CN115112146A - Method, computer system, and medium for generating an autopilot map - Google Patents
Method, computer system, and medium for generating an autopilot map Download PDFInfo
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
- G01C21/26—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 specially adapted for navigation in a road network
- G01C21/34—Route searching; Route guidance
- G01C21/36—Input/output arrangements for on-board computers
- G01C21/3679—Retrieval, searching and output of POI information, e.g. hotels, restaurants, shops, filling stations, parking facilities
- G01C21/3682—Retrieval, searching and output of POI information, e.g. hotels, restaurants, shops, filling stations, parking facilities output of POI information on a road map
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
- G01C21/26—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 specially adapted for navigation in a road network
- G01C21/34—Route searching; Route guidance
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
- G01C21/26—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 specially adapted for navigation in a road network
- G01C21/34—Route searching; Route guidance
- G01C21/36—Input/output arrangements for on-board computers
- G01C21/3679—Retrieval, searching and output of POI information, e.g. hotels, restaurants, shops, filling stations, parking facilities
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Abstract
The invention relates to a method, a computer system and a computer storage medium for generating an autopilot map. A method for generating an autopilot map according to one aspect of the invention includes the steps of: acquiring first measurement data at a single time by using a road test vehicle equipped with real-time dynamic measurement equipment, and processing the first measurement data to generate base map information of the automatic driving map; collecting second measurement data a plurality of times with a user vehicle and processing the second measurement data to generate supplemental information for the autonomous driving map; and generating the automatic driving map based on the base map information of the automatic driving map and the supplementary information of the automatic driving map.
Description
Technical Field
The present invention relates to the field of automated driving, and more particularly, to a method, computer system, and computer storage medium for generating an automated driving map.
Background
At present, two schemes for generating the automatic driving map are mainly adopted, one scheme is that pure visual data is used for not depending on high-precision positioning data, and the other scheme is that laser radar is used for depending on high-precision positioning data, and the automatic driving map is generated by performing three-dimensional reconstruction on point cloud data of a road collected by the laser radar.
However, the safety and reliability of map generation schemes using pure visual data need to be continuously improved and verified by actual landing, while the hardware cost and mapping cost of map generation schemes using lidar to collect point cloud data are high. In addition, as urban working condition road conditions are complex, the transverse and longitudinal positioning accuracy is more critical to improving the safety reliability and the use experience of automatic driving, and the signal of the global navigation satellite system in a partial region may have the condition of low reliability or even loss, so that a scheme of relying on high-precision positioning data is required. However, the cost of drawing with the high-precision positioning device and the drawing vehicle is too high, which cannot dynamically reflect the map updating elements, and the addition of the laser radar and the high-precision positioning device in the mass production vehicle also brings extra hardware cost.
Disclosure of Invention
To solve or at least alleviate one or more of the above problems, the following technical solutions are provided.
According to a first aspect of the present invention, there is provided a method for generating an autopilot map, the method comprising the steps of: acquiring first measurement data at a single time by using a road test vehicle equipped with real-time dynamic measurement equipment, and processing the first measurement data to generate base map information of the automatic driving map; collecting second measurement data a plurality of times with a user vehicle and processing the second measurement data to generate supplemental information for the autopilot map; and generating the automatic driving map based on the base map information of the automatic driving map and the supplementary information of the automatic driving map.
The method for generating an autopilot map according to an embodiment of the invention, wherein the first measurement data and the second measurement data include positioning data and perception data, the perception data including one or more of: stop line data, lane line data, road boundary data, lane bifurcation and junction point data, sign data and signal light data.
The method for generating an autopilot map according to one embodiment of the invention or any of the embodiments above, wherein processing the first measurement data to generate floor map information for the autopilot map includes: determining vehicle trajectory and intersection information based on the first measurement data; dividing the vehicle track into an inside sub-track and an outside sub-track according to the intersection information; converting a coordinate system from a vehicle coordinate system to a world coordinate system according to the positioning data in the first measurement data to project perception data in the first measurement data to the world coordinate system to generate a plurality of lane line tracks; matching the multiple sections of lane line tracks to the divided inside sub-track and outside sub-track of the intersection to generate the matched multiple sections of lane line tracks; and sampling and clustering each of the matched multiple sections of lane line tracks to obtain one or more of stop line information, sign board information, lane line information and road boundary information so as to generate base map information of the automatic driving map.
