Road hidden danger positioning method, device, electronic device and storage medium

Abstract

The embodiments of the present application disclose a method, device, electronic device and storage medium for locating road hidden dangers. The method includes: obtaining three-dimensional radar data of the road, trajectory data of a three-dimensional radar antenna for detecting road hidden dangers, and road surface marker data; converting the trajectory data into coordinate data; associating the road surface marker data with the coordinate data to obtain road surface marker positioning data corresponding to the road surface marker data; and performing slice analysis on the three-dimensional radar data in combination with the road surface marker positioning data and the coordinate data to determine the road hidden danger points. The technical solution provided by the embodiments of the present application can determine the location of road hidden danger points with the assistance of coordinate data and road surface markers, which can greatly improve the positioning accuracy of road hidden danger points.

Description

Technical Field

[0001] The embodiments of the present application relate to road hazard detection technology, and in particular to a road hazard positioning method, device, electronic device and storage medium. Background Art

[0002] In recent years, various provinces and cities have been carrying out large-scale inspections of urban road hazards (such as voids, cracks, looseness and water-rich bodies). Thousands or even tens of thousands of kilometers of roads need to be completed in a relatively short period of time, which places extremely high demands on construction efficiency.

[0003] Existing technologies typically use array radar detection solutions, which rely on Global Positioning System (GPS) and real-time kinematic (RTK) positioning, along with manual data processing and identification of road anomalies. Consequently, existing solutions not only have high labor costs and low data processing efficiency, but also suffer from low accuracy in detecting road hazards, making them unable to meet market demand. Summary of the Invention

[0004] The embodiments of the present application provide a road hazard positioning method, device, electronic device and storage medium, which can determine the location of road hazard points with the assistance of coordinate data and road surface markers, and can greatly improve the positioning accuracy of road hazard points.

[0005] In a first aspect, an embodiment of the present application provides a method for locating road hazards, the method comprising:

[0006] Acquire three-dimensional radar data of the road, trajectory data of the three-dimensional radar antenna, and road surface marker data;

[0007] Converting the trajectory data into coordinate data;

[0008] Associating the road surface marker data with the coordinate data to obtain road surface marker location data corresponding to the road surface marker data;

[0009] The three-dimensional radar data is sliced ​​and analyzed in combination with the road marker positioning data and the coordinate data to determine road hazard points.

[0010] In a second aspect, an embodiment of the present application provides a road hazard location device, which includes:

[0011] A data acquisition module is used to acquire three-dimensional radar data of the road, trajectory data of the three-dimensional radar antenna, and road surface marker data;

[0012] A data conversion module, used for converting the trajectory data into coordinate data;

[0013] a data association module, configured to associate the road marker data with the coordinate data to obtain road marker location data corresponding to the road marker data;

[0014] The hidden danger point determination module is used to perform slice analysis on the three-dimensional radar data in combination with the road marker positioning data and the coordinate data to determine the road hidden danger points.

[0015] In a third aspect, an embodiment of the present application provides an electronic device, the electronic device comprising:

[0016] one or more processors;

[0017] a storage device for storing one or more programs;

[0018] When the one or more programs are executed by the one or more processors, the one or more processors implement the road hazard location method described in any embodiment of the present application.

[0019] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium on which a computer program is stored, characterized in that when the computer program is executed by a processor, the road hazard locating method described in any embodiment of the present application is implemented.

[0020] The embodiments of the present application provide a method, device, electronic device and storage medium for locating road hidden dangers, which obtain three-dimensional radar data of the road, trajectory data of the three-dimensional radar antenna and road marker data; convert the trajectory data into coordinate data; associate the road marker data with the coordinate data to obtain road marker positioning data corresponding to the road marker data; and perform slice analysis on the three-dimensional radar data in combination with the road marker positioning data and coordinate data to determine the road hidden danger points. The present application can determine the location of road hidden danger points with the assistance of coordinate data and road markers, and can greatly improve the positioning accuracy of road hidden danger points. It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present application, nor is it used to limit the scope of the present application. Other features of the present application will become easy to understand through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] The accompanying drawings are provided to facilitate a better understanding of the present invention and do not constitute a limitation of the present application.

