Method, device, equipment and storage medium for determining road obstacles

Abstract

This disclosure provides a method for identifying road obstacles, relating to the field of artificial intelligence, specifically intelligent transportation, computer vision, and deep learning technologies. The method includes: acquiring a navigation-planned route and the corresponding actual navigation route; determining the difference points between the navigation-planned route and the actual navigation route; acquiring behavioral features at the locations of the difference points; and determining the type of road obstacle at the locations of the difference points based on the behavioral features. The method for identifying road obstacles provided by this disclosure can proactively detect changes in road obstacle types, improving the efficiency and accuracy of road obstacle detection.

Description

Technical Field

[0001] This disclosure relates to the field of artificial intelligence, specifically to intelligent transportation, computer vision and deep learning technologies, and in particular to methods, apparatus, devices and storage media for determining road obstacles. Background Technology

[0002] As urbanization accelerates, users' demands for a better travel environment are constantly increasing. However, various obstacles inevitably appear on urban roads as people travel and work. To provide users with a high-quality navigation experience, offering accurate and accessible route planning is a crucial aspect of user experience. Roadblocks, as important elements that physically obstruct road traffic, are particularly important in terms of their completeness and accuracy. Summary of the Invention

[0003] This disclosure provides a method, apparatus, device, and storage medium for determining road obstacles.

[0004] According to a first aspect of this disclosure, a method for determining road obstacles is provided, comprising: obtaining a navigation planned route and an actual navigation route corresponding to the navigation planned route; determining a difference point between the navigation planned route and the actual navigation route; obtaining behavioral characteristics at the location of the difference point; and determining the type of road obstacle at the location of the difference point based on the behavioral characteristics.

[0005] According to a second aspect of this disclosure, an apparatus for determining road obstacles is provided, comprising: a first acquisition module configured to acquire a navigation planned route and an actual navigation route corresponding to the navigation planned route; a first determination module configured to determine a difference point between the navigation planned route and the actual navigation route; a second acquisition module configured to acquire behavioral features at the location of the difference point; and a second determination module configured to determine the type of road obstacle at the location of the difference point based on the behavioral features.

[0006] According to a third aspect of this disclosure, an electronic device is provided, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform a method as described in any implementation of the first aspect.

[0007] According to a fourth aspect of this disclosure, a non-transitory computer-readable storage medium is provided storing computer instructions for causing a computer to perform a method as described in any implementation of the first aspect.

[0008] According to a fifth aspect of this disclosure, a computer program product is provided, including a computer program that, when executed by a processor, implements the method as described in any implementation of the first aspect.

[0009] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of this disclosure, nor is it intended to limit the scope of this disclosure. Other features of this disclosure will become readily apparent from the following description. Attached Figure Description

[0010] The accompanying drawings are provided to better understand this solution and do not constitute a limitation of this disclosure. Wherein:

[0011] Figure 1 This is an exemplary system architecture diagram to which this disclosure can be applied;

[0012] Figure 2 This is a flowchart of one embodiment of the method for determining road obstacles according to the present disclosure;

[0013] Figure 3 This is a flowchart of another embodiment of the method for determining road obstacles according to the present disclosure;

[0014] Figure 4 This is an application scenario diagram of the method for determining road obstacles according to this disclosure;

[0015] Figure 5 This is a schematic diagram of one embodiment of the device for determining road obstacles according to the present disclosure;

[0016] Figure 6 This is a block diagram of an electronic device used to implement the method for determining road obstacles according to embodiments of the present disclosure. Detailed Implementation

[0017] The exemplary embodiments of this disclosure are described below with reference to the accompanying drawings, including various details of the embodiments to aid understanding, and should be considered merely exemplary. Therefore, those skilled in the art will recognize that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of this disclosure. Similarly, for clarity and brevity, descriptions of well-known functions and structures are omitted in the following description.

