Vector Map Construction Method and Device for Driving Test Subjects, and Electronic Device

Abstract

The present application provides a method and apparatus for constructing a vector map based on driving test subjects, and an electronic device, relating to the fields of positioning and mapping. The method includes: acquiring point cloud data and inertial data of a target driving test area collected by a target sensor; generating a point cloud map of the target driving test area based on the point cloud data and the inertial data; drawing vector elements based on the environmental features included in the point cloud map; and generating a vector map of the target driving test area based on the vector elements. The present application improves the accuracy and robustness of positioning, realizes more stable and reliable positioning and mapping, and can better understand and adapt to the driving school scenario, realizing safer and more efficient driving behavior.

Description

Technical Field

[0001] This application relates to the technical field of positioning and mapping, and particularly to a method and device for constructing a vector map based on driving test subjects, and an electronic device. Background Art

[0002] With the progress of artificial intelligence and computer technology, driverless technology has become a research hotspot and an important direction in the field of artificial intelligence. Especially its application in the driving school scenario can save a large amount of manpower and material resources.

[0003] Although there are already some methods for autonomous positioning and mapping of robots in known environments, traditional GPS (Global Positioning System) positioning is unreliable due to errors, visual SLAM (Simultaneous Localization and Mapping) lacks accuracy and robustness in dynamic environments, and the sensor fusion algorithm based on Kalman filtering may accumulate large errors after long-term use. Therefore, in the process of a driverless vehicle from sensor scanning, generating a point cloud map to making a vector map, it needs to go through a series of complex processes, which are operationally difficult and require a high level of technical expertise. Summary of the Invention

[0004] In view of this, embodiments of this application provide a method and device for constructing a vector map based on driving test subjects, and an electronic device.

[0005] In a first aspect, an embodiment of this application provides a method for constructing a vector map based on driving test subjects, which is applied to a main control computer. The method includes: acquiring point cloud data and inertial data of a target driving test area collected by a target sensor; generating a point cloud map of the target driving test area based on the point cloud data and the inertial data; drawing vector elements based on the environmental features included in the point cloud map; and generating a vector map of the target driving test area based on the vector elements.

[0006] In combination with the first aspect, in some implementation manners of the first aspect, the environmental features include bay information corresponding to the target driving test subject. Drawing vector elements based on the environmental features included in the point cloud map includes: drawing a plurality of navigation points corresponding to the bay information based on the bay information corresponding to the target driving test subject, and each navigation point includes semantic information for marking the geographical coordinates of the operation positions in the bay information.

[0007] In combination with the first aspect, in certain implementations of the first aspect, based on vector elements, a vector map of the target driving test area is generated, including: moving multiple navigation points to the corresponding positions of the bay information based on the semantic information corresponding to each of the multiple navigation points; generating an electronic fence corresponding to the bay information based on the bay information to obtain a vector map of the target driving test subject in the target driving test area.

[0008] In combination with the first aspect, in certain implementations of the first aspect, an electronic fence corresponding to the bay information is generated based on the bay information, including: generating a virtual boundary based on the boundary information in the bay information; adding trigger points in the virtual boundary to generate an electronic fence corresponding to the bay information, where the trigger points are used to define key positions of the virtual boundary and trigger specific events.

[0009] In combination with the first aspect, in certain implementations of the first aspect, the target sensors include a lidar sensor and an inertial measurement unit. The point cloud data is collected by the lidar sensor, and the inertial data is collected by the inertial measurement unit. Based on the point cloud data and the inertial data, a point cloud map of the target driving test area is generated, including: synchronizing the point cloud data and the inertial data; obtaining the extrinsic parameter data of the lidar sensor and the inertial measurement unit in the configuration file of the target driver package, and estimating the initial motion trajectory of the target object based on the extrinsic parameter data and the synchronized point cloud data and inertial data; registering the point cloud data into the standard coordinate system based on the initial motion trajectory of the target object and cumulatively forming a point cloud map of the target driving test area.

[0010] In combination with the first aspect, in certain implementations of the first aspect, after generating a point cloud map of the target driving test area based on the point cloud data and the inertial data, it further includes: obtaining a first parameter value corresponding to the storage parameter in the configuration file of the target driver package; if the first parameter value is true, obtaining a second parameter value corresponding to the path parameter in the configuration file; storing the point cloud map to the storage path indicated by the second parameter value.

[0011] In combination with the first aspect, in certain implementations of the first aspect, before obtaining the point cloud data and the inertial data of the target driving test area, it further includes: testing the target sensors. Among them, testing the target sensors includes: determining a physical connection with the target sensors through the target port device file; setting communication parameters matching the configuration parameters of the target sensors; receiving the data stream sent by the target sensors and testing the target sensors based on the data stream.

[0012] In a second aspect, an embodiment of the present application provides a construction device based on a vector map, which is applied to a main control computer. The device includes: an acquisition module, configured to acquire point cloud data and inertial data of a target driving test area collected by a target sensor; a first generation module, configured to generate a point cloud map of the target driving test area based on the point cloud data and the inertial data; a drawing module, configured to draw vector elements based on environmental features included in the point cloud map; a second generation module, configured to generate a vector map of the target driving test area based on the vector elements.

[0013] In a third aspect, an embodiment of the present application provides a computer-readable storage medium storing a computer program for executing the vector map construction method based on driving test subjects described in the first aspect.

[0014] In a fourth aspect, an embodiment of the present application provides an electronic device, which includes: a processor; a memory for storing processor-executable instructions; the processor is configured to execute the vector map construction method based on driving test subjects described in the first aspect.

