CN110530389B - Intersection mode identification method and system based on high-precision navigation electronic map - Google Patents
Intersection mode identification method and system based on high-precision navigation electronic map Download PDFInfo
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- 238000003066 decision tree Methods 0.000 claims abstract description 23
- 238000013024 troubleshooting Methods 0.000 claims abstract description 10
- 238000004458 analytical method Methods 0.000 claims description 10
- 238000012567 pattern recognition method Methods 0.000 claims description 9
- 230000000875 corresponding effect Effects 0.000 description 12
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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
- G01C21/28—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 specially adapted for navigation in a road network with correlation of data from several navigational instruments
- G01C21/30—Map- or contour-matching
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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
- G01C21/3446—Details of route searching algorithms, e.g. Dijkstra, A*, arc-flags, using precalculated routes
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Abstract
The invention discloses a crossing mode recognition method and a crossing mode recognition system based on a high-precision navigation electronic map, wherein the recognition method comprises the following steps: s1, searching a planned path from a starting point to an end point of a planned vehicle on the high-precision map, and generating a local map containing the planned path; s2, searching all intersections which are relevant to the planned path in the local map according to the driving direction of the vehicle; s3, calculating by utilizing a preset decision tree structure to obtain the association type of each intersection; s4, acquiring troubleshooting standard data aiming at the association type of the intersection from a preset action table according to the association type of the intersection; and S5, calculating the action of the vehicle passing through the corresponding intersection by using the checking standard data, and assisting the automatic driving of the vehicle by using the action. The method realizes rapid intersection mode identification with strong expansibility.
Description
Technical Field
The invention relates to the technical field of map navigation engines, in particular to a crossing mode identification method and an intersection mode identification system based on a high-precision navigation electronic map.
Background
Autopilot, broadly refers to a technique that assists or replaces human driving of an automobile. With the development of the technology, the travel of people is more convenient, the influence of human factors of manual driving is reduced, and the driving safety can be further improved to a certain degree. In the field of automatic driving, map navigation is a precondition for end-to-end automatic driving. Navigation technology actually solves two problems, namely path planning, namely calculation of road or lane numbers covered by an end-to-end path; and secondly, according to the crossing passed in the path, making a corresponding action, guiding the vehicle to realize a correct action at the crossing, and ensuring that the vehicle is kept on the planned path.
At present, an automatic driving vehicle does not have strong perception and decision-making capability of human beings, and a map module is required to provide an action specification based on rules to regulate the driving of the automatic driving vehicle, so that accurate definition and identification of a road model are necessary preconditions for safe and smooth automatic driving. Meanwhile, for the existing method for automatically driving the vehicle at the intersection, several action points are generally set in a certain fixed area, the track lines which are required to be followed by relevant actions are prestored, and then the actions which can occur at each action point are specified.
Disclosure of Invention
An object of the present invention is to solve at least the above problems and to provide at least the advantages described later.
The invention also aims to provide an intersection mode identification method based on the high-precision navigation electronic map, which realizes quick and strong-expansibility intersection mode identification.
In order to achieve the above objects and other objects, the present invention adopts the following technical solutions:
a crossing mode identification method based on a high-precision navigation electronic map comprises the following steps:
s1, searching a planned path from a starting point to an end point of a planned vehicle on the high-precision map, and generating a local map containing the planned path;
s2, searching all intersections which are relevant to the planned path in the local map according to the driving direction of the vehicle;
s3, calculating by utilizing a preset decision tree structure to obtain the association type of each intersection;
s4, acquiring troubleshooting standard data aiming at the association type of the intersection from a preset action table according to the association type of the intersection;
and S5, calculating the action of the vehicle passing through the corresponding intersection by using the checking standard data, and assisting the automatic driving of the vehicle by using the action.
Preferably, in the intersection pattern recognition method based on the high-precision navigation electronic map, the generation method of the local map is as follows:
s1-1, searching a path related to the planned path in a length range contained in the planned path to form a local search path;
and S1-2, generating the local map by using the planned path and the local search path.
Preferably, in the intersection pattern recognition method based on the high-precision navigation electronic map, the relevance in S2 indicates the intersection between the planned path and the other roads.
Preferably, in the method for identifying an intersection pattern based on a high-precision navigation electronic map, the decision tree structure in S3 is a tree-like decision structure made according to the number of roads connected to the intersection and the types of the roads.