[0018] It should be noted that, unless otherwise specified, the embodiments and features described in this disclosure can be combined with each other. This disclosure will now be described in detail with reference to the accompanying drawings and embodiments.

[0019] Figure 1 An exemplary system architecture 100 is shown, in which embodiments of the method or apparatus for determining road obstacles of this disclosure can be applied.

[0020] like Figure 1 As shown, system architecture 100 may include terminal devices 101, 102, and 103, a network 104, and a server 105. Network 104 serves as the medium for providing communication links between terminal devices 101, 102, and 103 and server 105. Network 104 may include various connection types, such as wired or wireless communication links, or fiber optic cables, etc.

[0021] Users can use terminal devices 101, 102, and 103 to interact with server 105 via network 104 to receive or send information, etc. Various client applications can be installed on terminal devices 101, 102, and 103.

[0022] Terminal devices 101, 102, and 103 can be either hardware or software. When terminal devices 101, 102, and 103 are hardware, they can be various electronic devices, including but not limited to smartphones, tablets, laptops, and desktop computers. When terminal devices 101, 102, and 103 are software, they can be installed in the aforementioned electronic devices. They can be implemented as multiple software programs or software modules, or as a single software program or software module. No specific limitations are made here.

[0023] Server 105 can provide various services. For example, server 105 can analyze and process the navigation planning routes and the corresponding actual navigation routes obtained from terminal devices 101, 102, and 103, and generate processing results (such as road obstacle types).

[0024] It should be noted that server 105 can be either hardware or software. When server 105 is hardware, it can be implemented as a distributed server cluster consisting of multiple servers, or as a single server. When server 105 is software, it can be implemented as multiple software programs or software modules (e.g., used to provide distributed services), or as a single software program or software module. No specific limitations are made here.

[0025] It should be noted that the method for determining road obstacles provided in this embodiment is generally executed by server 105, and correspondingly, the device for determining road obstacles is generally located in server 105.

[0026] It should be understood that Figure 1 The number of terminal devices, networks, and servers shown is merely illustrative. Depending on implementation needs, any number of terminal devices, networks, and servers can be included.

[0027] Continue to refer to Figure 2The diagram illustrates a flow 200 of an embodiment of a method for determining road obstacles according to the present disclosure. The method for determining road obstacles includes the following steps:

[0028] Step 201: Obtain the planned navigation route and the actual navigation route corresponding to the planned navigation route.

[0029] In this embodiment, the entity executing the method for determining road obstacles (e.g.) Figure 1 The server 105 shown can obtain the planned navigation route and the corresponding actual navigation route. The planned navigation route is the route generated by the user using navigation software. For example, the user inputs a starting point and a destination location in the navigation software, and the software generates a navigation route with the starting point as the origin and the destination location as the destination. The actual planned route is the user's actual driving route based on the planned navigation route, that is, the actual trajectory route with the starting point as the origin and the destination location as the destination. It should be noted that the actual navigation route can be the same as or different from the planned navigation route. The aforementioned executing entity will obtain the user's planned navigation route and the corresponding actual navigation route.

[0030] Step 202: Identify the differences between the planned navigation route and the actual navigation route.

[0031] In this embodiment, the execution entity determines the differences between the planned navigation route and the actual navigation route. Because the user's actual walking route (i.e., the actual navigation route) may differ from the planned navigation route due to environmental factors, the execution entity determines these differences.

[0032] For example, the aforementioned execution entity will use the user's planned route during navigation as a basis, forming a planning reference plane with a buffer zone of approximately 5 meters (other values ​​can be set according to actual conditions). It will then determine whether the user's final driving trajectory (actual navigation route) falls within the reference plane. If it does not, the navigation planning experience is considered poor, and this case is identified as an instance to be inspected. The planned navigation route and the actual navigation route are then compared to determine the points of difference between them. These points of difference include points planned in the navigation route but not visited by the user, and also points passed through in the actual navigation route but not included in the planned route.