[0022] Figure 1 A schematic diagram of a first process flow of a road hazard location method provided in an embodiment of the present application;

[0023] Figure 2 A schematic diagram of a road hazard point provided in an embodiment of the present application;

[0024] Figure 3 A second flow chart of a method for locating road hazards provided in an embodiment of the present application;

[0025] Figure 4A A schematic diagram of three-dimensional radar positioning data provided by an embodiment of the present application;

[0026] Figure 4B A schematic diagram of performing slice analysis on three-dimensional radar positioning data provided in an embodiment of the present application;

[0027] Figure 5 A third flow chart of a method for locating road hazards provided in an embodiment of the present application;

[0028] Figure 6 A schematic diagram of the structure of a road hazard locating device provided in an embodiment of the present application;

[0029] Figure 7 This is a block diagram of an electronic device used to implement a road hazard location method in an embodiment of the present application. DETAILED DESCRIPTION

[0030] To make the purpose, technical solutions, and advantages of the embodiments of this application more clear, the technical solutions in the embodiments of this application will be clearly and completely described below in conjunction with the drawings in the embodiments of this application. Obviously, the described embodiments are part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0031] Example 1

[0032] Figure 1 A schematic diagram of a first process flow of a road hazard location method provided in an embodiment of the present application; Figure 2 A schematic diagram of a road hazard point provided in an embodiment of the present application. This embodiment can be applied to road inspections to identify road hazard points, such as voids, cracks, loose areas, and water-rich areas. A road hazard location method provided in this embodiment can be performed by a road hazard location device provided in an embodiment of the present application. This device can be implemented via software and / or hardware and integrated into the electronic device that performs this method. Preferably, the electronic device in the embodiment of the present application can be a road hazard detection device.

[0033] See also Figure 1 The method of this embodiment includes but is not limited to the following steps:

[0034] S110 , obtaining three-dimensional radar data of the road, trajectory data of the three-dimensional radar antenna, and road surface marker data.

[0035] 3D radar data refers to the 3D radar point cloud data generated by road detection. Trajectory data refers to the trajectory of the 3D radar antenna. Road hazard detection equipment also acts as the device that pulls the 3D radar antenna. Road surface marker data includes at least one of the following: images of manhole covers, images of road damage, and images of road repairs.

[0036] In the embodiments of the present application, the road hazard detection device is equipped with a 3D detection radar to obtain 3D radar data of the road. It is also equipped with real-time kinematic (RTK) and an inertial measurement unit (IMU) to obtain trajectory data of the 3D radar antenna. It is also equipped with a ground camera to capture road marking data by photographing the ground.

[0037] In the embodiments of this application, when a road hazard detection device is used to inspect a road, three-dimensional radar data, trajectory data, and road marker data are simultaneously collected. This application does not limit the frequency of collecting three-dimensional radar data, trajectory data, and road marker data, and those skilled in the art can set these frequencies based on actual needs when implementing the technical solutions of this application.

[0038] It should be noted that the 3D radar data, trajectory data and road marker data all carry the collection time (timestamp).

[0039] S120: Convert the trajectory data into coordinate data.

[0040] The present application does not limit the coordinate system used for the coordinate data. Preferably, the coordinate data may be in the world coordinate system or in other coordinate systems.

[0041] In this embodiment of the present application, the IMU includes at least an accelerometer and a gyroscope. The accelerometer is used to collect acceleration information from the road hazard detection equipment, and the gyroscope is used to collect heading angle information from the road hazard detection equipment. Therefore, each trajectory data of the 3D radar antenna contains at least corresponding acceleration information and heading angle information. In this embodiment, each trajectory data needs to be converted into corresponding coordinate data.

[0042] In this step, RTK and IMU are used to collect trajectory data from the 3D radar antenna, generating coordinate data. This setup provides more accurate coordinate data, meaning the coordinate data has a smaller error. This allows for more precise positioning of potential road hazards using this coordinate data and road marker data in the following steps.

[0043] Optionally, the specific process of converting each trajectory data into corresponding coordinate data in this step can be achieved through the following two steps:

[0044] S1201: Obtain coordinate data corresponding to initial trajectory data.

[0045] In an embodiment of the present application, the road hazard detection device is configured with RTK, which can measure the coordinate data corresponding to the initial trajectory data.

[0046] S1202 : Calculate coordinate information corresponding to each trajectory data according to the coordinate data corresponding to the initial trajectory data and the inertial navigation data increment of the adjacent trajectory data.