Preferably, in the intersection pattern recognition method based on the high-precision navigation electronic map, the association type of the intersection in S3 specifically includes: an entry direction travelable road, an entry direction non-travelable road, an exit direction travelable road, and an exit direction non-travelable road.
Preferably, in the intersection pattern recognition method based on the high-precision navigation electronic map, the association types of the intersection in S3 at least include a primary association type and a secondary association type;
the primary association category refers to: calculating the obtained association type of the intersection by using a preset decision tree structure;
the secondary association category refers to: judging the obtained association type of the intersection by using the first-level association type and the attributes of the roads around the intersection;
wherein, the attribute of each road around the intersection comprises: an entry road, an exit road, a perimeter passable road, and a perimeter nonpassable road.
Preferably, in the intersection pattern recognition method based on the high-precision navigation electronic map, the action table is a suggested action comparison table of vehicles corresponding to attributes of different intersections.
Preferably, in the intersection pattern recognition method based on the high-precision navigation electronic map, in S4, before the examination standard data for the association type of the intersection is obtained from the predetermined action table according to the association type of the intersection, the intersections are further sorted in the traveling direction of the vehicle.
An intersection mode recognition system based on a high-precision navigation electronic map comprises:
the map generation module is used for intercepting a part containing the planned path and other roads related to the roads in the planned path from a high-precision map according to the planned path of the vehicle to generate a local map;
the searching module is connected with the map generating module and searches all intersections which are related to the planned path on the local map;
the analysis module is connected with the search module, a decision tree framework is prestored in the analysis module, and the decision tree framework is utilized to calculate the associated types of all intersections searched by the search module;
the decision module is connected with the analysis module, and an action table is prestored in the decision module; the decision module calls corresponding data from the action table according to the association type of the intersection and calculates the action of the vehicle passing through the intersection according to the data;
and the execution module acquires the action from the decision module and assists the automatic driving of the vehicle through the intersection by utilizing the action.
The invention at least comprises the following beneficial effects:
according to the intersection mode identification method based on the high-precision navigation electronic map, firstly, a local map is generated by using a path planned for a vehicle, all intersections relevant to the path are searched on the local map, then, the relevance type of each intersection is calculated by using a preset decision tree framework, the troubleshooting standard data of the corresponding intersection is obtained in an action table through the relevance type, and finally, the action for guiding the vehicle to pass through the corresponding intersection is calculated according to the troubleshooting standard data and the attribute of the intersection, so that the safety of automatically driving the vehicle to pass through the intersection is improved.
In the intersection mode recognition method based on the high-precision navigation electronic map, the intersection mode recognition can be expanded to any area covered by the map only by defining the decision tree and the action table for each type of intersection according to experience, so that the expansibility of the intersection mode recognition is improved.
Additional advantages, objects, and features of the invention will be set forth in part in the description which follows and in part will become apparent to those having ordinary skill in the art upon examination of the following or may be learned from practice of the invention.
Detailed Description
The present invention is described in detail below to enable one skilled in the art to practice the invention in light of the description.
A crossing mode recognition based on a high-precision navigation electronic map comprises the following steps: s1, searching a planned path from a starting point to an end point of a planned vehicle on the high-precision map, and generating a local map containing the planned path;
s2, searching all intersections which are relevant to the planned path in the local map according to the driving direction of the vehicle;
s3, calculating by utilizing a preset decision tree structure to obtain the association type of each intersection;
s4, acquiring troubleshooting standard data aiming at the association type of the intersection from a preset action table according to the association type of the intersection;
and S5, calculating the action of the vehicle passing through the corresponding intersection by using the checking standard data, and assisting the automatic driving of the vehicle by using the action.
In the scheme, a local map is generated by using a path planned for a vehicle, all related intersections related to the path are searched on the local map, then the related types of all the intersections are calculated by using a preset decision tree framework, the troubleshooting standard data of the corresponding intersections are obtained in an action table through the related types, and finally the actions for guiding the vehicle to pass through the corresponding intersections are calculated according to the troubleshooting standard data and the attributes of the intersections, so that the safety of automatically driving the vehicle to pass through the intersections is improved.
In a preferred embodiment, the method for generating the local map includes:
s1-1, searching a path related to the planned path in a length range contained in the planned path to form a local search path;
and S1-2, generating the local map by using the planned path and the local search path.