The method for generating an autopilot map according to one embodiment or any of the above embodiments, wherein processing the first measurement data to generate floor map information for the autopilot map further includes: clustering the sub-tracks outside the intersection into clusters by using a line segment clustering method; and generating link information based on the cluster as base map information of the automatic driving map.
The method for generating an autopilot map according to one embodiment or any of the embodiments above wherein sampling and clustering each of the plurality of segments of lane line trajectory to obtain lane line information includes: clustering sampling points of the lane line information by using a density-based clustering method to generate a point cluster; determining a main direction of the point cluster by using a principal component analysis method or an endpoint method and rotating the main direction into a horizontal direction; dividing the point clusters with the length larger than a length threshold value into sub point clusters in the horizontal direction; sampling again after least square fitting is carried out on the point clusters and/or the sub-point clusters to obtain matching points and discrete points, and clustering the matching points and the discrete points by using a density-based clustering method to generate point clusters; and determining a lane line type based on the types of the points in the point cluster which are larger than a predetermined proportion.
The method for generating an autopilot map according to one embodiment or any of the embodiments above wherein sampling and clustering each of the plurality of segments of lane line trajectories to obtain stop-line information includes: determining a midpoint of a stop-line based on the stop-line information; clustering the midpoints of the stop lines using a density-based clustering method to generate a point cluster; and selecting a corresponding stop line from each point cluster, so that the midpoint of the stop line is the center of the point cluster, and the length and direction of the stop line are the average values of a plurality of stop lines corresponding to the center of the point cluster.
The method for generating an autonomous driving map according to an embodiment of the invention or any of the above embodiments, wherein processing the second measurement data to generate the supplementary information of the autonomous driving map comprises: determining vehicle trajectory and intersection information based on the second measurement data; dividing the vehicle track into an inside sub-track and an outside sub-track according to the intersection information; converting a coordinate system from a vehicle coordinate system to a world coordinate system according to the positioning data in the second measurement data to project perception data in the second measurement data to the world coordinate system to generate a plurality of lane line tracks; matching the multiple sections of lane line tracks to the divided inside sub-track and outside sub-track of the intersection to generate the matched multiple sections of lane line tracks; and sampling and clustering each of the matched plurality of segments of lane line trajectories to obtain one or more of stop line information, sign information, lane line information, road boundary information, and center line information to generate supplemental information for the autopilot map.
The method for generating an autonomous driving map according to an embodiment of the invention or any of the above embodiments, wherein processing the second measurement data to generate the supplementary information of the autonomous driving map further comprises: determining the intersection points of the multiple sections of lane line tracks and the intersection edges based on the multiple sections of lane line tracks and the intersection information; clustering the intersected points by using a density-based clustering method to obtain an entrance of the intersection and an exit of the intersection; and determining centerline information corresponding to inside and outside of the intersection, respectively.
The method for generating an autopilot map according to one embodiment or any of the embodiments above in which processing the second measurement data to generate supplemental information for the autopilot map further includes: and filtering the road boundary information and the center line information based on the Hausdorff distance and the boundary distance with the center line.
According to a second aspect of the present invention there is provided a computer system for generating an autopilot map, the system comprising: a memory; a processor; and a computer program stored on the memory and running on the processor, the running of the computer program causing the following steps to be performed: acquiring first measurement data at a single time by using a road test vehicle equipped with real-time dynamic measurement equipment, and processing the first measurement data to generate base map information of the automatic driving map; collecting second measurement data a plurality of times with a user vehicle and processing the second measurement data to generate supplemental information for the autonomous driving map; and generating the automatic driving map based on base map information of the automatic driving map and supplementary information of the automatic driving map.
The computer system for generating an autopilot map according to an embodiment of the invention, wherein the first measurement data and the second measurement data include positioning data and perception data, the perception data including one or more of: stop line data, lane line data, road boundary data, lane bifurcation and junction point data, sign data and signal light data.
