A method for generating a parking lot navigation road network for driverless AVP scenarios

Abstract

The present invention discloses a method for generating a parking lot navigation road network for an unmanned AVP scenario, belonging to the field of unmanned driving technology, and the specific steps are as follows: sensor deployment, vehicle data collection, map data collection, point cloud data processing, image data processing, data fusion, node determination, edge connection, path planning, and road network optimization. By judging obstacles and entering or marking the judged obstacles, the present invention can not only specifically improve the marked area in subsequent road network updates to accurately obtain information, but also avoid misjudging obstacles. Based on this, it can avoid errors in road network generation to a certain extent or correct errors in the road network caused by obstacles in a timely manner.

Description

Technical Field

[0001] The present invention relates to the technical field of driverless technology, and specifically provides a method for generating a parking lot navigation road network for driverless AVP scenarios. Background Art

[0002] The parking lot navigation road network for driverless AVP scenarios is a road network and related information system specifically constructed for vehicles to achieve autonomous navigation and parking in the parking lot.

[0003] However, when generating the existing parking lot navigation road network for driverless AVP scenarios, since there are various movable obstacles in the parking lot, when generating the navigation road network of the parking lot, the obstacles will not only block the perception of the sensors, making the information of some areas unable to be accurately obtained. In addition, there may be a phenomenon of misjudging movable obstacles as fixed obstacles, which will lead to errors in road network generation. Therefore, a method for generating a parking lot navigation road network for driverless AVP scenarios is invented. Summary of the Invention

[0004] To solve the above technical problems, according to one aspect of the present invention, the following technical solutions are provided:

[0005] A method for generating a parking lot navigation road network for driverless AVP scenarios, which includes the following specific steps:

[0006] S1, Data Acquisition:

[0007] S11, Sensor Deployment: Deploy multiple sensors inside the parking lot, including lidar, cameras, and ultrasonic sensors; the lidar is used to obtain the three-dimensional point cloud data of the parking lot, accurately measure the positions and shapes of obstacles, parking spaces, and road boundaries; the cameras are used to capture the visual images of the parking lot, provide texture and color information, and assist in identifying lane lines, traffic signs, and other vehicles and pedestrians; the ultrasonic sensors are used to detect obstacles at close range; during the process of sensor deployment, if an obstacle is encountered, the obstacle will be analyzed to determine whether it is a fixed obstacle or a moving obstacle. If it is a fixed obstacle, it will be recorded into the three-dimensional point cloud data. If it is a moving obstacle, it will be marked and the time will be recorded;

[0008] S12, Vehicle Data Acquisition: Make a collection vehicle equipped with a high-precision positioning system and an inertial measurement unit drive in the parking lot to collect the position, speed, and attitude information of the vehicle, and its data is used to determine the driving trajectory and attitude change of the vehicle, providing basic data for subsequent road network generation;

[0009] S13, Map data collection: Collect the electronic map data of the parking lot. The electronic map data can serve as the basic framework for road network generation, providing the general layout and geometric information of the parking lot;

[0010] S2, Data processing and analysis:

[0011] S21, Point cloud data processing: First, perform filtering and denoising preprocessing operations on the point cloud data collected by the lidar to remove abnormal points and noise points, improving the quality and accuracy of the data. Then, perform point cloud segmentation to divide the point cloud data into different regions;

[0012] S22, Image data processing: Perform image recognition and analysis on the images collected by the camera to identify feature information. Then, use computer vision technology to extract useful information from the images and fuse it with the point cloud data to more comprehensively understand the environment of the parking lot;

[0013] S23, Data fusion: Use algorithms to fuse the data of lidar, camera, ultrasonic sensor, and the collection vehicle to obtain more accurate and comprehensive information about the parking lot environment;

[0014] S3, Road network generation:

[0015] S31, Node determination: Based on the results of point cloud data and image data processing, determine the nodes of the parking lot navigation road network. These nodes will serve as the basic building blocks of the road network, used to connect and construct navigation paths;