In the scheme, the path related to the planned path and the planned path are combined to form the local map, so that the local map only comprises the planned path and the path related to the planned path, and the efficiency of subsequent intersection identification is improved.
In a preferred embodiment, the association in S2 refers to the intersection between the planned path and multiple other roads.
In the above scheme, the road having an intersection with the planned path can form an intersection on the planned path, and thus the relevance refers to the intersection between the planned path and other roads.
In a preferred embodiment, the decision tree structure in S3 refers to a tree-like decision structure made according to the number of roads connected to the intersection and the types of roads.
In the above scheme, a common decision tree is a judgment mechanism similar to a binary tree, each node is a condition to be judged, and its output is only yes and no, and is divided into left and right branches according to the output. In short, a clear organization mode for obtaining the final result is achieved by superposition of judgment conditions. In S3, the type of an intersection is mainly determined by the number and kind of roads connected through the intersection. The road types comprise passable roads, impassable roads, entrance roads and exit roads, so that the decision tree structure can be set according to the number of the roads connected with the intersection and the types of the roads, and further the associated types of the intersection can be judged.
In a preferred embodiment, the association category of the intersection in S3 specifically refers to: an entry direction travelable road, an entry direction non-travelable road, an exit direction travelable road, and an exit direction non-travelable road.
In the above solution, the related categories of the intersection include four types, i.e., a drivable road in the entering direction, an undrivable road in the entering direction, a drivable road in the exit direction, and an undrivable road in the exit direction.
In a preferred embodiment, the association categories of the intersection in S3 include at least a primary association category and a secondary association category;
the primary association category refers to: calculating the obtained association type of the intersection by using a preset decision tree structure;
the secondary association category refers to: and judging the obtained association type of the intersection by using the first-level association type and the attributes of the roads around the intersection.
Wherein, the attribute of each road around the intersection comprises: an entry road, an exit road, a perimeter passable road, and a perimeter nonpassable road.
In the above-mentioned solution, for the situation of the roads around the planned path, when the surrounding roads and the planned path have simple crossroads, etc., only the first-level association type needs to be calculated to extract data from the action table, and when the surrounding roads are complicated, for example, there are not only the main roads that are crossed, but also the intersections formed by the auxiliary roads, etc., the calculation accuracy of the association type of the intersection determined by the first-level association type and the attributes of the roads around the intersection can be further improved, so as to facilitate guidance of the subsequent vehicle action.
In a preferred embodiment, the action table is a suggested action comparison table of vehicles corresponding to attributes of different intersections.
In the above scenario, the action table actually defines what actions each intersection vehicle needs to do. For example, if the front needs to turn right at the intersection according to the planned path, the command of turning right can be obtained by searching the action table according to the current position, the front intersection information and the planned path, and the command is sent to the vehicle.
In a preferable embodiment, in S4, before the examination standard data for the association type of the intersection is obtained from the action table prepared in advance according to the association type of the intersection, the intersections are further sorted according to the driving direction of the vehicle.
In the scheme, the intersections are sequenced according to the driving direction of the vehicle, so that the actions of the intersections through which the vehicle passes are calculated according to the driving sequence, and the smooth driving of the vehicle is facilitated.
An intersection mode recognition system based on a high-precision navigation electronic map comprises:
the map generation module is used for intercepting a part containing the planned path and other roads related to the roads in the planned path from a high-precision map according to the planned path of the vehicle to generate a local map;
the searching module is connected with the map generating module and searches all intersections which are related to the planned path on the local map;
the analysis module is connected with the search module, a decision tree framework is prestored in the analysis module, and the decision tree framework is utilized to calculate the associated types of all intersections searched by the search module;
the decision module is connected with the analysis module, and an action table is prestored in the decision module; the decision module calls corresponding data from the action table according to the association type of the intersection and calculates the action of the vehicle passing through the intersection according to the data;
and the execution module acquires the action from the decision module and assists the automatic driving of the vehicle through the intersection by utilizing the action.
In the scheme, a map generation module intercepts a part containing a planned path and other roads related to the planned path on a high-precision map by using the planned path of a vehicle to generate a local map, a search module searches all intersections related to the planned path from the local map, an analysis module calculates the related types of all the intersections searched by the search module by using a decision tree framework, a decision module calls corresponding data from an action table according to the related types of the intersections, the action of the vehicle passing through the intersections is calculated according to the data, and an execution module assists the automatic driving of the vehicle passing through the intersections by using the action.
