to the initial planned path and the initial actual path in the target tile map, thereby reducing the impact of noise points contained in the path on the final path detection results.

[0078] In a feasible implementation manner, the initial path grid includes a first initial path grid and a second initial path grid. The step of "determining initial path grids corresponding to the initial planned path and the initial actual path in the target tile map" in the above step S10 may specifically include steps S101 to S103:

[0079] Step S101: determining a preset tile map level, and cutting a preset initial tile map according to the tile map level to obtain a target tile map;

[0080] Step S102: projecting each initial planning navigation point included in the initial planning path to the target tile map to determine a first initial path grid corresponding to each initial planning navigation point in the target tile map;

[0081] Step S103: Projecting each initial actual navigation point included in the initial actual path to the target tile map to determine that each initial actual navigation point corresponds to a second initial path grid in the target tile map.

[0082] It should be noted that the tile map level is used to cut the initial tile map to determine the level parameter of the grid size of each map grid contained in the target tile map. The higher the tile map level, the more map grids can be obtained after cutting the initial tile map, and the smaller the map grid size after cutting. It is understandable that the specific value of the tile map level can be set by the technician according to actual needs, and this application does not limit this.

[0083] In this embodiment, after obtaining the initial planned path and the initial actual path, the path detection device first reads the storage module configured by itself to obtain the preset tile map level. The path detection device then queries the preset grid size mapping table to determine the grid size corresponding to the target tile map according to the tile map level. The path detection device then cuts the initial tile map according to the grid size to obtain a target tile map containing multiple map grids that meet the grid size. Thereafter, the path detection device projects each initial planned navigation point contained in the initial planned path into the target tile map through Mercator projection, thereby determining the first initial path grid where each initial planned navigation point is located in the target tile map; at the same time, the path detection device projects each initial actual navigation point contained in the initial actual path into the target tile map through Mercator projection, thereby determining the second initial path grid where each initial actual navigation point is located in the target tile map.

[0084] For example, see Figure 3 , Figure 3A schematic diagram of a tile map involved in an embodiment of a method for detecting a vehicle driving path of the present application. After obtaining the initial planned path t0 and the initial actual path t1, the path detection device first reads the storage module configured by itself to obtain a preset tile map level of 20. The path detection device then queries a preset grid size mapping table containing multiple standard tile map levels and standard grid side lengths corresponding to each of the multiple standard tile map levels to determine that when the tile map level is 20, the grid size in the matching target tile map is 38m. The path detection device cuts the initial Gaode tile map according to the grid size of 38m to obtain the following: Figure 3 The target tile map shown contains multiple square grids of size 38m. After that, the path detection device further projects the initial planned navigation points contained in the initial planned path t0 to the target tile map through Mercator projection to determine the first initial path grid of each initial planned navigation point in the target tile map. At the same time, the path detection device projects the initial actual navigation points contained in the initial actual path t1 to the target tile map through Mercator projection to determine the second initial path grid of each initial actual navigation point in the target tile map.

[0085] In this way, the path detection device can obtain the initial planned path planned by the online car-hailing vehicle before departure, and the initial actual path generated by the online car-hailing vehicle in the process of driving to the target location. At the same time, the path detection device can project the navigation points contained in the initial planned path and the initial actual path into the target tile map, and determine the initial path grids corresponding to the initial planned path and the initial actual path in the target tile map, thereby reducing the impact of noise points contained in the path on the final path detection results.

[0086] Step S20: updating the initial planned path according to each of the initial path grids to obtain a target planned path, and updating the initial actual path according to each of the initial path grids to obtain a target actual path;

[0087] It should be noted that the target planning path is a path composed of multiple grids connected in the target tile map. It can be understood that after the above-mentioned initial planning path is projected to the target tile map, the first initial path grids corresponding to the initial planning path in the target tile map may be non-adjacent to each other. Therefore, in order to improve the accuracy of the actual driving trajectory detection, the initial planning path can be enhanced so that the first initial path grids of the initial planning path in the target tile map are connected to form a more complete target planning path.

[0088] In addition, the target actual path is a path composed of multiple grids connected in the target tile map. It can be understood that after the above-mentioned initial actual path is projected to the target tile map, the second initial path grids corresponding to the initial actual path in the target tile map may be non-adjacent to each other. Therefore, in order to improve the accuracy of actual driving trajectory detection, the initial actual path can be enhanced so that the first initial path grids of the initial actual path in the target tile map are connected to each other, thereby forming a more complete path.

[0089] In this embodiment, after obtaining each initial path grid, the path detection device further determines each first grid coordinate corresponding to the initial planned path according to each initial path grid, and the path detection device thereby performs a grid enhancement operation on each first grid coordinate to obtain a first enhanced path grid generated between multiple first initial path grids, and the path detection device then adds each first enhanced path grid to the initial planned path to generate a target planned path composed of multiple continuous grids within the target tile map. At the same time, the path detection device determines each second grid coordinate corresponding to the initial actual path according to each initial path grid, and the path detection device thereby performs a grid enhancement operation on each second grid coordinate to obtain a second enhanced path grid generated between multiple second initial path grids, and the path detection device then adds each second enhanced path grid to the initial actual path to generate a target actual path composed of multiple continuous grids within the target tile map.