[0016] S32, Edge connection: Based on the actual physical connection relationship between nodes and the feasibility of vehicle driving, determine the edges between nodes. These edges represent the paths that vehicles can drive on, connecting adjacent nodes; After determining the edges, the dimensions of the roads and parking spaces will be determined. After determination, it will be judged whether the vehicle can pass smoothly based on the vehicle size;

[0017] S33, Path planning: Based on the generated road network, use path planning algorithms to plan the optimal navigation path for the vehicle from the starting point to the ending point;

[0018] S34, Road network optimization: Optimize the generated navigation road network, remove unnecessary nodes and edges, simplify the road network structure, improve the efficiency and accuracy of path planning. At the same time, optimize the positions of curves and intersections in the road network to ensure that vehicles can pass smoothly and safely during driving;

[0019] S4, Map update and maintenance:

[0020] S41, New data update: According to the data collection, the environment of the parking lot is monitored in real time. During the monitoring process, if an obstacle appears in a space without obstacles, the data collection will be used to analyze whether the obstacle is a fixed obstacle or a moving obstacle. If it is a moving obstacle, no update will be performed. If it is a fixed obstacle, an update will be performed;

[0021] S42, Old data update: According to the recording time in the data collection, the marked area data is collected in sequence. If the moving obstacle has not moved, the time will be recorded again.

[0022] As a preferred solution of a method for generating a parking lot navigation road network for an unmanned AVP scenario according to the present invention, wherein: the electronic map data in S13 includes the floor plan, building structure, and entrance and exit positions of the parking lot.

[0023] As a preferred solution of a method for generating a parking lot navigation road network for an unmanned AVP scenario according to the present invention, wherein: the areas divided by the point cloud data in S21 include parking space areas, driving lane areas, and obstacle areas.

[0024] As a preferred solution of a method for generating a parking lot navigation road network for an unmanned AVP scenario according to the present invention, wherein: the feature information in S22 includes lane lines, traffic signs, and parking space markings, and the vision technology includes edge detection algorithms, feature extraction algorithms, and image classification algorithms.

[0025] As a preferred solution of a method for generating a parking lot navigation road network for an unmanned AVP scenario according to the present invention, wherein: the algorithms used in S23 include the Kalman filter algorithm and the particle filter algorithm.

[0026] As a preferred solution of a method for generating a parking lot navigation road network for an unmanned AVP scenario according to the present invention, wherein: the nodes in S31 include the entrances and exits of the parking lot, the centers of the parking spaces, the intersections of the roads, and the key points of the curves.

[0027] As a preferred solution of a method for generating a parking lot navigation road network for an unmanned AVP scenario according to the present invention, wherein: the steps for determining the road dimensions in S32 are as follows:

[0028] S3211, Image preprocessing:

[0029] S32111, Grayscale conversion: If the image is in color, it is converted to a grayscale image to reduce the computational complexity and the interference of color information;

[0030] S32112, Noise reduction: Use a filtering algorithm to perform noise reduction processing on the image to remove noise and interference in the image and improve the image quality;

[0031] S3212, Edge detection:

[0032] S32121, Selection algorithm: According to the characteristics and accuracy requirements of the image, select a suitable edge detection algorithm, and the algorithms include the Canny algorithm and the Sobel algorithm;

[0033] S32122, Application algorithm: Call the selected edge detection algorithm in an image processing software or programming language to perform edge detection on the preprocessed image. The algorithm will identify the edges of the road based on the gray-scale changes of the pixels in the image and represent them in the form of pixel points;

[0034] S3213, Measure the road dimensions:

[0035] S32131, Analyze the edge pixels: Analyze the detected road edge pixels and calculate the distances between the edge pixel points. By counting the number of adjacent edge pixel points and combining the resolution of the image, convert the pixel distance into the actual size.

