High-precision map element change detection method, device, equipment and autonomous driving vehicle

Abstract

The present disclosure provides a method, device, electronic device, readable storage medium and computer program product for detecting changes in high-precision map elements, which relate to the fields of autonomous driving and intelligent transportation. The specific implementation scheme is: during the operation of the current driving order of the autonomous driving vehicle, the triggering of manual takeover is detected; when the triggering of the manual takeover is detected, the vehicle driving data of the autonomous driving vehicle within a specified time range before and after the triggering of the manual takeover is generated; when the current driving order operation ends, the changes in the map elements in the high-precision map are detected based on the vehicle driving data. This scheme can detect the changes in the map elements in the high-precision map based on the vehicle driving data when the current driving order operation ends. Thereby ensuring the timeliness of the change detection of the map elements in the high-precision map.

Description

Technical Field

[0001] The present disclosure relates to the field of artificial intelligence, specifically to autonomous driving and intelligent transportation technology, and can be used in scenarios such as autonomous driving and data processing. Background Art

[0002] The driving of autonomous vehicles depends on the support of high-precision maps. Therefore, timely detection of changes in map elements in high-precision maps and changes to map elements in high-precision maps have become an important part of ensuring the timeliness of high-precision maps and the safety of autonomous vehicles.

[0003] However, existing solutions for detecting changes in map elements in high-precision maps are often less timely, which also leads to the poor timeliness of high-precision maps. Summary of the invention

[0004] The present disclosure provides a method, device, electronic device, readable storage medium and computer program product for detecting changes in high-precision map elements to improve the timeliness of change detection on map elements in high-precision maps.

[0005] According to one aspect of the present disclosure, a method for detecting changes in high-precision map elements is provided, and the method may include the following steps:

[0006] During the operation of the current driving order of the autonomous vehicle, the triggering of manual takeover is detected;

[0007] In the case where the triggering of the manual takeover is detected, generating vehicle driving data of the automatic driving vehicle within a specified time range before and after the triggering of the manual takeover;

[0008] When the current driving order operation is completed, the changes of the map elements in the high-precision map are detected based on the vehicle driving data.

[0009] According to a second aspect of the present disclosure, a high-precision map element change detection device is provided, which may include:

[0010] A manual takeover trigger detection module is used to detect the triggering of manual takeover during the current driving order operation of the autonomous driving vehicle;

[0011] A vehicle driving data generating module, configured to generate vehicle driving data of the automatic driving vehicle within a specified time range before and after the triggering of the manual takeover, when the triggering of the manual takeover is detected;

[0012] The map element change detection module is used to detect changes in map elements in the high-precision map based on the vehicle driving data when the current driving order operation is completed.

[0013] According to another aspect of the present disclosure, there is provided an electronic device, comprising:

[0014] at least one processor; and

[0015] a memory communicatively connected to the at least one processor; wherein,

[0016] The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the method in any embodiment of the present disclosure.

[0017] According to another aspect of the present disclosure, a non-transitory computer-readable storage medium storing computer instructions is provided. The computer instructions are used to enable a computer to execute the method in any embodiment of the present disclosure.

[0018] According to another aspect of the present disclosure, a computer program product is provided, including a computer program / instruction, wherein the computer program / instruction implements the method in any embodiment of the present disclosure when executed by a processor.

[0019] According to another aspect of the present disclosure, an autonomous driving vehicle is provided, comprising the electronic device provided in an embodiment of the present disclosure.

[0020] The disclosed technology can detect changes in map elements in a high-precision map based on vehicle driving data when the current driving order operation is completed, thereby ensuring the timeliness of change detection of map elements in the high-precision map.

[0021] In addition, during the operation of the current driving order of the autonomous vehicle, only vehicle driving data is generated, and only when the current driving order operation is completed, the map elements in the high-precision map are changed. This can greatly reduce the high-precision map element change detection and the occupation of vehicle-side resources during the driving process of the autonomous vehicle.

[0022] It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present disclosure, nor is it intended to limit the scope of the present disclosure. Other features of the present disclosure will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] The accompanying drawings are used to better understand the present solution and do not constitute a limitation of the present disclosure.