[0033] Step 203: Obtain the behavioral features at the location of the difference point.

[0034] In this embodiment, the execution entity acquires the behavioral characteristics at the location of the difference point. After determining the difference point between the planned navigation route and the actual navigation route, the execution entity aggregates the difference point location with the pre-constructed road network. Based on the intersection relationship, the difference point location is paired with the intersection to determine the location of the difference point. In this embodiment, the difference point location refers to the intersection closest to the difference point location.

[0035] Then, the aforementioned execution entity can obtain the behavioral characteristics of users passing through the intersection where the difference point is located, based on the pairing relationship between the location of the difference point and the intersection. These behavioral characteristics can refer to features such as detouring, making U-turns, and crossing. In other words, the aforementioned execution entity can obtain the actual walking routes of all users whose navigation-planned routes pass through the intersection where the difference point is located, and then extract the behavioral characteristics of the users at the intersection where the difference point is located.

[0036] Step 204: Determine the type of road obstacle at the location of the difference point based on behavioral characteristics.

[0037] In this embodiment, the execution entity determines the type of road obstacle at the location of the difference point based on the acquired behavioral characteristics. After acquiring the behavioral characteristics at the location of the difference point, the execution entity also determines from the road master database whether there is an obstacle at the location of the difference point, that is, whether there is already an obstacle at the location of the difference point. Then, the type of road obstacle at the location of the difference point is determined based on the behavioral characteristics.

[0038] For example, if the user behavior characteristics at the location of the difference point are all turning around or taking a detour, and it is determined from the road master database that there are no obstacle markers at the location of the difference point, then the location of the difference point can be identified as a suspected newly added road obstacle point.

[0039] For example, if the user behavior characteristics at the location of the difference point are all traversing, and it is determined from the road master database that there are already obstacle markers at the location of the difference point, then it can be determined that the location of the difference point is a suspected road obstacle removal point.

[0040] Optionally, after determining that the location of the discrepancy point is a suspected newly added or suspected deleted road obstacle, the aforementioned implementing entity will further verify the suspected addition or deletion of road obstacles by combining it with recently collected data from the surrounding area, thereby determining the type of road obstacle change and adding or deleting road obstacle element data from the road master database. For example, if the location of the discrepancy point is verified as a newly added road obstacle, a road obstacle element will be added to the road master database; if the location of the discrepancy point is verified as a deleted road obstacle, a road obstacle element will be deleted from the road master database.

[0041] The method for determining road obstacles provided in this embodiment first obtains the planned navigation route and the corresponding actual navigation route; then, it determines the difference points between the planned navigation route and the actual navigation route; subsequently, it obtains the behavioral features at the locations of the difference points; and finally, it determines the type of road obstacle at the location of the difference point based on the behavioral features. This method actively discovers the location and change type of road obstacles based on the user's planned navigation route and the actual navigation route, thereby improving the detection efficiency of road obstacles; furthermore, by determining the type of road obstacle at the location of the difference point based on the behavioral features, it improves the accuracy of the determined road obstacle type.

[0042] The collection, storage, use, processing, transmission, provision, and disclosure of user personal information involved in the technical solution disclosed herein comply with the provisions of relevant laws and regulations and do not violate public order and good morals.

[0043] Continue to refer to Figure 3 , Figure 3 A flow 300 of another embodiment of a method for determining road obstacles according to the present disclosure is shown. The method for determining road obstacles includes the following steps:

[0044] Step 301: Obtain the planned navigation route and the corresponding actual navigation route.

[0045] In this embodiment, the entity executing the method for determining road obstacles (e.g.) Figure 1 The server 105 shown will obtain the navigation plan route and the actual navigation route corresponding to the navigation plan route. Step 301 is basically the same as step 201 in the previous embodiment. For the specific implementation method, please refer to the above description of step 201, which will not be repeated here.

[0046] Step 302: Difference between the planned navigation route and the actual navigation route to determine the points of difference between them.

