Method, device, equipment, storage medium and program product for updating semantic map

Abstract

According to embodiments of the present disclosure, a method, apparatus, device, storage medium and program product for updating a semantic map are provided. The method comprises: obtaining a set of report messages uploaded from at least one autonomous vehicle, wherein the report messages indicate that the autonomous vehicle determines that a matching degree of a map element in the semantic map is lower than a preset degree in a positioning process; determining at least one map element to be updated based on the set of report messages; and updating the at least one map element in the semantic map by using vehicle perception data associated with the at least one map element. Based on such a manner, embodiments of the present disclosure can realize automatic updating of the semantic map.

Description

Technical Field

[0001] The exemplary embodiments disclosed herein generally relate to the field of computers, and particularly to a method, apparatus, device, computer-readable storage medium, and computer program product for updating semantic maps. Background Technology

[0002] With the rapid development of autonomous driving technology, based on semantic maps, autonomous vehicles can achieve more accurate positioning and make more intelligent decisions.

[0003] For autonomous vehicles to operate safely, semantic maps need to be maintained regularly. Currently, maintaining semantic maps requires significant human resources. Summary of the Invention

[0004] In a first aspect of this disclosure, a method for updating a semantic map is provided. The method includes: acquiring a set of report messages uploaded from at least one autonomous vehicle, wherein the report messages indicate that the autonomous vehicle determines during localization that the matching degree of map elements in the semantic map is lower than a preset degree; determining at least one map element to be updated based on the set of report messages; and updating the at least one map element in the semantic map using vehicle perception data associated with the at least one map element.

[0005] In a second aspect of this disclosure, an apparatus for updating a semantic map is provided. The apparatus includes: an acquisition module configured to acquire a set of report messages uploaded from at least one autonomous vehicle, wherein the report messages indicate that the autonomous vehicle determines during localization that the matching degree of map elements in the semantic map is lower than a preset degree; a determination module configured to determine at least one map element to be updated based on the set of report messages; and an update module configured to update the at least one map element in the semantic map using vehicle perception data associated with the at least one map element.

[0006] In a third aspect of this disclosure, an electronic device is provided. The device includes at least one processing unit; and at least one memory coupled to the at least one processing unit and storing instructions for execution by the at least one processing unit. When executed by the at least one processing unit, the instructions cause the device to perform the method of the first aspect.

[0007] In a fourth aspect of this disclosure, a computer-readable storage medium is provided. The computer-readable storage medium stores a computer program that can be executed by a processor to implement the method of the first aspect.

[0008] In a fifth aspect of this disclosure, a computer program product is provided. The computer program product includes computer-executable instructions that, when executed by a processor, implement the method of the first aspect.

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

[0010] The above and other features, advantages, and aspects of the embodiments of this disclosure will become more apparent from the accompanying drawings and the following detailed description. In the drawings, the same or similar reference numerals denote the same or similar elements, wherein:

[0011] Figure 1 A schematic diagram of an example environment in which embodiments of the present disclosure can be implemented is shown;

[0012] Figure 2 A flowchart illustrating an example process for updating a semantic map according to some embodiments of this disclosure is shown;

[0013] Figure 3A A flowchart illustrating an example process for updating a semantic map according to some embodiments of the present disclosure is shown;

[0014] Figure 3B Example record items are shown according to some embodiments of this disclosure;

[0015] Figure 4 A schematic structural block diagram of an example apparatus for updating semantic maps according to some embodiments of the present disclosure is shown; and

[0016] Figure 5 A block diagram of an apparatus capable of implementing several embodiments of the present disclosure is shown. Detailed Implementation

[0017] Embodiments of this disclosure will now be described in more detail with reference to the accompanying drawings. While some embodiments of this disclosure are shown in the drawings, it should be understood that this disclosure can be implemented in various forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided to provide a more thorough and complete understanding of this disclosure. It should be understood that the accompanying drawings and embodiments of this disclosure are for illustrative purposes only and are not intended to limit the scope of protection of this disclosure.