[0015] In the present application, first, the point cloud data provides the precise spatial structure of the environment, while the inertial data provides the motion state of the sensor or the vehicle. The two are used in combination to improve the accuracy and robustness of positioning, as well as the accuracy and reliability of map construction. Second, compared with visual SLAM, the present solution does not rely on the visual features of the environment, so it can still maintain good performance in dynamic or texture-lacking environments. Third, the present solution realizes more stable and reliable positioning and map construction by fusing multiple data, avoiding the cumulative error problem that may occur in the sensor fusion method that only relies on the Kalman filter algorithm after long-term operation. Finally, the present solution can generate a vector map containing rich environmental features. Through this method, driverless vehicles can better understand and adapt to the driving school scenario, realize safer and more efficient driving behaviors, and save manpower and material resources for the driving school. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] By describing the embodiments of the present application in more detail in conjunction with the accompanying drawings, the above and other objects, features, and advantages of the present application will become more obvious. The accompanying drawings are used to provide a further understanding of the embodiments of the present application, and constitute a part of the specification. They are used together with the embodiments of the present application to explain the present application, and do not constitute a limitation to the present application. In the accompanying drawings, the same reference numerals generally represent the same components or steps.

[0017] Figure 1 The figure shows a schematic flowchart of the vector map construction method provided by an embodiment of the present application.

[0018] Figure 2The figure shows a schematic flow chart of drawing vector elements provided by an embodiment of the present application.

[0019] Figure 3 The figure shows a schematic diagram of a point cloud map containing bin location information provided by an embodiment of the present application.

[0020] Figure 4 The figure shows a schematic diagram of a point cloud map of navigation points containing bin location information provided by an embodiment of the present application.

[0021] Figure 5 The figure shows a schematic flow chart of generating a vector map of a target driving test area provided by an embodiment of the present application.

[0022] Figure 6 The figure shows a schematic flow chart of generating an electronic fence corresponding to bin location information provided by an embodiment of the present application.

[0023] Figure 7 The figure shows a schematic diagram of an electronic fence containing trigger points provided by an embodiment of the present application.

[0024] Figure 8 The figure shows a schematic flow chart of generating a point cloud map of a target driving test area provided by an embodiment of the present application.

[0025] Figure 9 The figure shows a schematic flow chart of a method for constructing a vector map provided by another embodiment of the present application.

[0026] Figure 10 The figure shows a schematic flow chart of testing a target sensor provided by an embodiment of the present application.

[0027] Figure 11 The figure shows a schematic structural diagram of a vector map construction device based on driving test subjects provided by an embodiment of the present application.

[0028] Figure 12 The figure shows a schematic structural diagram of an electronic device provided by an embodiment of the present application. Detailed implementation manners

[0029] Next, the technical solutions in the embodiments of the present application will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present application.

[0030] Figure 1 The figure shows a schematic flow chart of a method for constructing a vector map provided by an embodiment of the present application. Exemplarily, this method is applied to a main control computer. As Figure 1As shown, the method includes the following steps.

[0031] Step S110: Obtain the point cloud data and inertial data of the target driving test area collected by the target sensor.

[0032] A point cloud is a set of points in three-dimensional space, and each point is associated with coordinates and other information such as color or intensity. In driverless vehicles, point cloud data is usually generated by lidar sensors. Lidar detects the environment by emitting laser pulses and measuring the time it takes for these pulses to reflect back, and these measurements are used to create an accurate three-dimensional map of the surrounding environment, including obstacles, road boundaries, and other features.

[0033] The inertial data is provided by an IMU (Inertial Measurement Unit), and this data is used to determine the orientation (attitude) of an object, as well as changes in linear and rotational motion. In driverless vehicles, the IMU can help the vehicle estimate its position changes in space, even indoors where GPS signals are unreliable or unavailable.

[0034] Exemplarily, the target sensor includes a lidar sensor and an inertial measurement unit. In this step, first ensure that the lidar and IMU are correctly installed and working properly. For example, use tools such as cutecom to test whether these two types of sensors can provide data. After confirming that the above sensors are working properly, start the IMU and use relevant instructions to obtain IMU data (i.e., inertial data), and at the same time configure the network interface of the lidar sensor to ensure that relevant data can be correctly received. Subsequently, execute the recording instruction on the main control computer to start collecting the real-time data of the lidar and IMU, and these data will be saved to a file for subsequent processing.

[0035] Step S120: Generate a point cloud map of the target driving test area based on the point cloud data and inertial data.

[0036] Exemplarily, in this embodiment, use the point cloud data collected by the lidar sensor, and this data contains the three-dimensional geometric information of the driving test area. Then, use the inertial data provided by the IMU to correct the point cloud data to ensure the precise alignment and stability of the point cloud. Further, through sensor fusion technology, such as using the LIO-mapping method, perform real-time three-dimensional modeling of the environment and maintain the consistency and accuracy of the point cloud map in a dynamic environment.

[0037] Step S130: Draw vector elements based on the environmental features included in the point cloud map.

[0038] Environmental features refer to the recognizable and useful geographical or physical attributes of the target driving test area included in the point cloud map. These features include, but are not limited to: roads and paths, obstacles, traffic signals, terrain features, special areas, etc.

[0039] Vector elements refer to converting these environmental features into graphics and objects that can be represented on a vector map. Usually, they include: lines (representing roads, boundaries, etc.), polygons (representing areas, such as parking spaces or special test areas), markers (representing specific points, such as points of interest or specific locations), and text labels (providing additional information about a feature, such as road names or text on signs). Additionally, during the process of drawing vector elements, software or tools such as Unity 3D can be used to convert environmental features into vector elements through the analysis of the point cloud map, which are used to guide the path planning, navigation, and decision-making of driverless vehicles.

[0040] Step S140: Generate a vector map of the target driving test area based on the vector elements.

[0041] A vector map is a type of map that uses mathematical algorithms to represent geospatial data. Different from raster maps (composed of pixel lattices), vector maps use object-based graphics to represent real-world entities, such as roads, buildings, rivers, etc. Each object is composed of geometric elements such as points, lines, and polygons, which are defined by mathematical formulas and can be precisely located and sized.