[0036] As a preferred solution of a method for generating a parking lot navigation road network for an unmanned AVP scenario according to the present invention, wherein: The steps for determining the size of the parking space in S32 are as follows:

[0037] S3221, Image acquisition: Simultaneously capture scenes containing parking spaces from different angles to obtain multiple images, ensuring that there is sufficient overlapping area of the parking spaces in different images;

[0038] S3222, Stereo matching: Perform stereo matching on the obtained multiple images to find the corresponding points of the same object in different images;

[0039] S3223, Depth calculation: According to the corresponding points obtained by stereo matching, use the principle of triangulation to calculate the depth information of each point, that is, the distance between the point and the camera;

[0040] S3224, Size calculation:

[0041] S32241: According to the depth information and the coordinate information in the image, convert the points on the parking space into a three-dimensional space coordinate system;

[0042] S32242: Calculate the distances and angles between the vertices of the parking space in the three-dimensional space, so as to obtain the actual shape and size of the parking space.

[0043] Compared with the prior art:

[0044] By judging obstacles and entering or marking the judged obstacles, the present invention can not only specifically improve the marked areas in subsequent road network updates to accurately obtain information, but also avoid misjudging obstacles. Based on this, it can to a certain extent avoid errors in road network generation or timely correct errors in the road network caused by obstacles. In addition, by determining the sizes of roads and parking spaces, it can judge whether vehicles can pass smoothly on roads and in parking spaces. Detailed implementation manners

[0045] To make the objectives, technical solutions and advantages of the present invention clearer, the following will further describe the implementation manners of the present invention in detail.

[0046] The present invention provides a method for generating a parking lot navigation road network for an unmanned AVP scenario, including the following specific steps:

[0047] S1, Data collection:

[0048] S11, Sensor deployment: Deploy a variety of sensors inside the parking lot, including lidar, cameras, and ultrasonic sensors; the lidar is used to obtain the three-dimensional point cloud data of the parking lot and accurately measure the positions and shapes of obstacles, parking spaces, and road boundaries; the cameras are used to capture the visual images of the parking lot, provide texture and color information, and assist in identifying lane lines, traffic signs, and other vehicles and pedestrians; the ultrasonic sensors are used to detect obstacles at close range; during the process of sensor deployment, if an obstacle is encountered, the obstacle will be analyzed to determine whether it is a fixed-point obstacle or a moving-point obstacle. If it is a fixed-point obstacle, it will be entered into the three-dimensional point cloud data. If it is a moving-point obstacle, it will be marked and the time will be recorded.

[0049] S12, Vehicle data collection: Let the collection vehicle equipped with a high-precision positioning system and an inertial measurement unit drive inside the parking lot to collect the position, speed, and attitude information of the vehicle. The data is used to determine the driving trajectory and attitude changes of the vehicle and provide basic data for subsequent road network generation.

[0050] S13, Map data collection: Collect the electronic map data of the parking lot. The electronic map data includes the floor plan, building structure, and entrance and exit positions of the parking lot. The electronic map data can serve as the basic framework for road network generation and provide the general layout and geometric information of the parking lot.

[0051] S2, Data processing and analysis:

[0052] S21, Point cloud data processing: First, perform filtering and denoising preprocessing operations on the point cloud data collected by the lidar to remove abnormal points and noise points, improve the quality and accuracy of the data, and then perform point cloud segmentation to divide the point cloud data into different regions. Among them, the regions into which the point cloud data is divided include parking space regions, driving lane regions, and obstacle regions;

[0053] S22, Image data processing: Perform image recognition and analysis on the images collected by the camera to identify feature information, including lane lines, traffic signs, and parking space markings. Then, use computer vision technology to extract useful information from the images and fuse it with the point cloud data to more comprehensively understand the parking lot environment. The vision technology includes edge detection algorithms, feature extraction algorithms, and image classification algorithms;