[0018] It should be noted that the headings of any section / subsection provided herein are not limiting. Various embodiments are described throughout this document, and embodiments of any type may be included under any section / subsection. Furthermore, embodiments described in any section / subsection may be combined in any way with any other embodiments described in the same section / subsection and / or different sections / subsections.

[0019] In the description of embodiments of this disclosure, the term "comprising" and similar terms should be understood as open-ended inclusion, i.e., "including but not limited to". The term "based on" should be understood as "at least partially based on". The term "one embodiment" or "the embodiment" should be understood as "at least one embodiment". The term "some embodiments" should be understood as "at least some embodiments". Other explicit and implicit definitions may also be included below. The terms "first", "second", etc., may refer to different or the same objects. Other explicit and implicit definitions may also be included below.

[0020] The embodiments of this disclosure may involve user data, data acquisition, and / or use. All of these aspects comply with applicable laws, regulations, and relevant provisions. In the embodiments of this disclosure, all data collection, acquisition, processing, manipulation, forwarding, and use are conducted with the user's knowledge and confirmation. Accordingly, in implementing the embodiments of this disclosure, the type, scope of use, and usage scenarios of any data or information that may be involved should be communicated to the user and their authorization obtained in accordance with relevant laws and regulations through appropriate means. The specific methods of notification and / or authorization may vary depending on the actual situation and application scenario, and the scope of this disclosure is not limited in this respect.

[0021] In this specification and the embodiments, any processing of personal information will be carried out only under the premise of legality (such as obtaining the consent of the personal information subject, or being necessary for the performance of a contract), and will only be carried out within the scope stipulated or agreed upon. A user's refusal to process personal information other than that necessary for basic functions will not affect the user's use of basic functions.

[0022] As briefly mentioned earlier, autonomous vehicles require accurate semantic maps to achieve precise positioning and make intelligent decisions. Maintaining semantic maps typically requires manual verification of the collected vehicle perception data to determine which map elements need updating. This process consumes significant human resources.

[0023] Embodiments of this disclosure propose a scheme for updating a semantic map. The scheme includes: acquiring a set of report messages uploaded from at least one vehicle, wherein the report messages indicate that the vehicle determined during the localization process that the matching degree of map elements in the semantic map was lower than a preset degree; determining at least one map element to be updated based on the set of report messages; and updating the at least one map element in the semantic map using vehicle perception data associated with the at least one map element.

[0024] In this way, embodiments of the present disclosure can identify map elements with low matching relevance to the semantic map based on a set of report messages uploaded by the vehicle, and then update these map elements in the semantic map based on relevant vehicle perception data. This enables automated updates to the semantic map, saving labor costs.

[0025] The following section provides a detailed description of various example implementations of this scheme, with reference to the accompanying drawings.

[0026] Example Environment

[0027] Figure 1 A schematic diagram of an example environment 100 in which embodiments of the present disclosure can be implemented is shown. For example... Figure 1 As shown, example environment 100 may include server 110.

[0028] In some embodiments, server 110 communicates with autonomous vehicle 120 to provide updated semantic map services. Server 110 can be a standalone physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, content delivery networks, and big data and artificial intelligence platforms. Server 110 may include, for example, computing systems / servers such as mainframes, edge computing nodes, computing devices in a cloud environment, etc.

[0029] A communication connection can be established between server 110 and autonomous vehicle 120. This communication connection can be established via wired or wireless means. The communication connection may include, but is not limited to, Bluetooth, mobile network, Universal Serial Bus (USB), and Wireless Fidelity (WiFi) connections; the embodiments of this disclosure are not limited in this respect. In the embodiments of this disclosure, server 130 and autonomous vehicle 120 can achieve signaling interaction through their communication connection.

[0030] It should be understood that the structure and function of the various elements in environment 100 are described for illustrative purposes only and do not imply any limitation on the scope of this disclosure.

[0031] The following description will continue with reference to the accompanying drawings, which will provide some exemplary embodiments of this disclosure.