[0042] Exemplarily, in this embodiment, the drawn vector elements are exported in a format suitable for a specific application, such as a specific vector map format for autonomous vehicle navigation. The exported vector map will contain all the key information of the driving test area, such as the road network, traffic rules, and specific driving test areas, providing necessary navigation and decision-making support for autonomous vehicles.

[0043] In this embodiment, the point cloud data provides the precise spatial structure of the environment, while the inertial data provides the motion state of the sensor or the vehicle. The two are used in combination to improve the accuracy and robustness of positioning, as well as the precision and reliability of map construction. Secondly, compared with visual SLAM, this solution does not rely on the visual features of the environment, so it can still maintain good performance in dynamic or texture-lacking environments. Moreover, this solution achieves more stable and reliable positioning and map construction by fusing multiple data, avoiding the cumulative error problem that may occur in sensor fusion methods that only rely on the Kalman filter algorithm after long-term operation. Finally, this solution can generate a vector map containing rich environmental features. Through this method, driverless vehicles can better understand and adapt to the driving school scenario, realizing safer and more efficient driving behaviors, saving manpower and material resources for the driving school.

[0044] Figure 2 The following is a schematic flowchart of drawing vector elements provided by an embodiment of the present application. In Figure 1 Based on the embodiment shown, Figure 2 an extended embodiment is derived. Figure 2 Below, the differences between the embodiment shown Figure 1 and the embodiment shown will be emphasized, and the similarities will not be elaborated.

[0045] In this embodiment, the environmental features include the bay information corresponding to the target driving test subject. The target driving test subject refers to a specific subject in the driving test, such as Subject 1, Subject 2, Subject 3, or Subject 4. The bay information refers to the data storage location or the record location in the database related to these subjects. Exemplarily, Figure 3 The following is a schematic diagram of a point cloud map containing bay information provided by an embodiment of the present application.

[0046] As Figure 2 shown, based on the environmental features included in the point cloud map, drawing vector elements includes step S210.

[0047] Step S210: Based on the bay information corresponding to the target driving test subject, draw a plurality of navigation points corresponding to the bay information.

[0048] Each navigation point contains semantic information for marking the geographical coordinates of the operation location in the bay information. Specifically, the navigation point is a specific point on the map used to indicate the position that the vehicle should reach or pay attention to during driving. These points can be actual geographical coordinates or virtual marker points for assisting navigation and path planning. In the driving test subject, the navigation points corresponding to the bay information refer to those points directly related to the test requirements. For example, if the subject requires the candidate to park at a specific location, then this parking point is a navigation point corresponding to the bay information. Exemplarily, Figure 4 The following is a schematic diagram of a point cloud map containing navigation points corresponding to bay information provided by an embodiment of the present application.

[0049] In some embodiments, first, according to the requirements of the driving test subject, determine the navigation points to be drawn. Then, assign precise geographical coordinates to each point to ensure that the vehicle can accurately navigate to these positions. Next, add semantic information to each point, which describes the specific role of the point in the test, such as a parking point, a turning indication, etc.

[0050] In this embodiment, by drawing vector elements based on the environmental features of the point cloud map and combining the bin location information to determine the geographical coordinates of the operation location, the accuracy and relevance of the navigation points are ensured. The semantic information contained in each navigation point not only provides a clear indication of the function and purpose of the point, but also enhances the understanding of the driving test environment, thereby improving the accuracy of path planning and vehicle navigation, as well as the efficiency and effectiveness of the driving test, providing strong support for the application of autonomous driving technology in the training field.

[0051] Figure 5 The following is a schematic flowchart of generating a vector map of the target driving test area provided by an embodiment of the present application. Figure 2 Based on the embodiment shown above, Figure 5 the following embodiment is extended. Figure 5 Hereinafter, the differences between the embodiment shown Figure 2 and the embodiment shown above will be mainly described, and the same parts will not be elaborated.

[0052] As Figure 5 shown, in this embodiment, based on the vector elements, a vector map of the target driving test area is generated, including the following steps.

[0053] Step S510: Move multiple navigation points to the corresponding positions of the bin location information based on the semantic information corresponding to each of the multiple navigation points.

[0054] Exemplarily, in this embodiment, the semantic information of each navigation point is parsed. After understanding this semantic information, the corresponding positions of the navigation points in the actual driving test area are identified according to these descriptions. Subsequently, the geographical coordinates of the navigation points are adjusted using this semantic information to ensure that they match the positions defined in the bin location information. For example, if the semantic information indicates that a certain navigation point is a parking point, it is necessary to ensure that the position of this point on the map corresponds to the actual parking area.

[0055] In addition, in some other embodiments, further refinement of the navigation points is also included, such as adjusting the size, shape or relative position of the navigation points with respect to other points to ensure that their semantic information can be clearly expressed on the vector map. In this way, the generated vector map can not only display the geometric features of the target driving test area, but also provide rich information on how to navigate and perform specific operations in this environment.

[0056] Step S520: Generate an electronic fence corresponding to the bin location information based on the bin location information to obtain a vector map of the target driving test subject in the target driving test area.

[0057] An electronic fence is a virtual boundary system that defines a specific area or space by using geographical coordinates, which can be two-dimensional (longitude and latitude) or three-dimensional (including altitude information). The electronic fence can be circular, rectangular, polygonal, or any other shape, depending on the area to be defined. In this embodiment, the electronic fence generated based on the storage location information is used to define the boundary of the target driving test subject in the target driving test area. Exemplarily, this electronic fence includes all the points and areas related to the driving test, ensuring that the vehicle does not exceed the predetermined range during the driving test.

