CN118262313A - Road area detection method and device and electronic equipment - Google Patents
Road area detection method and device and electronic equipment Download PDFInfo
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Abstract
The invention discloses a road area detection method, a device and electronic equipment, which relate to the technical field of automatic driving and comprise the following steps: acquiring original point cloud data of an area to be identified by using a laser radar arranged on a vehicle, and performing grid division on the original point cloud data to obtain a first type grid and a second type grid; wherein the first type of grid comprises at least one original point cloud data; the second class of grids have no original point cloud data; adopting a preset road detection model, and determining the road type of the first type of grids according to the original point cloud data of the first type of grids; the road type comprises a road area and a road boundary; and selecting a reference grid from the first type of grids, and determining the road type of the second type of grids by adopting the road type of the reference grid and the perception range parameters of the laser radar. The detection accuracy of the road area is improved.
Description
Technical Field
The present invention relates to the field of automatic driving technologies, and in particular, to a method and an apparatus for detecting a road area, and an electronic device.
Background
With the development of computer and artificial intelligence technologies, autopilot technology has become a hotspot for research and has been subject to rapid development iterations. The detection of the road area may provide information for decisions on driving behavior and path planning and control, and may also provide auxiliary information for obstacle detection. Thus, detection of road areas is a hotspot for concern and research within the field.
In the related art, the detection of the road area is generally performed using visual information and based on a deep learning model. However, visual information is easily affected by external light environment and lacks depth information of a scene, so that the detection accuracy of a road area is low and the requirement of high robustness of automatic driving is difficult to meet for the perception of some special or complex scenes.
Disclosure of Invention
The invention provides a road area detection method, a road area detection device and electronic equipment, and aims to solve the problem of low detection accuracy of a road area in the related technology.
According to an aspect of the present invention, there is provided a road area detection method including:
Acquiring original point cloud data of an area to be identified by using a laser radar arranged on a vehicle, and performing grid division on the original point cloud data to obtain a first type grid and a second type grid; wherein the first type of grid comprises at least one original point cloud data; the second class of grids do not have original point cloud data;
Determining the road type of the first type grid according to the original point cloud data of the first type grid by adopting a preset road detection model; wherein the road type comprises a road area and a road boundary;
and selecting a reference grid from the first type of grids, and determining the road type of the second type of grids by adopting the road type of the reference grid and the perception range parameter of the laser radar.
According to another aspect of the present invention, there is provided a road area detection apparatus including:
The acquisition unit is used for acquiring original point cloud data of the area to be identified by using a laser radar arranged on the vehicle;
The gridding unit is used for carrying out gridding on the original point cloud data to obtain a first type of grids and a second type of grids; wherein the first type of grid comprises at least one original point cloud data; the second class of grids do not have original point cloud data;
the detection unit is used for determining the road type of the first type grid according to the original point cloud data of the first type grid by adopting a preset road detection model; wherein the road type comprises a road area and a road boundary;
The detection unit is further configured to select a reference grid from the first type of grids, and determine a road type of the second type of grids by using a road type of the reference grid and a sensing range parameter of the laser radar.
According to another aspect of the present invention, there is provided an electronic apparatus including:
At least one processor; and
A memory communicatively coupled to the at least one processor; wherein,
The memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the road area detection method according to any one of the embodiments of the present invention.
According to another aspect of the present invention, there is provided a computer readable storage medium storing computer instructions for causing a processor to execute the road area detection method according to any one of the embodiments of the present invention.
According to the technical scheme provided by the embodiment of the invention, the road area can be detected by utilizing the point cloud data, and the point cloud data is not easily influenced by external light environments and has depth information of scenes, especially for some special or complex scenes such as extremely bad weather, the detection accuracy of the road area can be improved, and the requirement of high robustness of automatic driving can be met. And the original point cloud data is subjected to grid division, and detection is performed by taking grids as units, so that the calculation complexity can be reduced, and the detection speed can be improved. For the second type of grids without original point cloud data, road type prediction can be performed according to the road type of the reference grid in the first type of grids and the perception range parameters of the laser radar, so that the detectability of the region to be identified is improved.
