Map updating method, device, system, and corresponding equipment and medium

Abstract

The present disclosure provides a map updating method, device and system, and corresponding equipment and medium, relates to the technical field of automatic driving and intelligent cockpit, in particular to the technical field of high-precision map. The specific technical scheme comprises the following steps: obtaining first local map data from a server and sensor collected sensing data; updating the first local map data according to the sensing data to obtain second local map data after updating; determining the incremental data of the second local map data relative to the first local map data, and sending the incremental data to the server. The technical scheme of the present disclosure can improve the data security in the map updating process.

Description

Technical Field

[0001] This disclosure relates to the fields of autonomous driving and intelligent cockpit technology, and more particularly to the field of high-precision map technology, specifically to a map updating method, apparatus, system, and corresponding equipment and media. Background Technology

[0002] Crowdsourced mapping refers to high-precision maps that achieve real-time updates of static and dynamic road information through crowdsourcing, meeting the timeliness requirements of autonomous driving. Specifically, the map maker's server sends data collection tasks to OEM (Original Equipment Manufacturer) vehicles. The OEM vehicles then transmit the collected data back to the map maker's server. The map maker uses the data collected by the server to build a high-precision map that is updated in real time. This method can easily expose the OEM's private data. Summary of the Invention

[0003] This disclosure provides a map updating method, apparatus, system, and corresponding equipment and media.

[0004] According to a first aspect of this disclosure, a map updating method is provided, comprising:

[0005] Acquire the first partial map data from the server and the sensor data collected by the sensors;

[0006] The first local map data is updated based on the sensor data to obtain the updated second local map data;

[0007] Determine the incremental data of the second local map data relative to the first local map data, and send the incremental data to the server.

[0008] According to a second aspect of this disclosure, a map updating method is provided, comprising:

[0009] Send the first partial map data to the driving equipment;

[0010] The incremental data of the second local map data from the driving device relative to the first local map data is obtained; the second local map data is obtained after the driving device updates the first local map data;

[0011] Update the portion of the stored map data corresponding to the incremental data based on the incremental data.

[0012] According to a third aspect of this disclosure, a map updating apparatus is provided, comprising:

[0013] The first data acquisition module is used to acquire first local map data from the server and sensor data collected by the sensor.

[0014] The first map update module is used to update the first local map data based on the sensor data to obtain the updated second local map data.

[0015] The first data sending module is used to determine the incremental data of the second local map data relative to the first local map data and send the incremental data to the server.

[0016] According to a fourth aspect of this disclosure, a map updating apparatus is provided, comprising:

[0017] The second data sending module is used to send the first local map data to the driving equipment;

[0018] The second data acquisition module is used to acquire incremental data of the second local map data from the driving device relative to the first local map data; the second local map data is obtained after the driving device updates the first local map data;

[0019] The second map update module is used to update the portion of the stored map data corresponding to the incremental data based on the incremental data.

[0020] According to a fifth aspect of this disclosure, a driving 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 map update method for a driving device provided in any embodiment of this disclosure.

[0021] According to a sixth aspect of this disclosure, a server 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 to enable the at least one processor to perform a map update method for a server provided in any embodiment of this disclosure.

[0022] According to a seventh aspect of this disclosure, a map update system is provided, comprising: a driving device provided in any embodiment of this disclosure and a server provided in any embodiment of this disclosure;

[0023] Communication connection between the driving equipment and the server.

[0024] According to an eighth aspect of this disclosure, a non-transitory computer-readable storage medium storing computer instructions is provided, wherein the computer instructions are used to cause a computer to perform a map update method provided in any embodiment of this disclosure.

[0025] According to a ninth aspect of this disclosure, a computer program product is provided, including a computer program that, when executed by a processor, implements the map updating method provided in any embodiment of this disclosure.