[0024] Figure 1 A flowchart of a method for detecting changes in high-precision map elements provided in an embodiment of the present disclosure;

[0025] Figure 2A flowchart of a method for generating vehicle driving data provided in an embodiment of the present disclosure;

[0026] Figure 3 A flowchart of a method for detecting changes in map elements provided in an embodiment of the present disclosure;

[0027] Figure 4 A flow chart of a method for detecting changes in map elements provided in an embodiment of the present disclosure;

[0028] Figure 5 A schematic diagram of a map element change provided in an embodiment of the present disclosure;

[0029] Figure 6 A schematic diagram of a high-precision map element change detection device provided in an embodiment of the present disclosure;

[0030] Figure 7 A schematic diagram of an electronic device provided according to an embodiment of the present disclosure. DETAILED DESCRIPTION

[0031] The following is a description of exemplary embodiments of the present disclosure in conjunction with the accompanying drawings, including various details of the embodiments of the present disclosure to facilitate understanding, which should be considered as merely exemplary. Therefore, it should be recognized by those of ordinary skill in the art that various changes and modifications may be made to the embodiments described herein without departing from the scope and spirit of the present disclosure. Similarly, for the sake of clarity and conciseness, descriptions of well-known functions and structures are omitted in the following description.

[0032] This disclosure provides a method for detecting changes in high-precision map elements. Figure 1 , which is a flow chart of a method for detecting changes in high-precision map elements provided by an embodiment of the present disclosure. The method may include the following steps:

[0033] Step S101: During the operation of the current driving order of the autonomous driving vehicle, the triggering situation of manual takeover is detected.

[0034] Step S102: When a trigger for manual takeover is detected, vehicle driving data of the automatic driving vehicle within a specified time range before and after the trigger for manual takeover is generated.

[0035] Step S103: When the current driving order operation is completed, the changes of map elements in the high-precision map are detected based on the vehicle driving data.

[0036] The high-precision map element change detection method provided in the embodiments of the present disclosure is mainly executed by the client, and the so-called client can be a target program, application or software installed on the autonomous driving vehicle.

[0037] The so-called target program, application or software may be a program, application or software that has the function of detecting changes in high-precision map elements.

[0038] The high-precision map element change detection method provided in the embodiment of the present disclosure can detect the change of map elements in the high-precision map based on the vehicle driving data when the current driving order operation ends, thereby ensuring the timeliness of the change detection of map elements in the high-precision map.

[0039] In addition, during the operation of the current driving order of the autonomous vehicle, only vehicle driving data is generated, and only when the current driving order operation is completed, the map elements in the high-precision map are changed. This can greatly reduce the high-precision map element change detection and the occupation of vehicle-side resources during the driving process of the autonomous vehicle.

[0040] The so-called high-precision map generally refers to an electronic map with higher accuracy and richer and more detailed road traffic information elements contained in the electronic map.

[0041] Among them, higher accuracy means that the accuracy of electronic maps is generally at least accurate to the centimeter level.

[0042] The road traffic information contained in the electronic map includes: lane information such as the location, type, width, slope and curvature of the lane line; information such as traffic signs and traffic lights; lane height limits, sewer openings, obstacles and other road details, as well as infrastructure information such as elevated objects, guardrails, number, road edge type, roadside landmarks, etc.

[0043] The road traffic information elements contained in electronic maps are richer and more detailed. Generally speaking, electronic maps contain not only accurate road shapes, but also data on the slope, curvature, heading, elevation, and roll of each lane. Electronic maps not only depict roads, but also depict how many lanes there are on a road and the actual style of the road.

[0044] For an autonomous vehicle, after determining the starting point and the end point, the autonomous vehicle will automatically plan and generate the driving route and process from the starting point to the end point. At this point, the autonomous vehicle is considered to have obtained a driving order.

[0045] The operation process of a driving order refers to the process in which the autonomous vehicle executes the driving process from the starting point to the end point according to the planned driving route and process. The start of a driving order operation refers to the start of the autonomous vehicle driving to the end point according to the planned driving route and process. The end of a driving order operation refers to the arrival of the autonomous vehicle at the end point, or the end of the driving order operation before the autonomous vehicle reaches the end point.