[0047] In an embodiment of the present application, the road hazard detection device is equipped with an IMU that can measure the acceleration increment and heading angle increment of adjacent trajectory data. Based on the coordinate data corresponding to the initial trajectory data and the acceleration increment and heading angle increment corresponding to the second trajectory data, the coordinate information corresponding to the second trajectory data can be calculated. Similarly, based on the coordinate data corresponding to the initial trajectory data and the acceleration increment and heading angle increment between the two adjacent trajectory data, the coordinate information corresponding to each trajectory data is calculated.

[0048] S130: Associating the road surface marker data with the coordinate data to obtain road surface marker positioning data corresponding to the road surface marker data.

[0049] In the embodiment of the present application, after the trajectory data is converted into coordinate data in step S120, the road marker data is then associated with the coordinate data to obtain the road marker location data corresponding to the road marker data. The road marker location data refers to the coordinate data associated with the road marker data. The advantage of this arrangement is that each road marker data item has its corresponding location information, allowing for clear identification of the corresponding location on the measured road.

[0050] S140: Slice and analyze the three-dimensional radar data in combination with the road marker positioning data and coordinate data to determine potential road hazards.

[0051] In an embodiment of the present application, when performing slice analysis on 3D radar data, if an outlier is detected at a certain location in the 3D radar data, the outlier can be located based on the coordinate data, and then the road marker location data can be used to determine whether the outlier is a road hazard. Alternatively, the road marker location data can be used to determine whether the outlier is a road hazard. If so, the coordinate data can be used to locate the road hazard.

[0052] In this step, when performing slice analysis on the three-dimensional radar data in combination with the road marker positioning data and coordinate data, it is necessary not only to determine whether the abnormal points in the three-dimensional radar data are road hazard points, but also to determine the location information of the road hazard points.

[0053] Optionally, the road hazard detection device may also be equipped with a camera for capturing street scenes. The captured street scene images and road surface marker data may be used together to assist in identifying road hazard points on the road being tested.

[0054] For example, Figure 2 As shown in the figure, there is an anomaly at a certain location in the 3D radar positioning data (cross-section). A manhole cover can be seen in the street view imagery, and also in the road marker data (i.e., the manhole cover image). Therefore, it can be determined that the anomaly is not a road hazard, but rather a 3D radar data anomaly caused by the manhole cover.

[0055] It should be noted that usually only the road marker positioning data is needed (that is, street view images are not required) to assist in determining whether an abnormal point in the three-dimensional radar data is a road hazard point.

[0056] The technical solution provided by this embodiment first obtains three-dimensional radar data, trajectory data, and road marker data of the road; then converts the trajectory data into coordinate data; then associates the road marker data with the coordinate data to obtain road marker positioning data corresponding to the road marker data; finally, slices and analyzes the three-dimensional radar data in combination with the road marker positioning data and coordinate data to determine road hazard points. This application can solve the problem of manually identifying road hazard points in the existing technology, thereby also improving the detection efficiency of road hazard points; this application determines the location of road hazard points with the assistance of coordinate data and road markers, which can improve the detection accuracy and positioning precision of road hazard points and avoid secondary re-surveying of the road.

[0057] Example 2

[0058] Figure 3 A second flow chart of a method for locating road hazards provided in an embodiment of the present application; Figure 4A A schematic diagram of three-dimensional radar positioning data provided by an embodiment of the present application; Figure 4B This is a schematic diagram of slicing and analyzing 3D radar positioning data provided by an embodiment of the present application. This embodiment of the present application is an optimization based on the above embodiment. Specifically, the optimization is as follows: This embodiment provides a detailed explanation of the process of determining road hidden danger points.

[0059] See also Figure 3 The method of this embodiment includes but is not limited to the following steps:

[0060] S210: Acquire three-dimensional radar data of the road, trajectory data of the three-dimensional radar antenna, and road surface marker data.

[0061] In the embodiments of the present application, the road hazard detection device is equipped with a 3D detection radar to obtain 3D radar data of the road. It is also equipped with an RTK and IMU to obtain trajectory data of the 3D radar antenna. It is also equipped with a ground camera to capture road marking data by photographing the ground.

[0062] In the embodiments of this application, when a road hazard detection device is used to inspect a road, three-dimensional radar data, trajectory data, and road marker data are simultaneously collected. This application does not limit the frequency of collecting three-dimensional radar data, trajectory data, and road marker data, and those skilled in the art can set these frequencies based on actual needs when implementing the technical solutions of this application.