[0026] The technical solution disclosed herein can achieve at least the following beneficial effects:

[0027] The driving device can update the first partial map data sent by the server and send the updated incremental data back to the server. Based on the incremental data, the map data already stored on the server can be updated without sending back the OEM's private data. In the process of updating the map in a crowdsourcing manner, the privacy data of the OEM can be effectively protected and the data security can be improved.

[0028] 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

[0029] The accompanying drawings are provided to better understand this solution and do not constitute a limitation of this disclosure. Wherein:

[0030] Figure 1 This is a schematic diagram of the structural framework of a map update system provided in an embodiment of this disclosure;

[0031] Figure 2 This is a schematic flowchart of a map updating method provided in an embodiment of this disclosure;

[0032] Figure 3 This is an example diagram of an optional implementation of the map update method provided in this disclosure;

[0033] Figure 4 This is a schematic diagram of the structural framework of a map updating device provided in an embodiment of this disclosure;

[0034] Figure 5 This is a schematic diagram of the structural framework of another map updating device provided in this embodiment of the present disclosure;

[0035] Figure 6 This is a schematic diagram of the structural framework of an electronic device provided in an embodiment of this disclosure. Detailed Implementation

[0036] 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.

[0037] 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.

[0038] In the description of the embodiments of this disclosure, it should be understood that the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of the embodiments of this disclosure, unless otherwise stated, "a plurality of" means two or more.

[0039] It should be further understood that the term "and / or" as used in the embodiments of this disclosure includes all or any unit and all combination of one or more associated listed items.

[0040] It will be understood by those skilled in the art that, unless otherwise defined, all terms (including technical and scientific terms) used in the embodiments of this disclosure have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure pertains.

[0041] The inventors of this disclosure discovered in their research that in current crowdsourcing methods for building maps (i.e., crowdsourced maps), the data transmitted back to the server by the vehicle (hereinafter referred to as transmitted data) usually involves the OEM's private data, such as the vehicle's location, perception data, chassis data, etc. Among them, the location can be used for big data mining, and the perception data and chassis data can accelerate the iteration of autonomous driving software. They are all core data assets of the OEM and are private data that the OEM does not want to expose to map makers.

[0042] The data transmitted back from vehicles of different OEMs may be the same or different. The following table shows the data transmitted back from vehicles of three different OEMs.

[0043]

[0044] For each OEM, data marked with √ in the table above is included in the vehicle's backhaul data for that OEM, while data marked with × is not included in the vehicle's backhaul data for that OEM. For example, for the vehicle of the first OEM (A in the table), its backhaul data includes pose, chassis, lane lines, traffic signs, semantic lines, construction zones, merging and merging points, etc. For the vehicle of the second OEM (B in the table), its backhaul data includes pose, lane lines, traffic signs, etc. For the vehicle of the third OEM (C in the table), its backhaul data includes pose, images, etc.

[0045] The data in the table above is all OEM privacy data. Sending this data back to the map maker's server will expose the OEM's privacy data, which is not conducive to the privacy protection of each OEM and will reduce the willingness of OEMs to cooperate with map makers.

[0046] The technical solutions of this disclosure and how they solve the above-mentioned technical problems will be described in detail below with specific embodiments.

[0047] According to embodiments of this disclosure, this disclosure provides a map update system, such as... Figure 1 As shown, the system includes a driving device 101 and a server 102 that are connected in communication.

[0048] The driving device 101 can be any kind of device such as an autonomous vehicle, robot, or drone, or it can be any of the above devices that has computing functions.

[0049] The driving device 101 can execute the steps of the map update method applied to the driving device, and the server 102 can execute the steps of the map update method applied to the server.

[0050] According to embodiments of this disclosure, a map updating method is provided, which can be applied to the aforementioned map updating system, such as... Figure 2 As shown, the method includes:

[0051] S201, the server sends the first partial map data to the driving equipment.

[0052] The first local map data can be map data of any local area. The driving device can drive in the local area according to the map data and collect the required data.