It should be understood that the description in this section is not intended to identify key or critical features of the embodiments of the invention or to delineate the scope of the invention. Other features of the present invention will become apparent from the description that follows.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a flowchart of a road area detection method according to a first embodiment of the present invention;
fig. 2 is a schematic diagram of a road area detection result according to a first embodiment of the present invention;
Fig. 3 is a flowchart of a road area detection method according to a second embodiment of the present invention;
fig. 4 is a schematic structural diagram of a road area detecting device according to a third embodiment of the present invention;
Fig. 5 is a schematic structural diagram of an electronic device implementing a road area detection method according to an embodiment of the present invention.
Detailed Description
In order that those skilled in the art will better understand the present invention, a technical solution in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in which it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present invention without making any inventive effort, shall fall within the scope of the present invention.
It should be noted that the terms "original," "first," "second," and the like in the description and claims of the present invention and in the above figures are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments of the invention described herein may be implemented in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
Example 1
Fig. 1 is a flowchart of a road area detection method according to an embodiment of the present invention, which is applicable to road area and road boundary detection scenes of a running vehicle, and the method may be performed by an electronic device. As shown in fig. 1, the method includes:
step 101, acquiring original point cloud data of an area to be identified by using a laser radar arranged on a vehicle, and performing grid division on the original point cloud data to obtain a first type grid and a second type grid; wherein the first type of grid comprises at least one original point cloud data; there is no origin cloud data in the second class of grids.
Specifically, a laser radar may be respectively disposed in four directions of front, rear, left and right of the vehicle to obtain front, rear, left and right original point cloud data of the vehicle.
Specifically, a rectangular area of a preset size centered on the vehicle may be determined as the area to be identified. For example, a range of about 64 meters around 200 meters around the vehicle is determined as the area to be identified.
Specifically, the original point cloud data in the area to be identified can be subjected to grid division according to a preset unit grid size, so that a first type grid and a second type grid are obtained.
Step 102, determining the road type of the first type grid according to the original point cloud data of the first type grid by adopting a preset road detection model; wherein the road type includes a road area and a road boundary.
The road detection model is a neural network model trained in advance according to point cloud data to be trained.
Specifically, the first type of grid includes all original point cloud data and coordinates of the first type of grid relative to the area to be identified, the coordinates are input into the road detection model, and the road detection model outputs the road type of the area to be identified corresponding to the first type of grid.
Step 103, selecting a reference grid from the first type of grids, and determining the road type of the second type of grids by adopting the road type of the reference grid and the perception range parameters of the laser radar.
Specifically, considering that the laser radar point cloud is in a wide-angle scattering form and possibly cannot completely cover the area to be identified, when the road area detection is performed on the area to be identified, a second type grid which does not contain original point cloud data may exist. The second type of grid is typically present at the far end of the lidar sensing range, and therefore, a reference grid closer to the second type of grid may be selected from the first type of grids. In order to ensure the accuracy of prediction, whether the reference grid and the second type grid are in the sensing range of the laser radar or not can be determined according to the sensing range parameters of the laser radar, and if so, the road type of the second type grid can be determined according to the road type of the reference grid.
The sensing range parameters of the laser radar at least comprise a horizontal dynamic sensing range, an effective detection distance, a maximum detection distance and the like.
Fig. 2 is a schematic diagram of the road area detection result. Wherein 0 represents a grid area which does not belong to a road area nor a road boundary in the area to be identified; 2 represents a grid area belonging to a road boundary in the area to be identified; 1 denotes a mesh area belonging to a road area in an area to be identified. The grid areas shown as 2 are connected to form a road boundary during the running of the vehicle. The road area detection result can be displayed to the user so as to be convenient for the user to check.