[0058] Exemplarily, in some embodiments, first, according to the storage location information and driving test requirements, the geometric shape and size of the electronic fence are determined, and the boundary points of the electronic fence are marked on the map. Then, the semantic information related to the driving test is applied to the boundary points of the electronic fence to ensure that each part of the electronic fence corresponds to specific driving test requirements. Finally, the generated electronic fence is integrated into the vector map of the target driving test area, making it a part of the map and providing a reference for the autonomous driving system for navigation and operation.

[0059] In this embodiment, by moving the navigation points to the positions specified by the storage location information according to their respective semantic information, it is ensured that each point on the vector map accurately reflects its function and importance in the actual driving test environment, not only improving the accuracy and practicality of the vector map, but also enhancing the understanding of the driving test environment. By generating an electronic fence corresponding to the storage location information, the boundary of the target driving test subject in the target driving test area is defined, ensuring that the vehicle does not exceed the predetermined range during the driving test, and enhancing the standardization and safety of the test. At the same time, the electronic fence also provides necessary spatial limitations for the autonomous driving system, helping to perform effective path planning and avoid potential dangerous areas.

[0060] Figure 6 The following shows a schematic flow chart of generating an electronic fence corresponding to the storage location information provided by an embodiment of the present application. In Figure 5 Based on the embodiment shown, Figure 6 The embodiment shown is extended, Figure 6 The differences between the embodiment shown Figure 5 and the embodiment shown

[0061] are as follows. The same parts will not be elaborated. Figure 6 As

[0062] shown, in this embodiment, based on the storage location information, generating an electronic fence corresponding to the storage location information includes the following steps.

[0063] In the location information, the boundary information defines the outer boundaries of the driving test area, including the longitude and latitude coordinates, shape, size, etc. of the driving test area. Using the extracted boundary information, a virtual boundary is created to define the geographical scope of the driving test area. In this process, geographical coordinates are used to determine the exact position of the virtual boundary. These coordinates can be two-dimensional or three-dimensional, depending on the complexity of the area to be defined. According to the boundary information, the system will define the shape of the virtual boundary.

[0064] Step S620, in the virtual boundary, add trigger points to generate an electronic fence corresponding to the location information.

[0065] The trigger point is a specific geographical location preset in the electronic fence, used to monitor and respond to specific events or behaviors. That is, the trigger point is used to define the key positions of the virtual boundary and trigger specific events.

[0066] Specifically, the trigger points are predefined points within the electronic fence. For example, these points are the entrances and exits of the driving test area, the center points of specific operation areas, etc. When a vehicle or other object enters or leaves these points, preset events or actions can be triggered. For example, when a vehicle enters a certain point, the vehicle's position will be recorded, or a prompt sound of the navigation system will be triggered.

[0067] Exemplarily, in some embodiments, first, all relevant boundary information and key point information are extracted from the location information, including the role of the geographical coordinates of the points in the driving test and the specific events to be triggered. Then, using this information, trigger points are set at appropriate positions in the virtual boundary. Exemplarily, Figure 7 The following shows a schematic diagram of an electronic fence including trigger points provided by an embodiment of the present application. Next, corresponding logics and rules are configured for each trigger point, and these rules define the events that should occur when a vehicle or other object interacts with the point. For example, when a vehicle enters a specific point, a navigation prompt or a safety warning will be triggered. In addition, it is necessary to ensure that these trigger points can be accurately identified and responded to in the system. After the trigger points are set, the electronic fence needs to be tested to ensure that all points can work as expected and the entire system can operate stably. Finally, the generated electronic fence not only defines the geographical scope of the driving test area, but also provides a dynamic and interactive means of monitoring and management through trigger points.

[0068] Figure 8 The following shows a schematic flowchart of generating a point cloud map of the target driving test area provided by an embodiment of the present application. In Figure 1 Based on the embodiment shown, Figure 8 an embodiment shown is extended, and Figure 8 the embodiment shown below will be described in detail in comparison withFigure 1 The differences of the illustrated embodiments will not be elaborated, and the same parts will not be repeated.

[0069] As Figure 8 shown, in this embodiment, the target sensor includes a lidar sensor and an inertial measurement unit. The point cloud data is collected by the lidar sensor, and the inertial data is collected by the inertial measurement unit. Based on the point cloud data and the inertial data, a point cloud map of the target driving test area is generated, including the following steps.

[0070] Step S810: Synchronize the point cloud data and the inertial data.

[0071] Exemplarily, in some embodiments, first, the data collected by the lidar sensor and the inertial measurement unit are calibrated with timestamps to ensure that each data point has an accurate time stamp, so that the point cloud data can be accurately matched with the corresponding inertial data. In addition, since the data acquisition frequencies of different sensors may be different, data interpolation or resampling is required to ensure the temporal consistency of the data. Through the above synchronization process, the point cloud data and the inertial data can be accurately aligned.

[0072] Step S820: In the configuration file in the target driver package, obtain the extrinsic parameter data of the lidar sensor and the inertial measurement unit, and estimate the initial motion trajectory of the target object based on the extrinsic parameter data and the synchronized point cloud data and inertial data.

[0073] Exemplarily, first, read the relative position and orientation information between the lidar sensor and the IMU from the configuration file, that is, the extrinsic parameter data, which is used to describe the spatial relationship of the above sensors in the vehicle coordinate system. Exemplarily, the extrinsic parameter data includes the transformation matrix or transformation parameters between the lidar sensor and the inertial measurement unit, such as rotation and translation vectors.

[0074] It can be understood that the point cloud data provides accurate three-dimensional spatial information of the vehicle's surrounding environment, while the inertial data provides the vehicle's own motion state, including speed, acceleration, and attitude changes. By combining the extrinsic parameter data with these data, the position and motion state of the target object (such as a vehicle) in space can be estimated more accurately.