[0047] In this embodiment, the aforementioned execution entity can perform a difference analysis between the planned navigation route and the actual navigation route to determine the points of difference between them. Difference, also known as a difference function or difference operation, reflects a change between discrete quantities and is a tool for studying discrete mathematics. The difference of functions is often used to approximate the derivative.

[0048] In this embodiment, geometric coarse difference service can be used to determine the differences between the planned navigation route and the actual navigation route. These differences include points that appear in the planned navigation route but were not actually visited by the user (i.e., points not visited in the planned route) and points that appear in the actual navigation trajectory but were not included in the planned route (i.e., points not planned in the actual route). Based on the difference method, the differences between the planned and actual navigation routes can be determined more accurately.

[0049] Step 303: Based on the route binding service, bind the difference points to obtain the set of planned but untraveled routes and the set of routes that are not planned.

[0050] In this embodiment, the aforementioned execution entity performs route binding on the discrepancy points determined in step 302 based on the route binding service, that is, maps the discrepancy points to the corresponding roads, thereby obtaining the set of planned but untraveled routes and the set of routes that have not been planned. Here, the process of mapping the location points of discrepancy points to the corresponding roads can be called trajectory route binding. For example, the route binding service can be used to bind the navigation planned route and the actual navigation route that have been determined to have discrepancy points, thereby obtaining the map planned route sequence and the user driving route sequence. Then, the map planned sequence and the user driving route sequence are differentiated to obtain the set of planned but untraveled routes and the set of routes that have not been planned.

[0051] Step 304: Based on the intersection positioning service, the routes in the set of planned but not taken routes and the set of routes that pass through unplanned routes are paired to obtain the pairs of planned but not taken routes and the pairs of routes that pass through unplanned routes.

[0052] In this embodiment, the aforementioned execution entity uses intersection positioning services to pair routes in the planned untraveled routes set and the route-through-unplanned routes set, respectively, resulting in multiple planned untraveled route pairs and multiple route-through-unplanned route pairs. That is, the intersection positioning service is used to pair routes in the planned untraveled routes set in pairs, and then routes in the route-through-unplanned routes set in pairs, thereby obtaining planned untraveled route pairs and route-through-unplanned route pairs. This identifies the differences and yields planned untraveled route pairs and route-through-unplanned route pairs.

[0053] In some optional implementations of this embodiment, step 304 includes: determining the intersections in each planned untraveled route and each route with unplanned access based on the intersection location service; pairing the planned untraveled routes based on the intersections to obtain planned untraveled route pairs; and pairing the route with unplanned access based on the intersections to obtain route with unplanned access pairs.

[0054] In this implementation, the aforementioned execution entity uses unplanned routes and routes that pass through unplanned routes as guides, and locates the intersections closest to each unplanned route and the intersections closest to each route that passes through unplanned routes based on intersection location services. Then, based on the determined intersections, each unplanned route and each route that passes through unplanned routes is paired. That is, unplanned routes are paired at intersections to obtain unplanned route pairs. For example, unplanned route 1 and unplanned route 3 both pass through intersection A, so unplanned route 1 and unplanned route 3 can be considered as one unplanned route pair. Similarly, routes that pass through unplanned routes are paired at intersections to obtain routes that pass through unplanned routes. For example, routes that pass through unplanned routes 1 and routes that pass through unplanned routes both pass through intersection B, so routes that pass through unplanned routes 1 and routes that pass through unplanned routes can be considered as one route that passes through unplanned routes. Thus, unplanned route pairs and routes that pass through unplanned routes are obtained based on intersections to more accurately identify road obstacles.

[0055] It should be noted that each intersection may correspond to multiple planned but unused route pairs and multiple routes that are not planned.

[0056] Step 305: Based on the trajectory processing service, summarize the behavioral characteristics of the planned untraveled route pairs and the unplanned route pairs.