The technical scheme provided by the embodiment of the invention can detect the road area by utilizing the point cloud data, and the point cloud data is not easily influenced by external light environment and has depth information of scenes, especially for some special or complex scenes such as extremely bad weather and the like, the detection accuracy of the road area can be improved, and the requirement of high robustness of automatic driving can be further met. And the original point cloud data is subjected to grid division, and detection is performed by taking grids as units, so that the calculation complexity can be reduced, and the detection speed can be improved. For the second type of grids without original point cloud data, road type prediction can be performed according to the road type of the reference grid in the first type of grids and the perception range parameters of the laser radar, so that the detectability of the region to be identified is improved.
Example two
Fig. 3 is a flowchart of a road area detection method according to a second embodiment of the present invention, in which steps 101, 102 and 103 in the first embodiment are refined. As shown in fig. 3, the method includes:
Step 301, acquiring original point cloud data of an area to be identified by using a laser radar arranged on a vehicle, and performing projection processing on the original point cloud data to obtain a two-dimensional top view; grid division is carried out on the two-dimensional top view to obtain a first type grid and a second type grid; wherein the first type of grid comprises at least one original point cloud data; there is no origin cloud data in the second class of grids.
The original point cloud data can be projected by using a Bird's Eye View (BEV) technology to obtain a two-dimensional plan View. Specifically, the x-axis data and the y-axis data in the three-dimensional coordinate information in the original point cloud data are reserved, and the z-axis data are removed.
Specifically, the two-dimensional top view may be grid-divided according to a preset unit grid size, to obtain a first type grid and a second type grid. The preset unit grid size may be, for example, a square with a side length of 0.2 meters.
And step 302, processing the original point cloud data of the first type of grids by utilizing a feature extraction layer in the road detection model to obtain a process feature map.
The feature extraction layer may include a normalization processing layer, a linear rectification function layer, a convolution layer and a downsampling layer. Specifically, the normalization processing layer and the linear rectification function layer can be adopted to process the original point cloud data included in the first type of grids, and then the original feature map is obtained through the convolution layer processing. And processing the original feature map by adopting a downsampling layer to obtain a process feature map.
Wherein the downsampling layer may be a maximum pooling layer.
Specifically, the downsampling layer may compress the size of the original feature map and compress the information in the original feature map to obtain a process feature map, so as to extract more fine feature information in the original feature map.
Step 303, processing the process feature map by using a detection layer in the road detection model to obtain the road type of the first type grid; the input size of the feature extraction layer is the same as the output size of the detection layer; the road type includes a road area and a road boundary.
Specifically, the detection layer can be a reverse process of the feature extraction layer, the detection layer can be formed by a similar layer group with the feature extraction layer, and the input size of the feature extraction layer and the output size of the detection layer can be identical, so that the position information of the first type of grids is kept to the maximum extent, the erroneous classification is reduced, and the detection accuracy of the model is improved. Wherein, the input size of the feature extraction layer refers to the input data size of the input feature extraction layer. Wherein, the output size of the detection layer refers to the output data size of the detection layer.
And the full-connection layer is not used in the road detection model, so that the higher-resolution feature map is kept in the output of the detection layer, the size of the feature map is reduced, the number of parameters and the size of the model in model training are greatly reduced, the memory occupation in model training can be effectively reduced, and the calculation efficiency is improved.
In one implementation, the detection layers in the road detection model include an upsampling layer, a convolution layer, a feature fusion layer, and a Softmax activation function layer; processing the process feature map by using an up-sampling layer to obtain a sparse feature map; processing the sparse feature map by using a convolution layer to obtain a dense feature map; performing feature fusion processing on the sparse feature map and the dense feature map by using a feature fusion layer to obtain a fused feature map; and processing the fused feature map by using the Softmax activation function layer to obtain the road type of the first type of grid.
Wherein, the Softmax activation function layer can be used as an output layer of the detection layer; the normalization processing layer of the feature extraction layer serves as an input layer of the feature extraction layer. The input data size of the normalization processing layer is a first size, and the output data size of the Softmax activation function layer is a second size, then the first size is equal to the second size.
Specifically, the up-sampling layer is utilized to expand the size of the process feature map to the original feature map size, so as to obtain a sparse feature map.
The convolution layer can supplement information to the sparse feature map to obtain a dense feature map. The sparse and dense feature maps are the same size.