[0090] For example, after obtaining the above-mentioned initial path grids, the path detection device first determines the first grid coordinates (x t0) corresponding to each initial planning navigation point in the initial planning path t0 in the above-mentioned target tile map. ai ,y bi ), the path detection device then converts each first grid coordinate (x ai ,y bi ) is substituted into the preset grid enhancement formula to obtain the first grid coordinates (x ai ,y bi ) respectively correspond to the first enhanced path grid coordinates (x Ai ,y Bi ), the path detection device then converts each first enhanced path grid coordinate (x Ai ,y Bi ) are added to the initial planned route t0 to generate a target planned path T0 with continuous grids in the target tile map; at the same time, the path detection device determines the second grid coordinates (x ) of each initial actual navigation point in the initial actual path t1 corresponding to each other in the target tile map. ci ,y di ), the path detection device then converts each second grid coordinate (xci ,y di ) is substituted into the preset grid enhancement formula to obtain the second grid coordinates (x ci ,y di ) respectively correspond to the second enhanced path grid coordinates (x Ci ,y Di ), the path detection device then converts each second enhanced path grid coordinate (x Ci ,y Di ) is added to the initial actual path t1 to generate a target actual path T1 having a continuous grid in the target tile map.

[0091] In this way, the path detection device can enhance the trajectory formed by the initial planned path and the initial actual path in the target tile map, thereby obtaining a more complete planned path and actual path in the target tile map.

[0092] In a feasible implementation manner, the step of "updating the initial planned path according to each of the initial path grids to obtain a target planned path" in the above step S20 may specifically include steps S201 to S203:

[0093] Step S201: determining first grid coordinates corresponding to each of the first initial path grids;

[0094] Step S202: determining each first enhanced path grid coordinate based on each first grid coordinate, and determining each first enhanced path grid according to each first enhanced path grid coordinate;

[0095] Step S203: adding each of the first enhanced path grids to the initial planned path to update the target planned path.

[0096] In this embodiment, after obtaining each initial path grid, the path detection device first determines the first grid coordinates corresponding to the initial planned path in the target tile map. Then, the path detection device substitutes each first grid coordinate into a preset grid enhancement formula to obtain each first enhanced path grid coordinate. The path detection device thereby determines the first enhanced path grid corresponding to each first enhanced path grid coordinate. Finally, the path detection device adds each first enhanced path grid to the initial planned path, thereby generating a target planned path with continuous grids in the target tile map.

[0097] For example, see Figure 4 , Figure 4 This is a schematic diagram of a first enhanced path grid involved in an embodiment of a method for detecting a vehicle driving path of the present application. After obtaining the above-mentioned initial path grids, the path detection device first screens the initial path grids to determine the following in the target tile map: Figure 4The first initial path grid (3,1) and the first initial path grid (1,6) shown in FIG. 1 are used to determine the first grid coordinate (x a1 ,y b2 ) and the first grid coordinate (x) corresponding to the first initial path grid (1,6) a2 ,y b2 ), then the path detection device converts the first grid coordinate (x a1 ,y b2 ) and the first grid coordinate (x a2 ,y b2 ) into the preset coordinate enhancement formula:

[0098]

[0099] To get the first grid coordinate (x a1 ,y b2 ) and the first grid coordinate (x a2 ,y b2 ) corresponds to the first enhanced path grid coordinate (x A1 ,y B1 )、(x A2 ,y B2 )、(x A3 ,y B3 )、(x A4 ,y B4 ), the path detection device thus determines the first enhanced path grid coordinate (x A1 ,y B1 )、(x A2 ,y B2 )、(x A3 ,y B3 )、(x A4 ,y B4 ), determine that the first enhanced path grids between the first initial path grid (3,1) and the first initial path grid (1,6) are (3,2), (2,3), (2,4), (1,5), and finally, the path detection device combines the obtained first enhanced path grids (3,2), (2,3), (2,4), (1,5) with the first initial path grids (3,1), (1,6), thereby constructing a target planning route T0 composed of continuous grids.

[0100] In this way, the path detection device can enhance the trajectory formed by the initial planned path in the target tile map, thereby obtaining a more complete planned path in the target tile map.

[0101] In a feasible implementation manner, the step of "updating the initial actual path according to each of the initial path grids to obtain the target actual path" in the above step S20 may specifically include steps S204 to S206:

[0102] Step S204: determining the second grid coordinates corresponding to each of the second initial path grids;

[0103] Step S205: determining each second enhanced path grid coordinate based on each second grid coordinate, and determining each second enhanced path grid according to each second enhanced path grid coordinate;

[0104] Step S206: adding each of the second enhanced path grids to the initial actual path to update the target actual path.

[0105] In this embodiment, after obtaining each initial path grid, the path detection device first determines the second grid coordinates of the initial actual route in the target tile map. Then, the path detection device substitutes each second grid coordinate into the grid enhancement formula to obtain each second enhanced path grid coordinate. The path detection device thereby determines the second enhanced path grids corresponding to the respective coordinates of each second enhanced path grid. Finally, the path detection device adds each second enhanced path grid to the initial actual path, thereby generating a target actual path with continuous grids in the target tile map.