[0032] Example process

[0033] The following will combine Figure 2 and Figures 3A to 3B This describes the specific process of updating semantic map 340. Figure 2 A flowchart of an example process 200 for updating a semantic map 340 according to some embodiments of the present disclosure is shown. Figure 3A A flowchart of an example process 300A for updating a semantic map 340 according to some embodiments of the present disclosure is shown; Figure 3B Example record entries according to some embodiments of this disclosure are shown. Process 200 can be implemented at server 110. Reference is made below. Figure 1 and Figures 3A to 3B To describe process 200.

[0034] like Figure 2 As shown in box 210, server 110 receives a set of report messages 320 uploaded from at least one vehicle.

[0035] In some embodiments, the report message 320 may instruct the vehicle to determine during the localization process that the matching degree 310 of map elements 345 in the semantic map 340 is lower than a preset degree. Specifically, the report message 320 may be obtained based on vehicle perception data 130 collected by the autonomous vehicle 120.

[0036] In some embodiments, vehicle perception data 130 may indicate various map elements 345 corresponding to semantic map 340, as well as feature points for indicating map elements 345. The main control system of autonomous vehicle 120 can determine the map elements 345 in semantic map 340 that need to be updated by matching the map elements 345 in vehicle perception data 130 with the map elements 345 in semantic map 340.

[0037] In some embodiments, taking a map element 345 in the semantic map 340 as an example, the main control system can determine the degree of matching 310 between the map element 345 in the vehicle perception data 130 and the map element 345 in the semantic map 340 based on the following process: First, determine a first number of feature points in a first set that match the map element 345. Then, determine a second number of feature points in a second set that are associated with the map element 345. Finally, determine the degree of matching 310 based on the first and second numbers.

[0038] As an example, the main control system can determine whether a feature point matches based on the deviation distance between the first position of the same feature point in the semantic map 340 and the second position in the vehicle perception data 130. For instance, if the deviation distance is less than a first threshold, the main control system can consider that the feature point in the semantic map 340 matches the feature point in the vehicle perception data 130. If the deviation distance reaches the first threshold but is less than the second threshold, the main control system can consider that the feature point in the semantic map 340 does not match the feature point in the vehicle perception data 130.

[0039] In some embodiments, the main control system can first determine the deviation distance of all feature points associated with map element 345, and then determine the matching feature points among all feature points associated with map element 345 by comparing the deviation distance with a first threshold and a second threshold. Further, the main control system determines the number of all feature points associated with map element 345 and the number of matching feature points to determine the matching degree 310 of map element 345. Specifically, feature points with a deviation distance less than the first threshold can be denoted as the first group of feature points, the number of feature points with a deviation distance less than the first threshold can be denoted as the first number, the number of all feature points associated with map element 345 can be denoted as the second number, and feature points with a deviation distance reaching the first threshold but less than the second threshold can be denoted as the second group of feature points. It should be noted that the set of the first group of feature points and the set of the second group of feature points can be understood as all feature points associated with map element 345. Based on this, the number of feature points with a deviation distance reaching the first threshold but less than the second threshold is the difference between the second number and the first number.

[0040] In some embodiments, the first threshold and the second threshold can be preset by the main control system. The values ​​of the first threshold and the second threshold can be adaptively adjusted according to actual conditions.

[0041] In some embodiments, the matching degree 310 of map elements 345 can be obtained by determining the ratio of a first number to a second number. The higher the ratio of the first number to the second number, the higher the matching degree 310 of map elements 345. Conversely, the lower the ratio of the first number to the second number, the lower the matching degree 310 of map elements 345.

[0042] Based on the above steps, the main control system can determine the matching degree 310 of each map element 345 in the semantic map 340.

[0043] It should be understood that the lower the matching degree 310, the more likely the map element 345 is to have a problem and needs to be updated. Therefore, in some embodiments, the main control system can filter potentially problematic map elements 345 based on the matching degree 310 to generate a report message 320 for reporting. As an example, map elements 345 with a matching degree 310 lower than a preset degree can be generated into a report message 320 for reporting.