[0053] The map corresponding to the first local map data can be any form of map, such as a feature map, topological map, raster map, or vectorized map. Therefore, the first local map data can be any form of map data, such as feature map data, topological map data, raster map data, or vectorized map data.

[0054] S202, the driving equipment acquires a first partial map from the server and sensor data collected by the sensors.

[0055] Sensor data can include the location of the driving equipment, chassis, perception data, and other data that can be used to build maps.

[0056] S203, the driving equipment updates the first partial map data based on the sensor data to obtain the updated second partial map data.

[0057] S204, the driving device determines the incremental data of the second local map data relative to the first local map data and sends the incremental data to the server.

[0058] S205, the server obtains incremental data of the second local map data from the driving device relative to the first local map data.

[0059] The second partial map data is obtained by updating the first partial map data using the driving equipment.

[0060] S206, The server updates the portion of the stored map data corresponding to the incremental data based on the incremental data.

[0061] The map data stored on the server can be either first local map data or global map data that includes the first local map data. The global map data can be the latest version of the crowdsourced map data.

[0062] The map update method provided in this disclosure allows the driving device to update the first partial map data sent by the server and send the updated incremental data back to the server. Based on the incremental data, the map data already stored on the server can be updated without sending back the OEM's privacy data. In the process of updating map data in a crowdsourcing manner, the privacy data of the OEM can be effectively protected and the data security can be improved.

[0063] Optionally, in step S202, the driving device updates the first local map data based on the sensor data, including:

[0064] When there are multiple sensors, the driving device fuses the sensor data collected by the multiple sensors to obtain fused sensor data; and updates the first local map data based on the fused sensor data.

[0065] Fusing sensor data from different sensors can improve the reliability of the sensor data, thereby improving the reliability of the updated first local map data. At the same time, data fusion can compress the original data to a certain extent, making it easier to transmit.

[0066] In one example, the fusion of sensor data from different sensors can be achieved in the following way:

[0067] Based on the sensing data from different sensors, the variance of the error model of each sensor is determined. The sensing data of different sensors is then filtered based on this variance. For example, the sensing data of sensors with large variances are filtered out, and the filtered sensing data is used as the fused sensing data.

[0068] By estimating the error models of different sensors, the reliability of the sensor data can be judged. The larger the variance of the sensor's error model, the greater the error and the lower the reliability of the sensor data collected by that sensor. In this way, sensor data with lower reliability can be filtered out, making the final sensor data of each sensor smoother and with less noise.

[0069] In one optional implementation, in step S202, the driving device updates the first local map data based on the sensor data, including:

[0070] When the first local map data is non-raster map data (e.g., feature map data, topology map data, and vectorized map data), the driving device performs rasterization processing on the first local map data to obtain multiple grids; based on the prior probabilities of these multiple grids, it sets the attributes of these multiple grids and the confidence level (or probability) of these attributes; it determines the posterior probability of the grids corresponding to the sensing data based on the sensing data, and updates the confidence level of multiple grids within the specified grid area based on the posterior probability.

[0071] When rasterizing the first local map data, the processing can be based on a set side length, such as rasterizing with a side length of 50cm, so that the side length of each grid cell is 50cm.

[0072] Based on this method, the driving device can convert non-raster map data into raster map data, increasing the information content of the first local map data. By calculating the posterior probability and updating the confidence level, the information of the newly collected sensor data can be added to the first local map data, thereby realizing the update of the first local map data on the driving device side.

[0073] In another optional implementation, in step S202, the driving device updates the first local map data based on the sensor data, including:

[0074] When the first local map data is raster map data (each raster in the raster map data stores an attribute and the confidence level of that attribute), the driving device determines the posterior probability of the raster corresponding to the sensing data based on the sensing data, and updates the confidence levels of multiple raster cells within a specified raster area based on the posterior probability. Here, the confidence level is the confidence level of a certain attribute stored in the raster of the first local map data when the driving device receives the first local map data.