[0106] For example, see Figure 5 , Figure 5 This is a schematic diagram of a second enhanced path grid involved in an embodiment of a method for detecting a vehicle driving path of the present application. After obtaining the above-mentioned initial path grids, the path detection device first screens the initial path grids to determine the following in the target tile map: Figure 5 The second initial path grid (1, 2) and the second initial path grid (5, 3) shown in the figure, the path detection device thus reads the second grid coordinate of the second initial path grid (1, 2) in the target tile map as (x c1 ,y d1 ), and read the second grid coordinates of the second initial path grid (5,3) in the target tile map as (x c2 ,y d2 ), the path detection device then converts the second grid coordinate (x c1 ,y d1 )、(x c2 ,y d2 ) into the preset coordinate enhancement formula:

[0107]

[0108] To obtain the second grid coordinates (x c1,y d1 )、(x c2 ,y d2 ) corresponds to the second enhanced path grid coordinate (x C1 ,y D1 )、(x C2 ,y D2 )、(x C3 ,y D3 ), the path detection device thus determines the second enhanced path grid coordinate (x C1 ,y D1 )、(x C2 ,y D2 )、(x C3 ,y D3 ) determines that the second enhanced path grids between the second initial path grids (1,2) and (5,3) are (2,2), (3,3), and (4,3). Finally, the path detection device combines the second enhanced path grids (2,2), (3,3), and (4,3) with the second initial path grids (1,2) and (5,3) to construct a target actual route T1 composed of continuous grids.

[0109] In this way, the path detection device can enhance the grid track formed by the initial actual path in the target tile map, thereby obtaining a more complete actual path in the target tile map.

[0110] Step S30: determining the path similarity between the target planned path and the target actual path, and determining the path anomaly detection result corresponding to the target actual path based on the path similarity;

[0111] In this embodiment, after determining the target planned path and the target actual path, the path detection device calculates the path similarity between the target planned path and the target actual path. The path detection device then obtains the path anomaly detection result corresponding to the target actual path based on the path similarity, so as to determine whether the online car-hailing vehicle has taken a detour based on the path anomaly detection result.

[0112] Exemplarily, for example, after determining the target planned route T0 and the target actual route T1, the path detection device further calculates the path similarity S(T0, T1) between the target planned route T0 and the target actual route T1, and the path detection device then determines whether the target planned route T0 and the target actual route T1 are similar based on the path similarity S(T0, T1) to determine whether the path anomaly detection result is a normal path or an abnormal path.

[0113] In this way, the path detection device can determine the path similarity between the actual path and the planned path when using only one planned path, and then judge whether the vehicle deviates during driving based on the path similarity.

[0114] In a feasible implementation manner, the step of “determining the path similarity between the target planned path and the target actual path” in the above step S30 may specifically include steps S301 to S302:

[0115] Step S301: determining the number of first matching grids generated between the target planned path and the target actual path;

[0116] Step S302: determining the path similarity between the target planned path and the target actual path according to the first matching grid quantity.

[0117] It should be noted that the first matching grid number is the number of grids that match the target planned path and the target actual path.

[0118] In this embodiment, after determining the target planned path and the target actual path, the path detection device first processes the target planned path and the target actual path to determine the first number of matching grids generated between the target planned path and the target actual path. Thereafter, the path detection device obtains a preset similarity calculation formula and substitutes the first number of matching grids into the similarity calculation formula to calculate the path similarity generated between the target planned path and the target actual path.

[0119] For example, after determining the target planned route T0 and the target actual route T1, the path detection device first calculates the target planned route T0 and the target actual route T1 to calculate the first matching grid number generated between the target planned route T0 and the target actual route T1. Afterwards, the path detection device obtains a preset similarity calculation formula and converts the first matching grid number Substitute into the similarity calculation formula to calculate the path similarity S(T0, T1) between the target planned route T0 and the target actual route T1.

[0120] In this way, the path detection device can determine the path similarity between the actual path and the planned path when using only one planned path, and then judge whether the vehicle deviates during driving based on the path similarity.

[0121] In a feasible implementation manner, the above step S301 may specifically include steps S3011 to S3013:

[0122] Step S3011: determining each first character subscript corresponding to the target planned path, and determining each second character subscript corresponding to the target actual path;

[0123] Step S3012: determining first reverse character subscripts corresponding to the first character subscripts, and replacing the second character subscripts with the first reverse character subscripts to obtain a target subscript sequence;

[0124] Step S3013: determining a longest ascending subsequence according to the target index sequence, and determining the number of first matching grids generated between the target planned path and the target actual path based on the longest ascending subsequence.

[0125] In this embodiment, after determining the target planned path and the target actual path, the path detection device first identifies each first character subscript contained in the target planned path. At the same time, the path detection device identifies each second character subscript contained in the target actual path. Then, the path detection device determines the first reverse character subscript corresponding to each first character subscript. The path detection device then replaces each second character subscript with each first reverse character subscript to obtain a target subscript sequence. Finally, the path detection device determines the longest ascending subsequence based on the target subscript sequence, and determines the longest common subsequence generated between the target planned path and the target actual path based on the longest ascending subsequence. The path detection device determines the first matching grid number between the target planned path and the target actual path based on the longest common subsequence.

[0126] Exemplarily, for example, after determining the target planned route T0 and the target actual route T1, the path detection device first identifies each first grid contained in the target planned path T0, and records the first character subscripts corresponding to each first grid of the target planned path T0 in a hash table + vector manner. At the same time, the path detection device records the second character subscripts corresponding to each second grid of the target actual route T1 in a hash table + vector manner. Then, the path detection device performs reverse processing on each first character subscript to obtain each first reverse character subscript. The path detection device replaces each second character subscript contained in the target actual route T1 with each first reverse character subscript to obtain a target subscript sequence. Finally, the path detection device solves the target subscript sequence to obtain the longest ascending subsequence LIS. The path detection device converts the longest ascending subsequence LIS to obtain the longest common subsequence LSCC1(T0, T1) between the target planned route T0 and the target actual route T1. The path detection device determines the number of first matching grids between the target planned route T0 and the target actual route T1 based on the longest common subsequence LSCC1(T0, T1).