[0046] The map elements in the embodiments of the present disclosure often refer to traffic indicators in road traffic information, and the traffic indicators include but are not limited to traffic signs, traffic lights, lane lines, etc.

[0047] Common traffic signs include speed limit signs, height limit signs, and no turn signs, and traffic lights are generally red and green lights.

[0048] In addition, the map elements of the present disclosure may also be other road traffic information besides traffic indicators, such as guardrails, obstacles, etc.

[0049] The following specifically takes the case where the map element is a traffic indicator as an example to describe in detail the high-precision map element change detection method provided by the embodiment of the present disclosure. When the map element is other road traffic information other than a traffic indicator, the principle of high-precision map element change detection is the same. For details, please refer to the implementation process of the high-precision map element change detection method when the map element is a traffic indicator, which will not be repeated here.

[0050] The so-called map element change refers to that for a specified location and the map location that matches the specified location in the high-precision map, the map element corresponding to the map location in the high-precision map has changed compared to the actual map element corresponding to the specified location.

[0051] Specifically, for a certain intersection, the high-precision map shows that there is no red light at the intersection, but the road driving images collected by the autonomous vehicle reflect that there is actually a red light at the intersection. At this time, the map element of the high-precision map at the intersection is considered to have changed.

[0052] In addition, for a certain road, the high-precision map shows that the road is shown as a white dotted line at the specified position, but the road driving images collected by the autonomous driving vehicle show that the white dotted line at the specified position is actually a white solid line. At this time, the map element of the high-precision map at the specified position is considered to have changed.

[0053] For autonomous driving, if manual takeover occurs during this process, it is considered a failure of the driving order operation. Changes in map elements, vehicle system failures, and sudden road traffic conditions may trigger manual takeover. Therefore, changes in map elements will also lead to the failure of autonomous driving orders. If the map elements in the high-precision map are not promptly detected for changes and the map elements are not changed, then when the autonomous driving vehicle drives to the place where the map elements are changed, a manual takeover will occur, which will greatly increase the failure rate of driving orders.

[0054] It should be noted that the failure of a driving order does not mean the end of the driving order operation. For a driving order, multiple manual takeovers may occur during the driving order operation, but there is often no necessary management between the end of the driving order operation and the manual takeover.

[0055] Timely detection of map element changes can ensure the timeliness of map element change detection in high-precision maps, thereby promoting the speed of high-precision map changes to ensure the timeliness of high-precision maps. In addition, ensuring the timeliness of high-precision maps can also significantly reduce the failure rate of driving orders.

[0056] In the embodiment of the present disclosure, the steps of generating vehicle driving data are as follows: Figure 2 As shown, Figure 2 The present invention is a flowchart of a method for generating vehicle driving data provided in an embodiment of the present invention.

[0057] Step S201: When the current driving order operation starts, detecting the autonomous driving vehicle triggers manual takeover.

[0058] Step S202: When a trigger for manual takeover is detected, vehicle driving data is generated.

[0059] During the operation of a driving order, multiple changes in map elements may occur, and multiple manual takeovers may occur. Therefore, in order to ensure the accuracy of element change detection, real-time detection of the triggering of manual takeover by the autonomous driving vehicle begins when the current driving order operation begins. As long as the triggering of manual takeover is detected, the vehicle driving data of the autonomous driving vehicle within the specified time range before and after the triggering of manual takeover is generated. In this way, when the changes in map elements in the high-precision map are detected based on the vehicle driving data, it can be determined whether the manual takeover is triggered by the change of map elements, thereby ensuring that every change of map elements will be detected.

[0060] The so-called vehicle driving data refers to the data continuously collected by the driving recorder and different sensors during the driving process of the autonomous vehicle, which can be stored for subsequent extraction. The vehicle driving data includes but is not limited to vehicle trajectory data, vehicle speed data, road image collected by the image acquisition device on the vehicle, and radar scanning data collected by the vehicle-mounted radar on the vehicle.

