CN110942635A - Monitoring method and device for intelligent driving vehicle and computer equipment - Google Patents
Monitoring method and device for intelligent driving vehicle and computer equipment Download PDFInfo
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- CN110942635A CN110942635A CN201911294256.7A CN201911294256A CN110942635A CN 110942635 A CN110942635 A CN 110942635A CN 201911294256 A CN201911294256 A CN 201911294256A CN 110942635 A CN110942635 A CN 110942635A
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- G—PHYSICS
- G08—SIGNALLING
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- G08G1/00—Traffic control systems for road vehicles
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/09—Arrangements for giving variable traffic instructions
- G08G1/0962—Arrangements for giving variable traffic instructions having an indicator mounted inside the vehicle, e.g. giving voice messages
- G08G1/0967—Systems involving transmission of highway information, e.g. weather, speed limits
- G08G1/096708—Systems involving transmission of highway information, e.g. weather, speed limits where the received information might be used to generate an automatic action on the vehicle control
- G08G1/096725—Systems involving transmission of highway information, e.g. weather, speed limits where the received information might be used to generate an automatic action on the vehicle control where the received information generates an automatic action on the vehicle control
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/09—Arrangements for giving variable traffic instructions
- G08G1/0962—Arrangements for giving variable traffic instructions having an indicator mounted inside the vehicle, e.g. giving voice messages
- G08G1/0968—Systems involving transmission of navigation instructions to the vehicle
- G08G1/0969—Systems involving transmission of navigation instructions to the vehicle having a display in the form of a map
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Abstract
The embodiment of the disclosure relates to a monitoring method, a monitoring device and computer equipment for an intelligent driving vehicle, wherein the method comprises the following steps: determining a monitoring mode; acquiring running state data of an intelligent driving vehicle; acquiring a vehicle marking graph based on the running state data of the intelligent driving vehicle; determining map attitude data based on the operating state data of the intelligent driving vehicle and the monitoring mode; rendering based on the vehicle marking graph and the map posture data to obtain a monitoring map of the intelligent driving vehicle; or, based on the vehicle mark graph, obtaining the monitoring map of the intelligent driving vehicle so as to solve the problem that the existing method for monitoring the intelligent driving vehicle is not beneficial to workers to quickly know the state of the monitored vehicle.
Description
Technical Field
The embodiment of the disclosure relates to the technical field of intelligent driving vehicles, in particular to a monitoring method and device of an intelligent driving vehicle and computer equipment.
Background
The intelligent driving vehicle senses the road environment through a vehicle-mounted sensing system, automatically plans a driving route and controls the vehicle to reach a preset target. The intelligent control system integrates a plurality of technologies such as automatic control, a system structure, artificial intelligence, visual calculation and the like, is a product of high development of computer science, mode recognition and intelligent control technologies, is an important mark for measuring national scientific research strength and industrial level, and has wide application prospect in the fields of national defense and national economy.
Intelligently driving a vehicle may enable automated driving of the vehicle through a software program, which may be more complex than manually driven vehicles. In order to ensure normal unmanned operation, the vehicle state needs to be monitored manually, namely, a worker needs to monitor and manage the vehicle through a remote display beyond the visual range. However, the currently available methods for monitoring intelligently driven vehicles are not conducive to the ability of a worker to quickly learn about the status of the monitored vehicle.
The above description of the discovery process of the problems is only for the purpose of assisting understanding of the technical solutions of the present invention, and does not represent an admission that the above is prior art.
Disclosure of Invention
In order to solve at least one problem in the prior art, at least one embodiment of the invention provides a monitoring method and device for an intelligent driving vehicle and computer equipment, so as to solve the problem that the existing method for monitoring the intelligent driving vehicle is not beneficial to a worker to quickly know the state of the monitored vehicle.
In a first aspect, an embodiment of the present disclosure provides a monitoring method for an intelligent driving vehicle, where the method includes:
determining a monitoring mode;
acquiring running state data of an intelligent driving vehicle;
acquiring a vehicle marking graph based on the running state data of the intelligent driving vehicle;
determining map attitude data based on the operating state data of the intelligent driving vehicle and the monitoring mode; rendering based on the vehicle marking graph and the map posture data to obtain a monitoring map of the intelligent driving vehicle; or obtaining the monitoring map of the intelligent driving vehicle based on the vehicle mark graph.
In a second aspect, an embodiment of the present disclosure provides a monitoring device for an intelligent driving vehicle, including:
the mode determining module is used for determining a monitoring mode;
the state acquisition module is used for acquiring running state data of the intelligent driving vehicle;
the image acquisition module is used for acquiring a vehicle marking image based on the running state data of the intelligent driving vehicle;
the map rendering module is used for determining map attitude data based on the running state data of the intelligent driving vehicle and the monitoring mode; rendering based on the vehicle marking graph of the intelligent driving vehicle and the map posture data to obtain a monitoring map of the intelligent driving vehicle; or, the monitoring map for the intelligent driving vehicle is obtained based on the vehicle marking graph.
