Method, device and electronic equipment for improving map accuracy
By correcting the actual driving trajectory of intelligent vehicles to match the standard driving trajectory, the problem of low accuracy of high-precision maps caused by local map scale differences is solved, thus improving the accuracy of high-precision maps.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- ZHEJIANG GEELY HLDG GRP CO LTD
- Filing Date
- 2023-02-07
- Publication Date
- 2026-06-26
AI Technical Summary
The same trajectory can be viewed at different scales in different local maps, resulting in low accuracy in high-precision maps.
By obtaining the local coordinate set and world coordinate set of the intelligent vehicle, the actual driving trajectory and the standard driving trajectory are determined. The actual driving trajectory is corrected based on the standard driving trajectory, a mapping relationship between local coordinates and world coordinates is established, and vehicle detection information is associated to ensure that the target driving trajectory is consistent with the standard driving trajectory.
The accuracy of high-precision maps has been improved, ensuring that the deviation between the actual driving trajectory and the standard driving trajectory is small, and achieving consistency between local maps and world maps.
Smart Images
Figure CN116124155B_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of intelligent vehicle technology, and in particular to a method, apparatus and electronic device for improving map accuracy. Background Technology
[0002] With the development of intelligent vehicle technology, in order to improve the convenience of intelligent vehicle driving, high-precision maps have been introduced to provide navigation for intelligent vehicles. The generation of high-precision maps requires intelligent vehicles to provide vehicle detection information, which includes: the actual driving trajectory information of the vehicle, the lane information of the vehicle, and vehicle observation information. The accuracy of the vehicle detection information determines the accuracy of the high-precision map.
[0003] Currently, the methods for obtaining vehicle detection information specifically include: obtaining the local coordinates and world coordinates of the intelligent vehicle at each detection time point. The local coordinates are obtained based on a spatial local rectangular coordinate system constructed with the geographical location of the intelligent vehicle before it starts as the origin. The world coordinates are obtained based on the position of the intelligent vehicle in the world coordinate system. Then, the local coordinates of each detection time point are transformed to the world coordinate system based on a transformation matrix, thereby obtaining the target vehicle detection information.
[0004] Based on the above description, when acquiring local coordinates at various detection time points, the same trajectory has different scales in different local maps. Since high-precision maps are obtained by fusing multiple local maps, the greater the scale difference between local maps, the lower the accuracy of the high-precision map obtained based on the fusion of multiple local maps will be. Therefore, the accuracy of the obtained high-precision map will be low. Summary of the Invention
[0005] This application provides a method, apparatus, and electronic device for improving map accuracy, which addresses the problem of different scales of the same trajectory in different local maps and improves the accuracy of high-precision maps.
[0006] In a first aspect, this application provides a method for improving map accuracy, the method comprising:
[0007] Obtain the local coordinate set and the world coordinate set of the intelligent vehicle, wherein each coordinate in the local coordinate set and the world coordinate set is obtained based on each time detection point;
[0008] The actual driving trajectory of the intelligent vehicle is determined based on each local coordinate in the local coordinate set, and the standard driving trajectory of the intelligent vehicle is determined based on each world coordinate point in the world coordinate set.
[0009] The actual driving trajectory is corrected based on the standard driving trajectory to obtain the target driving trajectory corresponding to the actual trajectory;
[0010] The vehicle detection information corresponding to each local coordinate point in the local coordinate set is determined, and each vehicle detection information is associated with the corresponding coordinate point in the target driving trajectory to determine the target vehicle detection information of the intelligent vehicle.
[0011] By using the above method, the actual driving trajectory is corrected using a standard driving trajectory, thus correcting the actual driving trajectory to the target driving trajectory. This solves the problem of different scales for the same trajectory in different local maps, thereby ensuring improved accuracy of high-precision maps.
