CN113237483A - Vehicle navigation method, device, electronic equipment and storage medium - Google Patents
Vehicle navigation method, device, electronic equipment and storage medium Download PDFInfo
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- 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
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- G—PHYSICS
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- 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
- G01C21/3453—Special cost functions, i.e. other than distance or default speed limit of road segments
- G01C21/3492—Special cost functions, i.e. other than distance or default speed limit of road segments employing speed data or traffic data, e.g. real-time or historical
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Abstract
The embodiment of the application provides a vehicle navigation method, a vehicle navigation device, electronic equipment and a storage medium, wherein configuration data are loaded and used for representing a navigation scene of a navigation task to be executed by a vehicle; determining a target navigation model according to the configuration data, wherein the target navigation model is used for planning a driving path of the vehicle when the vehicle executes a navigation task; and controlling the vehicle to execute the navigation task according to the target navigation model. The configuration data represents the navigation scene of the navigation task to be executed by the vehicle, so that the target navigation model produced by loading the configuration data can be suitable for the current navigation scene, and is used for planning the driving path of the vehicle when the vehicle executes the navigation task, the accuracy of the driving path of the vehicle can be improved, and the execution efficiency and the accuracy of the navigation task can be improved.
Description
Technical Field
The present application relates to the field of automatic driving technologies, and in particular, to a vehicle navigation method and apparatus, an electronic device, and a storage medium.
Background
This section is intended to provide a background or context to the embodiments of the application that are recited in the claims. The description herein is not admitted to be prior art by inclusion in this section.
The automatic driving system is based on sensing technology, communication information, computer technology and control technology and can realize the real-time and continuous control of vehicle. The vehicle provided with the automatic driving system can control the vehicle to execute a navigation task after receiving the navigation instruction, so that the vehicle automatically drives to a destination.
In the prior art, navigation control is performed on a vehicle, generally based on a fixed navigation strategy, by acquiring map data in real time, a corresponding driving path is generated, and the vehicle is controlled to execute a navigation task.
However, in the actual execution process, because the driving environments of the vehicles when executing the navigation tasks are different, the navigation planning is performed through the fixed navigation strategy, and the problem of inaccurate planned driving path is caused.
Disclosure of Invention
The application provides a vehicle navigation method, a vehicle navigation device, electronic equipment and a storage medium, which are used for solving the problem of inaccurate planned driving path.
According to a first aspect of embodiments herein, there is provided a vehicle navigation method, the method comprising:
loading configuration data, wherein the configuration data is used for representing a navigation scene of a navigation task to be executed by a vehicle; determining a target navigation model according to the configuration data, wherein the target navigation model is used for planning a driving path of the vehicle when the vehicle executes the navigation task; and controlling the vehicle to execute the navigation task according to the target navigation model.
In a possible implementation manner, the target navigation model includes a navigation algorithm, and controlling a vehicle to execute the navigation task according to the target navigation model includes: acquiring a navigation map, wherein the navigation map is used for representing the position relation between an obstacle and the vehicle; calling the navigation algorithm by taking the navigation map as an input parameter to generate a driving path of the vehicle when the navigation task is executed; and controlling the vehicle to execute the navigation task according to the driving path.
In one possible implementation, the navigation map includes a global map and a local map, and the navigation algorithm includes a first scheduling algorithm and a second scheduling algorithm; the first scheduling algorithm is used for determining a global path of the navigation task; the second scheduling algorithm is used for determining a local path of the navigation task; controlling the vehicle to execute the navigation task according to the target navigation model, wherein the control method comprises the following steps: determining a global path of the navigation task according to the first scheduling algorithm and a global map, wherein the global path comprises a plurality of path intervals; on the basis of the global path, determining a local path in each path interval of the global path according to the second scheduling algorithm and a local map; and controlling the vehicle to execute the navigation task according to the global path and the local path in each path interval of the global path.
In one possible implementation, the acquiring the navigation map includes: acquiring a preset static map, wherein the static map comprises map data of an area where the navigation task is located; rendering the static map based on sensor data to generate a global map, wherein the sensor data represent the distance between an obstacle in the area where the navigation task is located and the vehicle, and the global map represents the position relation between the obstacle in the area where the navigation task is located and the vehicle.
In one possible implementation, rendering the static map based on the sensor data to generate a global map includes: according to the resolution of the static map, performing grid division on the static map to generate a rasterized static map; and superposing the sensor data to the rasterized static map, and rendering the distance between the obstacle represented by the sensor data and the vehicle in a color gradient form based on a Voronoi diagram to generate a global map.
In one possible implementation, the acquiring the navigation map includes: acquiring a dynamic map according to a preset first acquisition period, wherein the dynamic map comprises map data of a path interval where the vehicle arrives; rendering the dynamic map based on sensor data to generate a local map, wherein the sensor data represents the distance between an obstacle and the vehicle in a path interval reached by the vehicle; the local map represents the position relation between obstacles in a path section reached by the vehicle and the vehicle.
In one possible implementation, rendering the dynamic map based on the sensor data to generate a local map includes: according to the size of the dynamic map, carrying out grid division on the dynamic map by taking the current position of the dynamic map as a center to generate a rasterized dynamic map; and the sensor data superposition value is subjected to rasterization on the dynamic map, and the distance between the obstacle represented by the sensor data and the vehicle is rendered in a color gradient mode based on the voronoi diagram, so that a local map is generated.
In one possible implementation manner, controlling a vehicle to execute the navigation task according to the global path and a local path in each path section of the global path includes: controlling the vehicle to move according to the global path, and acquiring the running state of the vehicle in each path interval of the global path, wherein the running state is used for representing whether the vehicle is blocked by an obstacle; determining a real-time path according to the running state, wherein the real-time path comprises an original path or a modified path, and the original path is the global path and a local path in each path interval of the global path; the corrected path is a path obtained by correcting the global path or the local path of each path interval of the global path according to the running state; and controlling the vehicle to move according to the real-time path.