[0061] In order to reduce the amount of data that needs to be stored while ensuring that the generated vehicle driving data can support the requirements of map element change detection, in the embodiments of the present invention, the generation of vehicle driving data is implemented as follows: first, determine the time when manual takeover is triggered each time; then, based on the time when manual takeover is triggered each time and the specified time range, obtain the vehicle driving data.

[0062] Specifically, when the triggering of manual takeover is detected, the vehicle control platform on the vehicle records the time of each manual takeover, and automatically intercepts and stores the vehicle driving data of the autonomous driving vehicle within the specified time range before and after the triggering of manual takeover through the data storage module. After obtaining the vehicle driving data, a prompt message will be generated to prompt that the manual takeover is triggered and to obtain the vehicle driving data of the autonomous driving vehicle within the specified time range, and then the prompt message will be displayed through the human-computer interaction interface of the vehicle control platform to prompt the test driver.

[0063] The so-called data write-to-disk module is a module used to write vehicle driving data and the like to a preset disk.

[0064] The vehicle driving data automatically captured and stored by the data storage module is in the form of record packages. The so-called specified time range generally refers to the vehicle driving data within 10 seconds (seconds) before and after the triggering of manual takeover. Of course, the time is not limited to 10 seconds, and can also be set to 15 seconds, 20 seconds, etc. according to the prior value and the average driving speed of the autonomous driving vehicle.

[0065] In the embodiment of the present disclosure, the step of detecting the change detection of the map elements in the high-precision map is as follows: Figure 3 As shown, it is a flowchart of a method for detecting changes in map elements provided in an embodiment of the present disclosure.

[0066] Step S301: When the operation of the current travel order is completed, check whether the next travel order has started to operate.

[0067] Step S302: When the next travel order has not started operation, the changes of map elements in the high-precision map are detected based on the vehicle travel data.

[0068] For example, if the detection result of step S301 is no, the changes of map elements in the high-precision map are detected based on the vehicle driving data. That is, when the next driving order has not started operation, the changes of map elements in the high-precision map are detected based on the vehicle driving data.

[0069] If the detection result of step S301 is yes, then the detection of changes in map elements in the high-precision map is stopped. That is, when the next travel order starts operating, the detection of changes in map elements in the high-precision map is stopped.

[0070] In the embodiment of the present disclosure, changes in map elements in the high-precision map are detected based on vehicle driving data only when the current driving order operation is completed and the next driving order has not yet started, thereby ensuring the detection of map element changes and not occupying vehicle-side resources during the driving of the autonomous driving vehicle.

[0071] Detecting whether the next travel order has started operation is synchronized with detecting changes in map elements in the high-precision map. Once it is found that the next travel order has started operation, the detection of changes in map elements in the high-precision map will be stopped.

[0072] The steps for detecting changes in map elements in high-precision maps are as follows: Figure 4 As shown, it is a flow chart of a method for detecting changes in map elements provided in an embodiment of the present disclosure.

[0073] Step S401: Determine the position information of the autonomous driving vehicle on the driving road based on the vehicle driving data.

[0074] Step S402: Determine map location information in the high-precision map that matches location information on the driving road.

[0075] Step S403: Obtain the first map element corresponding to the map location information in the high-precision map.

[0076] Step S404: Determine the second map element corresponding to the position information on the driving road according to the vehicle driving data.

[0077] Step S405: Compare the first map element with the second map element to detect changes in the map element in the high-precision map.

[0078] If the detection result of step S405 is that a change has occurred, the vehicle driving data matching the map element change is sent to the server for executing the map element change of the high-precision map. That is, when the change of the map element in the high-precision map is that a map element change has occurred, the vehicle driving data matching the map element change is sent to the server for executing the map element change of the high-precision map.

[0079] If the detection result of step S405 is that no change has occurred, then the change detection of the next map element is performed.

[0080] First, the position information of the autonomous driving vehicle on the driving road is determined, and then the map position information that matches the position information on the driving road is obtained in the high-precision map, and then the first map element corresponding to the map position information in the high-precision map and the second map element corresponding to the position information on the driving road are obtained for element comparison, which can ensure the accuracy of the comparison and thus improve the accuracy of detecting changes in map elements.