[0127] In this way, the path detection device can determine the path similarity between the actual path and the planned path when using only one planned path, and then judge whether the vehicle deviates during driving based on the path similarity.

[0128] In a feasible implementation manner, the above step S302 may specifically include steps S3021 to S3023:

[0129] Step S3021: determining the number of first grids corresponding to the target planned path, and determining the number of second grids corresponding to the target actual path;

[0130] Step S3022: comparing the first number of grids with the second number of grids to determine a target number of grids;

[0131] Step S3023: Based on the target grid number and the first matching grid number, determine the path similarity between the target planned path and the target actual path.

[0132] In this embodiment, after determining the first matching grid number, the path detection device first determines the first grid number of the target planned path in the target tile map, and determines the second grid number of the target actual path in the target tile map. Then, the path detection device compares the first grid number and the second grid number to determine the target grid number with a higher value between the first grid number and the second grid number. Finally, the path detection device calculates the path similarity between the target planned path and the target actual path based on the first grid number and the target grid number.

[0133] Exemplarily, for example, the path detection device determines the first matching grid number After that, the first number of grids of the target planned path T0 in the target tile map is determined, and the second number of grids of the target actual path T1 in the target tile map is determined. Then, the path detection device compares the first number of grids with the second number of grids to determine the higher number of grids as the target number of grids MAX(T0, T1). Finally, the path detection device compares the first number of grids with the second number of grids based on the first matching number of grids. And the target grid number MAX(T0,T1), calculate the path similarity S(T0,T1) between the target planned path T0 and the target actual path T1:

[0134]

[0135] In this way, the path detection device can determine the path similarity between the actual path and the planned path when using only one planned path, and then judge whether the vehicle deviates during driving based on the path similarity.

[0136] In a feasible implementation manner, the step of "determining the path anomaly detection result corresponding to the target actual path based on the path similarity" in the above step S30 may specifically include steps S303 to S305:

[0137] Step S303: determining a preset similarity threshold;

[0138] Step S304: when it is detected that the path similarity reaches the similarity threshold, determining that the path anomaly detection result corresponding to the target actual path is that the driving route is normal;

[0139] Step S305: when it is detected that the path similarity does not reach the similarity threshold, determining that the path anomaly detection result corresponding to the target actual path is a driving route anomaly.

[0140] In this embodiment, after obtaining the path similarity, the path detection device further reads the storage module configured by itself to obtain a preset similarity threshold. The path detection device compares the path similarity with the similarity threshold to obtain a comparison result. Thereafter, when the path detection device detects that the comparison result is that the path similarity reaches the similarity threshold, it determines that the path anomaly detection result corresponding to the target actual path is that the driving route is normal. Similarly, when the path detection device detects that the comparison result is that the path similarity does not reach the similarity threshold, it determines that the path anomaly detection result corresponding to the target actual path is that the driving route is abnormal.

[0141] Exemplarily, for example, after obtaining the path similarity S(T0, T1), the path detection device further reads the storage module configured by itself to obtain a preset similarity threshold, and the path detection device then compares the path similarity S(T0, T1) with the similarity threshold to obtain a comparison result. Thereafter, when the path detection device detects that the comparison result is that the path similarity S(T0, T1) reaches the similarity threshold, it determines that the actual driving route of the vehicle is basically consistent with the initial planned driving route, and the path detection device thereby determines that the abnormal detection result of the target actual path T0 is that the driving route is normal. Similarly, when the path detection device detects that the comparison result is that the path similarity S(T0, T1) does not reach the similarity threshold, it determines that the actual driving route of the vehicle is inconsistent with the initial planned driving route, and the path detection device thereby determines that the abnormal detection result of the target actual path T0 is that the driving route is abnormal.

[0142] In this embodiment, when the online-hailing car completes the driving plan, the path detection device first accesses the map navigation device that is communicated with itself to obtain the initial planned path generated by the map navigation device based on the starting position of the online-hailing car and the destination position to which the online-hailing car needs to go. At the same time, the path detection device accesses the mobile terminal device used by the driver to obtain the longitude and latitude parameters recorded by the mobile terminal device during the vehicle's driving, and determines the initial actual path generated in the process of the online-hailing car moving from the starting position to the destination position based on the longitude and latitude parameters. The path detection device then grids the initial planned path and the initial actual path to determine the initial path grids corresponding to the initial planned path and the initial actual path in the target tile map. Afterwards, the path detection device determines the first grid coordinates corresponding to the initial planned path based on each initial path grid, and the path detection device performs a grid enhancement operation on each first grid coordinate to obtain multiple first The first enhanced path grid is generated between the initial path grids, and the path detection device then adds each first enhanced path grid to the initial planned path to generate a target planned path composed of multiple continuous grids in the target tile map. At the same time, the path detection device determines the second grid coordinates corresponding to the initial actual path according to each initial path grid, and the path detection device performs a grid enhancement operation on each second grid coordinate to obtain a second enhanced path grid generated between multiple second initial path grids, and the path detection device then adds each second enhanced path grid to the initial actual path to generate a target actual path composed of multiple continuous grids in the target tile map. Finally, the path detection device calculates the path similarity between the target planned path and the target actual path, and the path detection device obtains the path anomaly detection result corresponding to the target actual path based on the path similarity, so as to judge whether the online car-hailing vehicle has detour behavior based on the path anomaly detection result.