[0054] S23, Data fusion: Use algorithms (including Kalman filtering algorithms and particle filtering algorithms) to fuse the data of lidar, camera, ultrasonic sensors, and the collection vehicle to obtain more accurate and comprehensive parking lot environment information;

[0055] S3, Road network generation:

[0056] S31, Node determination: According to the results of point cloud data and image data processing, determine the nodes of the parking lot navigation road network. The nodes include the entrances and exits of the parking lot, the centers of parking spaces, the intersections of roads, and the key points of curves. These nodes will serve as the basic units of the road network for connecting and constructing navigation paths;

[0057] S32, Edge connection: According to the actual physical connection relationship between nodes and the feasibility of vehicle driving, determine the edges between nodes. These edges represent the paths that vehicles can drive on and connect adjacent nodes; After determining the edges, the dimensions of the roads and parking spaces will be determined. After determination, it will be judged whether the vehicle can pass smoothly based on the vehicle size;

[0058] Among them:

[0059] The steps for determining the road dimensions are as follows:

[0060] S3211, Image preprocessing:

[0061] S32111, Grayscale conversion: If the image is in color, convert it to a grayscale image to reduce the computational amount and interference of color information;

[0062] S32112, Denoising: Use a filtering algorithm to perform denoising processing on the image to remove noise and interference in the image and improve the image quality;

[0063] S3212, Edge detection:

[0064] S32121, Selection Algorithm: According to the characteristics of the image and the accuracy requirements, select an appropriate edge detection algorithm, which includes the Canny algorithm and the Sobel algorithm;

[0065] S32122, Algorithm Application: Call the selected edge detection algorithm in image processing software or programming language to perform edge detection on the preprocessed image. The algorithm will identify the edges of the road based on the gray-scale changes of the pixels in the image and represent them in the form of pixel points;

[0066] S3213, Measuring Road Dimensions:

[0067] S32131, Analyzing Edge Pixels: Analyze the detected road edge pixels and calculate the distances between the edge pixel points. By counting the number of adjacent edge pixel points and combining with the resolution of the image, convert the pixel distance into the actual size;

[0068] The steps for determining the parking space size are as follows:

[0069] S3221, Image Acquisition: Simultaneously capture scenes containing the parking space from different angles to obtain multiple images, ensuring that there is sufficient overlapping area of the parking space in different images;

[0070] S3222, Stereo Matching: Perform stereo matching on the obtained multiple images to find the corresponding points of the same object in different images;

[0071] S3223, Depth Calculation: Based on the corresponding points obtained from stereo matching, use the principle of triangulation to calculate the depth information of each point, that is, the distance between the point and the camera;

[0072] S3224, Size Calculation:

[0073] S32241: Convert the points on the parking space into a three-dimensional space coordinate system according to the depth information and the coordinate information in the image;

[0074] S32242: Calculate the distances and angles between the vertices of the parking space in three-dimensional space to obtain the actual shape and size of the parking space. The steps for determining the parking space size are as follows:

[0075] S3221, Image Acquisition: Simultaneously capture scenes containing the parking space from different angles to obtain multiple images, ensuring that there is sufficient overlapping area of the parking space in different images;

[0076] S3222, Stereo Matching: Perform stereo matching on the obtained multiple images to find the corresponding points of the same object in different images;

[0077] S3223, Depth calculation: Based on the corresponding points obtained from stereo matching, use the principle of triangulation to calculate the depth information of each point, that is, the distance between the point and the camera;

[0078] S3224, Dimension calculation:

[0079] S32241: According to the depth information and the coordinate information in the image, convert the points on the parking space into a three-dimensional space coordinate system;

[0080] S32242: Calculate the distances and angles between the vertices of the parking space in three-dimensional space, so as to obtain the actual shape and size of the parking space;

[0081] S33, Path planning: Based on the generated road network, use a path planning algorithm to plan the optimal navigation path for the vehicle from the starting point to the end point;