The computer system for generating an autopilot map according to one embodiment or any of the embodiments above of the invention wherein execution of the computer program further causes the following steps to be performed: determining vehicle trajectory and intersection information based on the first measurement data; dividing the vehicle track into an inside sub-track and an outside sub-track according to the intersection information; converting a coordinate system from a vehicle coordinate system to a world coordinate system according to the positioning data in the first measurement data to project perception data in the first measurement data to the world coordinate system to generate a plurality of lane line tracks; matching the multiple sections of lane line tracks to the divided sub-tracks inside the intersection and the sub-tracks outside the intersection to generate the matched multiple sections of lane line tracks; and sampling and clustering each of the matched multiple sections of lane line tracks to obtain one or more of stop line information, sign board information, lane line information and road boundary information so as to generate base map information of the automatic driving map.
The computer system for generating an autopilot map according to one embodiment of the invention or any of the embodiments above, wherein execution of the computer program further causes the following steps to be performed: clustering the sub-tracks outside the intersection into clusters by using a line segment clustering method; and generating link information based on the cluster as base map information of the automatic driving map.
The computer system for generating an autopilot map according to one embodiment of the invention or any of the embodiments above wherein sampling and clustering each segment of the plurality of segments of lane line trajectory to obtain lane line information comprises: clustering sampling points of the lane line information by using a density-based clustering method to generate a point cluster; determining a main direction of the point cluster by using a principal component analysis method or an endpoint method and rotating the main direction into a horizontal direction; dividing the point clusters with the length larger than the length threshold value into sub point clusters in the horizontal direction; sampling again after least square fitting is carried out on the point clusters and/or the sub-point clusters to obtain matching points and discrete points, and clustering the matching points and the discrete points by using a density-based clustering method to generate point clusters; and determining a lane line type based on the types of the points in the point cluster which are larger than a predetermined proportion.
The computer system for generating an autopilot map according to one embodiment of the invention or any of the embodiments above wherein sampling and clustering each segment of the multi-segment lane line trajectory to obtain stop-line information comprises: determining a midpoint of a stop-line based on the stop-line information; clustering the midpoints of the stop lines using a density-based clustering method to generate a point cluster; and selecting a corresponding stop line from each point cluster, so that the midpoint of the stop line is the center of the point cluster, and the length and direction of the stop line are the average values of a plurality of stop lines corresponding to the center of the point cluster.
The computer system for generating an autopilot map according to one embodiment of the invention or any of the embodiments above, wherein execution of the computer program further causes the following steps to be performed: determining vehicle trajectory and intersection information based on the second measurement data; dividing the vehicle track into an inside sub-track and an outside sub-track according to the intersection information; converting a coordinate system from a vehicle coordinate system to a world coordinate system according to the positioning data in the second measurement data to project perception data in the second measurement data to the world coordinate system to generate a plurality of lane line tracks; matching the multiple sections of lane line tracks to the divided inside sub-track and outside sub-track of the intersection to generate the matched multiple sections of lane line tracks; and sampling and clustering each of the matched plurality of segments of lane line trajectories to obtain one or more of stop line information, sign information, lane line information, road boundary information, and center line information to generate supplemental information for the autopilot map.
The computer system for generating an autopilot map according to one embodiment of the invention or any of the embodiments above, wherein execution of the computer program further causes the following steps to be performed: determining the intersection points of the multiple sections of lane line tracks and the intersection edges based on the multiple sections of lane line tracks and the intersection information; clustering the intersected points by using a density-based clustering method to obtain an entrance of the intersection and an exit of the intersection; and determining centerline information corresponding to inside and outside of the intersection, respectively.
The computer system for generating an autopilot map according to one embodiment of the invention or any of the embodiments above, wherein execution of the computer program further causes the following steps to be performed: and filtering the road boundary information and the center line information based on the Hausdorff distance and the boundary distance with the center line.
According to a third aspect of the present invention, there is provided a computer storage medium comprising instructions which, when executed, perform the steps of the method for generating an autopilot map according to the first aspect of the present invention.