[0143] In this way, the present application solves the technical problem of low accuracy of the path detection results obtained by the path detection device in the related technology, that is, the present application adopts a method of detecting whether the actual driving path generated by the vehicle matches a single standard driving path, thereby avoiding the situation that the path detection device cannot obtain accurate path detection results when the online car-hailing vehicle chooses a detour route that matches a certain standard path due to the use of multiple standard paths, thereby achieving the technical effect of improving the accuracy of the path detection results obtained by the path detection device, and protecting the travel rights and interests of users when taking online car-hailing vehicles. At the same time, the present application also projects the initial planned path and the actual planned path onto the target tile map and performs a grid enhancement operation to obtain a target planned path and a target actual path composed of multiple continuous grids, so that the path detection device can project each navigation point contained in each path into the grid in the tile map in the process of obtaining the path anomaly detection result, so as to process each path grid in the tile map, thereby reducing the influence of the noise points contained in the path on the detection result, and further improving the accuracy of the obtained detection result.

[0144] Based on the first embodiment of the present application, a second embodiment of the present application is proposed. In the second embodiment of the present application, the same or similar contents as the above embodiments can be referred to the above description and will not be repeated later. On this basis, after the above step S103, the vehicle driving path detection method of the present application can also include steps A10 to A30:

[0145] Step A10: determining the distances between adjacent grids corresponding to the second initial path grids;

[0146] Step A20: screening each of the second initial path grids according to the distances between each of the adjacent grids to determine abnormal second initial path grids included in each of the second initial path grids;

[0147] Step A30: deleting each of the abnormal second initial path grids, and executing the step of updating the initial planned path according to each of the initial path grids to obtain a target planned path.

[0148] In this embodiment, after determining each initial path grid, the path detection device further determines the second grid coordinates corresponding to each second initial path grid based on each initial path grid and the above-mentioned grid size, and the path detection device then determines the adjacent grid distances generated between each second initial path grid according to each second grid coordinate. After that, the path detection device obtains a preset grid distance threshold, and compares each adjacent grid distance with the grid distance threshold to obtain a plurality of second comparison results. The path detection device then screens each second initial path grid according to the plurality of second comparison results to determine abnormal second initial path grids whose adjacent grid distances are greater than the grid distance threshold in each second initial path grid. Finally, the path detection device deletes the abnormal second initial path grid, and performs a grid enhancement operation based on the second initial path grids remaining after the deletion to obtain the above-mentioned target planning path.

[0149] Exemplarily, for example, after determining each initial path grid, the path detection device further determines the second grid coordinates corresponding to each second initial path grid based on each initial path grid and the above-mentioned grid size. The path detection device then calculates the adjacent grid distance between any two adjacent second initial path grids based on each second grid coordinate. After that, the path detection device reads the above-mentioned storage module to obtain a preset grid distance threshold of 20km. The path detection device compares each obtained adjacent grid distance with the grid distance threshold to obtain multiple second comparison results. The path detection device screens each adjacent grid distance based on the multiple second comparison results to determine an abnormal adjacent grid distance with a distance greater than 20km, and determines the second initial path grid corresponding to the abnormal adjacent grid distance as the second abnormal initial path grid. Finally, the path detection device deletes each second abnormal initial path grid, and performs a grid enhancement operation based on the remaining second initial path grids after the deletion to obtain the above-mentioned target planned path T1.

[0150] In addition, after determining each second initial path grid, the path detection device can also first determine the actual terrain corresponding to each second initial path grid. The path detection device can then screen out each second abnormal path grid on the ocean from each second initial path grid based on the actual terrain, and delete each abnormal path grid.

[0151] In this way, the path detection device can determine multiple abnormal path grids contained in the actual path due to positioning drift, and determine the similarity between the actual path and the planned path after deleting each abnormal path grid, thereby further improving the accuracy of the acquired abnormal path detection results.

[0152] Based on the first embodiment and / or the second embodiment of the present application, the third embodiment of the present application is proposed here. In the third embodiment of the present application, the same or similar contents as the above embodiments can be referred to the above introduction, and will not be repeated later. On this basis, after the above step S301, the vehicle driving path detection method of the present application can also include steps B10 to B30:

[0153] Step B10: traversing the target actual path to obtain a grid matching result, and determining the matching abnormal grid contained in the target actual path according to the grid matching result;

[0154] Step B20: detecting each adjacent grid of the matching abnormal grid to obtain each detection result, and updating the first matching grid quantity according to each detection result to obtain the second matching grid quantity;

[0155] Step B30: determining the path similarity between the target planned path and the target actual path according to the second matching grid number.

[0156] In this embodiment, after determining the above-mentioned first matching grid number, the path detection device further traverses the target actual path to determine each matching abnormal grid in the target tile map of the target actual path. After that, the path detection device detects each matching abnormal grid to determine the adjacent grids of each matching abnormal grid, and judges whether the adjacent grids corresponding to each matching abnormal grid are the above-mentioned second enhanced path grids, thereby obtaining each detection result. When the path detection device determines that the detection result is that the second enhanced path grid exists in the adjacent grids corresponding to each matching abnormal grid, the path detection device modifies the matching abnormal grid to a matching normal grid, and updates the first matching grid number to obtain the second matching grid number. Finally, the path detection device determines the path similarity generated between the target planned path and the target actual path according to the second matching grid number.