[0082] S34, Road network optimization: Optimize the generated navigation road network, remove unnecessary nodes and edges, simplify the road network structure, improve the efficiency and accuracy of path planning. At the same time, optimize the positions of curves and intersections in the road network to ensure that the vehicle can pass smoothly and safely during driving;

[0083] S4, Map update and maintenance:

[0084] S41, New data update: According to data collection, monitor the environment of the parking lot in real time. During the monitoring process, if an obstacle appears in a space without obstacles, analyze whether the obstacle is a fixed-point obstacle or a moving-point obstacle through data collection. If it is a moving-point obstacle, no update will be performed. If it is a fixed-point obstacle, an update will be performed;

[0085] S42, Old data update: According to the recording time in data collection, collect the marked area data in sequence. If the moving-point obstacle has not moved, record the time again.

[0086] Although the present invention has been described above with reference to the embodiments, various improvements can be made to it and its components can be replaced with equivalents without departing from the scope of the present invention. In particular, as long as there is no structural conflict, the various features in the disclosed embodiments of the present invention can be combined with each other in any way. The exhaustive description of these combinations is not given in this specification only for the sake of saving space and resources. Therefore, the present invention is not limited to the specific embodiments disclosed herein, but includes all technical solutions falling within the scope of the claims.

Claims

1. A method for generating a parking lot navigation road network for driverless AVP scenarios, characterized in that, The specific steps are as follows: S1, Data collection: S11, Sensor deployment: Deploy a variety of sensors inside the parking lot, including lidar, cameras, and ultrasonic sensors; the lidar is used to obtain the three-dimensional point cloud data of the parking lot to accurately measure the positions and shapes of obstacles, parking spaces, and road boundaries; the cameras are used to capture the visual images of the parking lot to provide texture and color information to assist in identifying lane lines, traffic signs, and other vehicles and pedestrians; the ultrasonic sensors are used to detect obstacles at close range; during the sensor deployment process, if an obstacle is encountered, the obstacle will be analyzed to determine whether it is a fixed obstacle or a moving obstacle. If it is a fixed obstacle, it will be recorded into the three-dimensional point cloud data. If it is a moving obstacle, it will be marked and the time will be recorded; S12, Vehicle data collection: Let the collection vehicle equipped with a high-precision positioning system and an inertial measurement unit drive inside the parking lot to collect the position, speed, and attitude information of the vehicle. Its data is used to determine the driving trajectory and attitude changes of the vehicle, providing basic data for subsequent road network generation; S13, Map data collection: Collect the electronic map data of the parking lot. Its electronic map data can serve as the basic framework for road network generation, providing the general layout and geometric information of the parking lot; S2, Data processing and analysis: S21, Point cloud data processing: First, perform filtering and denoising preprocessing operations on the point cloud data collected by the lidar to remove abnormal points and noise points, improving the quality and accuracy of the data. Then, perform point cloud segmentation to divide the point cloud data into different regions; S22, Image data processing: Perform image recognition and analysis on the images collected by the cameras to identify the feature information. Then, use computer vision technology to extract useful information from the images and fuse it with the point cloud data to more comprehensively understand the environment of the parking lot; S23, Data fusion: Use algorithms to fuse the data of the lidar, cameras, ultrasonic sensors, and the collection vehicle to obtain more accurate and comprehensive parking lot environment information; S3, Road network generation: S31, Node determination: According to the results of point cloud data and image data processing, determine the nodes of the parking lot navigation road network. These nodes will serve as the basic components of the road network, used to connect and construct navigation paths; S32, Edge connection: According to the actual physical connection relationship between the nodes and the feasibility of vehicle driving, determine the edges between the nodes. These edges represent the paths that vehicles can drive on, connecting adjacent nodes; after determining the edges, the dimensions of the roads and parking spaces will be determined. After determination, it will be judged whether the vehicle can pass smoothly based on the vehicle size; S33, Path planning: Based on the generated road network, use path planning algorithms to plan the optimal navigation path for the vehicle from the starting point to the ending point; S34, Road network optimization: Optimize the generated navigation road network, remove unnecessary nodes and edges, simplify the road network structure, improve the efficiency and accuracy of path planning. At the same time, optimize the positions of curves and intersections in the road network to ensure that the vehicle can pass smoothly and safely during driving; S4, Map Update and Maintenance: S41, New Data Update: According to data collection, the environment of the parking lot is monitored in real time. During the monitoring process, if an obstacle appears in a space without obstacles, it will be analyzed through data collection to determine whether the obstacle is a fixed-point obstacle or a moving-point obstacle. If it is a moving-point obstacle, no update will be performed. If it is a fixed-point obstacle, an update will be performed; S42, Old Data Update: According to the recording time in data collection, the marked area data is collected in sequence. If the moving-point obstacle has not moved, the time will be recorded again.