[0081] In addition, when the vehicle driving data that matches the map element change is sent to the server for executing the map element change of the high-precision map, the server can change the high-precision map according to the vehicle driving data that matches the map element change. Compared with the prior art of uploading vehicle driving data to the server every day to detect the map element change, the map element change is performed after the driving order operation is completed, and the vehicle driving data that matches the map element change is sent to the server, which can increase the speed of the high-precision map element change, thereby ensuring the timeliness of the high-precision map.

[0082] The first map element is the map element recorded in the high-precision map, and the second map element is the real-time geographical element on the driving road.

[0083] In the embodiments of the present disclosure, the specific implementation method of obtaining the first map element and the second geographical element, and comparing the first map element with the second map element to detect the change of the map element in the high-precision map may be:

[0084] First, based on the road image of the vehicle driving data and the radar scanning data, the map elements of the specified area range of the position of the autonomous driving vehicle on the driving road are three-dimensionally constructed to obtain three-dimensional spatial data as the second map element data.

[0085] Then, data conversion is performed on the map elements in the designated area range of the map position to obtain three-dimensional space map element data as first map element data.

[0086] Finally, the first map element data is compared with the second map element data to detect changes in the map elements in the high-precision map.

[0087] In the embodiments of the present disclosure, the specific implementation method of obtaining the first map element data and the second geographical element, and comparing the first map element data with the second map element data to detect the change of the map element in the high-precision map may also be:

[0088] First, a traffic indicator image is segmented from a road image based on vehicle travel data as second map element data.

[0089] Among them, the image segmentation method includes but is not limited to at least one of the following: a threshold-based segmentation method, a watershed algorithm, a segmentation method based on edge detection, an image segmentation method based on wavelet analysis and wavelet transform, a segmentation method based on active contour models, and a segmentation model based on deep learning.

[0090] Then, the posture data of the image acquisition device corresponding to the road image is obtained.

[0091] The posture of the image acquisition device may be the posture of a vehicle-mounted camera.

[0092] Finally, according to the posture data of the image acquisition device, the map data corresponding to the map location data in the high-precision map is projected onto the plane where the road image is located, and the projection image is generated based on the comparison between the traffic indicator image and the projection image.

[0093] The map data includes three-dimensional data of traffic indicators whose positions and orientations meet preset requirements, and the three-dimensional data is the first map element data.

[0094] like Figure 5 As shown, Figure 5 A schematic diagram of a map element change provided in an embodiment of the present disclosure. Specifically, if a high-precision map shows that there is only a zebra crossing on a road, and there are no red lights at both ends of the zebra crossing, but in the road image corresponding to the road, there is not only a zebra crossing at that location, but also red lights at both ends of the zebra crossing, then the change of the map element in the high-precision map is a map element change.

[0095] The high-precision map element change detection method provided by the embodiment of the present disclosure can detect the change of map elements in the high-precision map based on the vehicle driving data when the current driving order operation ends, thereby ensuring the timeliness of the change detection of map elements in the high-precision map.

[0096] like Figure 6 As shown, a high-precision map element change detection device provided in an embodiment of the present disclosure includes:

[0097] A manual takeover trigger detection module 601 is used to detect the triggering situation of manual takeover during the current driving order operation process of the autonomous driving vehicle;

[0098] The vehicle driving data generating module 602 is used to generate the vehicle driving data of the automatic driving vehicle within a specified time range before and after the triggering of the manual takeover when the triggering of the manual takeover is detected;

[0099] The map element change detection module 603 is used to detect changes in map elements in the high-precision map based on vehicle driving data when the current driving order operation is completed.

[0100] In one implementation, the vehicle driving data generating module 602 may further include:

[0101] A manual takeover time determination submodule is used to determine the time when manual takeover is triggered each time;

[0102] The vehicle driving data acquisition submodule is used to acquire vehicle driving data based on the time when each manual takeover is triggered and the specified time range.