[0057] In this embodiment, the aforementioned execution entity summarizes the behavioral characteristics of both planned but untraveled routes and routes that pass through unplanned routes based on the trajectory processing service. These behavioral characteristics include: traversal characteristics, detour characteristics, and U-turn characteristics. That is, the trajectory processing service obtains the behavioral characteristics of each planned but untraveled route when it passes through this location, as well as the behavioral characteristics of routes that pass through this location, including traversal characteristics, U-turn characteristics, and detour characteristics. Therefore, the type of road obstacle can be determined based on these behavioral characteristics, including traversal characteristics, detour characteristics, and U-turn characteristics.

[0058] Step 306: In response to determining that the planned untraveled route has U-turn or detour characteristics, and that there are no road obstacles at the intersection corresponding to the planned untraveled route, a new road obstacle is added at the intersection.

[0059] In this embodiment, if the execution entity determines that the planned untraveled route has U-turn or detour characteristics, and there are no road obstacles at the intersection corresponding to the planned untraveled route, then it determines that the road obstacle at the intersection is a newly added road obstacle type, that is, a new road obstacle is added at the intersection. The existence of road obstacles at the intersection corresponding to the planned untraveled route can be determined based on information in the road master database.

[0060] Step 307: In response to the determination that there is a crossing feature for the unplanned route pair and that there is a road obstacle at the intersection corresponding to the unplanned route pair, the road obstacle at the intersection is deleted.

[0061] In this embodiment, if the execution entity determines that the unplanned route pair exhibits crossing characteristics and that there are road obstacles at the corresponding intersection, then it determines that the road obstacle at the intersection is a type of road obstacle to be deleted, i.e., the road obstacle at the intersection is deleted. The existence of road obstacles at the intersection corresponding to the unplanned route pair can be determined based on information in the road master database.

[0062] This allows us to determine the specific type of road obstacle based on different scenarios and behavioral characteristics.

[0063] In some optional embodiments of this example, the method for determining road obstacles further includes: verifying the type of road obstacle at the location of the difference point based on the collected road information; and adding or deleting road obstacle markers in a pre-built road database based on the verification results.

[0064] In this implementation, the aforementioned executing entity also acquires image data of the location of the difference point and verifies the type of road obstacle based on the acquired image data, i.e., verifying whether it is a newly added or deleted road obstacle marker. Then, based on the verification result, it adds or deletes the road obstacle marker in the pre-built road database map. If there is no pre-collected data at the location of the difference point or the collected data cannot be verified, the data acquisition process is triggered to re-collect the data and perform the verification operation. This allows for a more accurate determination of the road obstacle type.

[0065] from Figure 3 It can be seen from this that, with Figure 2 Compared to the corresponding embodiments, the method for determining road obstacles in this embodiment intelligently extracts the location of suspected road obstacles by being guided by user behavior, and performs efficient data updates by combining collected data, which further improves the accuracy and efficiency of road obstacle detection.

[0066] Further reference Figure 4 , Figure 4 The diagram illustrates an application scenario of the method for determining road obstacles according to this disclosure. In this application scenario, the executing entity first mines the differences between the user's navigation route (actual driving trajectory) and the planned route based on a geometric coarse difference service, identifies route discrepancy points, and forms a planning anomaly database.

[0067] Then, the executing entity will bind the planned route and the actual driving trajectory through the trajectory binding service to obtain the set of planned but not taken links and the set of links that are not planned.

[0068] Next, the intersection location service is used to identify the intersections in each planned but untraveled route and each unplanned route along the path. Based on the intersections, each planned but untraveled route is paired to obtain planned but untraveled link pairs. Based on the intersections, each unplanned route along the path is paired to obtain unplanned route link pairs. In other words, using the planned and unplanned links that the user has not taken as guides, the system locates the nearest intersection along the link direction to determine planned but untraveled link pairs and unplanned route link pairs.