[0075] Step S830: Based on the initial motion trajectory of the target object, register the point cloud data into the standard coordinate system and accumulate it to form a point cloud map of the target driving test area.

[0076] The initial motion trajectory provides the relative position and orientation changes of the target object in space. The registration process begins with combining the point cloud data collected in real time with the known motion trajectory. By calculating the position of the point cloud data relative to the standard coordinate system, the accurate spatial position of each point within the target driving test area can be determined. Additionally, this process requires applying a transformation matrix to convert the point cloud data from the sensor coordinate system to the vehicle coordinate system and then from the vehicle coordinate system to the standard coordinate system.

[0077] As the vehicle moves, the continuously collected point cloud data is continuously registered into the standard coordinate system, thereby gradually constructing a three-dimensional map of the target driving test area. During this cumulative process, real-time processing and fusion of the newly collected point cloud data are required to ensure the continuity and consistency of the map. Moreover, the cumulatively formed point cloud map not only provides an accurate environmental model for the autonomous vehicle but is also crucial for tasks such as path planning, obstacle avoidance, and navigation during the driving test. Through the registration and accumulation of the point cloud data, the autonomous driving system can better understand and predict the vehicle's motion within the driving test area, thereby improving driving safety and efficiency.

[0078] In this embodiment, by synchronizing the point cloud data and inertial data, the consistency of data from different sensors in time is ensured, providing an accurate and synchronized data source for subsequent processing. Using the extrinsic parameter data in the configuration file and combining the synchronized data, the initial motion trajectory of the target object is estimated, enabling accurate estimation of the vehicle's position and motion state, which provides a basis for subsequent map construction and navigation. Based on the estimated initial motion trajectory, the point cloud data is registered into the standard coordinate system, and by accumulating this data, a continuous point cloud map of the target driving test area is formed, providing the autonomous vehicle with a dynamic environmental perception ability and improving the navigation accuracy and safety of the autonomous vehicle. Through the continuously updated point cloud map, the autonomous driving system can better simulate the real driving test environment, providing a more realistic and challenging driving test experience for the driver or the autonomous vehicle.

[0079] Figure 9 The following shows a schematic flowchart of the method for constructing a vector map provided by another embodiment of the present application. Figure 1 Based on the embodiment shown Figure 9 the following embodiment is extended Figure 9 The differences between the embodiment shown Figure 1 and the embodiment shown

[0080] As shown Figure 9 in this embodiment, after generating the point cloud map of the target driving test area based on the point cloud data and inertial data, the following steps are further included.

[0081] Step S910: Obtain the first parameter value corresponding to the storage parameter from the configuration file in the target driver package.

[0082] The storage parameter is a series of settings that control how the point cloud map is stored or managed. For example, the storage format of the map, the compression level, the storage location, etc. The first parameter value is a key value among these storage parameters, which determines whether the map needs to be stored or triggers certain specific storage behaviors. For example, if the first parameter value is a boolean value, it indicates whether the system should save the generated point cloud map to persistent storage.

[0083] Step S920: If the first parameter value is true, obtain the second parameter value corresponding to the path parameter from the configuration file.

[0084] In this embodiment, if the first parameter value is true (for example, if it is a boolean flag indicating that a storage operation should be performed), further obtain the second parameter value corresponding to the path parameter from the configuration file. Specifically, the path parameter defines the specific path or directory where the point cloud map should be stored, and the second parameter value is the specific representation of this path, such as a file system path or a network storage location.

[0085] Step S930: Store the point cloud map into the storage path indicated by the second parameter value.

[0086] In this embodiment, storing the point cloud map into the storage path indicated by the second parameter value ensures that the point cloud map is saved to the appropriate location according to the predefined path parameter, thus facilitating subsequent access, analysis, or use. Exemplarily, the storage process includes operations such as writing the point cloud data to a file, a database, or transmitting it to a remote server through a network.

[0087] In this embodiment, by obtaining the first parameter value of the storage parameter from the configuration file, it is possible to determine whether a storage operation needs to be performed, thereby avoiding unnecessary storage operations and improving efficiency. Based on the truth or falsehood of the first parameter value, further obtain the second parameter value of the path parameter from the configuration file, ensuring the flexibility and configurability of the storage path, and allowing users or system administrators to specify different storage locations according to needs. Finally, storing the point cloud map into the path specified by the second parameter value not only ensures the persistence of the data but also provides convenience for subsequent use and analysis of the data.

[0088] In some embodiments of this application, before obtaining the point cloud data and inertial data of the target driving test area, it further includes: testing the target sensor. Figure 10 The following shows a schematic flowchart of testing the target sensor provided by an embodiment of this application. Figure 1 Based on the shown embodiment, it extends to Figure 10The described embodiments will be mainly described below. Figure 10 The described embodiment and Figure 1 the differences between the described embodiments will be described. The same parts will not be elaborated.

[0089] As Figure 10 shown, in this embodiment, the target sensor is tested, including the following steps.

[0090] Step S1010: Determine the physical connection with the target sensor through the target port device file.

[0091] The port device file refers to a file used to represent and access a specific hardware device in a computer operating system. In Unix-like systems, including Linux, device files are usually located in the / dev directory. Each device file corresponds to a hardware device in the system, allowing users or programs to interact with the hardware device through standard file operation interfaces.

[0092] In this embodiment, the target port device file specifically refers to the device file corresponding to the target sensor. Through this file, the test system can identify the sensor and establish communication with it. For example, if the target sensor is a USB device connected to a computer, then the target port device file is a device file representing this USB device, such as / dev / ttyUSB0. After determining the physical connection, the target sensor can be interacted with by reading or writing to the target port device file, such as sending instructions, configuring parameters, or receiving data.

[0093] Step S1020: Set communication parameters that match the configuration parameters of the target sensor.