[0012] In one possible design, the actual driving trajectory is corrected based on the standard driving trajectory to obtain the target driving trajectory corresponding to the actual trajectory, including:
[0013] Determine at least one first length between adjacent local coordinate points in the local coordinate set, and determine at least one second length between adjacent world coordinate points in the world coordinate set;
[0014] Calculate the first length ratio between the first length and the second length, adjust the actual driving trajectory based on the length ratio, and determine at least one third length between adjacent coordinate points in the adjusted actual driving trajectory;
[0015] In response to the second length ratio of the third length to the second length reaching a preset length ratio, the adjusted actual driving trajectory is taken as the target driving trajectory.
[0016] By using the above method, a second length ratio that reaches the preset length ratio is determined, thereby ensuring that the deviation between the target driving trajectory and the standard driving trajectory is small after the actual driving trajectory is corrected, thus ensuring the accuracy of the high-precision map.
[0017] In one possible design, after obtaining the target driving trajectory corresponding to the actual trajectory, the following is also included:
[0018] Based on each time detection point, the corresponding local coordinate point is extracted from the local coordinate set, and based on each time detection point, the corresponding world coordinate point is extracted from the world coordinate set.
[0019] Establish a mapping relationship between the local coordinates and the world coordinates.
[0020] By using the above method, a mapping relationship between local coordinate points and world coordinate points is established based on time detection points, thereby ensuring that there is a correspondence between the actual driving trajectory and the standard driving trajectory, and thus ensuring that the actual driving trajectory is corrected based on the standard driving trajectory.
[0021] In one possible design, the vehicle detection information corresponding to each local coordinate point in the local coordinate set is determined, and each piece of vehicle detection information is associated with the corresponding coordinate point in the target driving trajectory, including:
[0022] The vehicle speed and target object information of the intelligent vehicle at each local coordinate point are determined.
[0023] The vehicle speed and target object information at each of the local coordinate points are used as vehicle detection information.
[0024] The vehicle detection information corresponding to each coordinate point in the target driving trajectory is determined, and each coordinate point is associated with its corresponding vehicle detection information.
[0025] By using the above method, each coordinate point in the target driving trajectory is associated with its corresponding vehicle detection information, thereby improving the accuracy of the high-precision map.
[0026] Secondly, this application provides an apparatus for improving map accuracy, the apparatus comprising:
[0027] The acquisition module is used to obtain the local coordinate set and the world coordinate set of the intelligent vehicle;
[0028] The determination module is used to determine the actual driving trajectory of the intelligent vehicle based on each local coordinate in the local coordinate set, and to determine the standard driving trajectory of the intelligent vehicle based on each world coordinate point in the world coordinate set.
[0029] The correction module is used to correct the actual driving trajectory based on the standard driving trajectory to obtain the target driving trajectory corresponding to the actual trajectory;
[0030] The association module is used to determine the vehicle detection information corresponding to each local coordinate point in the local coordinate set, associate each vehicle detection information with the corresponding coordinate point in the target driving trajectory, and determine the target vehicle detection information of the intelligent vehicle.
[0031] In one possible design, the correction module is specifically used to determine at least one first length between adjacent local coordinate points in the local coordinate set and at least one second length between adjacent world coordinate points in the world coordinate set, calculate a first length ratio between the first length and the second length, adjust the actual driving trajectory based on the length ratio, and determine at least one third length between adjacent coordinate points in the adjusted actual driving trajectory. In response to the second length ratio of the third length to the second length reaching a preset length ratio, the adjusted actual driving trajectory is taken as the target driving trajectory.
[0032] In one possible design, the correction module is further configured to extract corresponding local coordinate points from the local coordinate set based on each time detection point, and extract corresponding world coordinate points from the world coordinate set based on each time detection point, thereby establishing a mapping relationship between the local coordinates and the world coordinates.
[0033] In one possible design, the association module is specifically used to determine the vehicle speed and target object information of the intelligent vehicle at each local coordinate point, use the vehicle speed and target object information of each local coordinate point as vehicle detection information, determine the vehicle detection information corresponding to each coordinate point in the target driving trajectory, and associate each coordinate point with its corresponding vehicle detection information.