[0075] Based on this method, when the driving device receives grid map data, it can directly update the grid map data without performing gridding processing. By calculating the posterior probability and updating the confidence level, the information of the newly collected sensor data can be added to the first local map data, thereby realizing the update of the first local map data on the driving device side.

[0076] In this embodiment of the disclosure, the attributes of the grid include semantic elements of semantic lines, color and line shape of lane lines, and the confidence level of an attribute is the probability that the attribute presents a certain feature, such as the probability that the color of the lane line is white.

[0077] In this embodiment of the disclosure, the designated grid area can be the area to which the grid corresponding to the sensing data belongs. The area may include the grid corresponding to the sensing data and the grids near the grid. The specific range of the area can be determined according to the confidence propagation algorithm.

[0078] Updating the confidence level of each grid cell within a specified grid area based on the posterior probability can include: updating the confidence level of each grid cell within the specified grid area using a confidence propagation algorithm based on the posterior probability.

[0079] Optionally, in step S204, the driving device determines incremental data of the second local map data relative to the first local map data, including:

[0080] The driving device performs differential calculations on the feature data in the second local map data and the feature data in the first local map data, and uses the calculation results as incremental data of the second local map data relative to the first local map data.

[0081] The differential calculation results can accurately reflect the differences between the second local map data and the first local map data, and can thus be used as incremental data to be sent back to the server.

[0082] In one example, when both the first local map data and the second local map data are raster map data, performing a difference calculation on the feature data in the second local map data and the feature data in the first local map data may include: normalizing the confidence scores of the rasters in the second local map data, performing a difference calculation on the confidence scores of the normalized rasters in the second local map data and the confidence scores of the rasters in the first local map data, and using the calculation result as the incremental data of the second local map data relative to the first local map data.

[0083] By using probability normalization, the confidence levels of multiple grids in the second local map data can be uniformly mapped to the same order of magnitude (the interval [0,1]), thereby improving the accuracy of differential calculation.

[0084] Figure 3 An example diagram illustrating an alternative implementation of the map updating method provided in this disclosure is shown, with reference to... Figure 3 The server distributes local raster map data to multiple vehicles based on the latest version of the crowdsourced map (local raster map data distribution). The vehicles collect sensor data through sensors (data acquisition), fuse the sensor data (data fusion), update the local raster map data based on the fused sensor data (local raster map data update), calculate the incremental data of the updated local raster map data relative to the original local raster map data (map increment calculation), and send it back to the server. The server updates the stored map data based on the returned incremental data (map data update).

[0085] According to embodiments of this disclosure, this disclosure also provides a map updating device that can be applied to driving equipment, such as... Figure 4 As shown, the device includes: a first data acquisition module 401, a first map update module 402, and a first data transmission module 403.

[0086] The first data acquisition module 401 is used to acquire first local map data from the server and sensor data collected by the sensor.

[0087] The first map update module 402 is used to update the first local map data based on the sensor data to obtain the updated second local map data.

[0088] The first data sending module 403 is used to determine the incremental data of the second local map data relative to the first local map data and send the incremental data to the server.

[0089] Optionally, the first map update module 402 is specifically used for: fusing the sensor data collected by multiple sensors when there are multiple sensors to obtain fused sensor data; and updating the first local map data based on the fused sensor data.

[0090] In one optional implementation, the first map update module 402 is specifically configured to: when the first local map data is non-raster map data, perform rasterization processing on the first local map data to obtain multiple graticles; set the attributes of the multiple graticles and the confidence level of the attributes based on the prior probabilities of the multiple graticles; determine the posterior probability of the graticle corresponding to the sensing data based on the sensing data; update the confidence level of multiple graticles within a specified graticle area based on the posterior probability; and specify the graticle area as the area to which the graticle corresponding to the sensing data belongs.

[0091] In another optional implementation, the first map update module 402 is specifically used to: determine the posterior probability of the grid corresponding to the sensing data based on the sensing data when the first local map data is a grid map; update the confidence of multiple grids in a specified grid area based on the posterior probability; and specify the grid area as the area to which the grid corresponding to the sensing data belongs.