[0094] The communication parameters that match the configuration parameters of the target sensor refer to a series of communication protocols and parameters that need to be set to ensure that the target sensor can correctly exchange data with the test system or control unit. These parameters must be consistent with the technical specifications and requirements of the target sensor. Exemplarily, the communication parameters include: Baud Rate: It refers to the rate of data transmission, that is, the number of bits transmitted per unit time; Data Bits: It refers to the number of bits sent in each transmission; Stop Bits: Stop bits are used to identify the end of a data packet and can be 1 or 2 bits; Parity Bit: It is an error detection mechanism and can be None, Odd, or Even; Interface Type: For example, the sensor communicates through interfaces such as SPI, I2C, UART, USB, etc., and each interface has its specific communication protocol; Address: In some communication protocols, such as I2C, each device has a unique address that must be correctly set to ensure that data can be sent to the correct device; Clock Rate: For some interfaces, such as SPI, the clock rate needs to be set to synchronize data transmission.

[0095] Step S1030, receive the data stream sent by the target sensor, and based on the data stream, test the target sensor.

[0096] The data stream refers to a continuous sequence of data sent by the target sensor in real-time or at regular time intervals, including various environmental parameters or status information measured by the target sensor. Exemplarily, the received data stream is parsed and converted into a format that can be further analyzed. By monitoring and recording the data stream in real-time, it can be monitored whether the output of the target sensor is stable and compared with the expected performance parameters.

[0097] In addition, the test system also performs quality checks on the data stream, including data integrity, accuracy, and whether it is within a preset range. Through statistical analysis, such as calculating the average value, standard deviation, maximum value, and minimum value, etc., the accuracy and stability of the target sensor are evaluated. In addition, by comparing the readings in the data stream with known standards or reference values, the accuracy of the sensor can be evaluated.

[0098] During the test process, if an abnormality or deviation is found in the data stream, the test system will trigger a fault diagnosis program to identify possible causes of the problem, such as sensor failure, signal interference, or other environmental factors. Through this analysis, the target sensor can be calibrated or repaired to restore its performance.

[0099] In this embodiment, the physical connection with the target sensor is determined through the target port device file, ensuring that the target sensor can be recognized and a communication link can be established. Then, communication parameters matching the configuration parameters of the target sensor are set, ensuring that data can be transmitted between the target sensor and the test system in the correct format and rate. Finally, based on the test results of the data stream, real-time monitoring, performance evaluation, and fault diagnosis of the target sensor can be carried out to ensure that the target sensor can provide high-quality data output under various working conditions.

[0100] As described above, the implementation process of the vector map construction method based on the driving test subjects has been elaborated in detail. Next, combined with specific examples, the generation process of the vector map will be briefly analyzed.

[0101] First, connect and test the sensors to ensure that the lidar and IMU are correctly installed and the data transmission is normal. Then, use the tool cutecom to test the sensor data. By entering the corresponding commands in the terminal, select the correct device and baud rate, and observe the data stream of the above sensors. Next, start the IMU and lidar sensors respectively, and use relevant commands to check the data stream of the IMU and lidar sensors to ensure the correct reception of the data.

[0102] Subsequently, record the data. Use the rosbag tool to record the point cloud data collected by the lidar and the inertial data of the IMU. During the recording process, the above sensor devices need to move sufficiently in the environment where mapping is required to capture the complete environmental information. After the recording is completed, use the rosbag play command to play the recorded data set to observe the mapping process in rviz.

[0103] After the data is prepared, use the LIO-mapping method to process the recorded data to generate a point cloud map in pcd format. Set the correct parameters in the configuration file of LIO-mapping, including the extrinsic parameter data of the IMU relative to the lidar sensor and the topic name. By modifying the configuration file, enable the automatic saving function of the point cloud data and specify the saving path.

[0104] After the point cloud map is generated, it needs to be further processed in Unity to generate a vector map. First, create a new project in Unity and use CreateMap to draw the vector map of the driving school scene. According to the actual point cloud map of the driving school, drag the pre-defined library positions to the corresponding navigation points to complete the drawing of the point position map. After the drawing is completed, export the library position file, which will contain the vector map information of the driving school.

[0105] Finally, to generate an electronic fence, continue to use the electronic fence function in CreateMap. Add the trigger points of the fence in Unity, generate a set of fence points according to the requirements, and export the electronic fence file. In this way, the entire process from sensor connection to electronic fence generation is completed, providing the necessary map support for the application of driverless cars in the driving school scenario.

[0106] As described above in conjunction with Figures 1 to 10 , the embodiments of the vector map construction method based on driving test subjects of the present application are described in detail. Below, in conjunction with Figure 11 , the embodiments of the vector map construction device based on driving test subjects of the present application are described in detail. It should be understood that the description of the embodiments of the vector map construction method based on driving test subjects corresponds to the description of the embodiments of the vector map construction device based on driving test subjects. Therefore, for the parts not described in detail, reference can be made to the foregoing method embodiments.

[0107] Figure 11 The following shows a schematic structural diagram of a vector map construction device based on driving test subjects provided by an embodiment of the present application. As shown in Figure 11 , the vector map construction device 110 based on driving test subjects provided by the embodiments of the present application includes:

[0108] An acquisition module 1110, configured to acquire point cloud data and inertial data of a target driving test area collected by a target sensor;

[0109] A first generation module 1120, configured to generate a point cloud map of the target driving test area based on the point cloud data and the inertial data;

[0110] A drawing module 1130, configured to draw vector elements based on the environmental features included in the point cloud map;

[0111] A second generation module 1140, configured to generate a vector map of the target driving test area based on the vector elements.

[0112] In an embodiment of the present application, the environmental features include bay information corresponding to the target driving test subject. The drawing module 1130 is further configured to draw a plurality of navigation points corresponding to the bay information based on the bay information corresponding to the target driving test subject. Each navigation point includes semantic information for marking the geographical coordinates of the operation position in the bay information.