[0157] For example, see Figure 6 and Figure 7 ,in, Figure 6 This is a schematic diagram of an abnormal matching grid involved in an embodiment of a method for detecting a vehicle driving path of the present application. Figure 7 This is a schematic diagram of matching abnormal grid correction involved in an embodiment of the vehicle driving path detection method of the present application. After determining the number of the first matching grids mentioned above, the path detection device further traverses the target actual path T1 to determine the target actual path T1 includes the following: Figure 6 As shown, the matching abnormal grid (i.e. Figure 6), then the path detection device detects each matching abnormal grid to determine the eight adjacent grids around each matching abnormal grid, and the path detection device determines whether there is at least one second enhanced path grid in the eight adjacent grids around each matching abnormal grid. If the path detection device detects that there is at least one second enhanced path grid in the eight adjacent grids around the matching abnormal grid, it determines that the matching abnormal grid is actually on the target planning path, and modifies the matching abnormal grid to Figure 7 The matching normal grid shown (i.e. Figure 7 The path detection device then updates the first matching grid number to obtain the second matching grid number. Finally, the path detection device obtains the path similarity between the target planned path and the target actual path based on the second matching grid number and the above-mentioned target grid number.

[0158] In this way, the path detection device can check and modify each matching abnormal grid in the actual path of the target by adopting the fuzzy matching method, thereby further improving the accuracy of the calculated path similarity.

[0159] This application also provides a vehicle driving path detection device, please refer to Figure 8 , the vehicle driving path detection device comprises:

[0160] A path projection module 10 is used to obtain an initial planned path and an initial actual path, and determine initial path grids corresponding to the initial planned path and the initial actual path in a target tile map;

[0161] A path enhancement module 20, configured to update the initial planned path according to each of the initial path grids to obtain a target planned path, and to update the initial actual path according to each of the initial path grids to obtain a target actual path;

[0162] The path comparison module 30 is used to determine the path similarity between the target planned path and the target actual path, and determine the path anomaly detection result corresponding to the target actual path based on the path similarity.

[0163] In a feasible implementation manner, the initial path grid includes a first initial path grid and a second initial path grid, and the path projection module 10 is further used for:

[0164] Determine a preset tile map level, and cut the preset initial tile map according to the tile map level to obtain a target tile map;

[0165] Projecting each initial planned navigation point included in the initial planned path to the target tile map to determine a first initial path grid corresponding to each initial planned navigation point in the target tile map;

[0166] Each initial actual navigation point included in the initial actual path is projected onto the target tile map to determine that each initial actual navigation point corresponds to a second initial path grid in the target tile map.

[0167] In a feasible implementation manner, the path enhancement module 20 is further used for:

[0168] Determine first grid coordinates corresponding to each of the first initial path grids;

[0169] determining each first enhanced path grid coordinate based on each of the first grid coordinates, and determining each first enhanced path grid according to each of the first enhanced path grid coordinates;

[0170] Each of the first enhanced path grids is added to the initial planned path to update the target planned path.

[0171] In a feasible implementation manner, the path enhancement module 20 is further used for:

[0172] Determine the second grid coordinates corresponding to each of the second initial path grids;

[0173] determining each second enhanced path grid coordinate based on each of the second grid coordinates, and determining each second enhanced path grid according to each of the second enhanced path grid coordinates;

[0174] Each of the second enhanced path grids is added to the initial actual path to update the target actual path.

[0175] In a feasible implementation manner, the path projection module 10 is further used for:

[0176] Determine the distances between adjacent grids corresponding to the second initial path grids;

[0177] Screening each of the second initial path grids according to each of the adjacent grid distances to determine an abnormal second initial path grid included in each of the second initial path grids;

[0178] Each of the abnormal second initial path grids is deleted, and the step of updating the initial planned path according to each of the initial path grids to obtain a target planned path is performed.

[0179] In a feasible implementation manner, the path comparison module 30 is further used for:

[0180] Determine the number of first matching grids generated between the target planned path and the target actual path;

[0181] The path similarity between the target planned path and the target actual path is determined according to the first matching grid number.

[0182] In a feasible implementation manner, the path comparison module 30 is further used for:

[0183] Determine each first character subscript corresponding to the target planned path, and determine each second character subscript corresponding to the target actual path;

[0184] Determine first reverse character subscripts corresponding to the first character subscripts, and replace the second character subscripts with the first reverse character subscripts to obtain a target subscript sequence;

[0185] A longest ascending subsequence is determined according to the target index sequence, and a first matching grid quantity generated between the target planned path and the target actual path is determined based on the longest ascending subsequence.

[0186] In a feasible implementation manner, the path comparison module 30 is further used for:

[0187] Determine the first number of grids corresponding to the target planned path, and determine the second number of grids corresponding to the target actual path;

[0188] Comparing the first number of grids with the second number of grids to determine a target number of grids;

[0189] Based on the target grid number and the first matching grid number, a path similarity between the target planned path and the target actual path is determined.

[0190] In a feasible implementation manner, the path comparison module 30 is further used for:

[0191] Traversing the target actual path to obtain a grid matching result, and determining a matching abnormal grid included in the target actual path according to the grid matching result;

[0192] Detecting each adjacent grid of the matching abnormal grid to obtain each detection result, and updating the first matching grid quantity according to each detection result to obtain a second matching grid quantity;

[0193] The path similarity between the target planned path and the target actual path is determined according to the second matching grid number.

[0194] In a feasible implementation manner, the path comparison module 30 is further used for:

[0195] Determining a preset similarity threshold;

[0196] When it is detected that the path similarity reaches the similarity threshold, determining that the path anomaly detection result corresponding to the target actual path is that the driving route is normal;

[0197] When it is detected that the path similarity does not reach the similarity threshold, it is determined that the path anomaly detection result corresponding to the target actual path is a driving route anomaly.