In a third aspect, an embodiment of the present disclosure provides a computer device, including: a processor and a memory;
the processor is used for executing the steps of any one of the methods provided by the embodiments of the present disclosure by calling the programs or instructions stored in the memory.
In a fourth aspect, the present disclosure provides a computer-readable storage medium storing a program or instructions for causing a computer to perform the steps of any one of the methods provided by the embodiments of the present disclosure.
As can be seen, in at least one embodiment of the present disclosure, a vehicle marking pattern is obtained based on the operating state data of the intelligent driving vehicle; determining map attitude data based on the operating state data of the intelligent driving vehicle and the monitoring mode; rendering based on the vehicle marking graph and the map posture data to obtain a monitoring map of the intelligent driving vehicle; or, based on the vehicle marking graph, the monitoring map of the intelligent driving vehicle is obtained, the monitoring map can be embodied on the monitoring map in a mode of combining the vehicle marking graph and the map posture data or in a mode of marking the vehicle graph, and therefore workers can know the state of the monitored vehicle visually and quickly. In addition, the embodiment of the disclosure can realize two monitoring modes, namely a single-vehicle monitoring mode, so that a worker can conveniently observe the behavior of a specific vehicle and analyze the system process; and in a multi-vehicle monitoring mode, workers can know the traffic condition, the service progress and find abnormal vehicles conveniently. The two monitoring modes have different information granularities, so that the state monitoring of the multidimensional intelligent driving vehicle can be realized, and one worker can conveniently monitor and manage a plurality of intelligent driving vehicles in an beyond-the-sight mode.
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To more clearly illustrate the technical solutions of the embodiments of the present disclosure, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and other drawings can be obtained by those skilled in the art according to the drawings.
Fig. 1 is a block diagram illustrating a monitoring apparatus for an intelligent driving vehicle according to an embodiment of the present disclosure;
2-4 are three vehicle marking patterns provided by the present disclosure;
FIG. 5 is a schematic structural diagram of a computer device provided by an embodiment of the present disclosure;
FIG. 6 is a flowchart of a monitoring method for an intelligent driving vehicle according to an embodiment of the present disclosure;
FIG. 7 is a flow chart of another monitoring method for a smart-driving vehicle provided by an embodiment of the present disclosure;
fig. 8 is a flowchart of another monitoring method for an intelligent driving vehicle according to an embodiment of the present disclosure.
Detailed Description
In order that the above objects, features and advantages of the present invention can be more clearly understood, the present invention will be further described in detail with reference to the accompanying drawings and examples. It is to be understood that the embodiments described are only a few embodiments of the present invention, and not all embodiments. The specific embodiments described herein are merely illustrative of the invention and are not to be construed as limiting the invention. All other embodiments, which can be derived by a person skilled in the art from the described embodiments of the invention, are within the scope of the invention.
It is noted that, in this document, relational terms such as "first" and "second," and the like, may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions.
In order to ensure normal unmanned operation of the intelligent driving vehicle, the vehicle state needs to be monitored manually, namely, a worker needs to monitor and manage the vehicle through a remote display beyond the visual range. However, the currently available methods for monitoring intelligently driven vehicles are not conducive to the ability of a worker to quickly learn about the status of the monitored vehicle. In order to solve the problem, the embodiment of the disclosure provides a monitoring scheme for an intelligent driving vehicle, which displays the state of the intelligent driving vehicle on a monitoring map in the form of a vehicle marking graph or in the form of combining the vehicle marking graph and a map gesture, and can help a worker to intuitively and quickly know the state of the monitoring vehicle.
In addition, the monitoring scheme of the intelligent driving vehicle provided by the embodiment of the disclosure comprises two monitoring modes, wherein the single vehicle monitoring mode is convenient for a worker to observe the behavior of a specific vehicle and analyze the system process; and in a multi-vehicle monitoring mode, workers can know the traffic condition, the service progress and find abnormal vehicles conveniently. The two monitoring modes have different information granularities, so that the state monitoring of the multidimensional intelligent driving vehicle can be realized, and one worker can conveniently monitor and manage a plurality of intelligent driving vehicles in an beyond-the-sight mode.
The monitoring method of the intelligent driving vehicle provided by the embodiment of the disclosure can be applied to monitoring the intelligent driving vehicle.