Specifically, the sparse feature map and the dense feature map may be feature fused by using a weighted summation method. The fused feature map has more comprehensive feature information, so that the road type detection by utilizing the fused feature map can improve the detection accuracy.
Optionally, the upsampling layer is an index maximum pooling layer.
Step 304, determining a first longitudinal distance and a first transverse distance of the first type of grid from the vehicle; and determining a second longitudinal distance and a second transverse distance of the second class of grids from the vehicle; and if the difference value between the first longitudinal distance and the second longitudinal distance is smaller than the preset threshold value and the first transverse distance is larger than the second transverse distance, determining the first type grid as the reference grid.
Specifically, in order to ensure detection accuracy, the preset threshold may be set to a smaller value.
In step 305, if the reference grid and the second type grid are determined to be within the sensing range of the laser radar according to the sensing range parameter of the laser radar, and the road type of the reference grid is the road boundary, the road type of the second type grid is determined to be the road area.
Specifically, to ensure the detection accuracy, if the road type of the reference grid is determined to be a road boundary, the road type of the second type grid may be determined to be a road area. If the road types of the reference grids are all road areas, the road types of the second type grids are not predicted.
Example III
Fig. 4 is a schematic structural diagram of a road area detecting device according to a third embodiment of the present invention. As shown in fig. 4, the apparatus 400 includes:
An acquiring unit 410, configured to acquire original point cloud data of an area to be identified using a lidar provided on a vehicle;
The gridding unit 420 is configured to grid-divide the original point cloud data to obtain a first type grid and a second type grid; wherein the first type of grid comprises at least one original point cloud data; the second class of grids have no original point cloud data;
The detecting unit 430 is configured to determine a road type of the first type of grid according to the original point cloud data of the first type of grid by using a preset road detection model; the road type comprises a road area and a road boundary;
The detecting unit 430 is further configured to select a reference grid from the first type of grids, and determine a road type of the second type of grids by using a road type of the reference grid and a sensing range parameter of the lidar.
The detection unit 430 specifically is configured to: processing the original point cloud data of the first type of grids by utilizing a feature extraction layer in the road detection model to obtain a process feature map;
processing the process feature map by using a detection layer in the road detection model to obtain the road type of the first type grid; the input size of the feature extraction layer is the same as the output size of the detection layer.
The detection layer in the road detection model comprises an up-sampling layer, a convolution layer, a characteristic fusion layer and a Softmax activation function layer; the detection unit 430 specifically is configured to: processing the process feature map by using an up-sampling layer to obtain a sparse feature map;
Processing the sparse feature map by using a convolution layer to obtain a dense feature map;
performing feature fusion processing on the sparse feature map and the dense feature map by using a feature fusion layer to obtain a fused feature map;
And processing the fused feature map by using the Softmax activation function layer to obtain the road type of the first type of grid.
In one implementation, the upsampling layer is an index maximization layer.
The detection unit 430 specifically is configured to: determining a first longitudinal distance and a first transverse distance of the first type of grid from the vehicle; and determining a second longitudinal distance and a second transverse distance of the second class of grids from the vehicle; if the difference value between the first longitudinal distance and the second longitudinal distance is smaller than a preset threshold value and the first transverse distance is larger than the second transverse distance, determining the first type of grids as reference grids;
If the reference grid and the second type grid are determined to be in the perception range of the laser radar according to the perception range parameters of the laser radar and the road type of the reference grid is a road boundary, the road type of the second type grid is determined to be a road area.
The gridding unit 420 is specifically configured to perform projection processing on the original point cloud data to obtain a two-dimensional top view;
And carrying out grid division on the two-dimensional top view to obtain a first type grid and a second type grid.
The road area detection device provided by the embodiment of the invention can execute the road area detection method provided by any embodiment of the invention, and has the corresponding functional modules and beneficial effects of executing the road area detection method.
Example IV
Fig. 5 shows a schematic diagram of the structure of an electronic device 10 that may be used to implement an embodiment of the invention. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. Electronic equipment may also represent various forms of mobile devices, such as personal digital processing, cellular telephones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations of the inventions described and/or claimed herein.