[0063] Preferably, the ground camera can be a 5-megapixel high-definition camera, with a peak acquisition rate of 80 road marking data frames per second. Both the road marking data and the 3D radar data are synchronized with the GPS clock. The acquisition frequency of the inertial measurement unit can be 1000 Hz, and the acquisition frequency of the 3D detection radar can be 200 Hz.

[0064] S220 , obtaining coordinate data corresponding to the initial trajectory data; and calculating coordinate information corresponding to each trajectory data according to the coordinate data corresponding to the initial trajectory data and the inertial navigation data increment of adjacent trajectory data.

[0065] In an embodiment of the present application, the road hazard detection device is configured with an RTK, which can measure the coordinate data corresponding to the initial trajectory data. The road hazard detection device is configured with an IMU, which can measure the acceleration increment and heading angle increment of adjacent trajectory data. Based on the coordinate data corresponding to the initial trajectory data and the acceleration increment and heading angle increment corresponding to the second trajectory data, the coordinate information corresponding to the second trajectory data can be calculated. By analogy, the coordinate information corresponding to each trajectory data is calculated based on the coordinate data corresponding to the initial trajectory data and the acceleration increment and heading angle increment between the two adjacent trajectory data. In the embodiment, the error accuracy of each coordinate information is within 20 centimeters.

[0066] S230: Associating the road surface marker data with the coordinate data to obtain road surface marker positioning data corresponding to the road surface marker data.

[0067] S240 , associating the coordinate data with the three-dimensional radar data to obtain three-dimensional radar positioning data, and performing slice analysis on the three-dimensional radar positioning data to determine initial road hazard points.

[0068] In the embodiment of the present application, the process of determining the initial road hidden danger point is as follows: First, the coordinate data and the three-dimensional radar data are associated to obtain the three-dimensional radar positioning data. Among them, the three-dimensional radar positioning data refers to the coordinate data corresponding to the three-dimensional radar data. The advantage of this setting is that each three-dimensional radar data can have its corresponding positioning information, and it can be clearly known which position of each three-dimensional radar data corresponds to the measured road. Second, the three-dimensional radar positioning data is sliced ​​and analyzed according to equal depth (or unequal depth) in the depth dimension of the road surface to determine the initial road hidden danger point. Figure 4A The following is a schematic diagram of three-dimensional radar positioning data. Figure 4B The figure shows a schematic diagram of slicing analysis of the three-dimensional radar positioning data. It can be seen from the figure that the three-dimensional radar positioning data map is divided into six slice data maps, and whether there are initial road hidden danger points can be analyzed from these six slice data maps.

[0069] S250: Filter out target road hidden danger points from the initial road hidden danger points based on the road marker positioning data.

[0070] In this embodiment of the present application, due to the presence of certain interfering objects on the road being measured (such as manhole covers, road damage, and road repairs), these objects can also cause anomalies in the 3D radar positioning data. Therefore, not all of the initial road potential risk points determined in step S240 are actual road potential risk points, but also include anomalies caused by certain interfering objects. Therefore, it is necessary to filter the target road potential risk points, i.e., the actual road potential risk points, from the initial road potential risk points based on the road marker positioning data.

[0071] Specifically, because the 3D radar positioning data contains positioning information, the location of the initial road hazard point can be determined. The road marker positioning data also contains positioning information. Based on the positioning information of the initial road hazard point, the road marker closest to the initial road hazard point can be identified. Based on the road marker, the road surface is judged to be normal. If not, the initial road hazard point is eliminated, thereby obtaining the target road hazard point.

[0072] For example, if the road surface marker is not an interference object (such as a manhole cover, road damage, road repair, etc.), indicating that the road surface is in normal condition, then the initial road hazard point is the target road hazard point, that is, the real road hazard point; if the road surface marker is an interference object, indicating that the road surface is in an abnormal condition, then the initial road hazard point is not the target road hazard point, that is, an abnormal point caused by some interference object.