In the process of executing the scheme, the motion parameters of all intelligent driving vehicles are continuously acquired at preset time intervals (such as 500 ms); the motion state data of all the intelligent driving vehicles is then determined, and the motion state data of the intelligent driving vehicles is related to the motion parameters of the intelligent driving vehicles.
In some embodiments, the motion parameters of the intelligent driving vehicle include speed, acceleration, duration from the last state change, longitude and latitude coordinates, yaw angle of the head direction thereof from a standard direction (e.g., due north), and the like.
The motion state data of the intelligent driving vehicle comprises at least one of operation state data, driving state data and abnormal state data; the operation state data comprises idle, in-flight, pause and the like; the driving state data comprises straight driving, turning, avoiding, accelerating, driving speed, decelerating, lane changing and the like; the abnormal state data includes faults, sudden stops, positioning abnormalities and the like.
Exemplarily, if the current intelligent driving vehicle does not carry passengers or execute transportation tasks, the current operation state is an idle state; if the current intelligent driving vehicle carries passengers or is on the way to receive the passengers, the operation state data is in the journey; and if the current intelligent driving vehicle does not receive the task within a period of time or the vehicle keeps still or the state of the current intelligent driving vehicle is set to be the operation suspension state, the operation state data is the operation suspension state.
The intelligent driving vehicle includes: sensor groups, intelligent driving systems, vehicle floor-based actuation systems, and other components that may be used to propel a vehicle and control the operation of the vehicle.
The sensor group is used for collecting data of the external environment of the vehicle and detecting position data of the vehicle. The sensor group includes, for example, but not limited to, at least one of a camera, a laser radar, a millimeter wave radar, a GPS (Global Positioning System), and an IMU (Inertial Measurement Unit). The motion parameters of the intelligent driving vehicle are acquired based on the sensor group arranged on the intelligent driving vehicle.
Fig. 1 is a block diagram of a monitoring device for an intelligent driving vehicle according to an embodiment of the present disclosure. The monitoring device of the intelligent driving vehicle provided by the embodiment of the disclosure can be integrated on a remote monitoring station. Referring to fig. 1, the monitoring apparatus of the smart driving vehicle includes: a mode determination module 110, a status acquisition module 120, a graphics acquisition module 130, and a map rendering module 140.
A mode determination module 110 for determining the monitoring mode.
In some embodiments, the mode determination module 110 includes a monitoring mode instruction receiving unit and a monitoring mode determination unit. The monitoring mode instruction receiving unit is used for receiving a monitoring mode instruction. The monitoring mode determining unit is used for determining monitoring modes based on the monitoring mode instructions, and the monitoring modes comprise a single-vehicle monitoring mode and a multi-vehicle monitoring mode.
In some embodiments, the monitoring mode command is sent by a worker clicking on a human-computer interaction interface icon.
In some embodiments, the single-vehicle monitoring mode refers to a mode in which only one smart-driving vehicle is monitored at a time. In the single-vehicle monitoring mode, only one monitored intelligent driving vehicle is displayed on the monitoring map. The multi-vehicle monitoring mode refers to a mode for monitoring at least two intelligent driving vehicles in the same time period. Likewise, in the multi-vehicle monitoring mode, only at least two monitored smart driving vehicles are displayed on the monitoring map. It should be noted that, in the multi-vehicle monitoring mode, all the intelligent driving vehicles can be monitored.
The status acquisition module 120 is used to acquire operating status data of the smart driving vehicle.
In some embodiments, if the monitoring mode is a single-vehicle monitoring mode, the state obtaining module 120 is specifically configured to determine an intelligent driving vehicle identifier that needs to be monitored; and then acquiring the motion state data of the intelligent driving vehicle based on the intelligent driving vehicle identification needing to be monitored. The intelligent driving vehicle identifier is used for distinguishing from other intelligent driving vehicles, and specifically can be a number, a name and the like of the intelligent driving vehicle. In the single-vehicle monitoring mode, only one intelligent driving vehicle is monitored, so that the purpose of the setting is to filter the motion state data of the intelligent driving vehicle which does not need to be monitored from the motion state data of all the intelligent driving vehicles and only obtain the motion state data of the intelligent driving vehicle which needs to be monitored in the single-vehicle monitoring mode.
In some embodiments, if the monitoring mode is a multi-vehicle monitoring mode, the state obtaining module 120 is specifically configured to determine a plurality of intelligent driving vehicle identifiers that need to be monitored; obtaining motion state data of the plurality of intelligently driven vehicles based on the plurality of intelligently driven vehicle identifications. The purpose of the arrangement is to acquire the motion state data of all intelligent driving vehicles needing to be monitored in the multi-vehicle monitoring mode.
The pattern obtaining module 130 is configured to obtain a vehicle marking pattern based on the operating state data of the smart driving vehicle.