In one possible implementation manner, acquiring the operating state of the vehicle in each of the path sections of the global path includes: determining whether the vehicle is currently blocked by an obstacle according to the local map; and if the vehicle is blocked by the obstacle, determining the running state according to the time length of the vehicle blocked by the obstacle or the times of the vehicle executing the evasive action aiming at the obstacle.
In a possible implementation manner, the second scheduling algorithm includes a local planning algorithm and a dynamic obstacle avoidance algorithm, and the local planning algorithm is configured to determine a local path in the first path interval according to the local map; and the dynamic obstacle avoidance algorithm is used for determining a local path in a second path interval according to the local map, wherein the first path interval is larger than the second path interval.
In a possible implementation manner, the target navigation model further includes a sensor identifier corresponding to the navigation algorithm, and the obtaining of the navigation map includes: determining a target sensor according to the sensor identification; and acquiring sensor data through the target sensor, and generating a navigation map according to the sensor data.
In one possible implementation, the loading the configuration data includes: detecting environmental data, wherein the environmental data is used for representing the current running environment of the vehicle; and determining and loading configuration data according to the environment data.
According to a second aspect of embodiments of the present application, there is provided a vehicular navigation apparatus including:
the loading module is used for loading configuration data, and the configuration data is used for representing a navigation scene of a navigation task to be executed by a vehicle;
the determining module is used for determining a target navigation model according to the configuration data, and the target navigation model is used for planning a driving path of the vehicle when the vehicle executes the navigation task;
and the control module is used for controlling the vehicle to execute the navigation task according to the target navigation model.
In a possible implementation manner, the target navigation model includes a navigation algorithm, and the control module is specifically configured to: acquiring a navigation map, wherein the navigation map is used for representing the position relation between an obstacle and the vehicle; calling the navigation algorithm by taking the navigation map as an input parameter to generate a driving path of the vehicle when the navigation task is executed; and controlling the vehicle to execute the navigation task according to the driving path.
In one possible implementation, the navigation map includes a global map and a local map, and the navigation algorithm includes a first scheduling algorithm and a second scheduling algorithm; the first scheduling algorithm is used for determining a global path of the navigation task; the second scheduling algorithm is used for determining a local path of the navigation task; the control module is specifically configured to, when controlling the vehicle to execute the navigation task according to the target navigation model: determining a global path of the navigation task according to the first scheduling algorithm and a global map, wherein the global path comprises a plurality of path intervals; on the basis of the global path, determining a local path in each path interval of the global path according to the second scheduling algorithm and a local map; and controlling the vehicle to execute the navigation task according to the global path and the local path in each path interval of the global path.
In a possible implementation manner, when the control module acquires the navigation map, the control module is specifically configured to: acquiring a preset static map, wherein the static map comprises map data of an area where the navigation task is located; rendering the static map based on sensor data to generate a global map, wherein the sensor data represent the distance between an obstacle in the area where the navigation task is located and the vehicle, and the global map represents the position relation between the obstacle in the area where the navigation task is located and the vehicle.
In a possible implementation manner, when the control module renders the static map based on the sensor data to generate the global map, the control module is specifically configured to: according to the resolution of the static map, performing grid division on the static map to generate a rasterized static map; and superposing the sensor data to the rasterized static map, and rendering the distance between the obstacle represented by the sensor data and the vehicle in a color gradient form based on a Voronoi diagram to generate a global map.
In one possible implementation manner, when acquiring the navigation map, the control module is further configured to: acquiring a dynamic map according to a preset first acquisition period, wherein the dynamic map comprises map data of a path interval where the vehicle arrives; rendering the dynamic map based on sensor data to generate a local map, wherein the sensor data represents the distance between an obstacle and the vehicle in a path interval reached by the vehicle; the local map represents the position relation between obstacles in a path section reached by the vehicle and the vehicle.
In a possible implementation manner, when the control module renders the dynamic map based on the sensor data and generates the local map, the control module is specifically configured to: according to the size of the dynamic map, carrying out grid division on the dynamic map by taking the current position of the dynamic map as a center to generate a rasterized dynamic map; and the sensor data superposition value is subjected to rasterization on the dynamic map, and the distance between the obstacle represented by the sensor data and the vehicle is rendered in a color gradient mode based on the voronoi diagram, so that a local map is generated.
In a possible implementation manner, when the control module controls the vehicle to execute the navigation task according to the global path and the local path in each path section of the global path, the control module is specifically configured to: controlling the vehicle to move according to the global path, and acquiring the running state of the vehicle in each path interval of the global path, wherein the running state is used for representing whether the vehicle is blocked by an obstacle; determining a real-time path according to the running state, wherein the real-time path comprises an original path or a modified path, and the original path is the global path and a local path in each path interval of the global path; the corrected path is a path obtained by correcting the global path or the local path of each path interval of the global path according to the running state; and controlling the vehicle to move according to the real-time path.
In a possible implementation manner, when acquiring the operating state of the vehicle in each of the path sections of the global path, the control module is specifically configured to: determining whether the vehicle is currently blocked by an obstacle according to the local map; and if the vehicle is blocked by the obstacle, determining the running state according to the time length of the vehicle blocked by the obstacle or the times of the vehicle executing the evasive action aiming at the obstacle.
In a possible implementation manner, the second scheduling algorithm includes a local planning algorithm and a dynamic obstacle avoidance algorithm, and the local planning algorithm is configured to determine a local path in the first path interval according to the local map; and the dynamic obstacle avoidance algorithm is used for determining a local path in a second path interval according to the local map, wherein the first path interval is larger than the second path interval.