[0103] In one implementation, the map element change detection module 603 may further include:

[0104] The next travel order detection submodule is used to detect whether the next travel order has started to operate when the current travel order operation ends;

[0105] The map element change detection submodule is used to detect changes in map elements in the high-precision map based on vehicle driving data when the next driving order has not yet started operation.

[0106] In one embodiment, the map element change detection submodule may further include:

[0107] The location information determination submodule is used to determine the location information of the autonomous driving vehicle on the high-precision map based on the vehicle driving data;

[0108] The map element acquisition submodule is used to obtain the map elements corresponding to the location information of the high-precision map;

[0109] A map element determination submodule, used to determine the map elements of the autonomous driving vehicle on the driving road based on the vehicle driving data;

[0110] The map element comparison submodule is used to compare the map elements of the autonomous driving vehicle on the road with the map elements corresponding to the location information of the high-precision map to detect changes in the map elements in the high-precision map.

[0111] In one embodiment, the device further comprises:

[0112] The vehicle driving data sending module is used to send the vehicle driving data matching the map element change to the server for executing the map element change of the high-precision map when the map element change in the high-precision map occurs.

[0113] In the technical solution disclosed herein, the acquisition, storage and application of user personal information involved are in compliance with the provisions of relevant laws and regulations and do not violate public order and good morals.

[0114] According to an embodiment of the present disclosure, the present disclosure also provides an electronic device and a readable storage medium.

[0115] In addition, the present disclosure also provides an autonomous driving vehicle including the electronic device provided by the present disclosure.

[0116] Figure 7A schematic block diagram of an example electronic device 700 that can be used to implement an embodiment 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 can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present disclosure described and / or required herein.

[0117] like Figure 7 As shown, the device 700 includes a computing unit 701, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 702 or a computer program loaded from a storage unit 708 into a random access memory (RAM) 703. In the RAM 703, various programs and data required for the operation of the device 700 can also be stored. The computing unit 701, the ROM 702, and the RAM 703 are connected to each other via a bus 704. An input / output (I / O) interface 705 is also connected to the bus 704.

[0118] A number of components in the device 700 are connected to the I / O interface 705, including: an input unit 706, such as a keyboard, a mouse, etc.; an output unit 707, such as various types of displays, speakers, etc.; a storage unit 708, such as a disk, an optical disk, etc.; and a communication unit 709, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 709 allows the device 700 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0119] The computing unit 701 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the computing unit 701 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, digital signal processors (DSPs), and any appropriate processors, controllers, microcontrollers, etc. The computing unit 701 performs the various methods and processes described above, such as a high-precision map element change detection method. For example, in some embodiments, the high-precision map element change detection method may be implemented as a computer software program, which is tangibly contained in a machine-readable medium, such as a storage unit 708. In some embodiments, part or all of the computer program may be loaded and / or installed on the device 700 via the ROM 702 and / or the communication unit 709. When the computer program is loaded into the RAM 703 and executed by the computing unit 701, one or more steps of the high-precision map element change detection method described above may be performed. Alternatively, in other embodiments, the computing unit 701 may be configured to execute the high-precision map element change detection method in any other appropriate manner (for example, by means of firmware).

[0120] Various implementations 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 chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0121] The program code for implementing the method of the present disclosure may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that the program code, when executed by the processor or controller, enables the functions / operations specified in the flow chart and / or block diagram to be implemented. The program code may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0122] In the context of the present disclosure, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or equipment. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or equipment, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0123] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the computer. Other types of devices can also be used to provide interaction with the user; for example, the 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 acoustic input, voice input, or tactile input).

[0124] The systems and techniques described herein may be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), and the Internet.

[0125] A computer system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The relationship of client and server is generated by computer programs running on respective computers and having a client-server relationship with each other. The server may be a cloud server, a server of a distributed system, or a server combined with a blockchain.

[0126] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps recorded in this disclosure can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solutions disclosed in this disclosure can be achieved, and this document does not limit this.

[0127] The above specific implementations do not constitute a limitation on the protection scope of the present disclosure. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present disclosure shall be included in the protection scope of the present disclosure.