[0034] Thirdly, this application provides an electronic device, comprising:
[0035] Memory, used to store computer programs;
[0036] When the processor executes the computer program stored in the memory, it implements the above-described method steps for improving map accuracy.
[0037] Fourthly, a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described method steps for improving map accuracy.
[0038] For details on each of the above-mentioned aspects one through four, and the technical effects that each aspect may achieve, please refer to the above description of the technical effects that can be achieved for the first aspect or the various possible solutions in the first aspect. These details will not be repeated here. Attached Figure Description
[0039] Figure 1 A flowchart of the steps of a method for improving map accuracy provided in this application;
[0040] Figure 2A schematic diagram of the actual driving trajectory of the intelligent vehicle provided in this application;
[0041] Figure 3 A schematic diagram of the standard driving trajectory of the intelligent vehicle provided in this application;
[0042] Figure 4 A schematic diagram of a device for improving map accuracy provided in this application;
[0043] Figure 5 This is a schematic diagram of the structure of an electronic device provided in this application. Detailed Implementation
[0044] To make the objectives, technical solutions, and advantages of this application clearer, the application will be further described in detail below with reference to the accompanying drawings. The specific operational methods in the method embodiments can also be applied to the device embodiments or system embodiments. It should be noted that in the description of this application, "multiple" is understood as "at least two". "And / or" describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone. A connected to B can represent: A and B directly connected, and A and B connected through C. Furthermore, in the description of this application, terms such as "first" and "second" are used only for distinguishing the purpose of description and should not be construed as indicating or implying relative importance or order.
[0045] In previous technologies, high-precision maps were formed by merging multiple local maps. However, the same trajectory has different scales in different local maps, resulting in low accuracy of the high-precision map formed by merging multiple local maps, which in turn leads to inaccuracy.
[0046] To address the problems described above, this application provides a method for improving map accuracy, specifically for enhancing the precision of high-precision maps. The methods and apparatus described in the embodiments of this application are based on the same technical concept. Since the principles by which the methods and apparatus solve the problems are similar, embodiments of the apparatus and methods can be referred to interchangeably, and repeated details will not be elaborated further.
[0047] The embodiments of this application will now be described in detail with reference to the accompanying drawings.
[0048] Reference Figure 1 This application provides a method for improving map accuracy, which is used to accelerate the progress of high-precision map production. The implementation process of this method is as follows:
[0049] Step S1: Obtain the local coordinate set and world coordinate set of the intelligent vehicle.
[0050] The embodiments of this application aim to improve the accuracy of high-precision maps. To improve the accuracy of local maps, it is necessary to first obtain the local coordinate set and the world coordinate set of the intelligent vehicle. Each coordinate in the local coordinate set and the world coordinate set is obtained from each time detection point. Each time detection point corresponds to a local coordinate pad and a world coordinate point. The local coordinate system can be established according to the position of the intelligent vehicle. For example, when the intelligent vehicle is stationary, a spatial rectangular coordinate system is established with the intelligent vehicle as the origin, and this spatial rectangular coordinate system is used as the local coordinate system. The world coordinate system is established based on latitude and longitude, and the world coordinates in the world coordinate system can be obtained from the Global Positioning System (GPS).
[0051] The local coordinate sets and world coordinate sets obtained based on each time detection point are shown in Table 1:
[0052] Time detection point Local coordinate points World coordinates T1 (1，2，3) (2，5，8) T2 (3，5，7) (6，1，2) T3 (1，4，8) (3，5，9) …… …… ……
[0053] Table 1
[0054] Based on Table 1 above, which lists multiple local coordinate points and multiple world coordinate points as examples, the local coordinate points in Table 1 can form a local coordinate set, and the world coordinate points can form a world coordinate set. The time detection points can be collected according to the actual traffic scenarios of intelligent vehicles, and no specific restrictions are imposed here.