The scheme for generating the automatic driving map according to one or more embodiments of the invention generates base map information of the automatic driving map by using high-precision measurement data acquired by a road test vehicle equipped with a real-time dynamic measurement device at a single time, and generates supplementary information of the automatic driving map by using navigation-level measurement data acquired by a user vehicle at multiple times, so as to realize refinement and update of the automatic driving map. According to the scheme for generating the automatic driving map, a map collecting vehicle is not required to carry out large-scale high-precision map collection and drawing, a high-precision positioning device is not required to be relied on during deployment and dynamic map updating, the drawing effect basically consistent with that of the high-precision map and the positioning device can be achieved, the automatic driving map is ensured to be in accordance with the design specification and the safety, the drawing efficiency is improved, and the manpower and material resource cost is reduced.
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The above and/or other aspects and advantages of the present invention will become more apparent and more readily appreciated from the following description of the various aspects taken in conjunction with the accompanying drawings, in which like or similar elements are designated with like reference numerals. In the drawings:
FIG. 1 is a flow diagram of a method for generating an autopilot map in accordance with one or more embodiments of the invention.
FIG. 2 is a flow diagram of a method for generating an autopilot map in accordance with one or more embodiments of the invention.
FIG. 3 is a schematic block diagram of a computer system for generating an autopilot map in accordance with one or more embodiments of the invention.
Detailed Description
The following description of the specific embodiments is merely exemplary in nature and is in no way intended to limit the disclosed technology or the application and uses of the disclosed technology. Furthermore, there is no intention to be bound by any expressed or implied theory presented in the preceding technical field, background, or the following detailed description.
In the following detailed description of embodiments, numerous specific details are set forth in order to provide a more thorough understanding of the disclosed technology. It will be apparent, however, to one of ordinary skill in the art that the disclosed techniques may be practiced without these specific details. In other instances, well-known features have not been described in detail to avoid unnecessarily complicating the description.
Words such as "comprising" and "comprises" mean that, in addition to having elements or steps which are directly and explicitly stated in the description, the solution of the invention does not exclude other elements or steps which are not directly or explicitly stated. Terms such as "first" and "second" do not denote an order of the elements in time, space, size, etc., but rather are used to distinguish one element from another.
It is noted that in the context of the present invention, the term "Real-time kinematic measuring device", also known as RTK (Real-time kinematic) device, is a measuring device that obtains positioning data with an accuracy of the order of centimeters in Real time by means of a Real-time differential positioning technique.
Hereinafter, exemplary embodiments according to the present invention will be described in detail with reference to the accompanying drawings.
FIG. 1 is a flow diagram of a method for generating an autopilot map according to one embodiment of the invention.
As shown in fig. 1, in step S101, first measurement data is acquired at a single time with a road test vehicle equipped with a real-time dynamic measurement device, and the first measurement data is processed to generate base map information of an automatic driving map. In step S103, second measurement data is acquired a plurality of times with the user vehicle, and the second measurement data is processed to generate supplementary information of the automatic driving map. Alternatively, steps S101 and S103 may be performed in parallel to improve the efficiency of acquiring data and generating maps.
Optionally, the first measurement data and the second measurement data may comprise positioning data and perception data. For example, the positioning data may include, but is not limited to, vehicle position, vehicle speed, yaw angle, yaw rate, and the like. The perception data may include, but is not limited to, stop-line data, lane-line data, road-boundary data, lane-bifurcation and junction data, sign data, and signal data, among others.
In one embodiment, a single acquisition of first measurement data with a road test vehicle equipped with a real-time dynamic measurement device may be performed at a data acquisition frequency of 20Hz, and a plurality of acquisitions of second measurement data with a user vehicle may be performed at a data acquisition frequency of 1 Hz.
In step S105, an automatic driving map is generated based on the base map information of the automatic driving map and the supplementary information of the automatic driving map. Alternatively, the generated autopilot map may include lane information (e.g., lane lines, stop lines, split junctions, etc.), sign information (e.g., sign boards, traffic lights, etc.), topological information of road connections, and driving experience information (e.g., center lines, guide lines, speed limits, etc.), among others.