As shown in fig. 5, the electronic device 10 includes at least one processor 11, and a memory, such as a Read Only Memory (ROM) 12, a Random Access Memory (RAM) 13, etc., communicatively connected to the at least one processor 11, in which the memory stores a computer program executable by the at least one processor, and the processor 11 may perform various appropriate actions and processes according to the computer program stored in the Read Only Memory (ROM) 12 or the computer program loaded from the storage unit 18 into the Random Access Memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 may also be stored. The processor 11, the ROM 12 and the RAM 13 are connected to each other via a bus 14. An input/output (I/O) interface 15 is also connected to bus 14.
Various components in the electronic device 10 are connected to the I/O interface 15, including: an input unit 16 such as a keyboard, a mouse, etc.; an output unit 17 such as various types of displays, speakers, and the like; a storage unit 18 such as a magnetic disk, an optical disk, or the like; and a communication unit 19 such as a network card, modem, wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information/data with other devices via a computer network, such as the internet, and/or various telecommunication networks.
The processor 11 may be a variety of general and/or special purpose processing components having processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various specialized Artificial Intelligence (AI) computing chips, various processors running machine learning model algorithms, digital Signal Processors (DSPs), and any suitable processor, controller, microcontroller, etc. The processor 11 performs the respective methods and processes described above, such as the road area detection method.
In some embodiments, any of the road area detection methods described above may be implemented as a computer program tangibly embodied on a computer-readable storage medium, such as the storage unit 18. In some embodiments, part or all of the computer program may be loaded and/or installed onto the electronic device 10 via the ROM 12 and/or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of any of the road area detection methods described above may be performed. Alternatively, in other embodiments, the processor 11 may be configured to perform any of the road area detection methods described above in any other suitable manner (e.g., by means of firmware).
Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuit systems, field Programmable Gate Arrays (FPGAs), application Specific Integrated Circuits (ASICs), application Specific Standard Products (ASSPs), systems On Chip (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs, the one or more computer programs may be executed and/or interpreted on a programmable system including at least one programmable processor, which may be a special purpose or general-purpose programmable processor, that may receive data and instructions from, and transmit data and instructions to, a storage system, at least one input device, and at least one output device.
A computer program for carrying out methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the computer programs, when executed by the processor, cause the functions/acts specified in the flowchart and/or block diagram block or blocks to be implemented. The computer program may execute entirely on the machine, partly on the machine, as a stand-alone software package, partly on the machine and partly on a remote machine or entirely on the remote machine or server.
In the context of the present invention, a computer-readable storage medium may be a tangible medium that can contain, or store a computer program for use by or in connection with an instruction execution system, apparatus, or device. The computer readable storage medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. Alternatively, the computer readable storage medium may be a machine readable signal medium. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, 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 foregoing.
To provide for interaction with a user, the systems and techniques described here can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and a pointing device (e.g., a mouse or a trackball) through which a user can provide input to the electronic device. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic input, speech input, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a background component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such background, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), wide Area Networks (WANs), blockchain networks, and the internet.
The computing system may include clients and servers. The client and server are typically remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server can be a cloud server, also called a cloud computing server or a cloud host, and is a host product in a cloud computing service system, so that the defects of high management difficulty and weak service expansibility in the traditional physical hosts and VPS service are overcome.
It should be appreciated that various forms of the flows shown above may be used to reorder, add, or delete steps. For example, the steps described in the present invention may be performed in parallel, sequentially, or in a different order, so long as the desired results of the technical solution of the present invention are achieved, and the present invention is not limited herein.
The above embodiments do not limit the scope of the present invention. It will be apparent to those skilled in the art that various modifications, combinations, sub-combinations and alternatives are possible, depending on design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the scope of the present invention.