[0198] The vehicle driving path detection device provided by the present application adopts the vehicle driving path detection method in the above embodiment, which can solve the technical problem of low accuracy of the path detection result obtained by the path detection device in the related art. Compared with the prior art, the beneficial effects of the vehicle driving path detection device provided by the present application are the same as the beneficial effects of the vehicle driving path detection method provided by the above embodiment, and the other technical features of the vehicle driving path detection device are the same as the features disclosed in the above embodiment method, which will not be repeated here.

[0199] The present application provides a path detection device, which includes: at least one processor; and a memory that is 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 so that the at least one processor can execute the vehicle driving path detection method in the above-mentioned embodiment 1.

[0200] Reference below Fig. 9 , which shows a schematic diagram of the structure of a path detection device suitable for implementing the embodiment of the present application. The path detection device in the embodiment of the present application may include but is not limited to a path detection device configured on a vehicle, or a mobile terminal, a data storage control terminal, a PC, etc. connected to an electronic control unit supporting the path detection device. Fig. 9 The path detection device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0201] like Fig. 9As shown, the path detection device may include a processing device 1001 (e.g., a central processing unit, a graphics processor, etc.), which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM: Read Only Memory) 1002 or a program loaded from a storage device 1003 to a random access memory (RAM: Random Access Memory) 1004. In RAM1004, various programs and data required for the operation of the path detection device are also stored. The processing device 1001, ROM1002, and RAM1004 are connected to each other through a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Generally, the following systems can be connected to the I / O interface 1006: an input device 1007 including, for example, a touch screen, a touch pad, a keyboard, a mouse, an image sensor, a microphone, an accelerometer, a gyroscope, etc.; an output device 1008 including, for example, a liquid crystal display (LCD: Liquid Crystal Display), a speaker, a vibrator, etc.; a storage device 1003 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 1009. The communication device 1009 can allow the path detection device to communicate with other devices wirelessly or by wire to exchange data. Although the figure shows a path detection device with various systems, it should be understood that it is not required to implement or have all the systems shown. More or fewer systems can be implemented or have alternatively.

[0202] In particular, according to the embodiments disclosed in the present application, the process described above with reference to the flowchart can be implemented as a computer software program. For example, the embodiments disclosed in the present application include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through a communication device, or installed from a storage device 1003, or installed from a ROM 1002. When the computer program is executed by the processing device 1001, the above-mentioned functions defined in the method of the embodiment disclosed in the present application are executed.

[0203] The path detection device provided by the present application adopts the vehicle driving path detection method in the above embodiment, which can solve the technical problem of low accuracy of the path detection result obtained by the path detection device in the related art. Compared with the prior art, the beneficial effects of the path detection device provided by the present application are the same as the beneficial effects of the vehicle driving path detection method provided by the above embodiment, and the other technical features in the path detection device are the same as the features disclosed in the method of the previous embodiment, which will not be repeated here.

[0204] It should be understood that the various parts disclosed in this application can be implemented by hardware, software, firmware or a combination thereof. In the description of the above embodiments, specific features, structures, materials or characteristics can be combined in any one or more embodiments or examples in a suitable manner.

[0205] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0206] The present application provides a computer-readable storage medium having computer-readable program instructions (ie, computer programs) stored thereon, and the computer-readable program instructions are used to execute the vehicle driving path detection method in the above-mentioned embodiment.

[0207] The computer-readable storage medium provided in the present application may be, for example, a USB flash drive, but is not limited to electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems or devices, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM: Random Access Memory), a read-only memory (ROM: Read Only Memory), an erasable programmable read-only memory (EPROM: Erasable Programmable Read Only Memory or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM: CD-Read Only Memory), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system or device. The program code contained on the computer-readable storage medium may be transmitted using any appropriate medium, including but not limited to: wires, optical cables, RF (Radio Frequency: Radio Frequency), etc., or any suitable combination of the above.

[0208] The computer-readable storage medium may be included in the path detection device; or may exist independently without being assembled into the path detection device.

[0209] The computer-readable storage medium carries one or more programs. When the one or more programs are executed by the path detection device, the path detection device: obtains an initial planned path and an initial actual path, and determines the initial path grids corresponding to the initial planned path and the initial actual path in the target tile map; updates the initial planned path according to each of the initial path grids to obtain a target planned path, and updates the initial actual path according to each of the initial path grids to obtain a target actual path; determines the path similarity between the target planned path and the target actual path, and determines the path anomaly detection result corresponding to the target actual path based on the path similarity.

[0210] Computer program code for performing the operations of the present application may be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0211] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present application. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0212] The modules involved in the embodiments described in this application may be implemented by software or hardware, wherein the name of the module does not constitute a limitation on the unit itself in some cases.

[0213] The readable storage medium provided by the present application is a computer-readable storage medium, which stores computer-readable program instructions (i.e., computer programs) for executing the above-mentioned vehicle driving path detection method, and can solve the technical problem of low accuracy of the path detection results obtained by the path detection device in the related art. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided by the present application are the same as the beneficial effects of the vehicle driving path detection method provided by the above-mentioned embodiment, and will not be repeated here.

[0214] The present application also provides a computer program product, including a computer program, which implements the steps of the above-mentioned vehicle driving path detection method when executed by a processor.

[0215] The computer program product provided by the present application can solve the technical problem of low accuracy of the path detection result obtained by the path detection device in the related art. Compared with the prior art, the beneficial effects of the computer program product provided by the present application are the same as the beneficial effects of the vehicle driving path detection method provided by the above embodiment, which will not be repeated here.