[0069] Next, the types of road obstacles at the locations of the discrepancies are determined using trajectory profiling and scenario analysis services. For example, behavioral characteristics such as crossing, U-turns, and detours are first summarized for planned and unplanned link pairs. If there are no crossings for any planned untraveled link pairs at a certain intersection, but U-turns and detours are present, and the road database shows no obstacles at that intersection, then the road obstacle type at that intersection should be considered a suspected newly added road obstacle. If there are crossings for all unplanned link pairs at a certain intersection, and the road database shows obstacles at that intersection, then the road obstacle type at that intersection is considered a suspected deleted road obstacle.

[0070] Finally, the executing entity will acquire image data of the intersection where the newly added roadblocks occurred, and use the verification data extraction service to verify whether the roadblock was newly added or removed. If verification data is available, verification will be performed based on the data, and the map in the road master database will be modified based on the verification results. If no data is collected or the collected data cannot be verified, the data collection process will be triggered to re-collect the collected data for verification.

[0071] Further reference Figure 5 As an implementation of the methods shown in the above figures, this disclosure provides an embodiment of a device for determining road obstacles, which is similar to... Figure 2 Corresponding to the method embodiments shown, this device can be specifically applied to various electronic devices.

[0072] like Figure 5As shown, the device 500 for determining road obstacles in this embodiment includes: a first acquisition module 501, a first determination module 502, a second acquisition module 503, and a second determination module 504. The first acquisition module 501 is configured to acquire a navigation planned route and the corresponding actual navigation route; the first determination module 502 is configured to determine the difference points between the navigation planned route and the actual navigation route; the second acquisition module 503 is configured to acquire behavioral characteristics at the location of the difference points; and the second determination module 504 is configured to determine the type of road obstacle at the location of the difference points based on the behavioral characteristics.

[0073] In this embodiment, the specific processing of the first acquisition module 501, the first determination module 502, the second acquisition module 503, and the second determination module 504 in the device 500 for determining road obstacles, and the resulting technical effects, can be found in the following references: Figure 2 The relevant descriptions of steps 201-204 in the corresponding embodiments will not be repeated here.

[0074] In some optional implementations of this embodiment, the first determining module includes: a first determining submodule, configured to perform differential analysis between the navigation planned route and the actual navigation route to determine the difference points between the navigation planned route and the actual navigation route.

[0075] In some optional implementations of this embodiment, the device 500 for determining road obstacles further includes: a road binding module, configured to bind the difference points based on the road binding service to obtain a set of planned untraveled routes and a set of routes passing through unplanned routes; and a pairing module, configured to pair the routes in the set of planned untraveled routes and the set of routes passing through unplanned routes based on the intersection positioning service to obtain a pair of planned untraveled routes and a pair of routes passing through unplanned routes.

[0076] In some optional implementations of this embodiment, the pairing module includes: a second determining submodule, configured to determine the intersections in each planned untraveled route and each unplanned route based on the intersection positioning service; a first pairing submodule, configured to pair each planned untraveled route based on the intersections to obtain a planned untraveled route pair; and a second pairing submodule, configured to pair each unplanned route based on the intersections to obtain a route unplanned route pair.

[0077] In some optional implementations of this embodiment, the second acquisition module includes: a summarization module, configured to summarize the behavioral characteristics of planned untraveled route pairs and unplanned route pairs based on the trajectory processing service, wherein the behavioral characteristics include: traversing characteristics, detour characteristics, or U-turn characteristics.

[0078] In some optional implementations of this embodiment, the second determining module includes: a adding submodule, configured to add a road obstacle at the intersection in response to determining that the planned untraveled route has a U-turn or detour feature and that there is no road obstacle at the intersection corresponding to the planned untraveled route; and a deleting submodule, configured to delete the road obstacle at the intersection in response to determining that the untraveled route has a crossing feature and that there is a road obstacle at the intersection corresponding to the untraveled route.