In a possible implementation manner, the target navigation model further includes a sensor identifier corresponding to the navigation algorithm, and the control module is specifically configured to: determining a target sensor according to the sensor identification; and acquiring sensor data through the target sensor, and generating a navigation map according to the sensor data.
In a possible implementation manner, when the loading module loads the configuration data, the loading module is specifically configured to: detecting environmental data, wherein the environmental data is used for representing the current running environment of the vehicle; and determining and loading configuration data according to the environment data.
According to a third aspect of embodiments of the present application, there is provided an electronic device, comprising: a memory, a processor, and a computer program;
wherein the computer program is stored in the memory and configured to be executed by the processor to perform the vehicle navigation method according to any one of the first aspect of the embodiments of the present application.
According to a fourth aspect of embodiments of the present application, there is provided a computer-readable storage medium having stored therein computer-executable instructions for implementing a vehicle navigation method according to any one of the first aspect of the embodiments of the present application when executed by a processor.
According to a fifth aspect of embodiments herein there is provided a computer program product comprising a computer program which, when executed by a processor, implements the first aspect as well as various possible vehicle navigation methods of the first aspect.
According to the vehicle navigation method, the vehicle navigation device, the electronic equipment and the storage medium, configuration data are loaded, and the configuration data are used for representing a navigation scene of a navigation task to be executed by a vehicle; determining a target navigation model according to the configuration data, wherein the target navigation model is used for planning a driving path of the vehicle when the vehicle executes the navigation task; and controlling the vehicle to execute the navigation task according to the target navigation model. The configuration data represents the navigation scene of the navigation task to be executed by the vehicle, so that the target navigation model produced by loading the configuration data can be suitable for the current navigation scene, and is used for planning the driving path of the vehicle when the vehicle executes the navigation task, the accuracy of the driving path of the vehicle can be improved, and the execution efficiency and the accuracy of the navigation task can be improved.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and together with the description, serve to explain the principles of the application.
Fig. 1 is an application scenario diagram of a vehicle navigation method according to an embodiment of the present application;
FIG. 2 is a flow chart of a vehicle navigation method provided in one embodiment of the present application;
FIG. 3 is a flow chart of a vehicle navigation method provided in another embodiment of the present application;
fig. 4 is a schematic diagram of a global path and a local path according to an embodiment of the present disclosure;
FIG. 5 is a flowchart of step S203 in the embodiment of FIG. 3;
FIG. 6 is a flowchart of step S205 in the embodiment shown in FIG. 3;
FIG. 7 is a flowchart of step S207 in the embodiment of FIG. 3;
fig. 8 is a schematic structural diagram of a vehicle navigation device according to an embodiment of the present application;
fig. 9 is a schematic structural diagram of an electronic device according to an embodiment of the present application.
With the above figures, there are shown specific embodiments of the present application, which will be described in more detail below. These drawings and written description are not intended to limit the scope of the inventive concepts in any manner, but rather to illustrate the inventive concepts to those skilled in the art by reference to specific embodiments.
Detailed Description
Reference will now be made in detail to the exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, like numbers in different drawings represent the same or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present application. Rather, they are merely examples of apparatus and methods consistent with aspects such as the present application.
The following explains an application scenario of the embodiment of the present application:
fig. 1 is an application scenario diagram of a vehicle navigation method according to an embodiment of the present application, and as shown in fig. 1, the vehicle navigation method according to the embodiment may be applied to a scenario of automatic driving control, and an execution subject of the method according to the embodiment may be a vehicle with an automatic driving function, such as an intelligent internet vehicle, or a vehicle controller of a vehicle with an automatic driving function. Specifically, as shown in fig. 1, a vehicle controller 11 for executing the method provided in this embodiment is disposed on the target vehicle, and after receiving a navigation instruction input by a user, the vehicle controller 11 plans a driving route in real time according to a current position of the target vehicle and a destination position in the navigation instruction, and controls the vehicle to move according to the real-time driving route, so as to implement automatic driving actions such as automatic starting, automatic obstacle avoidance 12, and automatic stopping after reaching the destination position, and complete a vehicle navigation task corresponding to the navigation instruction.
In the application scenario of the automatic driving control, in the prior art, when planning a navigation path, a vehicle controller generally acquires map data in real time based on a fixed navigation strategy to generate a corresponding driving path and control a vehicle to execute a corresponding navigation task. However, in actual execution, there is a difference in the running environment when the vehicle performs a navigation task, for example, the vehicle runs indoors, runs in an urban area, runs on a highway, and due to the difference in the running environment, there is also a difference in the map data suitable for the running environment. For example, when a vehicle travels on a highway or indoors, the accuracy and size of the map data required and the real-time requirement for acquiring the map data are different. Therefore, different driving environments correspond to different navigation scenes, and in the prior art, the same navigation strategy is adopted for the different navigation scenes, and the same map data acquisition method and the same map data processing algorithm are adopted, so that the fixed navigation strategy is not suitable for the navigation scene corresponding to the navigation task under some conditions, and further, the navigation planning is performed through the fixed navigation strategy, so that the problem of inaccurate planned driving path is caused, and the execution efficiency and accuracy of the navigation task are influenced.
The following describes the technical solutions of the present application and how to solve the above technical problems with specific embodiments. The following several specific embodiments may be combined with each other, and details of the same or similar concepts or processes may not be repeated in some embodiments. Embodiments of the present application will be described below with reference to the accompanying drawings.
Fig. 2 is a flowchart of a vehicle navigation method according to an embodiment of the present application, and as shown in fig. 2, for example, the vehicle navigation method according to the present embodiment may be applied to an intelligent vehicle, and a vehicle controller disposed in the intelligent vehicle executes the vehicle navigation method according to the present embodiment. The vehicle navigation method provided by the embodiment comprises the following steps:
step S101, loading configuration data, wherein the configuration data is used for representing a navigation scene of a navigation task to be executed by a vehicle.