In some embodiments, the vehicle indicia graphic is a predetermined image indicia for characterizing the motion state data thereof. Different motion state data correspond to different vehicle marking patterns, and the motion state data and the vehicle marking patterns should correspond one to one.
Fig. 2-4 are three vehicle marking patterns provided by the present disclosure. Illustratively, the vehicle marking graphic in FIG. 2 indicates that the intelligent driving vehicle is in an idle state. The vehicle label graphic in fig. 3 indicates that the smart driving vehicle is in a straight-ahead state. The vehicle signature graphic in fig. 4 indicates that the smart driving vehicle is in a fault state.
In some embodiments, the graph obtaining module 130 is specifically configured to preset a corresponding relationship between the motion state data of the intelligent driving vehicle and the vehicle marking graph; and acquiring a vehicle mark graph according to the running state data of the intelligent driving vehicle and the corresponding relation.
If the monitoring mode is a multi-vehicle monitoring mode, in some embodiments, the state obtaining module 120 obtains motion state data of a plurality of intelligent driving vehicles. The graph obtaining module 130 is specifically configured to distribute motion state data of a plurality of intelligent driving vehicles to different map nodes, where the map nodes correspond to positions of the intelligent driving vehicles; vehicle marker patterns of different map nodes are determined based on the motion state data of the intelligent driving vehicle. The essence of the arrangement is that in a multi-vehicle monitoring mode, the monitored motion state data of all the intelligent driving vehicles need to be displayed on the monitoring map, and each intelligent driving vehicle is represented by a corresponding vehicle mark graph, so that a worker can conveniently and intuitively and quickly know the state of the monitored vehicle.
The map rendering 140 may be understood as a module for rendering a 3D map.
If the monitoring mode is a single-vehicle monitoring mode, in some embodiments, the map rendering 140 is configured to determine map pose data based on the operating state data of the intelligent driving vehicle and the monitoring mode; and rendering based on the vehicle mark graph and the map posture data to obtain the monitoring map of the intelligent driving vehicle.
The map attitude data includes ground inclination, scale scaling, center point position, direction rotation angle, etc. The direction rotation angle refers to an included angle between the current vehicle head direction and a specified direction (such as a due north direction).
The map attitude data is determined based on the running state data and the monitoring mode of the intelligent driving vehicle, namely the running state data of the monitored vehicle adjusts the map attitude data, so that the monitoring map is appropriate to the running state of the monitored vehicle, and the current running state of the intelligent driving vehicle can be better shown.
In some embodiments, determining map pose data based on the operating state data and the monitoring mode of the smart-driving vehicle comprises: and acquiring the map attitude data corresponding to the running state data based on the functional relation between the running state data and the map attitude data in the single vehicle monitoring mode. The functional relationship between the operation state data and the map attitude data in the single vehicle monitoring mode is not limited by the disclosure.
For example, if the currently monitored vehicle is in an idle state, its speed is 0. Optionally, the ground inclination in the map pose data is 0; the scaling scale is 8, namely the scale is 1: 40; the central point position is unlimited (the central point position of the map can be monitored at the moment of entering the single-vehicle monitoring mode by default); the direction rotation angle is 0.
If the vehicle currently being monitored is in a driving state, the speed is v. Optionally, the ground inclination in the map pose data is kv (k is a constant coefficient); scale scaling scale is tv (t is a constant coefficient less than 1); the central point position is a vehicle position; the direction rotation angle is r, namely the included angle between the direction of the vehicle head and the designated direction (such as the true north direction).
If the currently monitored vehicle is in a suspended state, its speed is 0. Optionally, the ground inclination in the map pose data is 0; the scaling scale is 4, namely the scale is 1: 20; (ii) a The central point position is a vehicle position; the direction rotation angle is r, namely the included angle between the direction of the vehicle head and the designated direction (such as the true north direction).
If the monitoring mode is a multi-vehicle monitoring mode, in some embodiments, the map rendering 140 is used to obtain a monitoring map of the intelligent driving vehicle based on the vehicle marking graph.
The monitoring device for the intelligent driving vehicle provided by the embodiment of the disclosure displays the state of the intelligent driving vehicle on the monitoring map in the form of the vehicle marking graph or in the form of combining the vehicle marking graph and the map posture, and can help workers to visually and quickly know the state of the monitoring vehicle. In addition, the monitoring device comprises two monitoring modes, wherein the single-vehicle monitoring mode, namely the microscopic single-vehicle viewing angle, is convenient for a worker to observe the behavior of a specific vehicle and analyze the system process; the multi-vehicle monitoring mode, namely the macroscopic multi-vehicle view angle, is convenient for the working personnel to know the traffic condition, the service progress and find abnormal vehicles. The two monitoring modes have different information granularities. The monitoring device can realize multi-dimensional intelligent driving vehicle state monitoring, and can be convenient for one worker to monitor and manage a plurality of intelligent driving vehicles over the visual range.