2. The method for generating a parking lot navigation road network for an unmanned AVP scenario according to claim 1, wherein In S13, the electronic map data includes the floor plan of the parking lot, building structure, and entrance / exit positions.

3. A method for generating a parking lot navigation road network for an unmanned AVP scenario according to claim 1, characterized in that, In S21, the areas into which the point cloud data is divided include parking space areas, driving lane areas, and obstacle areas.

4. A method for generating a parking lot navigation road network for an unmanned AVP scenario according to claim 1, wherein, In S22, the characteristic information includes lane lines, traffic signs, and parking space markings, and the vision technology includes edge detection algorithms, feature extraction algorithms, and image classification algorithms.

5. A method for generating a parking lot navigation road network for an unmanned AVP scenario according to claim 1, characterized in that, In S23, the algorithms used include the Kalman filter algorithm and the particle filter algorithm.

6. The method for generating a parking lot navigation road network for an unmanned AVP scenario according to claim 1, wherein In S31, the nodes include the entrances and exits of the parking lot, the centers of parking spaces, the intersections of roads, and the key points of curves.

7. A method for generating a parking lot navigation road network for an unmanned AVP scenario according to claim 1, characterized in that In S32, the steps for determining the road dimensions are as follows: S3211, Image Preprocessing: S32111, Grayscale Conversion: If the image is in color, convert it to a grayscale image to reduce the amount of calculation and interference from color information; S32112, Noise Reduction: Use a filtering algorithm to perform noise reduction processing on the image to remove noise and interference in the image and improve the image quality; S3212, Edge Detection: S32121, Algorithm Selection: According to the characteristics and accuracy requirements of the image, select a suitable edge detection algorithm, including the Canny algorithm and the Sobel algorithm; S32122, Algorithm Application: Call the selected edge detection algorithm in an image processing software or programming language to perform edge detection on the preprocessed image. The algorithm will identify the edges of the road based on the gray level changes of the pixels in the image and represent them in the form of pixel points; S3213, Measuring Road Dimensions: S32131, Analyzing Edge Pixels: Analyze the detected road edge pixels and calculate the distance between the edge pixel points. By counting the number of adjacent edge pixel points and combining the resolution of the image, convert the pixel distance to the actual size.

8. A method for generating a parking lot navigation road network for an unmanned AVP scenario according to claim 1, characterized in that In S32, the steps for determining the parking space dimensions are as follows: S3221, Image Acquisition: Simultaneously capture scenes containing parking spaces from different angles to obtain multiple images, ensuring that there is sufficient overlapping area of the parking spaces in different images; S3222, Stereo Matching: Perform stereo matching on the obtained multiple images to find the corresponding points of the same object in different images; S3223, Depth Calculation: According to the corresponding points obtained from stereo matching, use the principle of triangulation to calculate the depth information of each point, that is, the distance between the point and the camera; S3224, Dimension Calculation: S32241: According to the depth information and coordinate information in the image, convert the points on the parking space into a three-dimensional space coordinate system; S32242: Calculate the distances and angles between the vertices of each parking space in three-dimensional space, so as to obtain the actual shape and size of the parking space.