Claims

1. A method for detecting changes in high-precision map elements, comprising: During the operation of the current driving order of the autonomous vehicle, the triggering of manual takeover is detected; In the case where the triggering of the manual takeover is detected, generating vehicle driving data of the automatic driving vehicle within a specified time range before and after the triggering of the manual takeover; When the operation of the current driving order ends, detecting changes in map elements in the high-precision map based on the vehicle driving data, and determining whether the manual takeover is triggered by changes in the map elements; The generating of the vehicle driving data of the automatic driving vehicle within a specified time range before and after triggering the manual takeover includes: Determine the time when the manual takeover is triggered each time; The vehicle driving data is acquired based on the time when the manual takeover is triggered each time and the specified time range.

2. The method according to claim 1, wherein: When the operation of the current driving order ends, detecting changes in map elements in the high-precision map according to the vehicle driving data includes: When the operation of the current travel order ends, detecting whether the next travel order starts to operate; When the next travel order has not started operation, the changes of the map elements in the high-precision map are detected based on the vehicle driving data.

3. The method according to claim 2, wherein: The detecting, based on the vehicle driving data, changes in map elements in the high-precision map includes: Determining the position information of the autonomous driving vehicle on the driving road according to the vehicle driving data; Determining map position information in the high-precision map that matches the position information on the driving road; Obtaining a first map element corresponding to the map location information in the high-precision map; Determining, based on the vehicle driving data, a second map element corresponding to the position information on the driving road; The first map element is compared with the second map element to detect changes in the map elements in the high-precision map.

4. The method according to claim 3, further comprising: When the change of the map element in the high-precision map is a case where a map element change occurs, the vehicle driving data matching the map element change is sent to a server for executing the map element change of the high-precision map.

5. A high-precision map element change detection device, comprising: A manual takeover trigger detection module is used to detect the triggering of manual takeover during the current driving order operation of the autonomous driving vehicle; A vehicle driving data generating module, configured to generate vehicle driving data of the automatic driving vehicle within a specified time range before and after the triggering of the manual takeover, when the triggering of the manual takeover is detected; A map element change detection module, used to detect changes in map elements in a high-precision map based on the vehicle driving data when the current driving order operation ends, and determine whether the manual takeover is triggered by a change in the map element; The vehicle driving data generating module comprises: A manual takeover time determination submodule, used to determine the time of triggering the manual takeover each time; The vehicle driving data acquisition submodule is used to acquire the vehicle driving data based on the time when the manual takeover is triggered each time and the specified time range.

6. The device according to claim 5, wherein: The map element change detection module includes: The next travel order detection submodule is used to detect whether the next travel order starts to operate when the operation of the current travel order ends; The map element change detection submodule is used to detect changes in map elements in the high-precision map based on the vehicle driving data when the next driving order has not started operation.

7. The device according to claim 6, wherein: The map element change detection submodule includes: A position information determination submodule, used to determine the position information of the autonomous driving vehicle on the high-precision map according to the vehicle driving data; A map element acquisition submodule, used to obtain the map element of the high-precision map corresponding to the location information; A map element determination submodule, for determining the map elements of the autonomous driving vehicle on the driving road according to the vehicle driving data; The map element comparison submodule is used to compare the map elements of the autonomous driving vehicle on the driving road with the map elements of the high-precision map corresponding to the position information to detect changes in the map elements in the high-precision map.

8. The apparatus according to claim 7, further comprising: The vehicle driving data sending module is used to send the vehicle driving data matching the map element change to the server for executing the map element change of the high-precision map when the map element change in the high-precision map is a map element change.

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 executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method according to any one of claims 1 to 4.

10. A non-transitory computer-readable storage medium storing computer instructions, wherein: The computer instructions are used to enable a computer to execute the method according to any one of claims 1 to 4.

11. A computer program product comprising a computer program / instructions, characterized in that When the computer program / instructions are executed by a processor, the steps of the method according to any one of claims 1 to 4 are implemented.

12. An autonomous driving vehicle comprising the electronic device as claimed in claim 9.

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