[0073] The technical solution provided by this embodiment first acquires three-dimensional radar data, trajectory data, and road marker data for a road; obtains coordinate data corresponding to the initial trajectory data; then calculates the coordinate information corresponding to each trajectory data based on the coordinate data corresponding to the initial trajectory data and the inertial navigation data increments of adjacent trajectory data; then correlates the road marker data with the coordinate data to obtain road marker location data; then correlates the coordinate data with the three-dimensional radar data to obtain three-dimensional radar location data, and performs slice analysis on the three-dimensional radar location data to determine initial road hazard points; and then selects target road hazard points from the initial road hazard points based on the road marker location data. This application first correlates the coordinate data with the three-dimensional radar data so that the determined initial road hazard points contain location information; then determines the road marker closest to the initial road hazard point; and finally, uses this road marker to assist in determining the target road hazard point. This technical solution, using coordinate data and road markers to determine the location of road hazard points, not only improves the accuracy of road hazard point detection but also significantly increases the accuracy of road hazard point location. This application can also improve road detection efficiency by avoiding secondary road retesting, thus meeting market demand for road detection.

[0074] Example 3

[0075] Figure 5 This is a third flow chart of a method for locating road hazards provided by an embodiment of the present application. This embodiment of the present application is an optimization based on the above embodiment. Specifically, the optimization is as follows: This embodiment provides a detailed explanation of the preprocessing process of 3D radar data and the thinning process of coordinate data.

[0076] refer to Figure 5 The method of this embodiment includes but is not limited to the following steps:

[0077] S310: Acquire three-dimensional radar data of the road, trajectory data of the three-dimensional radar antenna, and road surface marker data.

[0078] In the embodiments of the present application, the road hazard detection device is equipped with a 3D detection radar to obtain 3D radar data of the road. It is also equipped with an RTK and IMU to obtain trajectory data of the 3D radar antenna. It is also equipped with a ground camera to capture road marking data by photographing the ground.

[0079] In the embodiments of this application, when a road hazard detection device is used to inspect a road, three-dimensional radar data, trajectory data, and road marker data are simultaneously collected. This application does not limit the frequency of collecting three-dimensional radar data, trajectory data, and road marker data, and those skilled in the art can set these frequencies based on actual needs when implementing the technical solutions of this application.

[0080] Preferably, the ground camera can be a 5-megapixel high-definition camera, with a peak acquisition rate of 80 road marking data per second, and the acquisition of road marking data is based on GPS clock synchronization. The acquisition frequency of the inertial measurement unit can be 1000Hz, and the acquisition frequency of the 3D detection radar can be 200Hz.

[0081] S320: Perform data preprocessing on the three-dimensional radar data to obtain processed three-dimensional radar data.

[0082] In this embodiment of the present application, after acquiring 3D radar data of a road, data preprocessing is required to ensure that the data meets preset requirements. Data preprocessing includes at least removing direct waves, removing DC drift, and performing time zero correction on the 3D radar data. This embodiment addresses the existing practice of manual data processing, improving data processing efficiency and reducing labor costs.

[0083] Among them, this application does not limit the specific operation steps in data preprocessing, and may also include other data processing operation steps. Those skilled in the art can perform data preprocessing on the acquired three-dimensional radar data according to the actual needs during the implementation of this embodiment.

[0084] S330: Convert the trajectory data into coordinate data.

[0085] S340 , determining a thinning factor according to the timestamp of the three-dimensional radar data and the timestamp of the coordinate data; and performing thinning processing on the coordinate data according to the thinning factor to obtain processed coordinate data.

[0086] In an embodiment of the present application, when the acquisition frequencies of the three-dimensional radar data and the trajectory data are different, after the trajectory data is converted into coordinate data, the three-dimensional radar data or the coordinate data is thinned out. In actual situations, the acquisition frequency of the trajectory data is usually greater than the acquisition frequency of the three-dimensional radar data, and thus the coordinate data is thinned out so that each coordinate data has the same timestamp as each three-dimensional radar data. Specifically, a thinning factor is first determined based on the timestamp of the three-dimensional radar data and the timestamp of the coordinate data; the coordinate data is thinned out based on the thinning factor to obtain processed coordinate data. The advantage of this setting is that the timestamps of the coordinate data, the road marker data, and the three-dimensional radar data are all one-to-one corresponding, which can facilitate the subsequent association of the coordinate data with the road marker data and the three-dimensional radar data.

[0087] Optionally, before converting the trajectory data into coordinate data, thinning processing may be performed on the trajectory data.

[0088] S350: Correlate the road surface marker data with the processed coordinate data to obtain road surface marker positioning data corresponding to the road surface marker data.