The processing of the first and second measurement data will be described in detail below with the aid of the individual steps shown in fig. 2. It should be noted that the processing of the first measurement data and the second measurement data is similar, and therefore the steps in fig. 2 are described by means of the processing of the first measurement data.
FIG. 2 is a flow diagram of a method for generating an autopilot map in accordance with one or more embodiments of the invention.
As shown in fig. 2, in step S201, the vehicle trajectory and the intersection information are determined based on the first measurement data.
Alternatively, in step S201, the vehicle trajectory may be determined based on the positioning data in the first measurement data, and the intersection information may be further determined based on the perception data in the first measurement data and the vehicle trajectory.
Alternatively, in step S201, the intersection information may be determined based on the perception data and the vehicle trajectory in the first measurement data by: determining a midpoint of the stop line; clustering the midpoints of the stop lines using a density-based clustering method to generate a point cluster; selecting the circle center of the minimum outer wrapping circle of each point cluster as a center of the intersection and setting the minimum allowable radius of each intersection based on the radius of the minimum outer wrapping circle; and detecting whether the boundaries of the intersection overlap, and if so, reallocating the radii in proportion to the number of the midpoints of the stop lines contained in the boundaries until the boundaries of the intersection are detected not to overlap. It should be noted that if the reassigned radius is smaller than the minimum allowable radius of the intersection, the radius is reassigned based on the minimum allowable radius. By selecting the circle center of the minimum enclosing circle of each point cluster as the center of the intersection, the intersection position detection error caused when the number of stop lines detected by different branches of the same intersection is unbalanced can be prevented.
In step S203, the vehicle trajectory is divided into an inside-intersection sub-trajectory and an outside-intersection sub-trajectory according to the intersection information. Alternatively, the vehicle trajectory may be divided into an intra-intersection sub-trajectory and an extra-intersection sub-trajectory based on the minimum allowable radius of each intersection. Alternatively, the off-intersection sub-trajectories may be clustered into clusters using a line segment clustering method, and the section information may be generated based on the clusters as base map information of the automatic driving map.
In step S205, the coordinate system is converted from the host vehicle coordinate system to the world coordinate system according to the positioning data in the first measurement data to project the perception data in the first measurement data to the world coordinate system to generate a plurality of lane line trajectories. Illustratively, the corresponding perception data (e.g., lane lines, signboards, etc.) for each frame may be projected onto a world coordinate system to generate a segment of lane line trajectory.
In step S207, the multi-segment lane line trajectory is matched to the divided inside-intersection sub-trajectory and outside-intersection sub-trajectory to generate a matched multi-segment lane line trajectory. Alternatively, the multi-segment lane line trajectory may be matched to the divided sub-trajectory inside the intersection and the sub-trajectory outside the intersection by using an iterative closest point matching algorithm. By matching the multiple sections of lane line tracks to the inside sub-track and the outside sub-track of the intersection divided by the vehicle tracks, the error of the generated lane line track caused by the missing or unclear lane lines of the intersection can be avoided.
Optionally, in step S207, self-alignment and matching alignment may also be performed on the multiple lane line trajectories. In self-alignment, each segment of the multi-segment lane line trajectory may be aligned with the lane lines collected from the previous and subsequent frames in the world coordinate system. In the matching and aligning, each section of the multi-section lane line track, the lane lines acquired by the user vehicle for multiple times and the current optimal map lane line can be matched and aligned.
In step S209, each of the matched plurality of lane line trajectories is sampled and clustered to acquire one or more of stop line information, sign information, lane line information, and road boundary information to generate base map information of the automatic driving map. Optionally, sampling each segment of the matched multi-segment lane line trajectory may include selecting points for each segment to fit a curve.