For example, the configuration data may be data preset in the smart car, and the configuration data is loaded according to an instruction input by a user, or after the automatic driving program is started, the vehicle controller automatically loads according to the surrounding environment information and preset map data, so as to determine a navigation scene of a navigation task to be executed by the smart car at this time, where the navigation scene is, for example, indoor navigation, high-speed navigation, and the like. The configuration data may include parameters or scene identifiers for executing the navigation task, and the intelligent automobile is controlled by using a strategy matched with the navigation scene when executing the navigation task through the parameters or the scene identifiers for executing the navigation task.
In one possible implementation, the process of loading the configuration data includes:
detecting environmental data, wherein the environmental data are used for representing the current running environment of the vehicle; configuration data is determined and loaded according to the environment data. For example, the environment data may include environment information collected by a sensor provided on the smart vehicle, and by processing and characterizing the environment information, a driving environment in which the smart vehicle is currently located may be determined, wherein the driving environment characterizes a feature to which the vehicle is located, for example, whether the vehicle is in an open environment or a narrow environment, whether the obstacle density around the vehicle is present, and the like. And determining a corresponding navigation scene and configuration data corresponding to the navigation scene according to the driving environment, and loading.
The environmental data may also be sent to the vehicle controller serving as the execution subject of the method in this embodiment in a direct or indirect manner by other devices, for example, a server, a roadside device, and vehicle controllers of other intelligent vehicles, which are not described in detail here.
And S102, determining a target navigation model according to the configuration data, wherein the target navigation model is used for planning a driving path of the vehicle when the vehicle executes a navigation task.
For example, after the configuration data is obtained, a target navigation model for planning a driving path of the navigation task may be determined according to relevant information in the configuration data. The navigation model comprises information such as navigation algorithm, sensor configuration and the like, specific navigation planning can be realized through relevant navigation algorithms in the navigation model, and the intelligent vehicle runs according to the result of the navigation planning, namely a navigation path, and finally reaches a destination. Specifically, in a possible implementation manner, the configuration data may include an identifier of the target navigation model, and the target navigation model may be determined according to the identifier of the target navigation model and a preset mapping relationship; in another possible implementation manner, the configuration data may include identifiers corresponding to a plurality of navigation algorithms, and the target navigation model is constructed by loading the identifiers corresponding to the plurality of navigation algorithms, so that the target navigation model is produced in a modular manner.
And step S103, controlling the vehicle to execute a navigation task according to the target navigation model.
Further, after determining the target navigation model, when planning the navigation task through the target template navigation model determined by the configuration data, the strategy for matching the navigation scene can be used for navigation planning, for example, including using a navigation algorithm matched with the navigation scene, and/or collecting map data required by the navigation algorithm using a sensor matched with the navigation scene. Therefore, the driving path planned according to the target navigation model can be matched with the navigation scene, and the driving path is more reasonable.
In one possible implementation, the target navigation model includes a navigation algorithm, and the controlling the vehicle to perform the navigation task according to the target navigation model includes: acquiring a navigation map, wherein the navigation map is used for representing the position relation between the obstacle and the vehicle; taking the navigation map as an input parameter, calling a navigation algorithm, and generating a driving path of the vehicle when executing a navigation task; and controlling the vehicle to execute the navigation task according to the driving path.
The navigation map includes, for example, a static map and a dynamic map, where the static map is preset and represents map data of an area where the navigation task is located, for example, map data of a city a. The dynamic map is map data generated by processing data acquired by vehicle-mounted sensors such as laser radar and infrared sensors arranged on the intelligent vehicle, the dynamic map can be updated along with the movement of the position of the vehicle, and the intelligent vehicle acquires the dynamic map in a preset acquisition cycle.
Further, in the implementation process of the navigation algorithm, a navigation map is required to be used as an input quantity for calculation, the navigation map comprises a dynamic map, so that the planning of a driving path by a target navigation model is dynamic, the target navigation model dynamically plans the driving path according to the navigation map acquired in real time, and a vehicle controller controls a vehicle to move according to the dynamic driving path according to the driving path dynamically planned by the target navigation model until a destination position corresponding to a navigation task is reached, or the navigation task is stopped due to the occurrence of a preset abnormal condition.
In the embodiment, the configuration data is loaded and used for representing the navigation scene of the navigation task to be executed by the vehicle; determining a target navigation model according to the configuration data, wherein the target navigation model is used for planning a driving path of the vehicle when the vehicle executes a navigation task; and controlling the vehicle to execute the navigation task according to the target navigation model. The configuration data represents the navigation scene of the navigation task to be executed by the vehicle, so that the target navigation model produced by loading the configuration data can be suitable for the current navigation scene, and is used for planning the driving path of the vehicle when the vehicle executes the navigation task, the accuracy of the driving path of the vehicle can be improved, and the execution efficiency and the accuracy of the navigation task can be improved.
Fig. 3 is a flowchart of a vehicle navigation method according to another embodiment of the present application, and as shown in fig. 3, the vehicle navigation method according to this embodiment further details step S103 on the basis of the vehicle navigation method according to the embodiment shown in fig. 2, and then the vehicle navigation method according to this embodiment includes the following steps:
step S201, configuration data is loaded, and the configuration data is used for representing a navigation scene of a navigation task to be executed by the vehicle.
Step S202, determining a target navigation model according to the configuration data.
The target navigation model comprises a first scheduling algorithm and a second scheduling algorithm, wherein the first scheduling algorithm is used for determining a global path of a navigation task; the second scheduling algorithm is used to determine a local path for the navigation task. The target navigation model also includes a sensor identification corresponding to the second scheduling algorithm.