[0044] In some embodiments, considering the possibility that a low matching degree 310 of map element 345 may result from errors in vehicle perception data 130, the main control system may also report map elements 345 whose matching degree 310 is lower than a preset degree and whose difference between the second number and the first number is greater than a preset number by generating a report message 320. By setting a preset number, the robustness of the main control system in determining whether map element 345 may have a problem can be improved. The score corresponding to the preset degree and the preset number can be adaptively adjusted according to actual conditions.

[0045] In some embodiments, there may be multiple autonomous vehicles 120 for collecting vehicle perception data 130. Each autonomous vehicle 120 can generate a report message 320 when collecting vehicle perception data 130. When each autonomous vehicle 120 collects vehicle perception data 130 multiple times, it can generate multiple corresponding report messages 320. When multiple autonomous vehicles 120 collect vehicle perception data 130 multiple times, they can generate a corresponding set of report messages 320.

[0046] In some embodiments, a set of report messages 320 may also consist of multiple report messages 320 over a period of time; that is, a set of report messages 320 may be associated with a preset first time period. As an example, the first time period may be one week.

[0047] In box 220, server 110 determines at least one map element 345 to be updated based on a set of report messages 320.

[0048] In some embodiments, the server 110 can present a set of report messages 320 obtained through a graphical interface. Specifically, the server 110 can generate a set of record items corresponding to a set of map elements 345 based on a set of report messages 320, and present the set of record items through a graphical interface.

[0049] In some embodiments, each report message 320 in a set of report messages 320 corresponds to multiple map elements 345. Based on the report messages 320, record entries corresponding one-to-one with map elements 345 can be generated. As an example, a record entry may indicate at least one of the following: the identification information of the map element 345, the type of the map element 345, the location of the map element 345, and the number of messages corresponding to the map element 345. The identification information of the map element 345 may be the map element 345's number, and the number of messages corresponding to the map element 345 may be the total number of report messages 320 about that map element 345 obtained by the server 110.

[0050] In some embodiments, in addition to the above, the record item may also include detailed information associated with map element 345. Figure 3BExample record entries are shown according to some embodiments of this disclosure.

[0051] In some embodiments, server 110 can generate a target record item corresponding to the target map element by determining the latest report message associated with the target map element and based on vehicle perception data associated with the latest report message. The target record item indicates detailed information associated with the target map element. As an example, the target record item may indicate a timestamp corresponding to the latest report message, an index corresponding to the vehicle perception data, and the distance from the target map element to the autonomous vehicle 120.

[0052] In some embodiments, server 110 may first select a map element 345 from the map elements 345 corresponding to a set of report messages 320 as the target map element. Then, server 110 determines all report messages 320 associated with the target map element, and then determines the report message 320 with the most recent generation time from these report messages 320 to determine the latest report message. Once server 110 determines the latest report message, it can generate the target record item by parsing the latest report message.

[0053] Based on the above process, server 110 can generate a set of record items corresponding to a set of map elements 345 based on a set of report messages 320.

[0054] In some embodiments, considering that a set of report messages 320 obtained by server 110 may indicate map elements 345 that may have problems, but in fact these map elements 345 may not need to be updated, server 110 needs to further filter the map elements 345 indicated by the set of report messages 320 to determine at least one map element 345 that needs to be updated.

[0055] In some embodiments, server 110 may determine at least one map element 345 to be updated based on the following process: First, determine a set of map elements 345 corresponding to a set of report messages 320. Then, determine at least one map element 345 to be updated based on the number of report messages 320 corresponding to each map element 345.

[0056] As an example, server 110 can select one or more map elements 345 with the highest number of messages as the map element 345 to be updated. Server 110 can also select at least one map element 345 with a message count reaching a preset threshold as the map element 345 to be updated. The threshold can be adjusted adaptively according to the actual situation.

[0057] The above process can further improve the robustness of server 110 in determining whether map element 345 may have problems.