It is emphasized that in practice, it may happen that both monitoring modes are switched continuously or that one monitoring mode is refreshed continuously, depending on the monitoring requirements. For this purpose, optionally, the mode determination module 110 continuously obtains the monitoring mode instruction and performs the monitoring mode determination during the whole monitoring process (including but not limited to completing the 3D map rendering in the map rendering 140 and obtaining the monitoring map of the intelligent driving vehicle).
In some embodiments, a monitoring instruction icon is always displayed on a human-computer interaction page of the remote monitoring station, and when a worker clicks the monitoring instruction icon, a single-vehicle monitoring mode or a multi-vehicle monitoring mode is selected. If the single-vehicle monitoring mode is selected, an intelligent driving vehicle identification selection or input interface can be popped up. If the multi-vehicle monitoring mode is selected, part of vehicle monitoring or all vehicle monitoring options can be popped up. When a portion of vehicle monitoring is selected, a smart driving vehicle identification selection or input interface may also pop-up.
Fig. 5 is a schematic structural diagram of a computer device according to an embodiment of the present disclosure. As shown in fig. 5, the computer apparatus includes: at least one processor 201, at least one memory 202, and at least one communication interface 203. The various components in the computer device are coupled together by a bus system 204. A communication interface 203 for information transmission with an external device (e.g., a smart driving vehicle). It is understood that the bus system 204 is used to enable communications among the components. The bus system 204 includes a power bus, a control bus, and a status signal bus in addition to a data bus. For clarity of illustration, the various buses are labeled as bus system 204 in fig. 5.
It will be appreciated that the memory 202 in this embodiment can be either volatile memory or nonvolatile memory, or can include both volatile and nonvolatile memory.
In some embodiments, memory 202 stores the following elements, executable units or data structures, or a subset thereof, or an expanded set thereof: an operating system and an application program.
The operating system includes various system programs, such as a framework layer, a core library layer, a driver layer, and the like, and is used for implementing various basic services and processing hardware-based tasks. The application programs, including various application programs such as a Media Player (Media Player), a Browser (Browser), etc., are used to implement various application services. The program for implementing the monitoring method for the intelligent driving vehicle provided by the embodiment of the disclosure may be included in the application program.
In the embodiment of the present disclosure, the processor 201 is configured to execute the steps of the monitoring method for an intelligent driving vehicle provided by the embodiment of the present disclosure by calling a program or an instruction stored in the memory 202, specifically, a program or an instruction stored in an application program.
The monitoring method for the intelligent driving vehicle provided by the embodiment of the disclosure can be applied to the processor 201, or implemented by the processor 201. The processor 201 may be an integrated circuit chip having signal processing capabilities. In implementation, the steps of the above method may be performed by integrated logic circuits of hardware or instructions in the form of software in the processor 201. The Processor 201 may be a general-purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic device, or discrete hardware components. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The steps of the monitoring method for the intelligent driving vehicle provided by the embodiment of the disclosure can be directly embodied as the execution of a hardware decoding processor, or the execution of the hardware decoding processor and a software unit in the decoding processor is combined. The software elements may be located in ram, flash, rom, prom, or eprom, registers, among other storage media that are well known in the art. The storage medium is located in the memory 202, and the processor 201 reads the information in the memory 202, and performs the steps of the method in combination with the hardware thereof.
Fig. 6 is a flowchart of a monitoring method for an intelligent driving vehicle according to an embodiment of the present disclosure. The execution main body of the monitoring method of the intelligent driving vehicle is a remote monitoring station of the intelligent driving vehicle. Referring to fig. 6, the monitoring method of the smart driving vehicle includes: a mode determination step 310, a state acquisition step 320, a graphics acquisition step 330, and a map rendering step 340.
Wherein the mode determination step 310 is the basis for performing the subsequent steps.
In some embodiments, the mode determining step 310 comprises: receiving a monitoring mode instruction; and determining a monitoring mode based on the monitoring mode instruction, wherein the monitoring mode comprises a single-vehicle monitoring mode and a multi-vehicle monitoring mode.
The single-vehicle monitoring mode refers to a mode for monitoring only one intelligent driving vehicle at the same time. In the single-vehicle monitoring mode, only one monitored intelligent driving vehicle is displayed on the monitoring map. The multi-vehicle monitoring mode refers to a mode for monitoring at least two intelligent driving vehicles in the same time period. Likewise, in the multi-vehicle monitoring mode, only at least two monitored smart driving vehicles are displayed on the monitoring map.