Illustratively, the first scheduling algorithm and the second scheduling algorithm are independent, and are used for generating the global path and the local path in the navigation task respectively. The first scheduling algorithm and the second scheduling algorithm may be configured according to user needs. Specifically, the navigation task may be automatic driving task information generated according to a specific navigation instruction and executed by the intelligent vehicle, where the navigation instruction at least includes a destination location, and the intelligent vehicle automatically drives from a current location to the destination location according to the navigation instruction, thereby completing the navigation task.
Fig. 4 is a schematic diagram of a global path and a local path provided in an embodiment of the present application, and as shown in fig. 4, an exemplary global path refers to a rough path generated based on map data in a process of moving from a current location to a destination location. For example, if a vehicle needs to sequentially pass through a street a, a street B, a highway c, and a street d from the departure point of city a to the destination point of city B, the global route is a route including routes of the street a, the street B, the highway c, and the street d. The global path is generated by map data including a street, b street, c highway and d street, and a corresponding first scheduling algorithm. The local path refers to a path generated by planning a vehicle driving path in each road section on the basis of the global path and a path planned due to obstacle avoidance requirements. For example, when the vehicle travels in the a-block section, the vehicle may have a specific travel path within each lane, and/or the vehicle may have a planned path to avoid an obstacle when it encounters an obstacle (e.g., a stopped vehicle ahead). From the above description, the local path belongs to a dynamically planned path, and the local path may be generated by dynamically planning according to the map data acquired in real time and the second scheduling algorithm. The implementation manner of performing navigation planning according to the scheduling algorithm and the map data is known to those skilled in the art, and is not described herein again.
Step S203, a global map is acquired.
The global map is map data used for generating a global path as an input quantity of the first scheduling algorithm, and optionally, as shown in fig. 5, step S203 includes two specific implementation steps of step S2031 and step S2032:
step S2031, a preset static map is acquired, where the static map includes map data of an area where the navigation task is located.
Step S2032, rendering the static map based on the sensor data to generate a global map, wherein the sensor data represents the distance between the obstacle in the area where the navigation task is located and the vehicle, and the global map represents the position relation between the obstacle in the area where the navigation task is located and the vehicle.
Illustratively, the static map is preset map data, such as map data of city a, map data of the inside of a room, and the like. The static map is collected in advance and preset in the intelligent automobile, so that the static map is static, the static map can be updated in a preset period, and detailed description is omitted here.
In one possible implementation, an implementation of generating a global map includes:
according to the resolution of the static map, performing grid division on the static map to generate a rasterized static map; and superposing the sensor data to a rasterized static map, and rendering the distance between the obstacle represented by the sensor data and the vehicle in a color gradient form based on the voronoi diagram to generate a global map.
A static map is, for example, a map data for representing a drivable road in a large area, for a non-drivable area, corresponding to an obstacle for a vehicle. And rasterizing the static map on the basis of the static map to realize the partition of the static map. And then determining the position of the vehicle on the static map based on sensor data acquired by a sensor arranged on the vehicle, then performing superposition and voronoi diagram calculation on the rasterized static map, wherein the obtained calculation result can be used for estimating the distance between the obstacle and the vehicle, and further exemplarily rendering the calculation result in a color gradient manner to generate a global map, wherein the color map comprises multiple implementation forms such as RGB color gradient and gray scale gradient, which are not illustrated herein. In this embodiment, the voronoi diagram calculation method is also known in the prior art, and is not described herein again.
Step S204, determining a global path of the navigation task according to the first scheduling algorithm and the global map, wherein the global path comprises a plurality of path intervals.
For example, after generating a global map from the static map and the sensor data, the global map may characterize the distance of the vehicle from the obstacles in the area of the navigation task. I.e. the position of the vehicle in the road in the static map. Furthermore, according to a preset first scheduling algorithm, path planning is carried out based on the global map, and a global path in the area where the navigation task is located can be generated.
And S205, determining a target sensor according to the sensor identifier corresponding to the second scheduling algorithm, and acquiring a local map based on the target sensor.
Illustratively, the global map is map data for generating a local route as an input quantity of the second scheduling algorithm, and optionally, as shown in fig. 6, the step S205 includes two specific implementation steps of steps S2051 and S2052:
step S2051, acquiring a dynamic map according to a preset first acquisition cycle, where the dynamic map includes map data of a route section where the vehicle arrives.
Step S2052, rendering the dynamic map based on the sensor data to generate a local map, wherein the sensor data represents the distance between an obstacle and a vehicle in a path interval where the vehicle arrives; the local map represents the position relation between obstacles and the vehicle in the path section reached by the vehicle.
For example, the dynamic map is used to represent map data in a current path section of the vehicle, and the dynamic map may be map data acquired by a sensor provided on the vehicle to which the vehicle navigation method provided in this embodiment is applied, or may also be data acquired by other devices, such as other intelligent vehicles, roadside devices, and the like, and sent to the vehicle to which the vehicle navigation method provided in this embodiment is applied. The first acquisition period represents the interval of acquiring the dynamic map, the shorter the first acquisition period is, the greater the acquisition density of the dynamic map is, and correspondingly, the higher the vehicle control precision based on the dynamic map is, and the greater the computing resource consumption is. Thus, the first acquisition cycle is associated with a navigation scenario in which the vehicle performs a navigation task. For example, the first acquisition period may be a value corresponding to the second scheduling algorithm, set within or determined by the configuration data.
In one possible implementation, the implementation of generating the local map includes:
according to the size of the dynamic map, carrying out grid division on the dynamic map by taking the current position of the dynamic map as a center to generate a rasterized dynamic map; and rasterizing the sensor data superposition value into a dynamic map, and rendering the distance between the obstacle represented by the sensor data and the vehicle in a color gradient mode based on the Venno diagram to generate a local map.