In one embodiment, sampling and clustering each segment of the multiple segments of lane line trajectory to obtain lane line information may comprise: clustering the sampling points of the lane line information by using a density-based clustering method to generate a point cluster; determining a principal direction of the cluster of points using principal component analysis or end point methods and rotating the principal direction to a horizontal direction (e.g., parallel to the x-axis); dividing the point clusters with the length larger than the length threshold value into sub point clusters in the horizontal direction; sampling again after performing least square fitting on the point clusters and/or the sub-point clusters to obtain matching points and discrete points, and clustering the matching points and the discrete points by using a density-based clustering method to generate point clusters; and determining a lane line type based on the types of the points in the point cluster which are larger than a predetermined proportion. It should be noted that the above clustering process using the density-based clustering method and the least square fitting process for the point clusters and/or the sub-point clusters may be repeated to improve the accuracy of the acquired lane line information.
In one embodiment, sampling and clustering each segment of the multi-segment lane line trajectory to obtain stop-line information may include: determining a midpoint of the stop-line based on the stop-line information; clustering the midpoints of the stop lines using a density-based clustering method to generate a point cluster; and selecting a corresponding stop line from each point cluster, such that a midpoint of the corresponding stop line is a center of the point cluster, and a length and a direction of the corresponding stop line are an average of a plurality of stop lines corresponding to the center of the point cluster.
It should be noted that, in the process of processing the second measurement data to generate the supplementary information of the automatic driving map, each segment of the multiple segments of lane line tracks needs to be sampled and clustered to obtain the center line information, so as to generate the supplementary information of the automatic driving map. Optionally, in the process of processing the second measurement data to generate the supplementary information of the automatic driving map, the acquired road boundary information and the center line information may be filtered based on the Hausdorff distance and the boundary distance from the center line to filter out road boundaries and center lines that obviously do not belong to the same bar.
Optionally, in the process of sampling and clustering each segment of the multiple segments of lane line tracks to obtain center line information, determining the intersection points of the multiple segments of lane line tracks and the intersection edges based on the multiple segments of lane line tracks and the intersection information; clustering the intersected points by using a density-based clustering method to obtain an entrance of the intersection and an exit of the intersection; and determining centerline information corresponding to inside and outside of the intersection, respectively.
In one embodiment, centerline information corresponding to within an intersection may be determined by: numbering the entrances and exits of the intersections; grouping the entrances and exits of the intersections according to the road branches in which the entrances and the exits are located by using a clustering method; in each branch, the entries are incrementally numbered in a counter-clockwise direction and the exits are incrementally numbered in a clockwise direction; discarding the track interrupted in the intersection and leaving the track passing through the intersection; classifying the reserved tracks into clusters based on the entries and intersections of the intersections corresponding to the reserved tracks; and extracting a central line from each cluster of tracks by using a resampling and median position finding method, and associating the central line to the entrance and the intersection of the corresponding intersection.
In one embodiment, centerline information corresponding to outside of an intersection may be determined by: finding a principal direction by using a principal component analysis method aiming at the track points and fitting a guide line; making a vertical line along the guide line according to a fixed distance to cut the trajectory into sub-trajectories using the vertical line; clustering the intersection points of the vertical lines and the track to generate a node at the center of each point cluster, so that the track is divided into subsections connected through the nodes; grouping the subsegments according to the head node of each subsegment; one centerline is generated for each group of subsegments using the same method as determining the centerline within the street. Optionally, the number of incoming and outgoing centerlines for each node may also be analyzed to determine the type of node. If only one node enters the central line and one node leaves the central line, deleting the node and combining two connected central lines in sequence; if the number of the entering central lines of a node is larger than the number of the leaving central lines, determining the node as an merging point; if the number of entering centerlines of a node is less than or equal to the number of leaving centerlines, then the node is determined to be a bifurcation point.
According to the method for generating the automatic driving map, a map collecting vehicle is not needed for collecting and drawing large-scale high-precision maps, a high-precision positioning device is not needed during deployment and dynamic update of the maps, the map drawing effect basically consistent with that of the high-precision maps and the positioning device can be achieved, the automatic driving map is guaranteed to be in accordance with design specifications and safety, the map drawing efficiency is improved, and the manpower and material resource costs are reduced.
FIG. 3 is a schematic block diagram of a computer system for generating an autopilot map in accordance with one or more embodiments of the invention.