[0058] In box 230, server 110 uses vehicle perception data associated with at least one map element 345 to update at least one map element 345 in semantic map 340.

[0059] In some embodiments, server 110 may update semantic map 340 based on the following steps:

[0060] First, server 110 acquires a set of vehicle perception data associated with at least one map element 345. Here, the at least one map element 345 is the map element 345 to be updated.

[0061] Then, server 110 determines target vehicle perception data 330 from a set of vehicle perception data based on the distance from autonomous vehicle 120 to at least one map element 345 corresponding to a set of vehicle perception data. The distance from autonomous vehicle 120 to map element 345 reflected in the vehicle perception data determines the quality of that map element 345 in the updated semantic map 340. Specifically, the longer the distance from autonomous vehicle 120 to map element 345, the worse the quality of that map element 345 in the updated semantic map 340. Conversely, the shorter the distance from autonomous vehicle 120 to map element 345, the more abundant and detailed the data related to that map element 345 in the vehicle perception data, and the better the quality of that map element 345 in the updated semantic map 340.

[0062] Therefore, in some embodiments, server 110 can obtain target vehicle perception data 330 by filtering vehicle perception data from a set of vehicle perception data where the distance from autonomous vehicle 120 to map element 345 is less than a preset distance threshold. The target vehicle perception data 330 is used to update map element 345 in semantic map 340. The distance threshold can be adaptively adjusted according to actual conditions.

[0063] Finally, server 110 uses the target vehicle perception data 330 to update at least one map element 345 in semantic map 340.

[0064] Considering the quality requirements for map elements 345 in semantic map 340 and other requirements for map elements 345, the number of map elements 345 updated when updating semantic map 340 may be less than the number of map elements 345 to be updated.

[0065] In some embodiments, server 110 may generate a mapping result of a target area associated with at least one map element 345 based on target vehicle perception data 330. Then, server 110 updates at least one map element 345 in semantic map 340 based on the mapping result. The target area can be the region to which at least one map element 345 belongs.

[0066] In some embodiments, server 110 may also generate a mapping result of a target area associated with at least one map element 345 based on vehicle perception data. Then, server 110 updates at least one map element 345 in the semantic map 340 based on the mapping result. Here, the vehicle perception data refers to the vehicle perception data corresponding to the map element 345 to be updated. As an example, server 110 may obtain the mapping result using an offline mapping method.

[0067] In some embodiments, while updating at least one map element 345 in the semantic map 340 using the mapping results, the server 110 may also retain map elements 345 in the target area that have not been updated in the mapping results. By retaining map elements 345 in the target area that have not been updated in the mapping results, it can help with the subsequent maintenance of the semantic map 340.

[0068] In some embodiments, while updating at least one map element 345 in the semantic map 340 using the mapping results, the server 110 may also record historical map elements 345 to be updated, which can be used as a whitelist to prevent the autonomous vehicle 120 from repeatedly reporting the report message 320 associated with the map element 345 to be updated.

[0069] In some embodiments, during a second time period after at least one map element 345 has been updated, server 110 may filter report messages 320 reported by autonomous vehicle 120 by combining historical map elements 345 to be updated. For example, server 110 may distribute historical map elements 345 to be updated to autonomous vehicle 120 so that autonomous vehicle 120 does not send report messages 320 associated with at least one map element 345. Alternatively, server 110 may ignore report messages 320 associated with at least one map element 345. The second time period can be adaptively adjusted according to actual circumstances.

[0070] In some embodiments, the updated semantic map 340, the mapping results that retain the unupdated map elements 345 in the target area, and the historical map elements 345 to be updated can be stored in the server 110 or in a cloud server.

[0071] Based on the process described above, embodiments of this disclosure can automatically maintain or update the semantic map 340, enabling the autonomous vehicle 120 to use the high-quality semantic map 340 to achieve more accurate positioning and make more intelligent decisions.