Illustratively, a dynamic map is map data for characterizing a drivable road in a small area, and the dynamic map is map data for characterizing a positional relationship of an obstacle of a vehicle in a driving area of a static map with the vehicle. On the basis of the dynamic map, the static map is rasterized by taking the current position as the center, so that the partition of the dynamic map is realized. And then determining the position relation between the vehicle and the obstacle in the dynamic map based on sensor data acquired by a sensor arranged on the vehicle, then performing superposition and voronoi diagram calculation on the rasterized dynamic map, wherein the obtained calculation result can be used for estimating the distance between the obstacle and the vehicle, and further exemplarily rendering the calculation result in a color gradient form to generate a local map, wherein the color map comprises multiple implementation forms such as RGB color gradient, gray scale gradient and the like, which are not exemplified herein.
And step S206, determining the local path in each path interval of the global path according to the second scheduling algorithm and the local map on the basis of the global path.
Further, after determining the local map, on the basis of the global path, the driving path of the vehicle is locally planned according to the second scheduling algorithm and the local map, so that the vehicle has a more specific local path within one or more path sections of the global path.
Exemplarily, the second scheduling algorithm includes a local planning algorithm and a dynamic obstacle avoidance algorithm, and the local planning algorithm is configured to determine a local path in the first path interval according to the local map; and the dynamic obstacle avoidance algorithm is used for determining a local path in a second path interval according to the local map, wherein the first path interval is larger than the second path interval.
The local planning algorithm is an algorithm for planning a route in a smaller range than a global route, and aims to plan the driving of each route section in the global route based on the global route and further according to the actual driving condition of the vehicle so as to improve the driving safety and efficiency. The dynamic obstacle avoidance algorithm is a control algorithm for controlling a vehicle to avoid when an unpredictable obstacle appears, and for example, a vehicle, a pedestrian, and the like suddenly appear in front. Further, the local planning algorithm and the dynamic obstacle avoidance algorithm use different map data as input quantities, for example, the local map includes first map data corresponding to the local planning algorithm and second map data corresponding to the dynamic obstacle avoidance algorithm, wherein the second map data has a shorter acquisition cycle, is more accurate and has better dynamics, and the dynamic obstacle avoidance algorithm is operated based on the second map data, so that dynamic path planning with better real-time performance can be realized, and the driving safety of the vehicle is improved.
And step S207, controlling the vehicle to execute the navigation task according to the global path and the local path in each path section of the global path.
Optionally, as shown in fig. 7, step S207 includes three specific implementation steps of S2071, S2072 and S2073:
and step S2071, controlling the vehicle to move according to the global path, and acquiring the running state of the vehicle in each path section of the global path, wherein the running state is used for representing whether the vehicle is blocked by an obstacle.
The running state is a state for determining whether the vehicle is blocked by an obstacle during running, and may be represented by a state value, for example, "0" represents blocked, "1" represents unblocked. Of course, according to specific needs, under the condition that the vehicle is blocked, further subdivision may be further performed according to the state that the vehicle is blocked, for example, the first blocking state, the second blocking state, and the like, which are not described herein again.
Illustratively, acquiring the running state of the vehicle in each path section of the global path comprises the following steps: determining whether the vehicle is currently blocked by an obstacle according to the local map; and if the vehicle is blocked by the obstacle, determining the running state according to the time length of the vehicle blocked by the obstacle or the times of the vehicle executing the avoiding action aiming at the obstacle.
Step S2072, determine a real-time path according to the operation status.
And step S2073, controlling the vehicle to move according to the real-time path.
For example, in the process of controlling the vehicle to move according to the global path, when the operating states of the vehicle are different, the vehicle controller is required to plan a real-time path matched with the operating states according to the operating states and control the vehicle to move according to the implementation path, so that the vehicle can avoid obstacles. The real-time path comprises an original path or a modified path, wherein the original path is a global path and a local path in each path interval of the global path; the corrected path is a path obtained by correcting the global path or the local path of each path section of the global path according to the operation state. Specifically, when the running state represents that the current vehicle is not blocked currently, the vehicle is controlled to run according to the original path, namely the global path planned last time and the local paths in each path interval of the global path; when the running state represents that the current vehicle is blocked, the global path and/or the local path planned last time is invalid, the global path and/or the local path needs to be planned again, and the vehicle is controlled to run according to the revised path after the global path and/or the local path is planned again.
In this embodiment, the implementation manners of step S201 to step S202 are the same as the implementation manners of step S101 to step S102 in the embodiment shown in fig. 2 of this application, and are not described again.
Fig. 8 is a schematic structural diagram of a car navigation device according to an embodiment of the present application, which can be applied to an intelligent vehicle, and as shown in fig. 8, a car navigation device 3 according to the present embodiment includes:
the loading module 31 is configured to load configuration data, where the configuration data is used to represent a navigation scene of a navigation task to be executed by a vehicle.
And the determining module 32 is used for determining a target navigation model according to the configuration data, wherein the target navigation model is used for planning a driving path of the vehicle when the vehicle executes a navigation task.
And the control module 33 is used for controlling the vehicle to execute the navigation task according to the target navigation model.
In a possible implementation manner, the target navigation model includes a navigation algorithm, and the control module 33 is specifically configured to: acquiring a navigation map, wherein the navigation map is used for representing the position relation between the obstacle and the vehicle; taking the navigation map as an input parameter, calling a navigation algorithm, and generating a driving path of the vehicle when executing a navigation task; and controlling the vehicle to execute the navigation task according to the driving path.
In one possible implementation, the navigation map comprises a global map and a local map, and the navigation algorithm comprises a first scheduling algorithm and a second scheduling algorithm; the first scheduling algorithm is used for determining a global path of the navigation task; the second scheduling algorithm is used for determining a local path of the navigation task; when the control module 33 controls the vehicle to execute the navigation task according to the target navigation model, it is specifically configured to: determining a global path of the navigation task according to a first scheduling algorithm and a global map, wherein the global path comprises a plurality of path intervals; on the basis of the global path, determining a local path in each path interval of the global path according to a second scheduling algorithm and a local map; and controlling the vehicle to execute the navigation task according to the global path and the local path in each path section of the global path.