[0079] In some optional implementations of this embodiment, the device 500 for determining road obstacles further includes: a verification module configured to verify the type of road obstacle at the location of the difference point based on the collected road information; and a marking module configured to add or delete road obstacle marks in a pre-built road database based on the verification results.

[0080] According to embodiments of this disclosure, this disclosure also provides an electronic device, a readable storage medium, and a computer program product.

[0081] Figure 6 A schematic block diagram of an example electronic device 600 that can be used to implement embodiments of the present disclosure is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the present disclosure described and / or claimed herein.

[0082] like Figure 6 As shown, device 600 includes a computing unit 601, which can perform various appropriate actions and processes based on a computer program stored in read-only memory (ROM) 602 or a computer program loaded from storage unit 608 into random access memory (RAM) 603. RAM 603 may also store various programs and data required for the operation of device 600. The computing unit 601, ROM 602, and RAM 603 are interconnected via bus 604. Input / output (I / O) interface 605 is also connected to bus 604.

[0083] Multiple components in device 600 are connected to I / O interface 605, including: input unit 606, such as keyboard, mouse, etc.; output unit 607, such as various types of monitors, speakers, etc.; storage unit 608, such as disk, optical disk, etc.; and communication unit 609, such as network card, modem, wireless transceiver, etc. Communication unit 609 allows device 600 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0084] The computing unit 601 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the computing unit 601 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 601 performs the various methods and processes described above, such as the method of determining road obstacles. For example, in some embodiments, the method of determining road obstacles may be implemented as a computer software program tangibly contained in a machine-readable medium, such as storage unit 608. In some embodiments, part or all of the computer program may be loaded and / or installed on device 600 via ROM 602 and / or communication unit 609. When the computer program is loaded into RAM 603 and executed by the computing unit 601, one or more steps of the method of determining road obstacles described above may be performed. Alternatively, in other embodiments, the computing unit 601 may be configured to perform the method of determining road obstacles by any other suitable means (e.g., by means of firmware).

[0085] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0086] The program code used to implement the methods of this disclosure may be written in any combination of one or more programming languages. This program code may be provided to a processor or controller of a general-purpose computer, special-purpose computer, or other programmable data processing apparatus, such that when executed by the processor or controller, the program code causes the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The program code may be executed entirely on a machine, partially on a machine, as a standalone software package partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0087] In the context of this disclosure, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can be, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0088] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device for displaying information to the user (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor); and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the computer. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0089] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as a data server), or computing systems that include middleware components (e.g., an application server), or computing systems that include frontend components (e.g., a user computer with a graphical user interface or web browser through which a user can interact with embodiments of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., a communication network). Examples of communication networks include local area networks (LANs), wide area networks (WANs), and the Internet.

[0090] Cloud computing refers to a technology system that enables access to a shared pool of physical or virtual resources via a network. These resources can include servers, operating systems, networks, software, applications, and storage devices, and can be deployed and managed on demand and in a self-service manner. Cloud computing technology can provide efficient and powerful data processing capabilities for applications such as artificial intelligence and blockchain, as well as for model training.

[0091] Computer systems can include clients and servers. Clients and servers are generally located far apart and typically interact via communication networks. Client-server relationships are created by computer programs running on the respective computers and having a client-server relationship with each other. Servers can be cloud servers, servers in distributed systems, or servers incorporating blockchain technology.

[0092] It should be understood that the various forms of processes shown above can be used to rearrange, add, or delete steps. For example, the steps described in this disclosure can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution disclosed in this disclosure can be achieved, and this is not limited herein.

[0093] The specific embodiments described above do not constitute a limitation on the scope of protection of this disclosure. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this disclosure should be included within the scope of protection of this disclosure.