[0072] Example devices and equipment

[0073] Figure 4 A schematic structural block diagram of an apparatus 400 for a travel service according to certain embodiments of the present disclosure is shown. The apparatus 400 may be implemented as or included in a server 110. The various modules / components in the apparatus 400 may be implemented by hardware, software, firmware, or any combination thereof.

[0074] As shown in the figure, the device 400 includes an acquisition module 410 configured to acquire a set of report messages uploaded from at least one autonomous vehicle, wherein the report messages indicate that the autonomous vehicle determines during the localization process that the matching degree of map elements in the semantic map is lower than a preset degree; a determination module 420 configured to determine at least one map element to be updated based on the set of report messages; and an update module 430 configured to update at least one map element in the semantic map using vehicle perception data associated with at least one map element.

[0075] In some embodiments, determining at least one map element to be updated based on a set of report messages includes: determining a set of map elements corresponding to a set of report messages; and determining at least one map element to be updated based on the number of report messages corresponding to each map element.

[0076] In some embodiments, the method further includes: generating a set of record items corresponding to a set of map elements based on a set of report messages; and presenting the set of record items via a graphical interface.

[0077] In some embodiments, the record item indicates at least one of the following: identification information of the map element; type of the map element; location of the map element; number of messages corresponding to the map element.

[0078] In some embodiments, generating a set of record items corresponding to a set of map elements based on a set of report messages includes: determining the latest report message associated with the target map element; and generating a target record item corresponding to the target map element based on vehicle perception data associated with the latest report message.

[0079] In some embodiments, the target record item indicates: a timestamp corresponding to the latest reported message; an index corresponding to the vehicle perception data; and the distance from the target map element to the autonomous vehicle.

[0080] In some embodiments, the degree of matching is determined based on the following process: determining a first number of feature points in a first set that match a map element, wherein the deviation distance of the first set of feature points is less than a first threshold, the deviation distance being the distance between the location of the feature point in the semantic map and its location in the vehicle perception data; determining a second number of feature points in a second set that are associated with a map element, wherein the deviation distance of the second set of feature points is less than a second threshold, the second threshold being greater than the first threshold; and determining the degree of matching based on the first number and the second number.

[0081] In some embodiments, the report message is generated in response to the autonomous vehicle determining that the matching degree is lower than a preset degree and the difference between the second number and the first data is greater than a preset number.

[0082] In some embodiments, updating at least one map element in a semantic map using vehicle perception data associated with at least one map element includes: acquiring a set of vehicle perception data associated with at least one map element; determining target vehicle perception data from the set of vehicle perception data based on the distance from the autonomous vehicle corresponding to the set of vehicle perception data to at least one map element; and updating at least one map element in the semantic map using the target vehicle perception data.

[0083] In some embodiments, updating at least one map element in a semantic map using vehicle perception data associated with at least one map element includes: generating a mapping result of a target area associated with at least one map element based on the vehicle perception data, wherein the mapping result retains map elements in the target area that have not been updated.

[0084] In some embodiments, a set of reporting messages is associated with a preset first time period.

[0085] In some embodiments, during a second time period after at least one map element is updated: the autonomous vehicle does not send a report message associated with at least one map element; or the report message associated with at least one map element is ignored.

[0086] Figure 5 A block diagram is shown illustrating an electronic device 500 in which one or more embodiments of the present disclosure may be implemented. It should be understood that... Figure 5 The electronic device 500 shown is merely exemplary and should not be construed as limiting the functionality and scope of the embodiments described herein. Figure 5 The electronic device 500 shown can be used to achieve Figure 1 Server 110.

[0087] like Figure 5As shown, electronic device 500 is in the form of a general-purpose electronic device. Components of electronic device 500 may include, but are not limited to, one or more processors or processing units 510, memory 520, storage device 530, one or more communication units 540, one or more input devices 550, and one or more output devices 560. Processing unit 510 may be a physical or virtual processor and is capable of performing various processes according to programs stored in memory 520. In a multiprocessor system, multiple processing units execute computer-executable instructions in parallel to improve the parallel processing capability of electronic device 500.