[0055] Using the above method, during the driving process of an intelligent vehicle, the local coordinate set and world coordinate set of the intelligent vehicle are obtained based on time detection points, thereby enabling the intelligent vehicle to obtain its driving trajectory based on each local coordinate point and each world coordinate point.
[0056] Step S2: Determine the actual driving trajectory of the intelligent vehicle based on each local coordinate in the local coordinate set, and determine the standard driving trajectory of the intelligent vehicle based on each world coordinate point in the world coordinate set.
[0057] The local coordinate set and world coordinate set corresponding to the intelligent vehicle have been determined above. Based on each time detection point and each local coordinate point in the local coordinate set, the actual driving trajectory of the intelligent vehicle can be determined. A schematic diagram of the actual driving trajectory of the intelligent vehicle is shown below. Figure 2 As shown, Figure 2 The system records four time detection points and the actual driving trajectory composed of the layout coordinates corresponding to the four time detection points.
[0058] Furthermore, based on GPS, the world coordinates corresponding to each time detection point can be recorded, and then these world coordinates are connected sequentially to form a standard driving trajectory. A schematic diagram of the standard driving trajectory of an intelligent vehicle is shown below. Figure 3 As shown, in Figure 3 The system records the world coordinates corresponding to the four time detection points, as well as the standard driving trajectory generated based on the four world coordinates.
[0059] It should be noted that the difference between the actual driving trajectory and the standard driving trajectory can be adjusted according to the actual driving conditions of the intelligent vehicle and the traffic conditions, which will not be discussed in detail here.
[0060] The above method determines the actual driving trajectory of the intelligent vehicle using a local coordinate set and the standard driving trajectory of the intelligent vehicle using a world coordinate set. This facilitates the comparison between the actual driving trajectory and the standard driving trajectory, thereby enabling the adjustment of local coordinates in the local map.
[0061] Step S3: Correct the actual driving trajectory based on the standard driving trajectory to obtain the target driving trajectory corresponding to the actual trajectory.
[0062] The actual driving trajectory and standard driving trajectory of the intelligent vehicle have been determined above. In order to solve the problem of different scales of the same trajectory in multiple local maps, it is necessary to correct the actual driving trajectory based on the standard driving trajectory. The specific correction method is as follows:
[0063] Determine at least one first length between adjacent local coordinate points in the local coordinate set and at least one second length between adjacent local coordinate points in the world coordinate set. Calculate the ratio of the first length to the second length. Then, adjust the actual driving trajectory based on the first length ratio. To ensure that the adjusted actual driving trajectory is close to the standard driving trajectory, calculate at least one third length between adjacent coordinate points in the adjusted actual driving trajectory and check whether the ratio of the third length to the second length reaches a preset length ratio. For example, if the second length ratio is 0.6 and the preset length ratio is 0.9, then the second length ratio has not reached the preset length ratio, indicating a significant difference between the adjusted actual driving trajectory and the preset driving trajectory. When the second length ratio reaches the preset length ratio, the adjusted actual driving trajectory is taken as the target driving trajectory.
[0064] After determining the target driving trajectory, in order for the target driving trajectory to correspond with the standard driving trajectory, it is necessary to determine the local coordinate point and the world coordinate point corresponding to each time detection point, and then establish the association relationship between the local coordinate point and the world coordinate point, so that the local coordinate point in the local coordinate set and the world coordinate point in the world coordinate set form a mapping relationship, thereby making there a mapping relationship between the target driving trajectory and the standard driving trajectory.
[0065] By using the above method, the actual driving trajectory is corrected based on the standard driving trajectory, and the actual driving trajectory is adjusted to the target driving trajectory, thereby realizing the correction of the actual driving trajectory and thus the adjustment of the local map.
[0066] Step S4: Determine the vehicle detection information corresponding to each local coordinate point in the local coordinate set, associate each vehicle detection information with the corresponding coordinate point in the target driving trajectory, and determine the target vehicle detection information of the intelligent vehicle.