Claims

1. A method for determining road obstacles, comprising: Obtain the planned navigation route and the corresponding actual navigation route; Identify the differences between the planned navigation route and the actual navigation route; Obtain the behavioral characteristics at the location of the difference point; Based on the behavioral characteristics, determine the type of road obstacle at the location of the difference point; The method further includes: Based on the route binding service, the difference points are bound to routes to obtain a set of planned but untraveled routes and a set of routes that are not planned. Based on the intersection location service, identify the intersections in each planned but untraveled route and each route that is not planned; Based on the intersections, each planned untraveled route and each unplanned route along the route is paired to obtain pairs of planned untraveled routes and pairs of routes along the route; and The step of obtaining the behavioral features at the location of the difference point includes: Based on the trajectory processing service, the behavioral characteristics of the planned but not taken routes and the unplanned routes are summarized, wherein the behavioral characteristics include: traversing characteristics, detour characteristics, or U-turn characteristics.

2. The method according to claim 1, wherein, Determining the differences between the planned navigation route and the actual navigation route includes: The planned navigation route and the actual navigation route are compared to determine the points of difference between them.

3. The method according to claim 1, wherein, Determining the type of road obstacle at the location of the difference point based on the behavioral characteristics includes: In response to determining that the planned untraveled route pair has the U-turn feature or the detour feature, and that there are no road obstacles at the intersection corresponding to the planned untraveled route pair, a road obstacle is added at the intersection; In response to determining that the unplanned route pair has the crossing feature and that there is a road obstacle at the intersection corresponding to the unplanned route pair, the road obstacle at the intersection is deleted.

4. The method according to claim 3, further comprising: The types of road obstacles at the locations of the discrepancies are verified based on the collected road information; Based on the verification results, add or delete road obstacle markers in the pre-built road database.

5. A device for identifying road obstacles, comprising: The first acquisition module is configured to acquire the navigation planning route and the actual navigation route corresponding to the navigation planning route; The first determining module is configured to determine the difference points between the planned navigation route and the actual navigation route; The second acquisition module is configured to acquire behavioral features at the location of the difference point; The second determining module is configured to determine the type of road obstacle at the location of the difference point based on the behavioral characteristics; The device further includes: The route binding module is configured to bind the difference points to routes based on the route binding service, thereby obtaining a set of planned but untraveled routes and a set of routes that are not planned. The third determination module is configured to determine the intersections in each planned untraveled route and each unplanned route based on the intersection location service; The pairing module is configured to pair each planned untraveled route and each unplanned route along the route based on the intersection, resulting in pairs of planned untraveled routes and pairs of unplanned routes along the route; and The first acquisition module is further configured to: Based on the trajectory processing service, the behavioral characteristics of the planned but not taken routes and the unplanned routes are summarized, wherein the behavioral characteristics include: traversing characteristics, detour characteristics, or U-turn characteristics.

6. The apparatus according to claim 5, wherein, The first determining module includes: The first determining submodule is configured to perform a difference analysis between the planned navigation route and the actual navigation route to determine the difference points between the planned navigation route and the actual navigation route.

7. The apparatus according to claim 5, wherein, The second determining module includes: A new submodule is configured to add a road obstacle at the intersection in response to determining that the planned untraveled route pair has the U-turn feature or the detour feature, and that there is no road obstacle at the intersection corresponding to the planned untraveled route pair. The deletion submodule is configured to delete the road obstacle at the intersection in response to determining that the unplanned route pair has the crossing feature and that there is a road obstacle at the intersection corresponding to the unplanned route pair.

8. The apparatus according to claim 7, further comprising: The verification module is configured to verify the type of road obstacle at the location of the discrepancy point based on the collected road information; The tagging module is configured to add or delete road obstacle tags in a pre-built road database based on the verification results.

9. An electronic device, comprising: At least one processor; as well as A memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-4.

10. A non-transitory computer-readable storage medium storing computer instructions for causing the computer to perform the method of any one of claims 1-4.

11. A computer program product comprising a computer program that, when executed by a processor, implements the method according to any one of claims 1-4.

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