As shown in fig. 3, the computer system 30 for generating an autopilot map includes a communication unit 310, a memory 320 (e.g., non-volatile memory such as flash memory, read-only memory, a hard drive, a magnetic disk, an optical disk, etc.), a processor 330, and a computer program 340 stored on the memory 320 and operable on the processor 330.
The communication unit 310 serves as a communication interface configured to establish a communication connection between the computer system 30 and an external device or network (e.g., a mobile phone, a cloud, a remote server, etc.).
The memory 320 stores a computer program 340 executable by the processor 330. The processor 330 is configured to execute the computer program 340 to implement a method for generating an autopilot map according to one or more embodiments of the invention.
In addition, as described above, the present invention may also be embodied as a computer storage medium in which a program for causing a computer to execute the method for generating an automatic driving map according to an aspect of the present invention is stored.
Here, as the computer storage medium, various types of computer storage media such as a disk type (e.g., a magnetic disk, an optical disk, etc.), a card type (e.g., a memory card, an optical card, etc.), a semiconductor memory type (e.g., a read only memory, a nonvolatile memory, etc.), a tape type (e.g., a magnetic tape, a cassette tape, etc.), and the like can be used.
Where applicable, the various embodiments provided by the present invention can be implemented using hardware, software, or a combination of hardware and software. Also, where applicable, the various hardware components and/or software components set forth herein may be combined into composite components comprising software, hardware, and/or both without departing from the scope of the present disclosure. Where applicable, the various hardware components and/or software components set forth herein may be separated into sub-components comprising software, hardware, or both without departing from the scope of the present invention. Further, where applicable, it is contemplated that software components may be implemented as hardware components, and vice versa.
Software, such as program code and/or data, according to the present invention can be stored on one or more computer storage media. It is also contemplated that the software identified herein may be implemented using one or more general purpose or special purpose computers and/or computer systems that are networked and/or otherwise. Where applicable, the order of various steps described herein may be changed, combined into composite steps, and/or separated into sub-steps to provide features described herein.
The embodiments and examples set forth herein are presented to best explain embodiments in accordance with the invention and its particular application and to thereby enable those skilled in the art to make and utilize the invention. Those skilled in the art, however, will recognize that the foregoing description and examples have been presented for the purpose of illustration and example only. The description as set forth is not intended to cover all aspects of the invention or to limit the invention to the precise form disclosed.
Claims (10)
1. A method for generating an autopilot map, the method comprising the steps of:
acquiring first measurement data at a single time by using a road test vehicle equipped with real-time dynamic measurement equipment, and processing the first measurement data to generate base map information of the automatic driving map;
collecting second measurement data a plurality of times with a user vehicle and processing the second measurement data to generate supplemental information for the autonomous driving map; and
generating the automatic driving map based on base map information of the automatic driving map and supplementary information of the automatic driving map.
2. The method of claim 1, wherein the first measurement data and the second measurement data comprise positioning data and perception data, the perception data comprising one or more of: stop line data, lane line data, road boundary data, lane bifurcation and junction point data, sign data and signal light data.
3. The method of claim 1, wherein processing the first measurement data to generate base map information for the autopilot map comprises:
determining vehicle trajectory and intersection information based on the first measurement data;
dividing the vehicle track into an inside sub-track and an outside sub-track according to the intersection information;
converting a coordinate system from a vehicle coordinate system to a world coordinate system according to the positioning data in the first measurement data so as to project perception data in the first measurement data to the world coordinate system to generate a plurality of lane line tracks;
matching the multiple sections of lane line tracks to the divided inside sub-track and outside sub-track of the intersection to generate the matched multiple sections of lane line tracks; and
sampling and clustering each of the matched multiple sections of lane line tracks to obtain one or more of stop line information, sign board information, lane line information and road boundary information so as to generate base map information of the automatic driving map.
4. The method of claim 3, wherein processing the first measurement data to generate the base map information for the autopilot map further comprises:
clustering the sub-tracks outside the intersection into clusters by using a line segment clustering method; and
generating link information based on the cluster as base map information of the automatic driving map.