[0092] Optionally, the first data sending module 403 is specifically used to: perform differential calculation on the feature data in the second local map data and the feature data in the first local map data, and use the calculation result as the incremental data of the second local map data relative to the first local map data.

[0093] According to embodiments of this disclosure, this disclosure also provides a map updating device that can be applied to a server, such as... Figure 5 As shown, the device includes: a second data sending module 501, a second data acquisition module 502, and a second map updating module 503.

[0094] The second data sending module 501 is used to send the first local map data to the driving equipment.

[0095] The second data acquisition module 502 is used to acquire incremental data of the second local map data from the driving device relative to the first local map data; the second local map data is obtained after the driving device updates the first local map data.

[0096] The second map update module 503 is used to update the portion of the stored map data corresponding to the incremental data based on the incremental data.

[0097] The functions of each module in the map update device provided in this embodiment can be referred to the corresponding descriptions in the above method embodiments, and will not be repeated here.

[0098] According to embodiments of this disclosure, this disclosure also provides a driving device, a server, a non-transitory computer-readable storage medium, and a computer program product.

[0099] The driving device provided in this disclosure includes: 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, which, when executed by the at least one processor, enable the at least one processor to perform the steps of the map update method for the driving device provided in any embodiment of this disclosure.

[0100] The driving device may also include at least one sensor, which can collect sensing data. The at least one sensor may include at least one type of sensor such as an image sensor, lidar, or millimeter-wave radar.

[0101] At least one sensor may be integrated into the same device as the processor and memory, or it may be set independently outside the processor and memory.

[0102] The server provided in this disclosure includes: 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, which, when executed by the at least one processor, enable the at least one processor to perform the steps of the map update method applied to the server provided in any embodiment of this disclosure.

[0103] The non-transitory computer-readable storage medium provided in this disclosure stores computer instructions for causing a computer to execute the map update method provided in any embodiment of this disclosure.

[0104] The computer program product provided in this disclosure includes a computer program that, when executed by a processor, implements the map update method provided in any embodiment of this disclosure.

[0105] Figure 6 A schematic block diagram of an example electronic device 600 (which may serve as a driving device or a server) 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.

[0106] like Figure 6As shown, the electronic device 600 includes a computing unit 601, which can perform various appropriate actions and processes based on a computer program stored in a read-only memory (ROM) 602 or a computer program loaded from a storage unit 608 into a random access memory (RAM) 603. The RAM 603 may also store various programs and data required for the operation of the device 600. The computing unit 601, ROM 602, and RAM 603 are interconnected via a bus 604. An input / output (I / O) interface 605 is also connected to the bus 604.

[0107] Multiple components in electronic 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 displays, 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.

[0108] 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. For example, in some embodiments, the above methods can 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 can 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 methods described above can be performed. Alternatively, in other embodiments, the computing unit 601 can be configured to perform the above methods by any other suitable means (e.g., by means of firmware).

[0109] 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), complex 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.

[0110] 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.

[0111] 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.

[0112] 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).

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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 map updating method applied to a driving device, the method comprising: obtaining first local map data from a server and sensor data collected by a sensor in the driving device, wherein the sensor data relates to privacy data of the driving device, and the privacy data comprises location data, chassis data and perception data of the driving device; updating the first local map data according to the sensor data to obtain second local map data, including: in a case where the sensor is multiple, fusing the sensor data collected by the multiple sensors to obtain fused sensor data, and updating the first local map data according to the fused sensor data; performing differential calculation on feature data in the second local map data and feature data in the first local map data, taking a calculation result as incremental data of the second local map data relative to the first local map data, and sending the incremental data to the server, wherein the incremental data does not include the privacy data of the driving device; wherein the updating the first local map data according to the fused sensor data comprises: in a case where the first local map data is non-grid map data, performing rasterization processing on the first local map data to obtain multiple grids; setting attributes of the multiple grids and confidence degrees of the attributes based on prior probabilities of the multiple grids; determining posterior probabilities of grids corresponding to the sensor data according to the sensor data; and updating confidence degrees of multiple grids in a specified grid region according to the posterior probabilities; wherein the differential calculation on the feature data in the second local map data and the feature data in the first local map data comprises: normalizing confidence degrees of grids in the second local map data, and performing differential calculation on the confidence degrees of the grids in the second local map data and confidence degrees of grids in the first local map data.