[0089] In the embodiment of the present application, after the coordinate data is thinned out in step S340, the road marker data is associated with the processed coordinate data to obtain road marker positioning data corresponding to the road marker data.

[0090] S360: Perform slice analysis on the processed three-dimensional radar data in combination with the road marker positioning data and the processed coordinate data to determine potential road hazards.

[0091] In an embodiment of the present application, the three-dimensional radar data that has undergone data preprocessing and the coordinate data that has undergone thinning processing are first associated to obtain three-dimensional radar positioning data; then, the three-dimensional radar positioning data is sliced ​​and analyzed to determine the initial road hazard points; finally, the target road hazard points are screened out from the initial road hazard points based on the road surface marker positioning data.

[0092] The technical solution provided by this embodiment first obtains the three-dimensional radar data, trajectory data and road marker data of the road, then pre-processes the three-dimensional radar data to obtain processed three-dimensional radar data, and converts the trajectory data into coordinate data; then thins the coordinate data according to the determined thinning factor to obtain processed coordinate data; then associates the road marker data with the processed coordinate data to obtain road marker positioning data; finally, slices and analyzes the processed three-dimensional radar data in combination with the road marker positioning data and the processed coordinate data to determine the road hidden danger points. The technical solution of this application determines the location of road hidden danger points with the assistance of coordinate data and road markers, which can not only improve the detection accuracy of road hidden danger points, but also greatly improve the positioning accuracy of road hidden danger points. Since this application can avoid secondary re-surveying of the road, it can also improve the efficiency of road detection and meet the market demand for road detection.

[0093] Example 4

[0094] Figure 6 This is a schematic diagram of a road hazard location device provided in an embodiment of the present application. Figure 6 As shown, the apparatus 600 may include:

[0095] The data acquisition module 610 is used to acquire three-dimensional radar data of the road, trajectory data of the three-dimensional radar antenna, and road surface marker data.

[0096] The data conversion module 620 is configured to convert the trajectory data into coordinate data.

[0097] The data association module 630 is used to associate the road surface marker data with the coordinate data to obtain road surface marker positioning data corresponding to the road surface marker data.

[0098] The hidden danger point determination module 640 is used to perform slice analysis on the three-dimensional radar data in combination with the road marker positioning data and the coordinate data to determine the road hidden danger points.

[0099] Furthermore, the hidden danger point determination module 640 includes: a data analysis unit and a hidden danger point screening unit;

[0100] The data analysis unit is used to associate the coordinate data with the three-dimensional radar data to obtain three-dimensional radar positioning data, and perform slice analysis on the three-dimensional radar positioning data to determine initial road hazard points.

[0101] The potential danger point screening unit is used to screen out target road potential danger points from the initial road potential danger points according to the road marker positioning data.

[0102] Furthermore, the above-mentioned hidden danger point screening unit can be specifically used to determine the road marker closest to the initial road hidden danger point based on the road marker positioning data and the positioning information of the initial road hidden danger point; judge whether the road surface is in normal condition based on the road marker, and if not, exclude the initial road hidden danger point, thereby obtaining the target road hidden danger point.

[0103] Furthermore, the data conversion module 620 may be specifically configured to: obtain coordinate data corresponding to the initial trajectory data; and calculate coordinate information corresponding to each trajectory data based on the coordinate data corresponding to the initial trajectory data and the inertial navigation data increment of adjacent trajectory data.

[0104] Furthermore, the road hidden danger location device may further include: a data rarefaction module;

[0105] The data thinning module is configured to determine a thinning factor based on the timestamp of the three-dimensional radar data and the timestamp of the coordinate data; and perform thinning processing on the coordinate data according to the thinning factor to obtain processed coordinate data;

[0106] Correspondingly, the data association module 630 is configured to associate the road marker data with the processed coordinate data to obtain road marker positioning data corresponding to the road marker data.

[0107] Furthermore, the above-mentioned road hidden danger location device may further include: a data pre-processing module;

[0108] The data preprocessing module is used to perform data preprocessing on the three-dimensional radar data to obtain processed three-dimensional radar data, wherein the data preprocessing at least includes removing direct waves, removing DC drift, and performing time zero correction;

[0109] Correspondingly, the hidden danger point determination module 640 is configured to perform a slice analysis on the processed three-dimensional radar data in combination with the road marker positioning data and the coordinate data to determine the road hidden danger points.