[0216] The above descriptions are only some embodiments of the present application, and are not intended to limit the patent scope of the present application. All equivalent structural changes made using the contents of the present application specification and drawings under the technical concept of the present application, or direct / indirect applications in other related technical fields are included in the patent protection scope of the present application.

Claims

1. A method for detecting a vehicle driving path, characterized in that: The vehicle driving path detection method comprises: Acquire an initial planned path and an initial actual path, and determine initial path grids corresponding to the initial planned path and the initial actual path in the target tile map; The initial planned path is updated according to each of the initial path grids to obtain a target planned path, and the initial actual path is updated according to each of the initial path grids to obtain a target actual path; Determine a path similarity between the target planned path and the target actual path, and determine a path anomaly detection result corresponding to the target actual path based on the path similarity.

2. The method for detecting a vehicle driving path according to claim 1, characterized in that: The initial path grid includes a first initial path grid and a second initial path grid, and the step of determining the initial path grids corresponding to the initial planned path and the initial actual path in the target tile map includes: Determine a preset tile map level, and cut the preset initial tile map according to the tile map level to obtain a target tile map; Projecting each initial planned navigation point included in the initial planned path to the target tile map to determine a first initial path grid corresponding to each initial planned navigation point in the target tile map; Project each initial actual navigation point included in the initial actual path to the target tile map to determine that each initial actual navigation point corresponds to a second initial path grid in the target tile map.

3. The method for detecting a vehicle driving path according to claim 2, characterized in that: The step of updating the initial planned path according to each of the initial path grids to obtain a target planned path includes: Determine first grid coordinates corresponding to each of the first initial path grids; determining each first enhanced path grid coordinate based on each of the first grid coordinates, and determining each first enhanced path grid according to each of the first enhanced path grid coordinates; Each of the first enhanced path grids is added to the initial planned path to update the target planned path.

4. The method for detecting a vehicle driving path according to claim 2, characterized in that: The step of updating the initial actual path according to each of the initial path grids to obtain a target actual path includes: Determine the second grid coordinates corresponding to each of the second initial path grids; determining each second enhanced path grid coordinate based on each of the second grid coordinates, and determining each second enhanced path grid according to each of the second enhanced path grid coordinates; Each of the second enhanced path grids is added to the initial actual path to update the target actual path.

5. The method for detecting a vehicle driving path according to claim 2, characterized in that: After the step of determining that each of the initial actual navigation points corresponds to a second initial path grid in the target tile map, the method further includes: Determine the distances between adjacent grids corresponding to the second initial path grids; Screening each of the second initial path grids according to each of the adjacent grid distances to determine an abnormal second initial path grid included in each of the second initial path grids; Each of the abnormal second initial path grids is deleted, and the step of updating the initial planned path according to each of the initial path grids to obtain a target planned path is performed.

6. The method for detecting a vehicle driving path according to claim 1, characterized in that: The step of determining the path similarity between the target planned path and the target actual path comprises: Determine the number of first matching grids generated between the target planned path and the target actual path; The path similarity between the target planned path and the target actual path is determined according to the first matching grid number.

7. The method for detecting a vehicle driving path according to claim 6, characterized in that: The step of determining the number of first matching grids generated between the target planned path and the target actual path comprises: Determine each first character subscript corresponding to the target planned path, and determine each second character subscript corresponding to the target actual path; Determine first reverse character subscripts corresponding to the first character subscripts, and replace the second character subscripts with the first reverse character subscripts to obtain a target subscript sequence; A longest ascending subsequence is determined according to the target index sequence, and a first matching grid quantity generated between the target planned path and the target actual path is determined based on the longest ascending subsequence.

8. The method for detecting a vehicle driving path according to claim 6, characterized in that: The step of determining the path similarity between the target planned path and the target actual path according to the first matching grid number includes: Determine the first number of grids corresponding to the target planned path, and determine the second number of grids corresponding to the target actual path; Comparing the first number of grids with the second number of grids to determine a target number of grids; Based on the target grid number and the first matching grid number, a path similarity between the target planned path and the target actual path is determined.

9. The method for detecting a vehicle driving path according to claim 6, wherein: After the step of determining the number of first matching grids generated between the target planned path and the target actual path, the method further includes: Traversing the target actual path to obtain a grid matching result, and determining a matching abnormal grid included in the target actual path according to the grid matching result; Detecting each adjacent grid of the matching abnormal grid to obtain each detection result, and updating the first matching grid quantity according to each detection result to obtain a second matching grid quantity; The path similarity between the target planned path and the target actual path is determined according to the second matching grid number.

10. The method for detecting a vehicle driving path according to any one of claims 1 to 9, characterized in that: The step of determining the path anomaly detection result corresponding to the actual path of the target based on the path similarity includes: Determining a preset similarity threshold; When it is detected that the path similarity reaches the similarity threshold, determining that the path anomaly detection result corresponding to the target actual path is that the driving route is normal; When it is detected that the path similarity does not reach the similarity threshold, it is determined that the path anomaly detection result corresponding to the target actual path is a driving route anomaly.

11. A path detection device, characterized in that: The device comprises: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program is configured to implement the steps of the method for detecting a vehicle driving path as claimed in any one of claims 1 to 10.

12. A vehicle, characterized in that: A vehicle comprises the path detection device according to claim 11.

13. A storage medium, characterized in that: The storage medium is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by a processor, the steps of the vehicle driving path detection method according to any one of claims 1 to 10 are implemented.

14. A computer program product, characterized in that The computer program product comprises a computer program, and when the computer program is executed by a processor, the steps of the method for detecting a vehicle driving path according to any one of claims 1 to 10 are implemented.

Images