The state acquisition step 320 refers to acquiring motion state data of the smart driving vehicle related to the monitoring mode among the motion state data of all the smart driving vehicles.
If the monitoring mode is a single-vehicle monitoring mode, in some embodiments, the intelligent driving vehicle identifier needing to be monitored can be determined firstly; and then acquiring the motion state data of the intelligent driving vehicle based on the intelligent driving vehicle identification needing to be monitored. The intelligent driving vehicle identifier is used for distinguishing from other intelligent driving vehicles, and specifically can be a number, a name and the like of the intelligent driving vehicle. Because only one intelligent driving vehicle is monitored in the single vehicle monitoring mode, the purpose of the setting is to filter the motion state data of the intelligent driving vehicle which does not need to be monitored, and only obtain the motion state data of the intelligent driving vehicle which needs to be monitored in the single vehicle monitoring mode.
If the monitoring mode is a multi-vehicle monitoring mode, in some embodiments, a plurality of intelligent driving vehicle identifications to be monitored can be determined; obtaining motion state data of the plurality of intelligently driven vehicles based on the plurality of intelligently driven vehicle identifications. The purpose of the arrangement is to acquire the motion state data of all intelligent driving vehicles needing to be monitored in the multi-vehicle monitoring mode.
The pattern obtaining step 330 is to obtain a vehicle marking pattern according to the operation state data of the intelligent driving vehicle.
In some embodiments, the vehicle indicia graphic is a predetermined image indicia for characterizing the motion state data thereof. Different motion state data correspond to different vehicle marking patterns, and the motion state data and the vehicle marking patterns should correspond one to one.
Fig. 2-4 are three vehicle marking patterns provided by the present disclosure. Illustratively, the vehicle marking graphic in FIG. 2 indicates that the intelligent driving vehicle is in an idle state. The vehicle label graphic in fig. 3 indicates that the smart driving vehicle is in a straight-ahead state. The vehicle signature graphic in fig. 4 indicates that the smart driving vehicle is in a fault state.
In some embodiments, the corresponding relationship between the motion state data of the intelligent driving vehicle and the vehicle mark graph can be preset; and then, acquiring a vehicle mark graph according to the running state data of the intelligent driving vehicle and the corresponding relation.
If the monitoring mode is a multi-vehicle monitoring mode, the motion state data of a plurality of intelligent driving vehicles needs to be acquired. In some embodiments, the motion state data for a plurality of smart driven vehicles may be distributed to different map nodes, the map nodes corresponding to the smart driven vehicle locations; vehicle marker patterns of different map nodes are determined based on the motion state data of the intelligent driving vehicle. The essence of the arrangement is that in a multi-vehicle monitoring mode, the monitored motion state data of all the intelligent driving vehicles need to be displayed on the monitoring map, and each intelligent driving vehicle is represented by a corresponding vehicle mark graph, so that a worker can conveniently and intuitively and quickly know the state of the monitored vehicle.
A map rendering step 340 is used to render a 3D map forming the state for showing the smart driving vehicle. It includes two cases:
determining map attitude data based on running state data and a monitoring mode of an intelligent driving vehicle if the monitoring mode is a single-vehicle monitoring mode; and rendering based on the vehicle mark graph and the map posture data to obtain the monitoring map of the intelligent driving vehicle.
The map attitude data includes ground inclination, scale scaling, center point position, direction rotation angle, etc. The direction rotation angle refers to an included angle between the current vehicle head direction and a specified direction (such as a due north direction).
The map attitude data is determined based on the running state data and the monitoring mode of the intelligent driving vehicle, namely the running state data of the monitored vehicle adjusts the map attitude data, so that the monitoring map is appropriate to the running state of the monitored vehicle, and the current running state of the intelligent driving vehicle can be better shown.
In some embodiments, determining map pose data based on the operating state data and the monitoring mode of the smart-driving vehicle comprises: and acquiring the map attitude data corresponding to the running state data based on the functional relation between the running state data and the map attitude data in the single vehicle monitoring mode. The functional relationship between the operation state data and the map attitude data in the single vehicle monitoring mode is not limited by the disclosure.
For example, if the currently monitored vehicle is in an idle state, its speed is 0. Optionally, the ground inclination in the map pose data is 0; the scaling scale is 8, namely the scale is 1: 40; the central point position is unlimited (the central point position of the map can be monitored at the moment of entering the single-vehicle monitoring mode by default); the direction rotation angle is 0.
If the vehicle currently being monitored is in a driving state, the speed is v. Optionally, the ground inclination in the map pose data is kv (k is a constant coefficient); scale scaling scale is tv (t is a constant coefficient less than 1); the central point position is a vehicle position; the direction rotation angle is r, namely the included angle between the direction of the vehicle head and the designated direction (such as the true north direction).