[0110] The road hazard locating device provided in this embodiment can be applied to the road hazard locating method provided in any of the above embodiments, and has corresponding functions and beneficial effects.

[0111] Example 5

[0112] Figure 7 is a block diagram of an electronic device used to implement a road hazard location method according to an embodiment of the present application. Figure 7 A block diagram of an exemplary electronic device suitable for implementing the embodiments of the present application is shown. Figure 7The electronic device shown is only an example and should not limit the functionality and scope of use of the embodiments of the present application. The electronic device may typically be a smartphone, tablet computer, laptop computer, vehicle-mounted terminal, wearable device, etc.

[0113] like Figure 7 As shown, electronic device 700 is implemented as a general purpose computing device. Components of electronic device 700 may include, but are not limited to, one or more processors or processing units 716, memory 728, and a bus 718 connecting various system components (including memory 728 and processing unit 716).

[0114] Bus 718 represents one or more of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, a processor or a local bus using any of a variety of bus architectures. Examples of these architectures include, but are not limited to, an Industry Standard Architecture (ISA) bus, a Micro Channel Architecture (MAC) bus, an Enhanced ISA bus, a Video Electronics Standards Association (VESA) local bus, and a Peripheral Component Interconnect (PCI) bus.

[0115] The electronic device 700 typically includes a variety of computer system readable media. These media can be any available media that can be accessed by the electronic device 700, including volatile and non-volatile media, removable and non-removable media.

[0116] The memory 728 may include computer system readable media in the form of volatile memory, such as random access memory (RAM) 730 and / or cache memory 732. The electronic device 700 may further include other removable / non-removable, volatile / non-volatile computer system storage media. By way of example only, the storage system 734 may be used to read and write non-removable, non-volatile magnetic media ( Figure 7 Not shown, often called a "hard drive"). Although Figure 7 Not shown, a disk drive for reading and writing to a removable non-volatile disk (e.g., a "floppy disk"), and an optical drive for reading and writing to a removable non-volatile optical disk (e.g., a CD-ROM, DVD-ROM, or other optical media) may be provided. In these cases, each drive may be connected to the bus 718 via one or more data medium interfaces. The memory 728 may include at least one program product having a set (e.g., at least one) of program modules that are configured to perform the functions of each embodiment of the present application.

[0117] A program / utility 740 having a set (at least one) of program modules 742 may be stored, for example, in memory 728. Such program modules 742 include, but are not limited to, an operating system, one or more application programs, other program modules, and program data, each of which, or some combination thereof, may include an implementation of a network environment. Program modules 742 generally perform the functions and / or methods described in the embodiments of the present application.

[0118] The electronic device 700 may also communicate with one or more external devices 714 (e.g., a keyboard, a pointing device, a display 724, etc.), and may also communicate with one or more devices that enable a user to interact with the electronic device 700, and / or any device that enables the electronic device 700 to communicate with one or more other computing devices (e.g., a network card, a modem, etc.). Such communication may be performed through an input / output (I / O) interface 722. Furthermore, the electronic device 700 may also communicate with one or more networks (e.g., a local area network (LAN), a wide area network (WAN), and / or a public network, such as the Internet) through a network adapter 720. Figure 7 As shown, the network adapter 720 communicates with other modules of the electronic device 700 via the bus 718. Figure 7 Not shown, other hardware and / or software modules may be used in conjunction with electronic device 700, including but not limited to microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0119] The processing unit 716 executes various functional applications and data processing by running the programs stored in the memory 728, such as implementing the road hazard positioning method provided in any embodiment of the present application.

[0120] Example 6

[0121] Embodiment 6 of the present application also provides a computer-readable storage medium on which a computer program (or computer-executable instructions) is stored. When the program is executed by a processor, it can be used to execute the road hazard location method provided in any of the above embodiments of the present application.

[0122] The computer storage medium of the embodiment of the present application can adopt any combination of one or more computer-readable media.Computer-readable media can be computer-readable signal media or computer-readable storage media.Computer-readable storage media can be, for example, but not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices or components, or any combination thereof.More specific examples (non-exhaustive list) of computer-readable storage media include: electrical connections with one or more wires, portable computer disks, hard disks, random access memories (RAM), read-only memories (ROM), erasable programmable read-only memories (EPROM or flash memory), optical fibers, portable compact disk read-only memories (CD-ROMs), optical storage devices, magnetic storage devices, or any suitable combination thereof.In this document, computer-readable storage media can be any tangible medium containing or storing a program, which can be used by an instruction execution system, device or device or used in combination with it.