[0067] After correcting the actual driving trajectory to the target driving trajectory, in order to improve the accuracy of the high-precision map, it is necessary to determine the vehicle detection information corresponding to each local coordinate point in the local coordinate set. This vehicle detection information includes: the actual driving trajectory information of the vehicle, the lane information of the vehicle, and the vehicle observation information. This vehicle observation information is the information collected by the image acquisition device of the intelligent vehicle or the information collected by the radar device of the intelligent vehicle. No specific restrictions are made here.
[0068] To ensure consistency between the local map and the world map, it is necessary to determine the vehicle speed and target object information for each vehicle at this local coordinate point. The target object information refers to the information of the target object in the lane detected by the intelligent vehicle. The target object can be a vehicle sign detected by the intelligent vehicle, or other objects; there are no restrictions here.
[0069] After determining the vehicle speed and target object information, the vehicle speed and target object information are used as vehicle detection information. Based on the method described above, the vehicle detection information corresponding to each coordinate point in the target driving trajectory is determined. Each coordinate point is then associated with its corresponding vehicle detection information, and the target driving trajectory associated with the vehicle detection information is used as the target vehicle detection information of the intelligent vehicle, thereby ensuring that the target driving trajectory is consistent with the standard driving trajectory.
[0070] Based on the method described above, the actual driving trajectory and the standard driving trajectory are determined based on the time detection points, and the actual driving trajectory is corrected based on the standard driving trajectory to obtain the target driving trajectory corresponding to the actual driving trajectory. This allows for the adjustment of the local map based on the target driving trajectory, thereby improving the accuracy of the high-precision map.
[0071] Based on the same inventive concept, this application also provides a device for improving map accuracy. This device implements the function of a method for improving map accuracy, as described above. Figure 4 The device includes:
[0072] Module 401 is used to obtain the local coordinate set and the world coordinate set of the intelligent vehicle;
[0073] The determination module 402 is used to determine the actual driving trajectory of the intelligent vehicle based on each local coordinate in the local coordinate set, and to determine the standard driving trajectory of the intelligent vehicle based on each world coordinate point in the world coordinate set.
[0074] Correction module 403 is used to correct the actual driving trajectory based on the standard driving trajectory to obtain the target driving trajectory corresponding to the actual trajectory;
[0075] The association module 404 is used to determine the vehicle detection information corresponding to each local coordinate point in the local coordinate set, associate each vehicle detection information with the corresponding coordinate point in the target driving trajectory, and determine the target vehicle detection information of the intelligent vehicle.
[0076] In one possible design, the correction module 403 is specifically used to determine at least one first length between adjacent local coordinate points in the local coordinate set and at least one second length between adjacent world coordinate points in the world coordinate set, calculate a first length ratio between the first length and the second length, adjust the actual driving trajectory based on the length ratio, and determine at least one third length between adjacent coordinate points in the adjusted actual driving trajectory. In response to the second length ratio of the third length to the second length reaching a preset length ratio, the adjusted actual driving trajectory is used as the target driving trajectory.
[0077] In one possible design, the correction module 403 is further configured to extract corresponding local coordinate points from the local coordinate set based on each time detection point, and extract corresponding world coordinate points from the world coordinate set based on each time detection point, thereby establishing a mapping relationship between the local coordinates and the world coordinates.
[0078] In one possible design, the association module 404 is specifically used to determine the vehicle speed and target object information of the intelligent vehicle at each local coordinate point, use the vehicle speed and target object information of each local coordinate point as vehicle detection information, determine the vehicle detection information corresponding to each coordinate point in the target driving trajectory, and associate each coordinate point with its corresponding vehicle detection information.
[0079] Based on the same inventive concept, this application also provides an electronic device that can realize the function of the aforementioned device for improving map accuracy. (Refer to...) Figure 5 The electronic device includes:
[0080] At least one processor 501 and a memory 502 connected to at least one processor 501. In this embodiment, the specific connection medium between the processor 501 and the memory 502 is not limited. Figure 5 The example shown is the connection between processor 501 and memory 502 via bus 500. Bus 500 is... Figure 5 The connections between other components are indicated by thick lines and are for illustrative purposes only, not as limiting information. The Bus 500 can be divided into address bus, data bus, control bus, etc., for ease of representation. Figure 5 The term 501 is represented by a single thick line, but this does not imply that there is only one bus or one type of bus. Alternatively, the processor 501 can also be called a controller; there is no restriction on the name.