[0113] In an embodiment of the present application, the second generation module 1140 is further configured to move the plurality of navigation points to the corresponding positions of the bay information based on the semantic information corresponding to each of the plurality of navigation points; generate an electronic fence corresponding to the bay information based on the bay information, and obtain a vector map of the target driving test subject in the target driving test area.

[0114] In an embodiment of the present application, a second generation module 1140 generates a virtual boundary based on the boundary information in the storage location information; in the virtual boundary, trigger points are added to generate an electronic fence corresponding to the storage location information, where the trigger points are used to define key positions of the virtual boundary and trigger specific events.

[0115] In an embodiment of the present application, the target sensor includes a lidar sensor and an inertial measurement unit. The point cloud data is collected by the lidar sensor, and the inertial data is collected by the inertial measurement unit. The first generation module 1120 is further configured to synchronize the point cloud data and the inertial data; obtain the extrinsic parameter data of the lidar sensor and the inertial measurement unit in the configuration file of the target driver package, and estimate the initial motion trajectory of the target object based on the extrinsic parameter data and the synchronized point cloud data and inertial data; based on the initial motion trajectory of the target object, register the point cloud data into the standard coordinate system and accumulate to form a point cloud map of the target driving test area.

[0116] In an embodiment of the present application, the first generation module 1120 is further configured to obtain a first parameter value corresponding to the storage parameter in the configuration file of the target driver package; if the first parameter value is true, obtain a second parameter value corresponding to the path parameter in the configuration file; store the point cloud map to the storage path indicated by the second parameter value.

[0117] In an embodiment of the present application, the acquisition module 1110 is further configured to determine a physical connection with the target sensor through the target port device file; set communication parameters matching the configuration parameters of the target sensor; receive the data stream sent by the target sensor, and based on the data stream, test the target sensor.

[0118] Next, reference Figure 12 is made to describe the electronic device according to the embodiments of the present application. Figure 12 The following shows a schematic structural diagram of an electronic device provided by an exemplary embodiment of the present application.

[0119] As Figure 12 shown, the electronic device 120 includes one or more processors 1201 and a memory 1202.

[0120] The processor 1201 may be a central processing unit (CPU) or other forms of processing units with data processing capabilities and / or instruction execution capabilities, and may control other components in the electronic device 120 to perform desired functions.

[0121] The memory 1202 may include one or more computer program products, and the computer program products may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. The volatile memory may include, for example, random access memory (RAM) and / or cache memory, etc. The non-volatile memory may include, for example, read-only memory (ROM), hard disk, flash memory, etc. One or more computer program instructions may be stored on the computer-readable storage media, and the processor 1201 may run the program instructions to implement the vector map construction method based on driving test subjects and / or other desired functions of various embodiments of the present application described above. Various contents such as point cloud data, inertial data, point cloud map, vector elements, vector map, etc. may also be stored in the computer-readable storage media.

[0122] In one example, the electronic device 120 may further include: an input device 1203 and an output device 1204, and these components are interconnected through a bus system and / or other forms of connection mechanisms (not shown).

[0123] The input device 1203 may include, for example, a keyboard, a mouse, and so on.

[0124] The output device 1204 may output various information to the outside, including point cloud data, inertial data, point cloud map, vector elements, vector map, etc. The output device 1204 may include, for example, a display, a speaker, a printer, and a communication network and its connected remote output devices, and so on.

[0125] Of course, for simplicity, Figure 12 only some of the components related to the present application in the electronic device 120 are shown, and components such as buses, input / output interfaces, etc. are omitted. In addition, according to specific application scenarios, the electronic device 120 may further include any other appropriate components.

[0126] In addition to the above methods and devices, the embodiments of the present application may also be a computer program product, which includes computer program instructions, and when the computer program instructions are run by a processor, the processor is caused to execute the steps in the vector map construction method based on driving test subjects according to various embodiments of the present application described above in this specification.

[0127] The computer program product can be written in any combination of one or more programming languages for executing the program code of the operations of the embodiments of the present application. The programming languages include object-oriented programming languages such as Java, C++, etc., and also include conventional procedural programming languages such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computing device, partially on the user's device, executed as a stand-alone software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server.

[0128] In addition, an embodiment of the present application can also be a computer-readable storage medium, on which computer program instructions are stored. When the computer program instructions are run by a processor, the processor is caused to execute the steps in the method for constructing a vector map based on driving test subjects according to various embodiments of the present application described above in this specification.

[0129] The computer-readable storage medium can adopt any combination of one or more readable media. The readable medium can be a readable signal medium or a readable storage medium. The readable storage medium can, for example, include but is not limited to an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the above. More specific examples (non-exhaustive list) of the readable storage medium include: an electrical connection having one or more wires, a portable 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 disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0130] The basic principles of the present application have been described above in conjunction with specific embodiments. However, it should be noted that the advantages, benefits, effects, etc. mentioned in the present application are only examples and not limitations, and it cannot be considered that these advantages, benefits, effects, etc. are essential for each embodiment of the present application. In addition, the above-disclosed specific details are only for the purposes of illustration and easy understanding, and not for limitation. The above details do not limit the present application to necessarily adopt the above specific details for implementation.

[0131] The block diagrams of the devices, apparatuses, equipment, and systems involved in this application are only illustrative examples and are not intended to require or imply that they must be connected, arranged, and configured in the manner shown in the block diagrams. As those skilled in the art will recognize, these devices, apparatuses, equipment, and systems can be connected, arranged, and configured in any way. Words such as "comprising," "including," "having," etc. are open-ended terms that mean "including but not limited to" and can be used interchangeably with each other. The word "or" and "and" used herein refer to the phrase "and / or" and can be used interchangeably with it, unless the context clearly indicates otherwise. The phrase "such as" used herein refers to the phrase "such as but not limited to" and can be used interchangeably with it.