In a possible implementation manner, when the control module 33 acquires the navigation map, it is specifically configured to: acquiring a preset static map, wherein the static map comprises map data of an area where a navigation task is located; and rendering the static map based on the sensor data to generate a global map, wherein the sensor data represents the distance between the obstacle in the area where the navigation task is located and the vehicle, and the global map represents the position relation between the obstacle in the area where the navigation task is located and the vehicle.
In a possible implementation manner, when the control module 33 renders the static map based on the sensor data and generates the global map, it is specifically configured to: according to the resolution of the static map, performing grid division on the static map to generate a rasterized static map; and superposing the sensor data to a rasterized static map, and rendering the distance between the obstacle represented by the sensor data and the vehicle in a color gradient form based on the voronoi diagram to generate a global map.
In one possible implementation, the control module 33, when acquiring the navigation map, is further configured to: acquiring a dynamic map according to a preset first acquisition period, wherein the dynamic map comprises map data of a path interval where a vehicle arrives; rendering the dynamic map based on sensor data to generate a local map, wherein the sensor data represent the distance between an obstacle and a vehicle in a path interval where the vehicle arrives; the local map represents the position relation between obstacles and the vehicle in the path section reached by the vehicle.
In a possible implementation manner, the control module 33, when rendering the dynamic map based on the sensor data and generating the local map, is specifically configured to: according to the size of the dynamic map, carrying out grid division on the dynamic map by taking the current position of the dynamic map as a center to generate a rasterized dynamic map; and rasterizing the sensor data superposition value into a dynamic map, and rendering the distance between the obstacle represented by the sensor data and the vehicle in a color gradient mode based on the Venno diagram to generate a local map.
In a possible implementation manner, when the control module 33 controls the vehicle to execute the navigation task according to the global path and the local path in each path section of the global path, the control module is specifically configured to: controlling the vehicle to move according to the global path, and acquiring the running state of the vehicle in each path interval of the global path, wherein the running state is used for representing whether the vehicle is blocked by an obstacle or not; determining a real-time path according to the operation state, wherein the real-time path comprises an original path or a corrected path, and the original path is a global path and a local path in each path interval of the global path; the corrected path is a path obtained by correcting the global path or the local path of each path interval of the global path according to the running state; and controlling the vehicle to move according to a real-time path.
In one possible implementation manner, the control module 33, when acquiring the operating state of the vehicle in each path section of the global path, is specifically configured to: determining whether the vehicle is currently blocked by an obstacle according to the local map; and if the vehicle is blocked by the obstacle, determining the running state according to the time length of the vehicle blocked by the obstacle or the times of the vehicle executing the avoiding action aiming at the obstacle.
In one possible implementation manner, the second scheduling algorithm comprises a local planning algorithm and a dynamic obstacle avoidance algorithm, and the local planning algorithm is used for determining a local path in the first path interval according to a local map; and the dynamic obstacle avoidance algorithm is used for determining a local path in a second path interval according to the local map, wherein the first path interval is larger than the second path interval.
In a possible implementation manner, the target navigation model further includes a sensor identifier corresponding to a navigation algorithm, and the control module 33 is specifically configured to: determining a target sensor according to the sensor identifier; and acquiring sensor data through the target sensor, and generating a navigation map according to the sensor data.
In a possible implementation manner, when loading the configuration data, the loading module 31 is specifically configured to: detecting environmental data, wherein the environmental data are used for representing the current running environment of the vehicle; configuration data is determined and loaded according to the environment data.
The loading module 31, the determining module 32 and the control module 33 are connected in sequence. The car navigation device 3 provided in this embodiment can execute the technical solutions of any one of the method embodiments shown in fig. 2 to 7, and the implementation principles and technical effects thereof are similar, and are not described herein again.
Fig. 9 is a schematic view of an electronic device according to an embodiment of the present application, and as shown in fig. 9, an electronic device 4 according to the embodiment includes: a memory 41, a processor 42 and a computer program.
Wherein the computer program is stored in the memory 41 and configured to be executed by the processor 42 to implement the vehicle navigation algorithm provided by any one of the embodiments corresponding to fig. 2-7 of the present application.
The memory 41 and the processor 42 are connected by a bus 43.
The relevant descriptions and effects corresponding to the steps in the embodiments corresponding to fig. 2 to fig. 7 can be understood, and are not described in detail herein.
In one possible implementation, the electronic device 4 is a vehicle controller in the above-described method embodiment.
One embodiment of the present application provides a computer-readable storage medium, on which a computer program is stored, where the computer program is executed by a processor to implement a vehicle navigation method provided in any one of embodiments corresponding to fig. 2 to 7 of the present application.
The computer readable storage medium may be, among others, ROM, Random Access Memory (RAM), CD-ROM, magnetic tape, floppy disk, optical data storage device, and the like.
One embodiment of the present application provides a computer program product, which includes a computer program, and the computer program is executed by a processor to implement the vehicle navigation method provided in any one of the embodiments corresponding to fig. 2 to 7 of the present application.
In the several embodiments provided in the present application, it should be understood that the disclosed apparatus and method may be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, a division of modules is merely a division of logical functions, and an actual implementation may have another division, for example, a plurality of modules or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or modules, and may be in an electrical, mechanical or other form.
Other embodiments of the present application will be apparent to those skilled in the art from consideration of the specification and practice of the application disclosed herein. This application is intended to cover any variations, uses, or adaptations of the invention following, in general, the principles of the application and including such departures from the present disclosure as come within known or customary practice within the art to which the invention pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the application being indicated by the following claims.
It will be understood that the present application is not limited to the precise arrangements described above and shown in the drawings and that various modifications and changes may be made without departing from the scope thereof. The scope of the application is limited only by the appended claims.