[0081] In this embodiment, memory 502 stores instructions executable by at least one processor 501. By executing the instructions stored in memory 502, at least one processor 501 can perform a method for improving map accuracy as described above. Processor 501 can implement... Figure 4 The functions of each module in the device shown.
[0082] The processor 501 is the control center of the device. It can connect to various parts of the control device through various interfaces and lines. By running or executing instructions stored in memory 502 and calling data stored in memory 502, the processor can perform various functions and process data, thereby monitoring the device as a whole.
[0083] In one possible design, processor 501 may include one or more processing units. Processor 501 may integrate an application processor and a modem processor, wherein the application processor mainly handles the operating system, user interface, and applications, and the modem processor mainly handles wireless communication. It is understood that the modem processor may also not be integrated into processor 501. In some embodiments, processor 501 and memory 502 may be implemented on the same chip; in some embodiments, they may also be implemented on separate chips.
[0084] Processor 501 can be a general-purpose processor, such as a central processing unit (CPU), digital signal processor, application-specific integrated circuit, field-programmable gate array or other programmable logic device, discrete gate or transistor logic device, or discrete hardware component, capable of implementing or executing the methods, steps, and logic block diagrams disclosed in the embodiments of this application. The general-purpose processor can be a microprocessor or any conventional processor. The steps of a method for improving map accuracy disclosed in the embodiments of this application can be directly manifested as being executed by a hardware processor, or executed by a combination of hardware and software modules within the processor.
[0085] Memory 502, as a non-volatile computer-readable storage medium, can be used to store non-volatile software programs, non-volatile computer-executable programs, and modules. Memory 502 may include at least one type of storage medium, such as flash memory, hard disk, multimedia card, card-type memory, random access memory (RAM), static random access memory (SRAM), programmable read-only memory (PROM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), magnetic storage, magnetic disk, optical disk, etc. Memory 502 can be any other medium capable of carrying or storing desired program code in the form of instructions or data structures that can be accessed by a computer, but is not limited thereto. In the embodiments of this application, memory 502 can also be a circuit or any other device capable of implementing storage functions for storing program instructions and / or data.
[0086] By designing and programming the processor 501, the code corresponding to a method for improving map accuracy described in the foregoing embodiments can be embedded into the chip, enabling the chip to execute it during operation. Figure 1 The illustrated embodiment presents a step for improving map accuracy. How to design and program the processor 501 is a technique well-known to those skilled in the art and will not be described further here.
[0087] Based on the same inventive concept, embodiments of this application also provide a storage medium storing computer instructions that, when executed on a computer, cause the computer to perform a method for improving map accuracy as described above.
[0088] In some possible implementations, various aspects of the method for improving map accuracy provided by this application can also be implemented in the form of a program product, which includes program code that, when the program product is run on a device, causes the control device to perform the steps in a method for improving map accuracy according to various exemplary embodiments of this application as described above.
[0089] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.
[0090] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to this application. 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 program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.
[0091] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.
[0092] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.
[0093] Obviously, those skilled in the art can make various modifications and variations to this application without departing from the spirit and scope of this application. Therefore, if such modifications and variations fall within the scope of the claims of this application and their equivalents, this application also intends to include such modifications and variations.