[0123] A computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, which carries computer-readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium that can transmit, propagate, or transport a program for use by or in conjunction with an instruction execution system, apparatus, or device.

[0124] Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.

[0125] The computer program code for performing the operation of the embodiment of the application can be written in one or more programming languages ​​or a combination thereof, and the programming language includes object-oriented programming languages-such as Java, Smalltalk, C++, and also includes conventional procedural programming languages-such as "C" language or similar programming languages. The program code can be executed completely on the user's computer, partially on the user's computer, executed as an independent software package, partially on the user's computer and partially on the remote computer, or executed completely on the remote computer or server. In the case of a remote computer, the remote computer can be connected to the user's computer by any type of network-including local area network (LAN) or wide area network (WAN), or, it can be connected to an external computer (for example, utilizing an Internet service provider to connect by the Internet).

Claims

1. A method for locating road hazards, characterized in that: The method comprises: Acquire three-dimensional radar data of the road, trajectory data of the three-dimensional radar antenna, and road surface marker data; Converting the trajectory data into coordinate data; Associating the road surface marker data with the coordinate data to obtain road surface marker location data corresponding to the road surface marker data; performing a slicing analysis on the three-dimensional radar data in combination with the road marker positioning data and the coordinate data to determine a road potential hazard point, including: correlating the coordinate data with the three-dimensional radar data to obtain three-dimensional radar positioning data, and performing a slicing analysis on the three-dimensional radar positioning data to determine an initial road potential hazard point; and screening a target road potential hazard point from the initial road potential hazard points based on the road marker positioning data; Among them, the method of screening out the target road hazard point from the initial road hazard point based on the road marker positioning data includes: determining the road marker closest to the initial road hazard point based on the road marker positioning data and the positioning information of the initial road hazard point; judging whether the road surface is in normal condition based on the road marker, and if not, excluding the initial road hazard point to obtain the target road hazard point.

2. The road hidden danger location method according to claim 1, characterized in that: The converting the trajectory data into coordinate data includes: Get the coordinate data corresponding to the initial trajectory data; Coordinate information corresponding to each trajectory data is calculated according to the coordinate data corresponding to the initial trajectory data and the inertial navigation data increment of adjacent trajectory data.

3. The road hidden danger location method according to claim 1, characterized in that: Before associating the road surface marker data with the coordinate data to obtain road surface marker location data corresponding to the road surface marker data, the method includes: determining a thinning factor according to a timestamp of the three-dimensional radar data and a timestamp of the coordinate data; performing thinning processing on the coordinate data according to the thinning factor to obtain processed coordinate data; Correspondingly, the road surface marker data is associated with the processed coordinate data to obtain road surface marker positioning data corresponding to the road surface marker data.

4. The road hidden danger location method according to claim 1, characterized in that: After acquiring the 3D radar data of the road, including: Performing data preprocessing on the three-dimensional radar data to obtain processed three-dimensional radar data, wherein the data preprocessing at least includes removing direct waves, removing DC drift, and performing time zero correction; Accordingly, the processed three-dimensional radar data is sliced ​​and analyzed in combination with the road marker positioning data and the coordinate data to determine the road hazard points.

5. The road hidden danger location method according to claim 1, characterized in that: The road surface marker data includes at least one of the following: a road manhole cover image, a road damage image, and a road repair image.

6. A road hidden danger positioning device, characterized in that: For implementing the road hidden danger location method according to claim 1, the device comprises: A data acquisition module is used to acquire three-dimensional radar data of the road, trajectory data of the three-dimensional radar antenna, and road surface marker data; A data conversion module, used for converting the trajectory data into coordinate data; a data association module, configured to associate the road marker data with the coordinate data to obtain road marker location data corresponding to the road marker data; The hidden danger point determination module is used to perform slice analysis on the three-dimensional radar data in combination with the road marker positioning data and the coordinate data to determine the road hidden danger points.

7. An electronic device, characterized in that: The electronic device comprises: one or more processors; a storage device for storing one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement the road hazard location method as described in any one of claims 1 to 5.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the road hidden danger location method as claimed in any one of claims 1 to 5 is implemented.

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