[0132] It should also be noted that in the devices, equipment, and methods of this application, each component or each step can be decomposed and / or recombined. These decompositions and / or recombinations should be regarded as equivalent solutions of this application.

[0133] The above description of the disclosed aspects is provided to enable any person skilled in the art to make or use this application. Various modifications to these aspects will be readily apparent to those skilled in the art, and the general principles defined herein can be applied to other aspects without departing from the scope of this application. Therefore, this application is not intended to be limited to the aspects shown herein, but rather to the broadest scope consistent with the principles and novel features disclosed herein.

[0134] The above description has been given for purposes of illustration and description. In addition, this description is not intended to limit the embodiments of this application to the forms disclosed herein. Although multiple example aspects and embodiments have been discussed above, those skilled in the art will recognize certain variations, modifications, changes, additions, and sub-combinations thereof.

Claims

1. A method for constructing a vector map based on driving test subjects, characterized in that, Applied to a master computer, the method includes: Obtaining point cloud data and inertial data of a target driving test area collected by a target sensor; Generating a point cloud map of the target driving test area based on the point cloud data and the inertial data; Drawing vector elements based on the environmental features included in the point cloud map; Generating a vector map of the target driving test area based on the vector elements; Wherein, the environmental features include bay information corresponding to a target driving test subject; the drawing of vector elements based on the environmental features included in the point cloud map includes: Drawing a plurality of navigation points corresponding to the bay information based on the bay information corresponding to the target driving test subject, each of the navigation points including semantic information for marking the geographical coordinates of the operation positions in the bay information; Wherein, the generating of the vector map of the target driving test area based on the vector elements includes: Moving the plurality of navigation points to the corresponding positions of the bay information based on the semantic information corresponding to each of the plurality of navigation points; Generating a virtual boundary based on the boundary information in the bay information; Adding trigger points in the virtual boundary to generate an electronic fence corresponding to the bay information, and obtaining the vector map of the target driving test subject in the target driving test area, wherein the trigger points are used to define key positions of the virtual boundary and trigger specific events; Wherein, the generating of the point cloud map of the target driving test area based on the point cloud data and the inertial data further includes: Using the LIO-mapping method to perform real-time three-dimensional modeling of the environment; Wherein, the drawing of vector elements based on the environmental features included in the point cloud map includes: Using Unity 3D software to convert the environmental features into the vector elements through analysis of the point cloud map.

2. The method according to claim 1, wherein The target sensor includes a lidar sensor and an inertial measurement unit, the point cloud data is collected by the lidar sensor, and the inertial data is collected by the inertial measurement unit; wherein, the generating of the point cloud map of the target driving test area based on the point cloud data and the inertial data includes: Synchronizing the point cloud data and the inertial data; Obtaining the extrinsic parameter data of the lidar sensor and the inertial measurement unit in a configuration file in a target driver package, and estimating an initial motion trajectory of a target object based on the extrinsic parameter data and the synchronized point cloud data and inertial data; Registering the point cloud data into a standard coordinate system based on the initial motion trajectory of the target object, and cumulatively forming the point cloud map of the target driving test area.

3. The method according to claim 1, wherein After generating the point cloud map of the target driving test area based on the point cloud data and the inertial data, it further includes: Obtaining a first parameter value corresponding to a storage parameter in a configuration file in a target driver package; If the first parameter value is true, obtaining a second parameter value corresponding to a path parameter in the configuration file; Storing the point cloud map to the storage path indicated by the second parameter value.

4. The method according to claim 1, wherein Before obtaining the point cloud data and inertial data of the target driving test area, it further includes: testing the target sensor; Among them, the testing of the target sensor includes: Determining the physical connection with the target sensor through the target port device file; Setting communication parameters matching the configuration parameters of the target sensor; Receiving the data stream sent by the target sensor and testing the target sensor based on the data stream.

5. A vector map construction device based on driving test subjects, characterized in that, Applied to the main control computer, it includes: An acquisition module for acquiring the point cloud data and inertial data of the target driving test area collected by the target sensor; A first generation module for generating a point cloud map of the target driving test area based on the point cloud data and the inertial data; A drawing module for drawing vector elements based on the environmental features included in the point cloud map; A second generation module for generating a vector map of the target driving test area based on the vector elements; Among them, the environmental features include the bay information corresponding to the target driving test subject; the drawing module is further configured to draw a plurality of navigation points corresponding to the bay information based on the bay information corresponding to the target driving test subject, and each navigation point includes semantic information for marking the geographical coordinates of the operation position in the bay information; The second generation module is further configured to move the plurality of navigation points to the corresponding positions of the bay information based on the semantic information corresponding to each of the plurality of navigation points; generate a virtual boundary based on the boundary information in the bay information; add a trigger point in the virtual boundary to generate an electronic fence corresponding to the bay information, and obtain the vector map of the target driving test subject in the target driving test area, where the trigger point is used to define the key position of the virtual boundary and trigger a specific event; Among them, the generating of the point cloud map of the target driving test area based on the point cloud data and the inertial data further includes: Using the LIO-mapping method to perform real-time three-dimensional modeling of the environment; Among them, the drawing of vector elements based on the environmental features included in the point cloud map includes: Using Unity 3D software to convert the environmental features into the vector elements through the analysis of the point cloud map.

6. A computer-readable storage medium, characterized in that, The storage medium stores a computer program, and the computer program is used to execute the vector map construction method based on the driving test subject according to any one of claims 1 to 5 above.

7. An electronic device, characterized in that, It includes: A processor; A memory for storing the executable instructions of the processor; The processor is used to execute the vector map construction method based on the driving test subject according to any one of claims 1 to 5 above.

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