While embodiments of the invention have been disclosed above, it is not limited to the applications listed in the description and the embodiments, which are fully applicable in all kinds of fields of application of the invention, and further modifications may readily be effected by those skilled in the art, so that the invention is not limited to the specific details without departing from the general concept defined by the claims and the scope of equivalents.
Claims (4)
1. A crossing mode recognition method based on a high-precision navigation electronic map comprises the following steps:
s1, searching a planned path from a starting point to an end point of a planned vehicle on the high-precision map, and generating a local map containing the planned path;
s2, searching all intersections which are relevant to the planned path in the local map according to the driving direction of the vehicle;
s3, calculating by utilizing a preset decision tree structure to obtain the association type of each intersection; the decision tree structure is a tree decision structure made according to the number of roads connected with the intersection and the types of the roads; the association types of the intersection specifically refer to: a drivable road in an entering direction, a non-drivable road in an entering direction, a drivable road in an exit direction, and a non-drivable road in an exit direction;
s4, acquiring troubleshooting standard data aiming at the association type of the intersection from a preset action table according to the association type of the intersection; the action table is a vehicle suggested action comparison table corresponding to the attributes of different intersections;
s5, calculating the action of the vehicle passing through the corresponding intersection by using the troubleshooting standard data, and assisting the automatic driving of the vehicle by using the action;
the generation method of the local map comprises the following steps:
s1-1, searching a path related to the planned path in a length range contained in the planned path to form a local search path;
s1-2, generating the local map by using the planned path and the local search path;
in addition, the relevance in S2 refers to the intersection of the planned path with multiple other roads.
2. The intersection pattern recognition method based on the high-precision navigation electronic map as claimed in claim 1, wherein the association types of the intersection in S3 at least include a primary association type and a secondary association type;
the primary association category refers to: calculating the obtained association type of the intersection by using a preset decision tree structure;
the secondary association category refers to: judging the obtained association type of the intersection by using the first-level association type and the attributes of the roads around the intersection;
wherein, the attribute of each road around the intersection comprises: an entry road, an exit road, a perimeter passable road, and a perimeter nonpassable road.
3. The intersection pattern recognition method based on the high-precision navigation electronic map as claimed in claim 1, wherein in S4, before the data of the troubleshooting standards for the association types of the intersections is obtained from the action table prepared in advance according to the association types of the intersections, the intersections are further sorted according to the driving direction of the vehicle.
4. A system for applying the intersection pattern recognition method based on high-precision navigation electronic map according to any one of claims 1-3, wherein the method comprises the following steps:
the map generation module is used for intercepting a part containing the planned path and other roads related to the roads in the planned path from a high-precision map according to the planned path of the vehicle to generate a local map;
the searching module is connected with the map generating module and searches all intersections which are related to the planned path on the local map;
the analysis module is connected with the search module, a decision tree framework is prestored in the analysis module, and the decision tree framework is utilized to calculate the associated types of all intersections searched by the search module; the decision tree structure is a tree decision structure made according to the number of roads connected with the intersection and the types of the roads; the association types of the intersection specifically refer to: a drivable road in an entering direction, a non-drivable road in an entering direction, a drivable road in an exit direction, and a non-drivable road in an exit direction;
the decision module is connected with the analysis module, and an action table is prestored in the decision module; the decision module calls corresponding data from the action table according to the association type of the intersection and calculates the action of the vehicle passing through the intersection according to the data; the action table is a vehicle suggested action comparison table corresponding to the attributes of different intersections;
and the execution module acquires the action from the decision module and assists the automatic driving of the vehicle through the intersection by utilizing the action.
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Address after: 201, 202, 301, No. 56-4 Fenghuang South Road, Huadu District, Guangzhou City, Guangdong Province, 510806 Patentee after: Heduo Technology (Guangzhou) Co.,Ltd. Address before: 100089 21-14, 1st floor, building 21, Enji West Industrial Park, No.1, liangjiadian, Fuwai, Haidian District, Beijing Patentee before: HOLOMATIC TECHNOLOGY (BEIJING) Co.,Ltd. |
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Denomination of invention: Intersection pattern recognition method and recognition system based on high-precision navigation electronic map Granted publication date: 20210706 Pledgee: Bank of Shanghai Co.,Ltd. Beijing Branch Pledgor: Heduo Technology (Guangzhou) Co.,Ltd. Registration number: Y2024980009891 |