If the currently monitored vehicle is in a suspended state, its speed is 0. Optionally, the ground inclination in the map pose data is 0; the scaling scale is 4, namely the scale is 1: 20; (ii) a The central point position is a vehicle position; the direction rotation angle is r, namely the included angle between the direction of the vehicle head and the designated direction (such as the true north direction).
And in the second situation, if the monitoring mode is a multi-vehicle monitoring mode, obtaining a monitoring map of the intelligent driving vehicle based on the vehicle marking graph.
The monitoring method for the intelligent driving vehicle provided by the embodiment of the disclosure displays the state of the intelligent driving vehicle on the monitoring map in the form of the vehicle marking graph or in the form of combining the vehicle marking graph and the map posture, and can help workers to visually and quickly know the state of the monitoring vehicle. In addition, the monitoring method comprises two monitoring modes, wherein the single-vehicle monitoring mode, namely the microscopic single-vehicle viewing angle, is convenient for workers to observe the behavior of a specific vehicle and analyze the system process; the multi-vehicle monitoring mode, namely the macroscopic multi-vehicle view angle, is convenient for the working personnel to know the traffic condition, the service progress and find abnormal vehicles. The two monitoring modes have different information granularities. The monitoring method can realize multi-dimensional state monitoring of the intelligent driving vehicles, and can facilitate the over-the-horizon monitoring and management of a plurality of intelligent driving vehicles by one worker.
Fig. 7 is a flowchart of another monitoring method for an intelligent driving vehicle according to an embodiment of the present disclosure. Referring to fig. 7, the monitoring method of the smart driving vehicle includes:
and S410, determining a monitoring mode.
And S420, acquiring the running state data of the intelligent driving vehicle.
And S430, acquiring a vehicle mark graph based on the running state data of the intelligent driving vehicle.
S440, determining map attitude data based on the running state data and the monitoring mode of the intelligent driving vehicle; rendering is carried out on the basis of the vehicle mark graph and the map posture data to obtain a monitoring map of the intelligent driving vehicle; or, based on the vehicle mark graph, a monitoring map of the intelligent driving vehicle is obtained.
Fig. 8 is a flowchart of another monitoring method for an intelligent driving vehicle according to an embodiment of the present disclosure. Referring to fig. 8, the monitoring method of the smart driving vehicle includes:
and S501, continuously acquiring the motion parameters of each intelligent driving vehicle related to the remote monitoring station at set intervals, and executing S502.
S502, obtaining the running state data of each intelligent driving vehicle based on the motion parameters of each intelligent driving vehicle, and executing S503.
S503, receiving a monitoring mode instruction, and judging whether the monitoring mode of the monitoring mode instruction is a multi-vehicle monitoring mode. If yes, executing S504; if not, go to S509.
And S504, determining a plurality of intelligent driving vehicle identifications needing to be monitored according to the monitoring mode command, and executing S505.
And S505, acquiring motion state data of a plurality of intelligent driving vehicles needing to be monitored based on the plurality of intelligent driving vehicle identifications, and executing S506.
And S506, distributing the motion state data of the plurality of intelligent driving vehicles to be monitored to different map nodes, and executing S507.
And S507, determining vehicle marking graphs of different map nodes based on the motion state data of the intelligent driving vehicle, and executing S508.
And S508, acquiring a monitoring map of the intelligent driving vehicle based on the vehicle mark graph, and executing S503.
And S509, determining a single intelligent driving vehicle identifier needing to be monitored according to the monitoring mode command, and executing S510.
And S510, acquiring motion state data of the intelligent driving vehicle based on the intelligent driving vehicle identifier needing to be monitored, and executing S511.
And S511, acquiring map attitude data corresponding to the running state data based on the mapping relation between the running state data and the map attitude data in the single-vehicle monitoring mode, and executing S512.
S512, rendering is carried out based on the vehicle mark graph and the map posture data, a monitoring map of the intelligent driving vehicle is obtained, and S503 is executed.
According to the monitoring method of the intelligent driving vehicle, the state of the intelligent driving vehicle is displayed on the monitoring map in the form of the vehicle marking graph or the combination of the vehicle marking graph and the map posture, and workers can be helped to visually and quickly know the state of the monitoring vehicle. In addition, the monitoring method comprises two monitoring modes, wherein the single-vehicle monitoring mode, namely the microscopic single-vehicle viewing angle, is convenient for workers to observe the behavior of a specific vehicle and analyze the system process; the multi-vehicle monitoring mode, namely the macroscopic multi-vehicle view angle, is convenient for the working personnel to know the traffic condition, the service progress and find abnormal vehicles. The two monitoring modes have different information granularities. The monitoring method can realize multi-dimensional state monitoring of the intelligent driving vehicles, and can facilitate the over-the-horizon monitoring and management of a plurality of intelligent driving vehicles by one worker.