Claims (10)
1. A road area detection method, characterized by comprising:
Acquiring original point cloud data of an area to be identified by using a laser radar arranged on a vehicle, and performing grid division on the original point cloud data to obtain a first type grid and a second type grid; wherein the first type of grid comprises at least one original point cloud data; the second class of grids do not have original point cloud data;
Determining the road type of the first type grid according to the original point cloud data of the first type grid by adopting a preset road detection model; wherein the road type comprises a road area and a road boundary;
and selecting a reference grid from the first type of grids, and determining the road type of the second type of grids by adopting the road type of the reference grid and the perception range parameter of the laser radar.
2. The method of claim 1, wherein the determining, using a preset road detection model, the road type of the first type of mesh according to the original point cloud data of the first type of mesh comprises:
Processing the original point cloud data of the first type of grids by utilizing a feature extraction layer in the road detection model to obtain a process feature map;
Processing the process feature map by using a detection layer in the road detection model to obtain the road type of the first type grid; wherein the input size of the feature extraction layer is the same as the output size of the detection layer.
3. The method of claim 2, wherein the detection layers in the road detection model include an upsampling layer, a convolution layer, a feature fusion layer, and a Softmax activation function layer; the processing the process feature map by using a detection layer in the road detection model to obtain the road type of the first type of grid comprises the following steps:
processing the process feature map by using the up-sampling layer to obtain a sparse feature map;
processing the sparse feature map by using the convolution layer to obtain a dense feature map;
Performing feature fusion processing on the sparse feature map and the dense feature map by using the feature fusion layer to obtain a fused feature map;
and processing the fused feature map by using the Softmax activation function layer to obtain the road type of the first type grid.
4. A method according to claim 3, characterized in that the upsampling layer is an index maximization pooling layer.
5. The method of claim 1, wherein selecting a reference grid from the first type of grid and determining the road type of the second type of grid using the road type of the reference grid and the range of perception parameters of the lidar comprises:
Determining a first longitudinal distance and a first lateral distance of the first type of mesh from the vehicle; and determining a second longitudinal distance and a second lateral distance of the second class of grids from the vehicle; if the difference value between the first longitudinal distance and the second longitudinal distance is smaller than a preset threshold value and the first transverse distance is larger than the second transverse distance, determining the first type of grid as a reference grid;
and if the reference grid and the second type grid are determined to be in the perception range of the laser radar according to the perception range parameters of the laser radar and the road type of the reference grid is a road boundary, determining the road type of the second type grid as a road area.
6. The method of claim 1, wherein the meshing the raw point cloud data to obtain a first type mesh and a second type mesh comprises:
performing projection processing on the original point cloud data to obtain a two-dimensional top view;
And carrying out grid division on the two-dimensional top view to obtain a first type grid and a second type grid.
7. A road area detection apparatus, characterized by comprising:
The acquisition unit is used for acquiring original point cloud data of the area to be identified by using a laser radar arranged on the vehicle;
The gridding unit is used for carrying out gridding on the original point cloud data to obtain a first type of grids and a second type of grids; wherein the first type of grid comprises at least one original point cloud data; the second class of grids do not have original point cloud data;
the detection unit is used for determining the road type of the first type grid according to the original point cloud data of the first type grid by adopting a preset road detection model; wherein the road type comprises a road area and a road boundary;
The detection unit is further configured to select a reference grid from the first type of grids, and determine a road type of the second type of grids by using a road type of the reference grid and a sensing range parameter of the laser radar.
8. The device according to claim 7, wherein the detection unit is specifically configured to:
Processing the original point cloud data of the first type of grids by utilizing a feature extraction layer in the road detection model to obtain a process feature map;
Processing the process feature map by using a detection layer in the road detection model to obtain the road type of the first type grid; wherein the input size of the feature extraction layer is the same as the output size of the detection layer.
9. An electronic device, the electronic device comprising:
At least one processor; and
A memory communicatively coupled to the at least one processor; wherein,
The memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the road area detection method of any one of claims 1-6.
10. A computer readable storage medium, characterized in that the computer readable storage medium stores computer instructions for causing a processor to implement the road area detection method of any one of claims 1-6 when executed.
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| CN202410244067.3A CN118262313A (en) | 2024-03-04 | 2024-03-04 | Road area detection method and device and electronic equipment |
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