5. The method of claim 3, wherein sampling and clustering each of the plurality of segments of lane line trajectory to obtain lane line information comprises:
clustering sampling points of the lane line information by using a density-based clustering method to generate a point cluster;
determining a main direction of the point cluster by using a principal component analysis method or an endpoint method and rotating the main direction into a horizontal direction;
dividing the point clusters with the length larger than the length threshold value into sub point clusters in the horizontal direction;
sampling again after least square fitting is carried out on the point clusters and/or the sub-point clusters to obtain matching points and discrete points, and clustering the matching points and the discrete points by using a density-based clustering method to generate point clusters; and
determining a lane line type based on the types of the points in the point cluster which are larger than a predetermined proportion.
6. The method of claim 3, wherein sampling and clustering each of the plurality of segments of the lane line trajectory to obtain stop-line information comprises:
determining a midpoint of a stop-line based on the stop-line information;
clustering the midpoints of the stop lines using a density-based clustering method to generate a point cluster; and
selecting a respective stop-line from each point cluster such that a midpoint of the stop-line is a center of the point cluster, and a length and a direction of the stop-line are an average of a plurality of stop-lines corresponding to the center of the point cluster.
7. The method of claim 1, wherein processing the second measurement data to generate supplemental information for the autopilot map comprises:
determining vehicle trajectory and intersection information based on the second measurement data;
dividing the vehicle track into an inside sub-track and an outside sub-track according to the intersection information;
converting a coordinate system from a vehicle coordinate system to a world coordinate system according to the positioning data in the second measurement data so as to project perception data in the second measurement data to the world coordinate system to generate a plurality of lane line tracks;
matching the multiple sections of lane line tracks to the divided inside sub-track and outside sub-track of the intersection to generate the matched multiple sections of lane line tracks; and
sampling and clustering each of the matched plurality of segments of lane line trajectories to obtain one or more of stop line information, sign information, lane line information, road boundary information, and center line information to generate supplemental information for the autopilot map.
8. The method of claim 7, wherein processing the second measurement data to generate supplemental information for the autopilot map further comprises:
determining the intersection points of the multiple sections of lane line tracks and the intersection edges based on the multiple sections of lane line tracks and the intersection information;
clustering the intersected points by using a density-based clustering method to obtain an entrance of the intersection and an exit of the intersection; and
centerline information corresponding to inside and outside of the intersection is determined,
wherein processing the second measurement data to generate supplemental information for the autopilot map further comprises:
and filtering the road boundary information and the center line information based on the Hausdorff distance and the boundary distance with the center line.
9. A computer system for generating an autopilot map, the system comprising:
a memory;
a processor; and
a computer program stored on the memory and executed on the processor, the execution of the computer program causing the following steps to be performed:
acquiring first measurement data at a single time by using a road test vehicle equipped with real-time dynamic measurement equipment, and processing the first measurement data to generate base map information of the automatic driving map;
collecting second measurement data a plurality of times with a user vehicle and processing the second measurement data to generate supplemental information for the autonomous driving map; and
generating the automatic driving map based on base map information of the automatic driving map and supplementary information of the automatic driving map.
10. A computer storage medium comprising instructions that when executed perform the method of any one of claims 1 to 8.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN116499477A (en) * | 2023-06-21 | 2023-07-28 | 小米汽车科技有限公司 | Map fusion method, device, medium and vehicle |
| CN116578891A (en) * | 2023-07-14 | 2023-08-11 | 天津所托瑞安汽车科技有限公司 | Road information reconstruction method, terminal and storage medium |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN116499477A (en) * | 2023-06-21 | 2023-07-28 | 小米汽车科技有限公司 | Map fusion method, device, medium and vehicle |
| CN116499477B (en) * | 2023-06-21 | 2023-09-26 | 小米汽车科技有限公司 | Map fusion method, device, medium and vehicle |
| CN116578891A (en) * | 2023-07-14 | 2023-08-11 | 天津所托瑞安汽车科技有限公司 | Road information reconstruction method, terminal and storage medium |
| CN116578891B (en) * | 2023-07-14 | 2023-10-03 | 天津所托瑞安汽车科技有限公司 | Road information reconstruction method, terminal and storage medium |
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