[0088] Electronic device 500 typically includes multiple computer storage media. Such media can be any accessible media that is accessible to electronic device 500, including but not limited to volatile and non-volatile media, removable and non-removable media. Memory 520 can be volatile memory (e.g., registers, cache, random access memory (RAM)), non-volatile memory (e.g., read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory), or some combination thereof. Storage device 530 can be a removable or non-removable medium and can include machine-readable media, such as flash drives, disks, or any other media that can be used to store information and / or data (e.g., training data for training) and can be accessed within electronic device 500.

[0089] Electronic device 500 may further include additional removable / non-removable, volatile / non-volatile storage media. Although not explicitly stated... Figure 5 As shown, disk drives for reading from or writing to removable, non-volatile disks (e.g., "floppy disks") and optical disk drives for reading from or writing to removable, non-volatile optical disks can be provided. In these cases, each drive can be connected to a bus (not shown) via one or more data media interfaces. Memory 520 may include computer program product 525 having one or more program modules configured to perform various methods or actions of various embodiments of this disclosure.

[0090] Communication unit 540 enables communication with other electronic devices via a communication medium. Additionally, the functionality of components of electronic device 500 can be implemented using a single computing cluster or multiple computing machines capable of communicating via communication connections. Therefore, electronic device 500 can operate in a networked environment using logical connections to one or more other servers, network personal computers (PCs), or another network node.

[0091] Input device 550 can be one or more input devices, such as a mouse, keyboard, trackball, etc. Output device 560 can be one or more output devices, such as a monitor, speaker, printer, etc. Electronic device 500 can also communicate with one or more external devices (not shown) via communication unit 540 as needed. These external devices include storage devices, display devices, etc., and can communicate with one or more devices that enable user interaction with electronic device 500, or with any device that enables electronic device 500 to communicate with one or more other electronic devices (e.g., network card, modem, etc.). Such communication can be performed via input / output (I / O) interface (not shown).

[0092] According to an exemplary implementation of this disclosure, a computer-readable storage medium is provided that stores computer-executable instructions thereon, wherein the computer-executable instructions are executed by a processor to implement the methods described above. According to an exemplary implementation of this disclosure, a computer program product is also provided, which is tangibly stored on a non-transitory computer-readable medium and includes computer-executable instructions, which are executed by a processor to implement the methods described above.

[0093] Various aspects of this disclosure are described herein with reference to flowchart illustrations and / or block diagrams of methods, apparatuses, devices, and computer program products implemented according to this disclosure. It should be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer-readable program instructions.

[0094] These computer-readable program instructions can be provided to a processing unit of a general-purpose computer, a special-purpose computer, or other programmable data processing apparatus to produce a machine such that, when executed by the processing unit of the computer or other programmable data processing apparatus, they create means for implementing the functions / actions specified in one or more blocks of the flowchart and / or block diagram. These computer-readable program instructions can also be stored in a computer-readable storage medium that causes a computer, programmable data processing apparatus, and / or other device to operate in a particular manner. Thus, the computer-readable medium storing the instructions comprises an article of manufacture that includes instructions for implementing aspects of the functions / actions specified in one or more blocks of the flowchart and / or block diagram.

[0095] Computer-readable program instructions can be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational steps to be performed on the computer, other programmable data processing apparatus, or other device to produce a computer-implemented process, thereby causing the instructions that execute on the computer, other programmable data processing apparatus, or other device to perform the functions / actions specified in one or more boxes of a flowchart and / or block diagram.

[0096] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of an instruction, which contains one or more executable instructions for implementing the specified logical function. In some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, may be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0097] Various implementations of this disclosure have been described above. These descriptions are exemplary and not exhaustive, nor are they limited to the disclosed implementations. Many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the described implementations. The terminology used herein is chosen to best explain the principles, practical applications, or improvements to technology in the market, or to enable others skilled in the art to understand the various implementations disclosed herein.