2. The map update method according to claim 1, wherein The specified grid region is a region to which the grids corresponding to the sensor data belong.

3. The map update method according to claim 1, wherein The updating the first local map data according to the sensor data comprises: in a case where the first local map data is grid map data, determining posterior probabilities of grids corresponding to the sensor data according to the sensor data; and updating confidence degrees of multiple grids in a specified grid region according to the posterior probabilities; the specified grid region is a region to which the grids corresponding to the sensor data belong. 4.A map updating method, comprising: sending first local map data to a driving device; obtaining incremental data of second local map data relative to the first local map data from the driving device; the second local map data is obtained by updating the first local map data according to the map updating method of any one of claims 1-3; updating a part corresponding to the incremental data in stored map data according to the incremental data. 5.A map updating apparatus applied to a driving device, the apparatus comprising: ​ The first data acquisition module is configured to acquire first local map data from a server and sensor data collected by sensors in the driving device, wherein the sensor data comprises privacy data of the driving device, and the privacy data comprises location data, chassis data and perception data of the driving device. The first map updating module is configured to update the first local map data according to the sensor data to obtain second local map data. The first data sending module is configured to perform differential calculation on feature data in the second local map data and feature data in the first local map data, take the calculation result as incremental data of the second local map data relative to the first local map data, and send the incremental data to the server, wherein the incremental data does not comprise the privacy data of the driving device. In a case where the first local map data is non-grid map data, the first map updating module is configured to perform rasterization processing on the first local map data to obtain a plurality of grids, set attributes and confidence degrees of the plurality of grids based on prior probabilities of the plurality of grids, determine posterior probabilities of grids corresponding to the sensor data according to the sensor data, and update the confidence degrees of a plurality of grids in a specified grid region according to the posterior probabilities. In a case where the first local map data is grid map data, the first map updating module is configured to determine posterior probabilities of grids corresponding to the sensor data according to the sensor data, and update the confidence degrees of a plurality of grids in a specified grid region according to the posterior probabilities.

6. The map update apparatus according to claim 5, wherein The specified grid region is a region to which the grids corresponding to the sensor data belong.

7. The map update apparatus according to claim 5, wherein The first map updating module is specifically configured to: In a case where the first local map data is grid map data, determine posterior probabilities of grids corresponding to the sensor data according to the sensor data, and update the confidence degrees of a plurality of grids in a specified grid region according to the posterior probabilities. The specified grid region is a region to which the grids corresponding to the sensor data belong.

8. A map updating apparatus, comprising: A second data sending module configured to send first local map data to a driving device. A second data acquisition module configured to acquire incremental data of second local map data relative to the first local map data from the driving device, wherein the second local map data is obtained by updating the first local map data according to the map updating method of any one of claims 1-3. A second map updating module configured to update a part corresponding to the incremental data in stored map data according to the incremental data.

9. A traveling apparatus comprising: at least one processor; and a memory in communication with 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 the map updating method of any one of claims 1-3.

10. A server comprising: at least one processor; and a memory in communication with 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 the map updating method of claim 4.

11. A map update system, comprising: the traveling apparatus as claimed in claim 9, and the server as claimed in claim 10; the traveling apparatus and the server are in communication.

12. A non-transitory computer readable storage medium having stored thereon computer instructions, wherein, the computer instructions are for causing the computer to perform the map updating method of any one of claims 1-4.

13. A computer program product comprising a computer program which, when executed by a processor, implements the map updating method of any one of claims 1-4.

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