Claims (16)
1. A method for navigating a vehicle, the method comprising:
loading configuration data, wherein the configuration data is used for representing a navigation scene of a navigation task to be executed by a vehicle;
determining a target navigation model according to the configuration data, wherein the target navigation model is used for planning a driving path of the vehicle when the vehicle executes the navigation task;
and controlling the vehicle to execute the navigation task according to the target navigation model.
2. The method of claim 1, wherein the target navigation model includes a navigation algorithm therein, and wherein controlling the vehicle to perform the navigation task according to the target navigation model comprises:
acquiring a navigation map, wherein the navigation map is used for representing the position relation between an obstacle and the vehicle;
calling the navigation algorithm by taking the navigation map as an input parameter to generate a driving path of the vehicle when the navigation task is executed;
and controlling the vehicle to execute the navigation task according to the driving path.
3. The method of claim 2, wherein the navigation map comprises a global map and a local map, and wherein the navigation algorithm comprises a first scheduling algorithm and a second scheduling algorithm; the first scheduling algorithm is used for determining a global path of the navigation task; the second scheduling algorithm is used for determining a local path of the navigation task;
controlling the vehicle to execute the navigation task according to the target navigation model, wherein the control method comprises the following steps:
determining a global path of the navigation task according to the first scheduling algorithm and a global map, wherein the global path comprises a plurality of path intervals;
on the basis of the global path, determining a local path in each path interval of the global path according to the second scheduling algorithm and a local map;
and controlling the vehicle to execute the navigation task according to the global path and the local path in each path interval of the global path.
4. The method of claim 3, wherein the obtaining the navigation map comprises:
acquiring a preset static map, wherein the static map comprises map data of an area where the navigation task is located;
rendering the static map based on sensor data to generate a global map, wherein the sensor data represent the distance between an obstacle in the area where the navigation task is located and the vehicle, and the global map represents the position relation between the obstacle in the area where the navigation task is located and the vehicle.
5. The method of claim 4, wherein rendering the static map based on sensor data to generate a global map comprises:
according to the resolution of the static map, performing grid division on the static map to generate a rasterized static map;
and superposing the sensor data to the rasterized static map, and rendering the distance between the obstacle represented by the sensor data and the vehicle in a color gradient form based on a Voronoi diagram to generate a global map.
6. The method of claim 3, wherein the obtaining the navigation map comprises:
acquiring a dynamic map according to a preset first acquisition period, wherein the dynamic map comprises map data of a path interval where the vehicle arrives;
rendering the dynamic map based on sensor data to generate a local map, wherein the sensor data represents the distance between an obstacle and the vehicle in a path interval reached by the vehicle; the local map represents the position relation between obstacles in a path section reached by the vehicle and the vehicle.
7. The method of claim 6, wherein rendering the dynamic map based on sensor data, generating a local map, comprises:
according to the size of the dynamic map, carrying out grid division on the dynamic map by taking the current position of the dynamic map as a center to generate a rasterized dynamic map;
and the sensor data superposition value is subjected to rasterization on the dynamic map, and the distance between the obstacle represented by the sensor data and the vehicle is rendered in a color gradient mode based on the voronoi diagram, so that a local map is generated.
8. The method of claim 3, wherein controlling a vehicle to perform the navigation task based on the global path and a local path within each path segment of the global path comprises:
controlling the vehicle to move according to the global path, and acquiring the running state of the vehicle in each path interval of the global path, wherein the running state is used for representing whether the vehicle is blocked by an obstacle;
determining a real-time path according to the running state, wherein the real-time path comprises an original path or a modified path, and the original path is the global path and a local path in each path interval of the global path; the corrected path is a path obtained by correcting the global path or the local path of each path interval of the global path according to the running state;
and controlling the vehicle to move according to the real-time path.
9. The method of claim 8, wherein obtaining the operating state of the vehicle within each of the path segments of the global path comprises:
determining whether the vehicle is currently blocked by an obstacle according to the local map;
and if the vehicle is blocked by the obstacle, determining the running state according to the time length of the vehicle blocked by the obstacle or the times of the vehicle executing the evasive action aiming at the obstacle.
10. The method of claim 3, wherein the second scheduling algorithm comprises a local planning algorithm and a dynamic obstacle avoidance algorithm, and the local planning algorithm is used for determining a local path in the first path interval according to the local map;
and the dynamic obstacle avoidance algorithm is used for determining a local path in a second path interval according to the local map, wherein the first path interval is larger than the second path interval.
11. The method of claim 2, wherein the target navigation model further comprises a sensor identifier corresponding to the navigation algorithm, and the obtaining of the navigation map comprises:
determining a target sensor according to the sensor identification;
and acquiring sensor data through the target sensor, and generating a navigation map according to the sensor data.
12. The method of any of claims 1-11, wherein loading configuration data comprises:
detecting environmental data, wherein the environmental data is used for representing the current running environment of the vehicle;
and determining and loading configuration data according to the environment data.
13. A vehicular navigation apparatus, characterized by comprising:
the loading module is used for loading configuration data, and the configuration data is used for representing a navigation scene of a navigation task to be executed by a vehicle;
the determining module is used for determining a target navigation model according to the configuration data, and the target navigation model is used for planning a driving path of the vehicle when the vehicle executes the navigation task;
and the control module is used for controlling the vehicle to execute the navigation task according to the target navigation model.
14. An electronic device, comprising: a memory, a processor, and a computer program;
wherein the computer program is stored in the memory and configured to be executed by the processor to implement the vehicle navigation method of any one of claims 1 to 12.
15. A computer-readable storage medium having computer-executable instructions stored thereon for implementing the vehicle navigation method of any one of claims 1 to 12 when executed by a processor.
16. A computer program product comprising a computer program which, when executed by a processor, implements a vehicle navigation method as claimed in any one of claims 1 to 12.
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