Claims

1. A method for updating a semantic map, comprising: Obtain a set of report messages uploaded from at least one autonomous vehicle, wherein the report messages indicate that the autonomous vehicle determined during the localization process that the degree of matching of map elements in the semantic map was lower than a preset degree; Based on the set of report messages, at least one map element to be updated is determined; as well as The at least one map element in the semantic map is updated using vehicle perception data associated with the at least one map element.

2. The method of claim 1, wherein determining at least one map element to be updated based on the set of report messages comprises: Determine a set of map elements corresponding to the set of report messages; as well as Based on the number of reports corresponding to each map element, the at least one map element to be updated is determined.

3. The method according to claim 2, further comprising: Based on the set of report messages, generate a set of record items corresponding to the set of map elements; as well as The set of records is presented via a graphical interface.

4. The method of claim 3, wherein the record item indicates at least one of the following: The identification information of the map elements; The type of the map element; The location of the map element; The number of messages corresponding to the map element.

5. The method according to claim 3, wherein generating a set of record items corresponding to the set of map elements based on the set of report messages includes: Determine the latest report message associated with the target map element; as well as Based on the vehicle perception data associated with the latest report message, a target record item corresponding to the target map element is generated.

6. The method of claim 5, wherein the target record item indicates: The timestamp corresponding to the latest report message; The index corresponding to the vehicle perception data; The distance from the target map element to the autonomous vehicle.

7. The method of claim 1, wherein the degree of matching is determined based on the following process: A first number of feature points matching the map element is determined, wherein the deviation distance of the first set of feature points is less than a first threshold, and the deviation distance is the distance between the position of the feature point in the semantic map and its position in the vehicle perception data; Determine a second number of feature points in a second set associated with the map element, wherein the distance from the second set of feature points to the deviation is less than a second threshold, and the second threshold is greater than a first threshold; and The degree of matching is determined based on the first number and the second number.

8. The method of claim 7, wherein the report message is generated in response to the autonomous vehicle determining that the matching degree is lower than the preset degree and the difference between the second number and the first data is greater than the preset number.

9. The method of claim 1, wherein updating the at least one map element in the semantic map using vehicle perception data associated with the at least one map element comprises: Acquire a set of vehicle perception data associated with the at least one map element; Based on the distance from the autonomous vehicle to the at least one map element corresponding to the set of vehicle perception data, the target vehicle perception data is determined from the set of vehicle perception data; and The target vehicle perception data is used to update at least one map element in the semantic map.

10. The method of claim 1, wherein updating the at least one map element in the semantic map using vehicle perception data associated with the at least one map element comprises: Based on the vehicle perception data, a mapping result of the target area associated with the at least one map element is generated, and the mapping result retains the map elements in the target area that have not been updated.

11. The method of claim 1, wherein the set of reporting messages is associated with a preset first time period.

12. The method according to claim 11, wherein, During the second time period after at least one map element is updated: The autonomous vehicle does not send a report message associated with the at least one map element; or The report message associated with the at least one map element is ignored.

13. An apparatus for updating a semantic map, comprising: The acquisition module is configured to acquire a set of report messages uploaded from at least one autonomous vehicle, wherein the report messages indicate that the autonomous vehicle determined during the localization process that the degree of matching of map elements in the semantic map was lower than a preset degree; The determination module is configured to determine at least one map element to be updated based on the set of report messages; as well as An update module is configured to update the at least one map element in the semantic map using vehicle perception data associated with the at least one map element.

14. An electronic device comprising: At least one processing unit; as well as At least one memory, coupled to the at least one processing unit and storing instructions for execution by the at least one processing unit, the instructions causing the electronic device to perform the method according to any one of claims 1 to 12 when executed by the at least one processing unit.

15. A computer-readable storage medium having a computer program stored thereon, the computer program being executable by a processor to implement the method according to any one of claims 1 to 12.

16. A computer program product comprising computer-executable instructions, wherein the computer-executable instructions, when executed by a processor, implement the method according to any one of claims 1 to 12.

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