Embodiments of the present disclosure also provide a non-transitory computer-readable storage medium storing a program or instructions, where the program or instructions cause a computer to perform steps of various embodiments of a monitoring method for an intelligent driving vehicle, and in order to avoid repeated descriptions, the steps are not described herein again.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
Those skilled in the art will appreciate that although some embodiments described herein include some features included in other embodiments instead of others, combinations of features of different embodiments are meant to be within the scope of the invention and form different embodiments.
Those skilled in the art will appreciate that the description of each embodiment has a respective emphasis, and reference may be made to the related description of other embodiments for those parts of an embodiment that are not described in detail.
Although the embodiments of the present invention have been described in conjunction with the accompanying drawings, those skilled in the art may make various modifications and variations without departing from the spirit and scope of the invention, and such modifications and variations fall within the scope defined by the appended claims.
Claims (11)
1. A monitoring method for an intelligent driving vehicle, comprising:
determining a monitoring mode;
acquiring running state data of an intelligent driving vehicle;
acquiring a vehicle marking graph based on the running state data of the intelligent driving vehicle;
determining map attitude data based on the operating state data of the intelligent driving vehicle and the monitoring mode; rendering based on the vehicle marking graph and the map posture data to obtain a monitoring map of the intelligent driving vehicle; or obtaining the monitoring map of the intelligent driving vehicle based on the vehicle mark graph.
2. The monitoring method of claim 1, wherein the determining the monitoring mode comprises:
receiving a monitoring mode instruction;
and determining a monitoring mode based on the monitoring mode instruction, wherein the monitoring mode comprises a single-vehicle monitoring mode and a multi-vehicle monitoring mode.
3. The monitoring method according to claim 2, wherein the monitoring mode is a single-vehicle monitoring mode, and the acquiring motion state data of the smart-driving vehicle comprises:
determining an intelligent driving vehicle identifier needing to be monitored;
and acquiring the motion state data of the intelligent driving vehicle based on the intelligent driving vehicle identification needing to be monitored.
4. The monitoring method of claim 3, wherein the determining map pose data based on the operating state data of the smart-driving vehicle and the monitoring mode comprises:
and obtaining map attitude data corresponding to the running state data based on the mapping relation between the running state data and the map attitude data in the single vehicle monitoring mode.
5. The monitoring method according to claim 2, wherein the monitoring mode is a multi-vehicle monitoring mode, and the acquiring the operating state data of the smart driving vehicle comprises:
determining a plurality of intelligent driving vehicle identifications needing to be monitored;
obtaining motion state data for the plurality of smart-driven vehicles based on the plurality of smart-driven vehicle identifications.
6. The monitoring method of claim 5, wherein the obtaining a vehicle signature based on the operating state data of the smart driving vehicle comprises:
distributing the motion state data of the plurality of intelligent driving vehicles to different map nodes, wherein the map nodes correspond to the positions of the intelligent driving vehicles;
determining vehicle marker graphs for different map nodes based on the motion state data of the intelligent driving vehicle.
7. The monitoring method according to claim 1, wherein the motion state data of the smart-driving vehicle includes at least one of operation state data, driving state data, and abnormal state data;
the operational status data includes idle, in-flight, and suspended;
the driving state data comprises straight driving, turning, avoiding, accelerating, driving speed, decelerating and lane changing;
the abnormal state data includes faults, sudden stops and positioning anomalies.
8. The monitoring method of a smart driving vehicle of claim 1, wherein the map pose data comprises ground inclination, scale scaling, center point position and direction rotation angle.
9. A monitoring device for a smart-driving vehicle, comprising:
the mode determining module is used for determining a monitoring mode;
the state acquisition module is used for acquiring running state data of the intelligent driving vehicle;
the image acquisition module is used for acquiring a vehicle marking image based on the running state data of the intelligent driving vehicle;
the map rendering module is used for determining map attitude data based on the running state data of the intelligent driving vehicle and the monitoring mode; rendering based on the vehicle marking graph of the intelligent driving vehicle and the map posture data to obtain a monitoring map of the intelligent driving vehicle; or, the monitoring map for the intelligent driving vehicle is obtained based on the vehicle marking graph.
10. A computer device, comprising: a processor and a memory;
the processor is adapted to perform the steps of the method of any one of claims 1 to 8 by calling a program or instructions stored in the memory.
11. A computer-readable storage medium, characterized in that it stores a program or instructions for causing a computer to carry out the steps of the method according to any one of claims 1 to 8.
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