Claims
1. A method for improving map accuracy, characterized in that, include: Obtain the local coordinate set and the world coordinate set of the intelligent vehicle, wherein each coordinate in the local coordinate set and the world coordinate set is obtained based on each time detection point; The actual driving trajectory of the intelligent vehicle is determined based on each local coordinate in the local coordinate set, and the standard driving trajectory of the intelligent vehicle is determined based on each world coordinate point in the world coordinate set. The actual driving trajectory is corrected based on the standard driving trajectory to obtain the target driving trajectory corresponding to the actual driving trajectory; The vehicle detection information corresponding to each local coordinate point in the local coordinate set is determined, and each vehicle detection information is associated with the corresponding coordinate point in the target driving trajectory to determine the target vehicle detection information of the intelligent vehicle. The step of correcting the actual driving trajectory based on the standard driving trajectory to obtain the target driving trajectory corresponding to the actual trajectory includes: Determine at least one first length between adjacent local coordinate points in the local coordinate set, and determine at least one second length between adjacent world coordinate points in the world coordinate set; Calculate the first length ratio between the first length and the second length, adjust the actual driving trajectory based on the length ratio, and determine at least one third length between adjacent coordinate points in the adjusted actual driving trajectory; In response to the second length ratio of the third length to the second length reaching a preset length ratio, the adjusted actual driving trajectory is taken as the target driving trajectory.
2. The method as described in claim 1, characterized in that, After obtaining the target driving trajectory corresponding to the actual trajectory, the process also includes: Based on each time detection point, the corresponding local coordinate point is extracted from the local coordinate set, and based on each time detection point, the corresponding world coordinate point is extracted from the world coordinate set. Establish a mapping relationship between the local coordinate points and the world coordinate points.
3. The method as described in claim 1, characterized in that, Determine the vehicle detection information corresponding to each local coordinate point in the local coordinate set, and associate each vehicle detection information with the corresponding coordinate point in the target driving trajectory, including: The vehicle speed and target object information of the intelligent vehicle at each local coordinate point are determined. The vehicle speed and target object information at each of the local coordinate points are used as vehicle detection information. The vehicle detection information corresponding to each coordinate point in the target driving trajectory is determined, and each coordinate point is associated with its corresponding vehicle detection information.
4. A device for improving map accuracy, characterized in that, include: The acquisition module is used to obtain the local coordinate set and the world coordinate set of the intelligent vehicle; The determination module is used to determine the actual driving trajectory of the intelligent vehicle based on each local coordinate in the local coordinate set, and to determine the standard driving trajectory of the intelligent vehicle based on each world coordinate point in the world coordinate set. The correction module is used to correct the actual driving trajectory based on the standard driving trajectory to obtain the target driving trajectory corresponding to the actual driving trajectory; The association module is used to determine the vehicle detection information corresponding to each local coordinate point in the local coordinate set, associate each vehicle detection information with the corresponding coordinate point in the target driving trajectory, and determine the target vehicle detection information of the intelligent vehicle. Specifically, the correction module is used to determine at least one first length between adjacent local coordinate points in the local coordinate set and at least one second length between adjacent world coordinate points in the world coordinate set, calculate a first length ratio between the first length and the second length, adjust the actual driving trajectory based on the length ratio, and determine at least one third length between adjacent coordinate points in the adjusted actual driving trajectory. In response to the second length ratio between the third length and the second length reaching a preset length ratio, the adjusted actual driving trajectory is used as the target driving trajectory.
5. The apparatus as described in claim 4, characterized in that, The correction module is further configured to extract corresponding local coordinate points from the local coordinate set based on each time detection point, and extract corresponding world coordinate points from the world coordinate set based on each time detection point, and establish a mapping relationship between the local coordinates and the world coordinates.
6. The apparatus as claimed in claim 4, characterized in that, The association module is specifically used to determine the vehicle speed and target object information of the intelligent vehicle at each local coordinate point, use the vehicle speed and target object information of each local coordinate point as vehicle detection information, determine the vehicle detection information corresponding to each coordinate point in the target driving trajectory, and associate each coordinate point with its corresponding vehicle detection information.
7. An electronic device, characterized in that, include: Memory, used to store computer programs; A processor, when executing a computer program stored in the memory, implements the steps of the method according to any one of claims 1-3.
8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the steps of the method described in any one of claims 1-3.